COST09-BP01 Perform an analysis on the workload demand - Cost Optimization Pillar

COST09-BP01 Perform an analysis on the workload demand

Analyze the demand of the workload over time. Verify that the analysis covers seasonal trends and accurately represents operating conditions over the full workload lifetime. Analysis effort should reflect the potential benefit, for example, time spent is proportional to the workload cost.

Level of risk exposed if this best practice is not established: High

Implementation guidance

Analyzing workload demand for cloud computing involves understanding the patterns and characteristics of computing tasks that are initiated in the cloud environment. This analysis helps users optimize resource allocation, manage costs, and verify that performance meets required levels.

Know the requirements of the workload. Your organization's requirements should indicate the workload response times for requests. The response time can be used to determine if the demand is managed, or if the supply of resources should change to meet the demand.

The analysis should include the predictability and repeatability of the demand, the rate of change in demand, and the amount of change in demand. Perform the analysis over a long enough period to incorporate any seasonal variance, such as end-of-month processing or holiday peaks.

Analysis effort should reflect the potential benefits of implementing scaling. Look at the expected total cost of the component and any increases or decreases in usage and cost over the workload's lifetime.

The following are some key aspects to consider when performing workload demand analysis for cloud computing:

  1. Resource utilization and performance metrics: Analyze how AWS resources are being used over time. Determine peak and off-peak usage patterns to optimize resource allocation and scaling strategies. Monitor performance metrics such as response times, latency, throughput, and error rates. These metrics help assess the overall health and efficiency of the cloud infrastructure.

  2. User and application scaling behaviour: Understand user behavior and how it affects workload demand. Examining the patterns of user traffic assists in enhancing the delivery of content and the responsiveness of applications. Analyze how workloads scale with increasing demand. Determine whether auto-scaling parameters are configured correctly and effectively for handling load fluctuations.

  3. Workload types: Identify the different types of workloads running in the cloud, such as batch processing, real-time data processing, web applications, databases, or machine learning. Each type of workload may have different resource requirements and performance profiles.

  4. Service-level agreements (SLAs): Compare actual performance with SLAs to ensure compliance and identify areas that need improvement.

You can use Amazon CloudWatch to collect and track metrics, monitor log files, set alarms, and automatically react to changes in your AWS resources. You can also use Amazon CloudWatch to gain system-wide visibility into resource utilization, application performance, and operational health.

With AWS Trusted Advisor, you can provision your resources following best practices to improve system performance and reliability, increase security, and look for opportunities to save money. You can also turn off non-production instances and use Amazon CloudWatch and Auto Scaling to match increases or reductions in demand.

Finally, you can use AWS Cost Explorer or Amazon QuickSight with the AWS Cost and Usage Report (CUR) file or your application logs to perform advanced analysis of workload demand.

Overall, a comprehensive workload demand analysis allows organizations to make informed decisions about resource provisioning, scaling, and optimization, leading to better performance, cost efficiency, and user satisfaction.

Implementation steps

  • Analyze existing workload data: Analyze data from the existing workload, previous versions of the workload, or predicted usage patterns. Use Amazon CloudWatch, log files and monitoring data to gain insight on how workload was used. Analyze a full cycle of the workload, and collect data for any seasonal changes such as end-of-month or end-of-year events. The effort reflected in the analysis should reflect the workload characteristics. The largest effort should be placed on high-value workloads that have the largest changes in demand. The least effort should be placed on low-value workloads that have minimal changes in demand.

  • Forecast outside influence: Meet with team members from across the organization that can influence or change the demand in the workload. Common teams would be sales, marketing, or business development. Work with them to know the cycles they operate within, and if there are any events that would change the demand of the workload. Forecast the workload demand with this data.


Related documents:

Related videos:

Related examples: