Managing the capacity of your AWS infrastructure

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As a Capacity Planner or AWS Technology Specialist, you can use BMC Helix Capacity Optimization to configure, administer, and manage the capacity of your AWS infrastructure. 

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As the flow diagram illustrates, the data source (AWS API ETL) collects data from the AWS portal. The collected data is transferred to Capacity Optimization where it is processed, and then displayed on the user interface. You can use the product features to review, analyze, and manage the capacity of your AWS infrastructure. 

The following sections describe how you can achieve these goals:

Managing the capacity of AWS infrastructure

You can analyze and manage the capacity of your AWS infrastructure elements by using the AWS EC2 Instances view. For the AWS data to be available in the view, the Administrator must first set up the data sources to collect data. 

Step 1. Collect data and install the views

As an Administrator, you can use the Amazon-Web-Services-AWS-API-Extractor to collect data from the AWS EC2 instances:

  • Configure and run this ETL to collect the required configuration and performance metrics from VMs. In the ETL configuration, you can choose to collect data from a single or multiple AWS accounts depending on your AWS account setup. 
  • In addition to the required metrics collected using the AWS API ETL, you can install, configure, and start the CloudWatch agent on your EC2 instances to collect the system-level metrics from them. These metrics are useful for investigating the capacity-related issues that might occur in your AWS cloud environment. The CloudWatch agent collects these metrics and sends them to Amazon CloudWatch. When you run the AWS API ETL, these metrics are imported into the Capacity Optimization database. For more information, see Collecting-EC2-instance-metrics-using-the-CloudWatch-agent.

(Optional) You can also use BMC-TrueSight-Capacity-Optimization-Gateway-VIS-files-parser to collect additional metrics at a higher granularity. Before you use this ETL, you must instrument the VMs. 

Instrumenting VMs

The memory utilization value that is collected from an instrumented VM is based on the actual memory of the VM. The Agent collects resource consumption breakdown at process or workload level and helps you to detect specific in-guest OS level resource constraints (for example, in-guest paging due to the physical memory configuration of the VM being too low).

BMC recommends that you instrument your business-critical VMs to collect OS-level memory usage values. 

To instrument a VM: 

  1. Install a Capacity Agent inside the VM from which you want to collect metrics
  2. Configure the Gateway Server and Capacity Agent to initiate data collection. For more information, see Collecting-data-via-Capacity-Agents. 
  3. Configure and use the out-of-the-box BMC-TrueSight-Capacity-Optimization-Gateway-VIS-files-parser to collect the required metrics from the VM.

After data collection starts, data is loaded daily and Indicators are available in the Workspace.

As an Administrator, you must install the AWS views and Business Services view and grant the necessary permissions to Capacity Planners and AWS Technology Specialists to access these views. While creating the service pools, ensure to select AWS domains to view AWS only data.

Step 2. Analyze the collected data

For detailed analysis of AWS infrastructure elements, use the AWS-views. 

The following common use cases are described here:

Identify overallocated VMs

Use the Recommendations-page-in-the-AWS-EC2-Instances-view to identify AWS EC2 instances that are overallocated. The page also provides actionable recommendations to help you resolve the issue.

Identify idle or unused VMs

Use the Recommendations-page-in-the-AWS-EC2-Instances-view to identify AWS EC2 instances that are idle. The page also provides actionable recommendations to help you resolve the issue.

The EC2-Idle-Instances-page-in-the-AWS-EC2-Instances-view displays a list of all such EC2 instances along with some of their key metric details.

Determine and analyze the available resources and their utilization per EC2 instance

Review and analyze the relevant metrics on the EC2-Instances-page-in-the-AWS-EC2-Instances-view to determine the available resources and their utilization per instance. For example, utilization metrics for CPU, Memory, and datastore.

Determine and analyze the available resources for EBS volumes

Review the details on the EBS-Volumes-page-in-the-AWS-EC2-Instances-view to analyze the available resources for EBS volume per EC2 instance.

Performing advanced analysis

The earlier sections explained how you can use the out-of-the-box capacity views to manage your environment. These capacity views help you analyze your AWS infrastructure using a predefined set of metrics.

To perform advanced analysis on the imported AWS data, such as identifying specific performance issues, trends, and bottlenecks, you can use Analysis.

About Analysis

An analysis is a visual tool that you can use to identify the behavior of a set of metrics and the relationships among them. Each analysis can focus on the business driver metrics of an application, on the performance of an application's systems, and on the events related to an application. Analyses can also be used to compare performance and business driver metrics to determine a system's behavior under load. 

Here are some use cases for which you can create and use Analyses:

For more examples, see Creating-an-analysis.

Analyze the CPU and memory utilization of EC2 instances

Analyse the resource utilization pattern of AWS VMs

  1. Create an analysis. 
    See sample configuration values: 
    aws_global_metrics_analysis_01.png
    aws_global_metrics_analysis_02.png
  2. Review the analysis results.
    The analysis results are shown in a tabular format. The summary table shows the resource utilization metric values for the virtual machines.
    aws_global_metrics_analysis_03.png
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Managing future demand

By using the capacity views and analysis charts, you can analyze the data of your existing capacity. Use Models to predict service performance and obtain forecasts of historical series of metrics, including deep details on the modeling techniques used in forecasts and how to interpret the results of model runs.

About models

A model is a simplified mathematical description of service components that evaluates historical data, predicts future behavior, and simulates what-if scenarios. Models are always built on existing data and analysis. After you create a model, define scenarios to perform multiple predictions under different conditions. With Models you can forecast and model changes in service demands.

For more information, see Modeling-capacity-usage.

 

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