Managing the capacity of your Azure infrastructure

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

msazure_use_case.png

As the flow diagram illustrates, the data source (Azure API Extractor) collects data from the Azure resources. The collected data is transferred to the Capacity Optimization data warehouse 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 Azure infrastructure.

The following sections describe how you can achieve these goals:

Managing the capacity of Azure infrastructure

You can analyze and manage the capacity of your Azure infrastructure elements by using the Azure Virtual Machines view. For the Azure 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 Microsoft-Azure-Azure-API-Extractor to collect data from the Azure virtual machines:

  • Configure and run this ETL to collect the required configuration and performance metrics from VMs. Depending on your requirement, you can configure the ETL to collect data from the Azure Resource Manager model or Classic model.
  • In addition to the required metrics collected using the Azure API ETL, you can enable guest-level monitoring to collect metrics of your guest virtual machines. These metrics are useful for investigating the capacity-related issues that might occur in your Azure environment. When you run the Microsoft Azure API ETL, these metrics are imported into the Capacity Optimization database. For more information, see Collecting-additional-metrics-using-Guest-OS-diagnostics.

(Optional) You can also use the BMC-TrueSight-Capacity-Optimization-Gateway-VIS-files-parser to collect additional metrics, more accurate memory utilization metrics, and performance 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 in the data warehouse daily and Indicators are available in the Workspace.

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

Step 2. Analyze the collected data

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

The following common use cases are described here:

Identify overallocated VMs

Use the Recommendations-page-in-the-Azure-Virtual-Machines-view to identify Azure VMs 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-Azure-Virtual-Machines-view to identify Azure VMs that are idle. The page also provides actionable recommendations to help you resolve the issue.

The Idle-Virtual-Machines-page-in-the-Azure-Virtual-Machines-view displays a list of all such VMs along with some of their key metric details.

Determine and analyze the available resources and their utilization per virtual machine

Review and analyze the relevant metrics on the Virtual-Machines-page-in-the-Azure-Virtual-Machines-view to determine the available resources and their utilization per VM. For example, utilization metrics for CPU, Memory, and datastore.

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 Azure infrastructure using a predefined set of metrics.

To perform advanced analysis on the imported Azure 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 global metrics utilization of the Azure VM over time and understand the trend

Analyse the resource (CPU) utilization pattern of Azure VMs

  1. Create an analysis. 
    See sample configuration values: 
    azure_vm_cpu_utilization_01.png
    azure_vm_cpu_utilization_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.
    azure_vm_cpu_utilization_03.png

Managing future demand

By using the capacity views and analysis charts, you can analyze the data of your existing capacity. To predict and plan your IT resource needs, use Models.

This topic describes the following use case:

Predicting the behavior of your resources

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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