Viewing an AWS Resilience Hub application summary
The application summary page in the AWS Resilience Hub console provides an overview of your application information and resiliency health.
To view an application summary
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Choose Applications from the navigation pane.
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On the Applications page, choose the name of the application you want to view.
The applications summary page has the following sections.
Assessment Summary
This section provides a summary of the last successful assessment and highlights critical recommendations as actionable insights. AWS Resilience Hub uses Amazon Bedrock generative AI capabilities to help focus users on the most critical resilience recommendations provided by AWS Resilience Hub. By focusing on the critical items, you can focus on the most critical recommendations that improves the resilience posture of your application. Choose a recommendation to view its summary and choose View details to view more details about the recommendations in the relevant section of the assessment report. For more information about reviewing the assessment report, see Reviewing assessments reports.
Note
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This assessment summary is available only in US East (N. Virginia) Region.
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The assessment summary generated by large language models (LLMs) on Amazon Bedrock are only suggestions. The current level of generative AI technology is not perfect and LLMs are not infallible. Bias and incorrect answers, although rare, should be expected. Review each recommendation in the Assessment summary before you use the output from an LLM.
Summary
This section provides a summary of the selected application in the following sections:
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Application info – This section provides the following information about the selected application:
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Application status – Indicates the status of the application.
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Description – The description of the application.
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Version – Indicates the currently assessed version of the application.
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Resiliency policy – Indicates the resiliency policy that is attached the application. For more information about resiliency policies, see Managing resiliency policies.
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Application drifts – This section highlights the drifts detected while running an assessment for the selected application to check if it is compliant with its resiliency policy. Additionally, it also checks if any of the resources have been added or removed since the last time the application version was published. This section displays the following information:
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Policy drifts – Choose the number below to view all the Application Components that complied with the policy in the previous assessment but failed to comply in the current assessment.
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Resource drifts – Choose the number below to view all the drifted resources in the latest assessment.
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Application resiliency
The metrics shown on the Resiliency score section are from the most recent resiliency assessment of the application.
Resiliency score
The resiliency score helps you quantify your readiness to handle a potential disruption. This score reflects how closely your application has followed the AWS Resilience Hub recommendations for meeting the application's resiliency policy, alarms, standard operating procedures (SOPs), and tests.
The maximum resiliency score that your application can achieve is 100%. The score represents all recommended tests that run in a predefined period of time. It indicates that the tests are initiating the correct alarm, and that the alarm initiates the correct SOP.
For example, suppose that AWS Resilience Hub recommends one test with one alarm and one SOP. When the test runs, the alarm initiates the associated SOP, and then runs successfully. For more information about the resiliency score, see Understanding resiliency scores.
Implemented alarms
The application summary Implemented alarms section lists the alarms that you set up in Amazon CloudWatch to monitor the application. For more information about alarms, see Managing alarms.
Implemented experiments
The application summary Fault injection experiments section shows a list of the fault injection experiments. For more information about fault injection experiments, see Managing Amazon Fault Injection Service experiments.
