InstanceRecommendation (AWS SDK for Java

java.lang.Object
- com.amazonaws.services.computeoptimizer.model.InstanceRecommendation

All Implemented Interfaces:: StructuredPojo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class InstanceRecommendation
extends Object
implements Serializable, Cloneable, StructuredPojo

Describes an Amazon EC2 instance recommendation.

See Also:: AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

InstanceRecommendation()

Constructors
Constructor and Description
`InstanceRecommendation()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`InstanceRecommendation`	`clone()`
`boolean`	`equals(Object obj)`
`String`	`getAccountId()` The Amazon Web Services account ID of the instance.
`GpuInfo`	`getCurrentInstanceGpuInfo()` Describes the GPU accelerator settings for the current instance type.
`String`	`getCurrentInstanceType()` The instance type of the current instance.
`String`	`getCurrentPerformanceRisk()` The risk of the current instance not meeting the performance needs of its workloads.
`EffectiveRecommendationPreferences`	`getEffectiveRecommendationPreferences()` An object that describes the effective recommendation preferences for the instance.
`ExternalMetricStatus`	`getExternalMetricStatus()` An object that describes Compute Optimizer's integration status with your external metrics provider.
`String`	`getFinding()` The finding classification of the instance.
`List<String>`	`getFindingReasonCodes()` The reason for the finding classification of the instance.
`String`	`getIdle()` Describes if an Amazon EC2 instance is idle.
`List<String>`	`getInferredWorkloadTypes()` The applications that might be running on the instance as inferred by Compute Optimizer.
`String`	`getInstanceArn()` The Amazon Resource Name (ARN) of the current instance.
`String`	`getInstanceName()` The name of the current instance.
`String`	`getInstanceState()` The state of the instance when the recommendation was generated.
`Date`	`getLastRefreshTimestamp()` The timestamp of when the instance recommendation was last generated.
`Double`	`getLookBackPeriodInDays()` The number of days for which utilization metrics were analyzed for the instance.
`List<InstanceRecommendationOption>`	`getRecommendationOptions()` An array of objects that describe the recommendation options for the instance.
`List<RecommendationSource>`	`getRecommendationSources()` An array of objects that describe the source resource of the recommendation.
`List<Tag>`	`getTags()` A list of tags assigned to your Amazon EC2 instance recommendations.
`List<UtilizationMetric>`	`getUtilizationMetrics()` An array of objects that describe the utilization metrics of the instance.
`int`	`hashCode()`
`void`	`marshall(ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setAccountId(String accountId)` The Amazon Web Services account ID of the instance.
`void`	`setCurrentInstanceGpuInfo(GpuInfo currentInstanceGpuInfo)` Describes the GPU accelerator settings for the current instance type.
`void`	`setCurrentInstanceType(String currentInstanceType)` The instance type of the current instance.
`void`	`setCurrentPerformanceRisk(String currentPerformanceRisk)` The risk of the current instance not meeting the performance needs of its workloads.
`void`	`setEffectiveRecommendationPreferences(EffectiveRecommendationPreferences effectiveRecommendationPreferences)` An object that describes the effective recommendation preferences for the instance.
`void`	`setExternalMetricStatus(ExternalMetricStatus externalMetricStatus)` An object that describes Compute Optimizer's integration status with your external metrics provider.
`void`	`setFinding(String finding)` The finding classification of the instance.
`void`	`setFindingReasonCodes(Collection<String> findingReasonCodes)` The reason for the finding classification of the instance.
`void`	`setIdle(String idle)` Describes if an Amazon EC2 instance is idle.
`void`	`setInferredWorkloadTypes(Collection<String> inferredWorkloadTypes)` The applications that might be running on the instance as inferred by Compute Optimizer.
`void`	`setInstanceArn(String instanceArn)` The Amazon Resource Name (ARN) of the current instance.
`void`	`setInstanceName(String instanceName)` The name of the current instance.
`void`	`setInstanceState(String instanceState)` The state of the instance when the recommendation was generated.
`void`	`setLastRefreshTimestamp(Date lastRefreshTimestamp)` The timestamp of when the instance recommendation was last generated.
`void`	`setLookBackPeriodInDays(Double lookBackPeriodInDays)` The number of days for which utilization metrics were analyzed for the instance.
`void`	`setRecommendationOptions(Collection<InstanceRecommendationOption> recommendationOptions)` An array of objects that describe the recommendation options for the instance.
`void`	`setRecommendationSources(Collection<RecommendationSource> recommendationSources)` An array of objects that describe the source resource of the recommendation.
`void`	`setTags(Collection<Tag> tags)` A list of tags assigned to your Amazon EC2 instance recommendations.
`void`	`setUtilizationMetrics(Collection<UtilizationMetric> utilizationMetrics)` An array of objects that describe the utilization metrics of the instance.
`String`	`toString()` Returns a string representation of this object.
`InstanceRecommendation`	`withAccountId(String accountId)` The Amazon Web Services account ID of the instance.
`InstanceRecommendation`	`withCurrentInstanceGpuInfo(GpuInfo currentInstanceGpuInfo)` Describes the GPU accelerator settings for the current instance type.
`InstanceRecommendation`	`withCurrentInstanceType(String currentInstanceType)` The instance type of the current instance.
`InstanceRecommendation`	`withCurrentPerformanceRisk(CurrentPerformanceRisk currentPerformanceRisk)` The risk of the current instance not meeting the performance needs of its workloads.
`InstanceRecommendation`	`withCurrentPerformanceRisk(String currentPerformanceRisk)` The risk of the current instance not meeting the performance needs of its workloads.
`InstanceRecommendation`	`withEffectiveRecommendationPreferences(EffectiveRecommendationPreferences effectiveRecommendationPreferences)` An object that describes the effective recommendation preferences for the instance.
`InstanceRecommendation`	`withExternalMetricStatus(ExternalMetricStatus externalMetricStatus)` An object that describes Compute Optimizer's integration status with your external metrics provider.
`InstanceRecommendation`	`withFinding(Finding finding)` The finding classification of the instance.
`InstanceRecommendation`	`withFinding(String finding)` The finding classification of the instance.
`InstanceRecommendation`	`withFindingReasonCodes(Collection<String> findingReasonCodes)` The reason for the finding classification of the instance.
`InstanceRecommendation`	`withFindingReasonCodes(InstanceRecommendationFindingReasonCode... findingReasonCodes)` The reason for the finding classification of the instance.
`InstanceRecommendation`	`withFindingReasonCodes(String... findingReasonCodes)` The reason for the finding classification of the instance.
`InstanceRecommendation`	`withIdle(InstanceIdle idle)` Describes if an Amazon EC2 instance is idle.
`InstanceRecommendation`	`withIdle(String idle)` Describes if an Amazon EC2 instance is idle.
`InstanceRecommendation`	`withInferredWorkloadTypes(Collection<String> inferredWorkloadTypes)` The applications that might be running on the instance as inferred by Compute Optimizer.
`InstanceRecommendation`	`withInferredWorkloadTypes(InferredWorkloadType... inferredWorkloadTypes)` The applications that might be running on the instance as inferred by Compute Optimizer.
`InstanceRecommendation`	`withInferredWorkloadTypes(String... inferredWorkloadTypes)` The applications that might be running on the instance as inferred by Compute Optimizer.
`InstanceRecommendation`	`withInstanceArn(String instanceArn)` The Amazon Resource Name (ARN) of the current instance.
`InstanceRecommendation`	`withInstanceName(String instanceName)` The name of the current instance.
`InstanceRecommendation`	`withInstanceState(InstanceState instanceState)` The state of the instance when the recommendation was generated.
`InstanceRecommendation`	`withInstanceState(String instanceState)` The state of the instance when the recommendation was generated.
`InstanceRecommendation`	`withLastRefreshTimestamp(Date lastRefreshTimestamp)` The timestamp of when the instance recommendation was last generated.
`InstanceRecommendation`	`withLookBackPeriodInDays(Double lookBackPeriodInDays)` The number of days for which utilization metrics were analyzed for the instance.
`InstanceRecommendation`	`withRecommendationOptions(Collection<InstanceRecommendationOption> recommendationOptions)` An array of objects that describe the recommendation options for the instance.
`InstanceRecommendation`	`withRecommendationOptions(InstanceRecommendationOption... recommendationOptions)` An array of objects that describe the recommendation options for the instance.
`InstanceRecommendation`	`withRecommendationSources(Collection<RecommendationSource> recommendationSources)` An array of objects that describe the source resource of the recommendation.
`InstanceRecommendation`	`withRecommendationSources(RecommendationSource... recommendationSources)` An array of objects that describe the source resource of the recommendation.
`InstanceRecommendation`	`withTags(Collection<Tag> tags)` A list of tags assigned to your Amazon EC2 instance recommendations.
`InstanceRecommendation`	`withTags(Tag... tags)` A list of tags assigned to your Amazon EC2 instance recommendations.
`InstanceRecommendation`	`withUtilizationMetrics(Collection<UtilizationMetric> utilizationMetrics)` An array of objects that describe the utilization metrics of the instance.
`InstanceRecommendation`	`withUtilizationMetrics(UtilizationMetric... utilizationMetrics)` An array of objects that describe the utilization metrics of the instance.

Methods inherited from class java.lang.Object
getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - InstanceRecommendation
```
public InstanceRecommendation()
```
- Method Detail
  - setInstanceArn
```
public void setInstanceArn(String instanceArn)
```
    The Amazon Resource Name (ARN) of the current instance.
    
    Parameters:
    
    instanceArn - The Amazon Resource Name (ARN) of the current instance.
  - getInstanceArn
```
public String getInstanceArn()
```
    The Amazon Resource Name (ARN) of the current instance.
    
    Returns:
    
    The Amazon Resource Name (ARN) of the current instance.
  - withInstanceArn
```
public InstanceRecommendation withInstanceArn(String instanceArn)
```
    The Amazon Resource Name (ARN) of the current instance.
    
    Parameters:
    
    instanceArn - The Amazon Resource Name (ARN) of the current instance.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setAccountId
```
public void setAccountId(String accountId)
```
    The Amazon Web Services account ID of the instance.
    
    Parameters:
    
    accountId - The Amazon Web Services account ID of the instance.
  - getAccountId
```
public String getAccountId()
```
    The Amazon Web Services account ID of the instance.
    
    Returns:
    
    The Amazon Web Services account ID of the instance.
  - withAccountId
```
public InstanceRecommendation withAccountId(String accountId)
```
    The Amazon Web Services account ID of the instance.
    
    Parameters:
    
    accountId - The Amazon Web Services account ID of the instance.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setInstanceName
```
public void setInstanceName(String instanceName)
```
    The name of the current instance.
    
    Parameters:
    
    instanceName - The name of the current instance.
  - getInstanceName
```
public String getInstanceName()
```
    The name of the current instance.
    
    Returns:
    
    The name of the current instance.
  - withInstanceName
```
public InstanceRecommendation withInstanceName(String instanceName)
```
    The name of the current instance.
    
    Parameters:
    
    instanceName - The name of the current instance.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setCurrentInstanceType
```
public void setCurrentInstanceType(String currentInstanceType)
```
    The instance type of the current instance.
    
    Parameters:
    
    currentInstanceType - The instance type of the current instance.
  - getCurrentInstanceType
```
public String getCurrentInstanceType()
```
    The instance type of the current instance.
    
    Returns:
    
    The instance type of the current instance.
  - withCurrentInstanceType
```
public InstanceRecommendation withCurrentInstanceType(String currentInstanceType)
```
    The instance type of the current instance.
    
    Parameters:
    
    currentInstanceType - The instance type of the current instance.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setFinding
```
public void setFinding(String finding)
```
    The finding classification of the instance.
    
    Findings for instances include:
    - Underprovisioned —An instance is considered under-provisioned when at least one specification of your instance, such as CPU, memory, or network, does not meet the performance requirements of your workload. Under-provisioned instances may lead to poor application performance.
    - Overprovisioned —An instance is considered over-provisioned when at least one specification of your instance, such as CPU, memory, or network, can be sized down while still meeting the performance requirements of your workload, and no specification is under-provisioned. Over-provisioned instances may lead to unnecessary infrastructure cost.
    - Optimized —An instance is considered optimized when all specifications of your instance, such as CPU, memory, and network, meet the performance requirements of your workload and is not over provisioned. For optimized resources, Compute Optimizer might recommend a new generation instance type.
    The valid values in your API responses appear as OVER_PROVISIONED, UNDER_PROVISIONED, or OPTIMIZED.
    Parameters:
    finding - The finding classification of the instance.
    
    Findings for instances include:
    
    Underprovisioned —An instance is considered under-provisioned when at least one specification of your instance, such as CPU, memory, or network, does not meet the performance requirements of your workload. Under-provisioned instances may lead to poor application performance.
    
    Overprovisioned —An instance is considered over-provisioned when at least one specification of your instance, such as CPU, memory, or network, can be sized down while still meeting the performance requirements of your workload, and no specification is under-provisioned. Over-provisioned instances may lead to unnecessary infrastructure cost.
    
    Optimized —An instance is considered optimized when all specifications of your instance, such as CPU, memory, and network, meet the performance requirements of your workload and is not over provisioned. For optimized resources, Compute Optimizer might recommend a new generation instance type.
    
    The valid values in your API responses appear as OVER_PROVISIONED, UNDER_PROVISIONED, or OPTIMIZED.
    See Also:
    
    Finding
  - getFinding
```
public String getFinding()
```
    The finding classification of the instance.
    
    Findings for instances include:
    - Underprovisioned —An instance is considered under-provisioned when at least one specification of your instance, such as CPU, memory, or network, does not meet the performance requirements of your workload. Under-provisioned instances may lead to poor application performance.
    - Overprovisioned —An instance is considered over-provisioned when at least one specification of your instance, such as CPU, memory, or network, can be sized down while still meeting the performance requirements of your workload, and no specification is under-provisioned. Over-provisioned instances may lead to unnecessary infrastructure cost.
    - Optimized —An instance is considered optimized when all specifications of your instance, such as CPU, memory, and network, meet the performance requirements of your workload and is not over provisioned. For optimized resources, Compute Optimizer might recommend a new generation instance type.
    The valid values in your API responses appear as OVER_PROVISIONED, UNDER_PROVISIONED, or OPTIMIZED.
    Returns:
    The finding classification of the instance.
    
    Findings for instances include:
    
    Underprovisioned —An instance is considered under-provisioned when at least one specification of your instance, such as CPU, memory, or network, does not meet the performance requirements of your workload. Under-provisioned instances may lead to poor application performance.
    
    Overprovisioned —An instance is considered over-provisioned when at least one specification of your instance, such as CPU, memory, or network, can be sized down while still meeting the performance requirements of your workload, and no specification is under-provisioned. Over-provisioned instances may lead to unnecessary infrastructure cost.
    
    Optimized —An instance is considered optimized when all specifications of your instance, such as CPU, memory, and network, meet the performance requirements of your workload and is not over provisioned. For optimized resources, Compute Optimizer might recommend a new generation instance type.
    
    The valid values in your API responses appear as OVER_PROVISIONED, UNDER_PROVISIONED, or OPTIMIZED.
    See Also:
    
    Finding
  - withFinding
```
public InstanceRecommendation withFinding(String finding)
```
    The finding classification of the instance.
    
    Findings for instances include:
    - Underprovisioned —An instance is considered under-provisioned when at least one specification of your instance, such as CPU, memory, or network, does not meet the performance requirements of your workload. Under-provisioned instances may lead to poor application performance.
    - Overprovisioned —An instance is considered over-provisioned when at least one specification of your instance, such as CPU, memory, or network, can be sized down while still meeting the performance requirements of your workload, and no specification is under-provisioned. Over-provisioned instances may lead to unnecessary infrastructure cost.
    - Optimized —An instance is considered optimized when all specifications of your instance, such as CPU, memory, and network, meet the performance requirements of your workload and is not over provisioned. For optimized resources, Compute Optimizer might recommend a new generation instance type.
    The valid values in your API responses appear as OVER_PROVISIONED, UNDER_PROVISIONED, or OPTIMIZED.
    Parameters:
    finding - The finding classification of the instance.
    
    Findings for instances include:
    
    Underprovisioned —An instance is considered under-provisioned when at least one specification of your instance, such as CPU, memory, or network, does not meet the performance requirements of your workload. Under-provisioned instances may lead to poor application performance.
    
    Overprovisioned —An instance is considered over-provisioned when at least one specification of your instance, such as CPU, memory, or network, can be sized down while still meeting the performance requirements of your workload, and no specification is under-provisioned. Over-provisioned instances may lead to unnecessary infrastructure cost.
    
    Optimized —An instance is considered optimized when all specifications of your instance, such as CPU, memory, and network, meet the performance requirements of your workload and is not over provisioned. For optimized resources, Compute Optimizer might recommend a new generation instance type.
    
    The valid values in your API responses appear as OVER_PROVISIONED, UNDER_PROVISIONED, or OPTIMIZED.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    Finding
  - withFinding
```
public InstanceRecommendation withFinding(Finding finding)
```
    The finding classification of the instance.
    
    Findings for instances include:
    - Underprovisioned —An instance is considered under-provisioned when at least one specification of your instance, such as CPU, memory, or network, does not meet the performance requirements of your workload. Under-provisioned instances may lead to poor application performance.
    - Overprovisioned —An instance is considered over-provisioned when at least one specification of your instance, such as CPU, memory, or network, can be sized down while still meeting the performance requirements of your workload, and no specification is under-provisioned. Over-provisioned instances may lead to unnecessary infrastructure cost.
    - Optimized —An instance is considered optimized when all specifications of your instance, such as CPU, memory, and network, meet the performance requirements of your workload and is not over provisioned. For optimized resources, Compute Optimizer might recommend a new generation instance type.
    The valid values in your API responses appear as OVER_PROVISIONED, UNDER_PROVISIONED, or OPTIMIZED.
    Parameters:
    finding - The finding classification of the instance.
    
    Findings for instances include:
    
    Underprovisioned —An instance is considered under-provisioned when at least one specification of your instance, such as CPU, memory, or network, does not meet the performance requirements of your workload. Under-provisioned instances may lead to poor application performance.
    
    Overprovisioned —An instance is considered over-provisioned when at least one specification of your instance, such as CPU, memory, or network, can be sized down while still meeting the performance requirements of your workload, and no specification is under-provisioned. Over-provisioned instances may lead to unnecessary infrastructure cost.
    
    Optimized —An instance is considered optimized when all specifications of your instance, such as CPU, memory, and network, meet the performance requirements of your workload and is not over provisioned. For optimized resources, Compute Optimizer might recommend a new generation instance type.
    
    The valid values in your API responses appear as OVER_PROVISIONED, UNDER_PROVISIONED, or OPTIMIZED.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    Finding
  - getFindingReasonCodes
```
public List<String> getFindingReasonCodes()
```
    The reason for the finding classification of the instance.
    
    Finding reason codes for instances include:
    - CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    - CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    - MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    - MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
      
      Memory utilization is analyzed only for resources that have the unified CloudWatch agent installed on them. For more information, see Enabling memory utilization with the Amazon CloudWatch Agent in the Compute Optimizer User Guide. On Linux instances, Compute Optimizer analyses the mem_used_percent metric in the CWAgent namespace, or the legacy MemoryUtilization metric in the System/Linux namespace. On Windows instances, Compute Optimizer analyses the Memory % Committed Bytes In Use metric in the CWAgent namespace.
    - EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    - EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    - EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    - EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    - NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period.
    - NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
    - NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    - NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    - DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    - DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    - DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    - DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    For more information about instance metrics, see List the available CloudWatch metrics for your instances in the Amazon Elastic Compute Cloud User Guide. For more information about EBS volume metrics, see Amazon CloudWatch metrics for Amazon EBS in the Amazon Elastic Compute Cloud User Guide.
    Returns:
    The reason for the finding classification of the instance.
    
    Finding reason codes for instances include:
    
    CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    
    CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    
    MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    
    MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    
    Memory utilization is analyzed only for resources that have the unified CloudWatch agent installed on them. For more information, see Enabling memory utilization with the Amazon CloudWatch Agent in the Compute Optimizer User Guide. On Linux instances, Compute Optimizer analyses the mem_used_percent metric in the CWAgent namespace, or the legacy MemoryUtilization metric in the System/Linux namespace. On Windows instances, Compute Optimizer analyses the Memory % Committed Bytes In Use metric in the CWAgent namespace.
    
    EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    
    EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    
    EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    
    EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    
    NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period.
    
    NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
    
    NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    
    NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    
    DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    
    DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    
    DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    
    DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    
    For more information about instance metrics, see List the available CloudWatch metrics for your instances in the Amazon Elastic Compute Cloud User Guide. For more information about EBS volume metrics, see Amazon CloudWatch metrics for Amazon EBS in the Amazon Elastic Compute Cloud User Guide.
    See Also:
    
    InstanceRecommendationFindingReasonCode
  - setFindingReasonCodes
```
public void setFindingReasonCodes(Collection<String> findingReasonCodes)
```
    The reason for the finding classification of the instance.
    
    Finding reason codes for instances include:
    - CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    - CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    - MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    - MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
      
      Memory utilization is analyzed only for resources that have the unified CloudWatch agent installed on them. For more information, see Enabling memory utilization with the Amazon CloudWatch Agent in the Compute Optimizer User Guide. On Linux instances, Compute Optimizer analyses the mem_used_percent metric in the CWAgent namespace, or the legacy MemoryUtilization metric in the System/Linux namespace. On Windows instances, Compute Optimizer analyses the Memory % Committed Bytes In Use metric in the CWAgent namespace.
    - EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    - EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    - EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    - EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    - NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period.
    - NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
    - NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    - NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    - DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    - DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    - DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    - DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    For more information about instance metrics, see List the available CloudWatch metrics for your instances in the Amazon Elastic Compute Cloud User Guide. For more information about EBS volume metrics, see Amazon CloudWatch metrics for Amazon EBS in the Amazon Elastic Compute Cloud User Guide.
    Parameters:
    findingReasonCodes - The reason for the finding classification of the instance.
    
    Finding reason codes for instances include:
    
    CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    
    CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    
    MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    
    MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    
    Memory utilization is analyzed only for resources that have the unified CloudWatch agent installed on them. For more information, see Enabling memory utilization with the Amazon CloudWatch Agent in the Compute Optimizer User Guide. On Linux instances, Compute Optimizer analyses the mem_used_percent metric in the CWAgent namespace, or the legacy MemoryUtilization metric in the System/Linux namespace. On Windows instances, Compute Optimizer analyses the Memory % Committed Bytes In Use metric in the CWAgent namespace.
    
    EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    
    EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    
    EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    
    EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    
    NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period.
    
    NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
    
    NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    
    NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    
    DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    
    DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    
    DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    
    DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    
    For more information about instance metrics, see List the available CloudWatch metrics for your instances in the Amazon Elastic Compute Cloud User Guide. For more information about EBS volume metrics, see Amazon CloudWatch metrics for Amazon EBS in the Amazon Elastic Compute Cloud User Guide.
    See Also:
    
    InstanceRecommendationFindingReasonCode
  - withFindingReasonCodes
```
public InstanceRecommendation withFindingReasonCodes(String... findingReasonCodes)
```
    The reason for the finding classification of the instance.
    
    Finding reason codes for instances include:
    - CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    - CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    - MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    - MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
      
      Memory utilization is analyzed only for resources that have the unified CloudWatch agent installed on them. For more information, see Enabling memory utilization with the Amazon CloudWatch Agent in the Compute Optimizer User Guide. On Linux instances, Compute Optimizer analyses the mem_used_percent metric in the CWAgent namespace, or the legacy MemoryUtilization metric in the System/Linux namespace. On Windows instances, Compute Optimizer analyses the Memory % Committed Bytes In Use metric in the CWAgent namespace.
    - EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    - EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    - EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    - EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    - NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period.
    - NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
    - NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    - NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    - DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    - DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    - DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    - DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    For more information about instance metrics, see List the available CloudWatch metrics for your instances in the Amazon Elastic Compute Cloud User Guide. For more information about EBS volume metrics, see Amazon CloudWatch metrics for Amazon EBS in the Amazon Elastic Compute Cloud User Guide.
    
    NOTE: This method appends the values to the existing list (if any). Use setFindingReasonCodes(java.util.Collection) or withFindingReasonCodes(java.util.Collection) if you want to override the existing values.
    Parameters:
    findingReasonCodes - The reason for the finding classification of the instance.
    
    Finding reason codes for instances include:
    
    CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    
    CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    
    MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    
    MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    
    Memory utilization is analyzed only for resources that have the unified CloudWatch agent installed on them. For more information, see Enabling memory utilization with the Amazon CloudWatch Agent in the Compute Optimizer User Guide. On Linux instances, Compute Optimizer analyses the mem_used_percent metric in the CWAgent namespace, or the legacy MemoryUtilization metric in the System/Linux namespace. On Windows instances, Compute Optimizer analyses the Memory % Committed Bytes In Use metric in the CWAgent namespace.
    
    EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    
    EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    
    EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    
    EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    
    NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period.
    
    NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
    
    NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    
    NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    
    DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    
    DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    
    DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    
    DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    
    For more information about instance metrics, see List the available CloudWatch metrics for your instances in the Amazon Elastic Compute Cloud User Guide. For more information about EBS volume metrics, see Amazon CloudWatch metrics for Amazon EBS in the Amazon Elastic Compute Cloud User Guide.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    InstanceRecommendationFindingReasonCode
  - withFindingReasonCodes
```
public InstanceRecommendation withFindingReasonCodes(Collection<String> findingReasonCodes)
```
    The reason for the finding classification of the instance.
    
    Finding reason codes for instances include:
    - CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    - CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    - MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    - MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
      
      Memory utilization is analyzed only for resources that have the unified CloudWatch agent installed on them. For more information, see Enabling memory utilization with the Amazon CloudWatch Agent in the Compute Optimizer User Guide. On Linux instances, Compute Optimizer analyses the mem_used_percent metric in the CWAgent namespace, or the legacy MemoryUtilization metric in the System/Linux namespace. On Windows instances, Compute Optimizer analyses the Memory % Committed Bytes In Use metric in the CWAgent namespace.
    - EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    - EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    - EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    - EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    - NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period.
    - NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
    - NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    - NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    - DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    - DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    - DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    - DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    For more information about instance metrics, see List the available CloudWatch metrics for your instances in the Amazon Elastic Compute Cloud User Guide. For more information about EBS volume metrics, see Amazon CloudWatch metrics for Amazon EBS in the Amazon Elastic Compute Cloud User Guide.
    Parameters:
    findingReasonCodes - The reason for the finding classification of the instance.
    
    Finding reason codes for instances include:
    
    CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    
    CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    
    MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    
    MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    
    Memory utilization is analyzed only for resources that have the unified CloudWatch agent installed on them. For more information, see Enabling memory utilization with the Amazon CloudWatch Agent in the Compute Optimizer User Guide. On Linux instances, Compute Optimizer analyses the mem_used_percent metric in the CWAgent namespace, or the legacy MemoryUtilization metric in the System/Linux namespace. On Windows instances, Compute Optimizer analyses the Memory % Committed Bytes In Use metric in the CWAgent namespace.
    
    EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    
    EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    
    EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    
    EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    
    NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period.
    
    NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
    
    NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    
    NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    
    DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    
    DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    
    DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    
    DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    
    For more information about instance metrics, see List the available CloudWatch metrics for your instances in the Amazon Elastic Compute Cloud User Guide. For more information about EBS volume metrics, see Amazon CloudWatch metrics for Amazon EBS in the Amazon Elastic Compute Cloud User Guide.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    InstanceRecommendationFindingReasonCode
  - withFindingReasonCodes
```
public InstanceRecommendation withFindingReasonCodes(InstanceRecommendationFindingReasonCode... findingReasonCodes)
```
    The reason for the finding classification of the instance.
    
    Finding reason codes for instances include:
    - CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    - CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    - MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    - MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
      
      Memory utilization is analyzed only for resources that have the unified CloudWatch agent installed on them. For more information, see Enabling memory utilization with the Amazon CloudWatch Agent in the Compute Optimizer User Guide. On Linux instances, Compute Optimizer analyses the mem_used_percent metric in the CWAgent namespace, or the legacy MemoryUtilization metric in the System/Linux namespace. On Windows instances, Compute Optimizer analyses the Memory % Committed Bytes In Use metric in the CWAgent namespace.
    - EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    - EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    - EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    - EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    - NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period.
    - NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
    - NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    - NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    - DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    - DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    - DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    - DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    For more information about instance metrics, see List the available CloudWatch metrics for your instances in the Amazon Elastic Compute Cloud User Guide. For more information about EBS volume metrics, see Amazon CloudWatch metrics for Amazon EBS in the Amazon Elastic Compute Cloud User Guide.
    Parameters:
    findingReasonCodes - The reason for the finding classification of the instance.
    
    Finding reason codes for instances include:
    
    CPUOverprovisioned — The instance’s CPU configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    
    CPUUnderprovisioned — The instance’s CPU configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better CPU performance. This is identified by analyzing the CPUUtilization metric of the current instance during the look-back period.
    
    MemoryOverprovisioned — The instance’s memory configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    
    MemoryUnderprovisioned — The instance’s memory configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better memory performance. This is identified by analyzing the memory utilization metric of the current instance during the look-back period.
    
    Memory utilization is analyzed only for resources that have the unified CloudWatch agent installed on them. For more information, see Enabling memory utilization with the Amazon CloudWatch Agent in the Compute Optimizer User Guide. On Linux instances, Compute Optimizer analyses the mem_used_percent metric in the CWAgent namespace, or the legacy MemoryUtilization metric in the System/Linux namespace. On Windows instances, Compute Optimizer analyses the Memory % Committed Bytes In Use metric in the CWAgent namespace.
    
    EBSThroughputOverprovisioned — The instance’s EBS throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    
    EBSThroughputUnderprovisioned — The instance’s EBS throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS throughput performance. This is identified by analyzing the VolumeReadBytes and VolumeWriteBytes metrics of EBS volumes attached to the current instance during the look-back period.
    
    EBSIOPSOverprovisioned — The instance’s EBS IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    
    EBSIOPSUnderprovisioned — The instance’s EBS IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better EBS IOPS performance. This is identified by analyzing the VolumeReadOps and VolumeWriteOps metric of EBS volumes attached to the current instance during the look-back period.
    
    NetworkBandwidthOverprovisioned — The instance’s network bandwidth configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period.
    
    NetworkBandwidthUnderprovisioned — The instance’s network bandwidth configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network bandwidth performance. This is identified by analyzing the NetworkIn and NetworkOut metrics of the current instance during the look-back period. This finding reason happens when the NetworkIn or NetworkOut performance of an instance is impacted.
    
    NetworkPPSOverprovisioned — The instance’s network PPS (packets per second) configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    
    NetworkPPSUnderprovisioned — The instance’s network PPS (packets per second) configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better network PPS performance. This is identified by analyzing the NetworkPacketsIn and NetworkPacketsIn metrics of the current instance during the look-back period.
    
    DiskIOPSOverprovisioned — The instance’s disk IOPS configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    
    DiskIOPSUnderprovisioned — The instance’s disk IOPS configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk IOPS performance. This is identified by analyzing the DiskReadOps and DiskWriteOps metrics of the current instance during the look-back period.
    
    DiskThroughputOverprovisioned — The instance’s disk throughput configuration can be sized down while still meeting the performance requirements of your workload. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    
    DiskThroughputUnderprovisioned — The instance’s disk throughput configuration doesn't meet the performance requirements of your workload and there is an alternative instance type that provides better disk throughput performance. This is identified by analyzing the DiskReadBytes and DiskWriteBytes metrics of the current instance during the look-back period.
    
    For more information about instance metrics, see List the available CloudWatch metrics for your instances in the Amazon Elastic Compute Cloud User Guide. For more information about EBS volume metrics, see Amazon CloudWatch metrics for Amazon EBS in the Amazon Elastic Compute Cloud User Guide.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    InstanceRecommendationFindingReasonCode
  - getUtilizationMetrics
```
public List<UtilizationMetric> getUtilizationMetrics()
```
    An array of objects that describe the utilization metrics of the instance.
    
    Returns:
    
    An array of objects that describe the utilization metrics of the instance.
  - setUtilizationMetrics
```
public void setUtilizationMetrics(Collection<UtilizationMetric> utilizationMetrics)
```
    An array of objects that describe the utilization metrics of the instance.
    
    Parameters:
    
    utilizationMetrics - An array of objects that describe the utilization metrics of the instance.
  - withUtilizationMetrics
```
public InstanceRecommendation withUtilizationMetrics(UtilizationMetric... utilizationMetrics)
```
    An array of objects that describe the utilization metrics of the instance.
    
    NOTE: This method appends the values to the existing list (if any). Use setUtilizationMetrics(java.util.Collection) or withUtilizationMetrics(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    utilizationMetrics - An array of objects that describe the utilization metrics of the instance.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withUtilizationMetrics
```
public InstanceRecommendation withUtilizationMetrics(Collection<UtilizationMetric> utilizationMetrics)
```
    An array of objects that describe the utilization metrics of the instance.
    
    Parameters:
    
    utilizationMetrics - An array of objects that describe the utilization metrics of the instance.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setLookBackPeriodInDays
```
public void setLookBackPeriodInDays(Double lookBackPeriodInDays)
```
    The number of days for which utilization metrics were analyzed for the instance.
    
    Parameters:
    
    lookBackPeriodInDays - The number of days for which utilization metrics were analyzed for the instance.
  - getLookBackPeriodInDays
```
public Double getLookBackPeriodInDays()
```
    The number of days for which utilization metrics were analyzed for the instance.
    
    Returns:
    
    The number of days for which utilization metrics were analyzed for the instance.
  - withLookBackPeriodInDays
```
public InstanceRecommendation withLookBackPeriodInDays(Double lookBackPeriodInDays)
```
    The number of days for which utilization metrics were analyzed for the instance.
    
    Parameters:
    
    lookBackPeriodInDays - The number of days for which utilization metrics were analyzed for the instance.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getRecommendationOptions
```
public List<InstanceRecommendationOption> getRecommendationOptions()
```
    An array of objects that describe the recommendation options for the instance.
    
    Returns:
    
    An array of objects that describe the recommendation options for the instance.
  - setRecommendationOptions
```
public void setRecommendationOptions(Collection<InstanceRecommendationOption> recommendationOptions)
```
    An array of objects that describe the recommendation options for the instance.
    
    Parameters:
    
    recommendationOptions - An array of objects that describe the recommendation options for the instance.
  - withRecommendationOptions
```
public InstanceRecommendation withRecommendationOptions(InstanceRecommendationOption... recommendationOptions)
```
    An array of objects that describe the recommendation options for the instance.
    
    NOTE: This method appends the values to the existing list (if any). Use setRecommendationOptions(java.util.Collection) or withRecommendationOptions(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    recommendationOptions - An array of objects that describe the recommendation options for the instance.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withRecommendationOptions
```
public InstanceRecommendation withRecommendationOptions(Collection<InstanceRecommendationOption> recommendationOptions)
```
    An array of objects that describe the recommendation options for the instance.
    
    Parameters:
    
    recommendationOptions - An array of objects that describe the recommendation options for the instance.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getRecommendationSources
```
public List<RecommendationSource> getRecommendationSources()
```
    An array of objects that describe the source resource of the recommendation.
    
    Returns:
    
    An array of objects that describe the source resource of the recommendation.
  - setRecommendationSources
```
public void setRecommendationSources(Collection<RecommendationSource> recommendationSources)
```
    An array of objects that describe the source resource of the recommendation.
    
    Parameters:
    
    recommendationSources - An array of objects that describe the source resource of the recommendation.
  - withRecommendationSources
```
public InstanceRecommendation withRecommendationSources(RecommendationSource... recommendationSources)
```
    An array of objects that describe the source resource of the recommendation.
    
    NOTE: This method appends the values to the existing list (if any). Use setRecommendationSources(java.util.Collection) or withRecommendationSources(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    recommendationSources - An array of objects that describe the source resource of the recommendation.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withRecommendationSources
```
public InstanceRecommendation withRecommendationSources(Collection<RecommendationSource> recommendationSources)
```
    An array of objects that describe the source resource of the recommendation.
    
    Parameters:
    
    recommendationSources - An array of objects that describe the source resource of the recommendation.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setLastRefreshTimestamp
```
public void setLastRefreshTimestamp(Date lastRefreshTimestamp)
```
    The timestamp of when the instance recommendation was last generated.
    
    Parameters:
    
    lastRefreshTimestamp - The timestamp of when the instance recommendation was last generated.
  - getLastRefreshTimestamp
```
public Date getLastRefreshTimestamp()
```
    The timestamp of when the instance recommendation was last generated.
    
    Returns:
    
    The timestamp of when the instance recommendation was last generated.
  - withLastRefreshTimestamp
```
public InstanceRecommendation withLastRefreshTimestamp(Date lastRefreshTimestamp)
```
    The timestamp of when the instance recommendation was last generated.
    
    Parameters:
    
    lastRefreshTimestamp - The timestamp of when the instance recommendation was last generated.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setCurrentPerformanceRisk
```
public void setCurrentPerformanceRisk(String currentPerformanceRisk)
```
    The risk of the current instance not meeting the performance needs of its workloads. The higher the risk, the more likely the current instance cannot meet the performance requirements of its workload.
    
    Parameters:
    
    currentPerformanceRisk - The risk of the current instance not meeting the performance needs of its workloads. The higher the risk, the more likely the current instance cannot meet the performance requirements of its workload.
    
    See Also:
    
    CurrentPerformanceRisk
  - getCurrentPerformanceRisk
```
public String getCurrentPerformanceRisk()
```
    The risk of the current instance not meeting the performance needs of its workloads. The higher the risk, the more likely the current instance cannot meet the performance requirements of its workload.
    
    Returns:
    
    The risk of the current instance not meeting the performance needs of its workloads. The higher the risk, the more likely the current instance cannot meet the performance requirements of its workload.
    
    See Also:
    
    CurrentPerformanceRisk
  - withCurrentPerformanceRisk
```
public InstanceRecommendation withCurrentPerformanceRisk(String currentPerformanceRisk)
```
    The risk of the current instance not meeting the performance needs of its workloads. The higher the risk, the more likely the current instance cannot meet the performance requirements of its workload.
    
    Parameters:
    
    currentPerformanceRisk - The risk of the current instance not meeting the performance needs of its workloads. The higher the risk, the more likely the current instance cannot meet the performance requirements of its workload.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    CurrentPerformanceRisk
  - withCurrentPerformanceRisk
```
public InstanceRecommendation withCurrentPerformanceRisk(CurrentPerformanceRisk currentPerformanceRisk)
```
    The risk of the current instance not meeting the performance needs of its workloads. The higher the risk, the more likely the current instance cannot meet the performance requirements of its workload.
    
    Parameters:
    
    currentPerformanceRisk - The risk of the current instance not meeting the performance needs of its workloads. The higher the risk, the more likely the current instance cannot meet the performance requirements of its workload.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    CurrentPerformanceRisk
  - setEffectiveRecommendationPreferences
```
public void setEffectiveRecommendationPreferences(EffectiveRecommendationPreferences effectiveRecommendationPreferences)
```
    An object that describes the effective recommendation preferences for the instance.
    
    Parameters:
    
    effectiveRecommendationPreferences - An object that describes the effective recommendation preferences for the instance.
  - getEffectiveRecommendationPreferences
```
public EffectiveRecommendationPreferences getEffectiveRecommendationPreferences()
```
    An object that describes the effective recommendation preferences for the instance.
    
    Returns:
    
    An object that describes the effective recommendation preferences for the instance.
  - withEffectiveRecommendationPreferences
```
public InstanceRecommendation withEffectiveRecommendationPreferences(EffectiveRecommendationPreferences effectiveRecommendationPreferences)
```
    An object that describes the effective recommendation preferences for the instance.
    
    Parameters:
    
    effectiveRecommendationPreferences - An object that describes the effective recommendation preferences for the instance.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getInferredWorkloadTypes
```
public List<String> getInferredWorkloadTypes()
```
    The applications that might be running on the instance as inferred by Compute Optimizer.
    
    Compute Optimizer can infer if one of the following applications might be running on the instance:
    - AmazonEmr - Infers that Amazon EMR might be running on the instance.
    - ApacheCassandra - Infers that Apache Cassandra might be running on the instance.
    - ApacheHadoop - Infers that Apache Hadoop might be running on the instance.
    - Memcached - Infers that Memcached might be running on the instance.
    - NGINX - Infers that NGINX might be running on the instance.
    - PostgreSql - Infers that PostgreSQL might be running on the instance.
    - Redis - Infers that Redis might be running on the instance.
    - Kafka - Infers that Kafka might be running on the instance.
    - SQLServer - Infers that SQLServer might be running on the instance.
    Returns:
    The applications that might be running on the instance as inferred by Compute Optimizer.
    
    Compute Optimizer can infer if one of the following applications might be running on the instance:
    
    AmazonEmr - Infers that Amazon EMR might be running on the instance.
    
    ApacheCassandra - Infers that Apache Cassandra might be running on the instance.
    
    ApacheHadoop - Infers that Apache Hadoop might be running on the instance.
    
    Memcached - Infers that Memcached might be running on the instance.
    
    NGINX - Infers that NGINX might be running on the instance.
    
    PostgreSql - Infers that PostgreSQL might be running on the instance.
    
    Redis - Infers that Redis might be running on the instance.
    
    Kafka - Infers that Kafka might be running on the instance.
    
    SQLServer - Infers that SQLServer might be running on the instance.
    See Also:
    
    InferredWorkloadType
  - setInferredWorkloadTypes
```
public void setInferredWorkloadTypes(Collection<String> inferredWorkloadTypes)
```
    The applications that might be running on the instance as inferred by Compute Optimizer.
    
    Compute Optimizer can infer if one of the following applications might be running on the instance:
    - AmazonEmr - Infers that Amazon EMR might be running on the instance.
    - ApacheCassandra - Infers that Apache Cassandra might be running on the instance.
    - ApacheHadoop - Infers that Apache Hadoop might be running on the instance.
    - Memcached - Infers that Memcached might be running on the instance.
    - NGINX - Infers that NGINX might be running on the instance.
    - PostgreSql - Infers that PostgreSQL might be running on the instance.
    - Redis - Infers that Redis might be running on the instance.
    - Kafka - Infers that Kafka might be running on the instance.
    - SQLServer - Infers that SQLServer might be running on the instance.
    Parameters:
    inferredWorkloadTypes - The applications that might be running on the instance as inferred by Compute Optimizer.
    
    Compute Optimizer can infer if one of the following applications might be running on the instance:
    
    AmazonEmr - Infers that Amazon EMR might be running on the instance.
    
    ApacheCassandra - Infers that Apache Cassandra might be running on the instance.
    
    ApacheHadoop - Infers that Apache Hadoop might be running on the instance.
    
    Memcached - Infers that Memcached might be running on the instance.
    
    NGINX - Infers that NGINX might be running on the instance.
    
    PostgreSql - Infers that PostgreSQL might be running on the instance.
    
    Redis - Infers that Redis might be running on the instance.
    
    Kafka - Infers that Kafka might be running on the instance.
    
    SQLServer - Infers that SQLServer might be running on the instance.
    See Also:
    
    InferredWorkloadType
  - withInferredWorkloadTypes
```
public InstanceRecommendation withInferredWorkloadTypes(String... inferredWorkloadTypes)
```
    The applications that might be running on the instance as inferred by Compute Optimizer.
    
    Compute Optimizer can infer if one of the following applications might be running on the instance:
    - AmazonEmr - Infers that Amazon EMR might be running on the instance.
    - ApacheCassandra - Infers that Apache Cassandra might be running on the instance.
    - ApacheHadoop - Infers that Apache Hadoop might be running on the instance.
    - Memcached - Infers that Memcached might be running on the instance.
    - NGINX - Infers that NGINX might be running on the instance.
    - PostgreSql - Infers that PostgreSQL might be running on the instance.
    - Redis - Infers that Redis might be running on the instance.
    - Kafka - Infers that Kafka might be running on the instance.
    - SQLServer - Infers that SQLServer might be running on the instance.
    NOTE: This method appends the values to the existing list (if any). Use setInferredWorkloadTypes(java.util.Collection) or withInferredWorkloadTypes(java.util.Collection) if you want to override the existing values.
    Parameters:
    inferredWorkloadTypes - The applications that might be running on the instance as inferred by Compute Optimizer.
    
    Compute Optimizer can infer if one of the following applications might be running on the instance:
    
    AmazonEmr - Infers that Amazon EMR might be running on the instance.
    
    ApacheCassandra - Infers that Apache Cassandra might be running on the instance.
    
    ApacheHadoop - Infers that Apache Hadoop might be running on the instance.
    
    Memcached - Infers that Memcached might be running on the instance.
    
    NGINX - Infers that NGINX might be running on the instance.
    
    PostgreSql - Infers that PostgreSQL might be running on the instance.
    
    Redis - Infers that Redis might be running on the instance.
    
    Kafka - Infers that Kafka might be running on the instance.
    
    SQLServer - Infers that SQLServer might be running on the instance.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    InferredWorkloadType
  - withInferredWorkloadTypes
```
public InstanceRecommendation withInferredWorkloadTypes(Collection<String> inferredWorkloadTypes)
```
    The applications that might be running on the instance as inferred by Compute Optimizer.
    
    Compute Optimizer can infer if one of the following applications might be running on the instance:
    - AmazonEmr - Infers that Amazon EMR might be running on the instance.
    - ApacheCassandra - Infers that Apache Cassandra might be running on the instance.
    - ApacheHadoop - Infers that Apache Hadoop might be running on the instance.
    - Memcached - Infers that Memcached might be running on the instance.
    - NGINX - Infers that NGINX might be running on the instance.
    - PostgreSql - Infers that PostgreSQL might be running on the instance.
    - Redis - Infers that Redis might be running on the instance.
    - Kafka - Infers that Kafka might be running on the instance.
    - SQLServer - Infers that SQLServer might be running on the instance.
    Parameters:
    inferredWorkloadTypes - The applications that might be running on the instance as inferred by Compute Optimizer.
    
    Compute Optimizer can infer if one of the following applications might be running on the instance:
    
    AmazonEmr - Infers that Amazon EMR might be running on the instance.
    
    ApacheCassandra - Infers that Apache Cassandra might be running on the instance.
    
    ApacheHadoop - Infers that Apache Hadoop might be running on the instance.
    
    Memcached - Infers that Memcached might be running on the instance.
    
    NGINX - Infers that NGINX might be running on the instance.
    
    PostgreSql - Infers that PostgreSQL might be running on the instance.
    
    Redis - Infers that Redis might be running on the instance.
    
    Kafka - Infers that Kafka might be running on the instance.
    
    SQLServer - Infers that SQLServer might be running on the instance.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    InferredWorkloadType
  - withInferredWorkloadTypes
```
public InstanceRecommendation withInferredWorkloadTypes(InferredWorkloadType... inferredWorkloadTypes)
```
    The applications that might be running on the instance as inferred by Compute Optimizer.
    
    Compute Optimizer can infer if one of the following applications might be running on the instance:
    - AmazonEmr - Infers that Amazon EMR might be running on the instance.
    - ApacheCassandra - Infers that Apache Cassandra might be running on the instance.
    - ApacheHadoop - Infers that Apache Hadoop might be running on the instance.
    - Memcached - Infers that Memcached might be running on the instance.
    - NGINX - Infers that NGINX might be running on the instance.
    - PostgreSql - Infers that PostgreSQL might be running on the instance.
    - Redis - Infers that Redis might be running on the instance.
    - Kafka - Infers that Kafka might be running on the instance.
    - SQLServer - Infers that SQLServer might be running on the instance.
    Parameters:
    inferredWorkloadTypes - The applications that might be running on the instance as inferred by Compute Optimizer.
    
    Compute Optimizer can infer if one of the following applications might be running on the instance:
    
    AmazonEmr - Infers that Amazon EMR might be running on the instance.
    
    ApacheCassandra - Infers that Apache Cassandra might be running on the instance.
    
    ApacheHadoop - Infers that Apache Hadoop might be running on the instance.
    
    Memcached - Infers that Memcached might be running on the instance.
    
    NGINX - Infers that NGINX might be running on the instance.
    
    PostgreSql - Infers that PostgreSQL might be running on the instance.
    
    Redis - Infers that Redis might be running on the instance.
    
    Kafka - Infers that Kafka might be running on the instance.
    
    SQLServer - Infers that SQLServer might be running on the instance.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    InferredWorkloadType
  - setInstanceState
```
public void setInstanceState(String instanceState)
```
    The state of the instance when the recommendation was generated.
    
    Parameters:
    
    instanceState - The state of the instance when the recommendation was generated.
    
    See Also:
    
    InstanceState
  - getInstanceState
```
public String getInstanceState()
```
    The state of the instance when the recommendation was generated.
    
    Returns:
    
    The state of the instance when the recommendation was generated.
    
    See Also:
    
    InstanceState
  - withInstanceState
```
public InstanceRecommendation withInstanceState(String instanceState)
```
    The state of the instance when the recommendation was generated.
    
    Parameters:
    
    instanceState - The state of the instance when the recommendation was generated.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    InstanceState
  - withInstanceState
```
public InstanceRecommendation withInstanceState(InstanceState instanceState)
```
    The state of the instance when the recommendation was generated.
    
    Parameters:
    
    instanceState - The state of the instance when the recommendation was generated.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    InstanceState
  - getTags
```
public List<Tag> getTags()
```
    A list of tags assigned to your Amazon EC2 instance recommendations.
    
    Returns:
    
    A list of tags assigned to your Amazon EC2 instance recommendations.
  - setTags
```
public void setTags(Collection<Tag> tags)
```
    A list of tags assigned to your Amazon EC2 instance recommendations.
    
    Parameters:
    
    tags - A list of tags assigned to your Amazon EC2 instance recommendations.
  - withTags
```
public InstanceRecommendation withTags(Tag... tags)
```
    A list of tags assigned to your Amazon EC2 instance recommendations.
    
    NOTE: This method appends the values to the existing list (if any). Use setTags(java.util.Collection) or withTags(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    tags - A list of tags assigned to your Amazon EC2 instance recommendations.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withTags
```
public InstanceRecommendation withTags(Collection<Tag> tags)
```
    A list of tags assigned to your Amazon EC2 instance recommendations.
    
    Parameters:
    
    tags - A list of tags assigned to your Amazon EC2 instance recommendations.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setExternalMetricStatus
```
public void setExternalMetricStatus(ExternalMetricStatus externalMetricStatus)
```
    An object that describes Compute Optimizer's integration status with your external metrics provider.
    
    Parameters:
    
    externalMetricStatus - An object that describes Compute Optimizer's integration status with your external metrics provider.
  - getExternalMetricStatus
```
public ExternalMetricStatus getExternalMetricStatus()
```
    An object that describes Compute Optimizer's integration status with your external metrics provider.
    
    Returns:
    
    An object that describes Compute Optimizer's integration status with your external metrics provider.
  - withExternalMetricStatus
```
public InstanceRecommendation withExternalMetricStatus(ExternalMetricStatus externalMetricStatus)
```
    An object that describes Compute Optimizer's integration status with your external metrics provider.
    
    Parameters:
    
    externalMetricStatus - An object that describes Compute Optimizer's integration status with your external metrics provider.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setCurrentInstanceGpuInfo
```
public void setCurrentInstanceGpuInfo(GpuInfo currentInstanceGpuInfo)
```
    Describes the GPU accelerator settings for the current instance type.
    
    Parameters:
    
    currentInstanceGpuInfo - Describes the GPU accelerator settings for the current instance type.
  - getCurrentInstanceGpuInfo
```
public GpuInfo getCurrentInstanceGpuInfo()
```
    Describes the GPU accelerator settings for the current instance type.
    
    Returns:
    
    Describes the GPU accelerator settings for the current instance type.
  - withCurrentInstanceGpuInfo
```
public InstanceRecommendation withCurrentInstanceGpuInfo(GpuInfo currentInstanceGpuInfo)
```
    Describes the GPU accelerator settings for the current instance type.
    
    Parameters:
    
    currentInstanceGpuInfo - Describes the GPU accelerator settings for the current instance type.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setIdle
```
public void setIdle(String idle)
```
    Describes if an Amazon EC2 instance is idle.
    
    Parameters:
    
    idle - Describes if an Amazon EC2 instance is idle.
    
    See Also:
    
    InstanceIdle
  - getIdle
```
public String getIdle()
```
    Describes if an Amazon EC2 instance is idle.
    
    Returns:
    
    Describes if an Amazon EC2 instance is idle.
    
    See Also:
    
    InstanceIdle
  - withIdle
```
public InstanceRecommendation withIdle(String idle)
```
    Describes if an Amazon EC2 instance is idle.
    
    Parameters:
    
    idle - Describes if an Amazon EC2 instance is idle.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    InstanceIdle
  - withIdle
```
public InstanceRecommendation withIdle(InstanceIdle idle)
```
    Describes if an Amazon EC2 instance is idle.
    
    Parameters:
    
    idle - Describes if an Amazon EC2 instance is idle.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    InstanceIdle
  - toString
```
public String toString()
```
    Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    A string representation of this object.
    
    See Also:
    
    Object.toString()
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - clone
```
public InstanceRecommendation clone()
```
    Overrides:
    
    clone in class Object
  - marshall
```
public void marshall(ProtocolMarshaller protocolMarshaller)
```
    Description copied from interface: StructuredPojo
    
    Marshalls this structured data using the given ProtocolMarshaller.
    
    Specified by:
    
    marshall in interface StructuredPojo
    
    Parameters:
    
    protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.

AWS SDK for Java 1.x API Reference - 1.12.779

Class InstanceRecommendation

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

InstanceRecommendation

Method Detail

setInstanceArn

getInstanceArn

withInstanceArn

setAccountId

getAccountId

withAccountId

setInstanceName

getInstanceName

withInstanceName

setCurrentInstanceType

getCurrentInstanceType

withCurrentInstanceType

setFinding

getFinding

withFinding

withFinding

getFindingReasonCodes

setFindingReasonCodes

withFindingReasonCodes

withFindingReasonCodes

withFindingReasonCodes

getUtilizationMetrics

setUtilizationMetrics

withUtilizationMetrics

withUtilizationMetrics

setLookBackPeriodInDays

getLookBackPeriodInDays

withLookBackPeriodInDays

getRecommendationOptions

setRecommendationOptions

withRecommendationOptions

withRecommendationOptions

getRecommendationSources

setRecommendationSources

withRecommendationSources

withRecommendationSources

setLastRefreshTimestamp

getLastRefreshTimestamp

withLastRefreshTimestamp

setCurrentPerformanceRisk

getCurrentPerformanceRisk

withCurrentPerformanceRisk

withCurrentPerformanceRisk

setEffectiveRecommendationPreferences

getEffectiveRecommendationPreferences

withEffectiveRecommendationPreferences

getInferredWorkloadTypes

setInferredWorkloadTypes

withInferredWorkloadTypes

withInferredWorkloadTypes

withInferredWorkloadTypes

setInstanceState

getInstanceState

withInstanceState

withInstanceState

getTags

setTags

withTags

withTags

setExternalMetricStatus

getExternalMetricStatus

withExternalMetricStatus

setCurrentInstanceGpuInfo

getCurrentInstanceGpuInfo

withCurrentInstanceGpuInfo

setIdle

getIdle

withIdle

withIdle

toString

equals

hashCode