Managing the capacity of your RHEV infrastructure
As a Capacity Planner or RHEV Technology Specialist, you can use BMC Helix Continuous Optimization to configure, administer, and manage the capacity of your RHEV (Red Hat Enterprise Virtualization) infrastructure. BMC Helix Continuous Optimization enables you to collect and analyze data from RHEV infrastructure elements:
- Providers (hosts)
- Consumers (virtual machines)
As the flow diagram illustrates, the BMC Helix Continuous Optimization data source collects data from the RHEV infrastructure elements. The collected data is transferred to BMC Helix Continuous Optimization where it is processed, and then displayed on the user interface. You can use the product functionalities to review, analyze, and manage capacity of your RHEV infrastructure providers and consumers.
The following sections describe how you can achieve these goals:
Collecting the data of RHEV infrastructure providers
As an Administrator, configure and use one of the following methods for data collection:
- Using the out-of-the-box ETLs:
- Using the Continuous Optimization Agents
After data collection starts, data is loaded daily and Indicators are available in the Workspace tab.
Collecting the data of RHEV infrastructure consumers
As an Administrator, you can use the following data sources to collect data from the KVM virtual machines managed by RHEV:
- Red-Hat-Enterprise-Virtualization-Extractor-service: Configure and run this ETL to collect the required configuration and performance metrics from VMs.
- BMC-TrueSight-Capacity-Optimization-Gateway-VIS-files-parser: Configure and run this ETL 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. 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
- Install a Continuous Optimization Agent inside the VM from which you want to collect metrics
- Configure the Gateway Server and Continuous Optimization Agent to initiate data collection.
For more information, see Collecting data via Continuous Optimization Agents. - 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 tab.
In the Workspace tab:
- Virtual machines whose data is collected by the Red Hat Enterprise Virtualization - Extractor service ETL are represented by the Virtual Machines - RHEV system type.
- Instrumented VMs are represented by the Virtual Node - RHEV system type.
- If you have configured the ETLs to share the same entity catalog, then these two system types, Virtual Machine - RHEV and Virtual Node - RHEV, are internally linked and you can view their relationship for each VM.
Performing advanced analysis
To perform advanced analysis on the imported KVM data managed by RHEV, such as identifying specific performance issues, trends, and bottlenecks, you can use Analysis. For more information, see Creating-an-analysis.
Here are some use cases for which you can create and use Analysis:
Analyze the CPU utilization
The following video explains how to create an analysis to analyze the trend of CPU utilization of a host over time.
Analyze the resource utilization pattern of KVM hosts managed by RHEV
- Create an analysis.
See sample configuration values:
- Review the analysis results.
The analysis results are shown in a tabular format. The summary table shows the resource utilization metric values for a host. The page shows the memory usage of the host.
Managing the future demand
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. For more information, see Modeling-capacity-usage.
Forecast the saturation of a resource
The following video explains how to create a model to predict when a resource of a host completely saturates.