Since the goal of this scenario is to determine whether a single nPartition with eight CPUs and 16 GB of memory will adequately meet the resource requirements for these three workloads, a hy- pothetical system will be added to this scenario. The screen shown in Figure 18-16 shows the tool for defining a new hypothetical system. As an alternative to creating a new system, the system ca- pacity for one of the existing nPartitions could have been hypothetically increased. After creating the hypothetical system, what-if experiments can be performed that simulate the consolidation of the three workloads to the hypothetical system.
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Before performing experiments using this scenario, the simulation interval should be set to reflect the time period to be used for performing the capacity-planning analysis. In this situa- tion, the interval has been set two years in the future. Using a time interval in the future allows the workloads’ forecast to be taken into account. This ensures that the workloads will have ad- equate resources at a future point. Failure to set the simulation interval appropriately could pro- duce the result that although a workload consolidation appears to fit on the target system, a year or two in the future the system doesn’t have the capacity to meet the expected growth in the workloads’ resource requirements.
The next step in this scenario is performing a what-if experiment using the newly defined hy- pothetical system and the three workloads that have been edited to accurately reflect their his- toric and future resource requirements.
The screen shown in Figure 18-17 is the What-If Move Workload screen. This interface allows the selected workloads to be moved to the hypothetical system. This interface does not actually move the workloads. Instead, it serves as a test-bed for performing capacity-planning scenarios.
In this case, the three workloads are selected to be moved to the hypothetical system. The upper portion of the screen shows each of the workloads and their resource profiles. The middle of the screen shows the current nPartition servers where the workloads are currently running. Finally, the bottom of the screen shows the hypothetical system as the target for the workloads. When this What-If Move Workload screen is approved, the follow-on screens for this capacity-planning scenario will show the workloads as if they have been moved. From these screens, reports can be generated to evaluate whether the hypothetical system and the workloads will receive their re- quired resources.
Since the hypothetical system in the scenario now contains the three workloads being eval- uated for consolidation, the system is selected as the target for reports. Figure 18-18 shows the interface for creating a system report. This interface provides a wide variety of options for cus- tomizing the report. In this example, the options to generate overall system and workload summaries are selected along with the utilization profiles and sustained load reports for the hypothetical system and the workloads.
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As with the simulation interval for the what-if scenarios, the date range for generating sys- tem reports should generally be set to a point in the future. In this case, the time interval for the report has been set to the entire month of March two years in the future. This takes the forecast for the workloads into account. The generated report will then indicate the resource utilization at the specified time period in the future.
The screen shown in Figure 18-19 illustrates one of the many graphs that are generated in the resulting Capacity Advisor report. This graph shows what the sustained CPU utilization will be for the hypothetical system during March of 2007. The maximum sustained utilization (at least 15 minutes in duration) is 92% for this hypothetical system. This means that the highest work- load peak that will be sustained for at least 15 minutes will consume at least 92% of the hypo- thetical system’s resource. An important distinction to understand from the sustained load graph is that the graph doesn’t show utilization spikes that are shorter than 15 minutes in duration. In many environments, such short spikes aren’t a concern from a capacity-planning perspective; the sustained peaks are generally most significant. In addition to verifying that the CPU load can be
handled by the target system, you should also examine other system resources to ensure that memory and I/O resources will be able to handle the consolidated load.
The graphs and data provided in the Capacity Planning reporting facilities are extensive. These reports provide a wealth of information that is based on historic data and workload- specific baselines. The reports can also be built from hypothetical systems and workloads. As a result of this combination of historic data and fine tuning, the models generated by the Capac- ity Advisor are highly accurate and reliable.
From the experiment performed in this example scenario it is clear that the three workloads can be safely combined to a single hardware nPartition. This will release one-third of the com- puting resources for other workloads and will result in higher utilization of the hardware in the consolidated environment. In addition, fewer operating system images will be required to host the workloads; this translates into lower system administration costs to keep the operating sys- tems up to date and running properly.
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Summary
The Capacity Advisor component of the VSE management suite provides the data, tools, and re- ports necessary to make informed decisions regarding workload placement, migration, and con- solidation. Using this product, capacity planners are able to analyze each workload’s utilization profile. With data in hand, they can create baselines as the foundation for performing what-if sce- narios. The baseline can be finely tuned to accurately represent the nature of the workloads, which creates more accurate representations of the workload’s resource requirements. Specific workloads may require that you build elaborate recurrences into the baseline, such as peaks on a biweekly basis, in order to reflect the nature of workloads. After adjusting the baseline, you can build a forecast to extrapolate the workload’s resource requirements into the future. This fore- cast can be adjusted based on expected changes in the workload’s resource requirements.
After going through the baseline and forecast modification steps, you can create capacity- planning scenarios as a test bed for evaluating workload consolidations, migrations, or place- ments. These scenarios allow what-if experiments to be performed on both real and hypothetical workloads and systems. These experiments provide an accurate preview of the expected resource utilization because they are based on actual workload utilization metrics.
Capacity Advisor is a crucial component of HP’s Virtual Server Environment. It allows capacity planners to more fully utilize the existing resources in the datacenter and provide assistance when placing new workloads or performing workload consolidation. These steps are tradition- ally difficult to perform and require extensive manual data collection and manipulation. Using Capacity Advisor takes the manual steps out of the process and allows capacity planners to focus on planning the datacenter.
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© 2006 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. The only warranties for HP products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. HP shall not be liable for technical or editorial errors or omissions contained herein.