A Reason for Capacity Planning


Before diving in to the Capacity Advisor example scenario, one of the many reasons for capacity planning is illustrated using an experiment that was performed using data from production workloads in HP's internal IT department. In this experiment, the data for two production workloads was collected. The data was then used to determine whether consolidating the two workloads would result in reduced hardware requirements. Of course, reducing the number of required CPUs results in a reduction in both hardware costs and software licensing costs.

The graph shown in Figure 18-1 depicts the resource utilization for the first workload to be examined in HP's internal IT department. The graph shows the CPU utilization for the month of January. The scale of the y-axis is based on CPU shares, with 100 shares equaling one physical CPU. This workload consumed roughly seven CPUs at its highest peaks.

Figure 18-1. First Workload's Resource Utilization for the Month of January


The graph shown in Figure 18-2 is the CPU utilization for a second workload HP's internal IT department examined. As with the previous graph, the y-axis of the graph is based on CPU shares. This workload consumed almost eight CPUs at its highest peak. When the two graphs are examined together, it appears the utilization patterns of the workloads are closely aligned and experience peaks at roughly the same time. However, as shown in the next graph, that is not necessarily the case.

Figure 18-2. Second Workload's Resource Utilization for the Month of January


After seeing these two graphs, the question arises, "How much hardware, if any, can I save by consolidating these workloads?" The initial answer from looking only at the previous two graphs could be "None." The first workload requires seven CPUs and the second requires eight CPUs, so the consolidated system must have 15 CPUs. However, Figure 18-3 shows the aggregation of the two consolidated workloads. From this graph it is clear that while the peaks appear to line up very closely, they are offset enough to provide an opportunity for hardware savings. In this case, the sum of the two workloads is less than 12 CPUs. This is a 20% savings in hardware without a reduction in the quality of service or response time.

Figure 18-3. Combined Utilization for Both Workloads


From this simple experiment based on production workloads in HP's internal IT department, it is obvious that careful capacity planning can truly result in higher system utilization and reduced hardware requirements; both will result in cost savings. The following section walks through an example Capacity Advisor scenario that describes how the product can be used to effectively and reliably plan for workload consolidations.



The HP Virtual Server Environment. Making the Adaptive Enterprise Vision a Reality in Your Datacenter
The HP Virtual Server Environment: Making the Adaptive Enterprise Vision a Reality in Your Datacenter
ISBN: 0131855220
EAN: 2147483647
Year: 2003
Pages: 197

flylib.com © 2008-2017.
If you may any questions please contact us: flylib@qtcs.net