In the previous chapters we presented a method, a software toolkit and experimental results on the determination of resource profiles for customer-oriented services. We will now analyze whether the determined consumption estimates are appropriate input parameters for analytical performance models. The basic approach is to set up a performance model according to the respective guidelines in the literature and calculate model predictions of system performance in different workload scenarios. We then validate model and input parameters by comparing the results, i.e., processor utilizations, response times, throughput with measurements from load tests. The motivation therefore stems from the overall requirements on resource profiles (see section 3.4):
  1. 1.

    Accuracy The resource profiles should be unbiased even if the workload at the respective resources varies. We have already addressed this central requirement in the experiments presented in section 4.3.4. However, during those load tests the resources were at all times exclusively used by a single service. Furthermore, the profiling workload consisting of x concurrent services invocations is not comparable to the workload during regular operations. In contrast, in load tests for the validation of performance models realistic scenarios with multiple concurrently active services and varying workload can be simulated. We claim that if performance models parameterized with consumption estimates from the resource profiles accurately predict system performance (utilization, response time, throughput) during those load tests, the accuracy of the resource profiles is also sufficient for cost allocation.

  2. 2.

    Capacity Planning In contrast to arbitrarily chosen cost allocation keys, the resource profiles should bridge the gap between business forecasts and IT resource requirements. The major advantage of an analytical performance model is that once has it has been validated it can be readily used for capacity planning. It enables capacity planners to conduct “what-if” analyses and thus anticipate the effects of changes in the workload composition, hardware configuration or system architecture.



Load Test Processor Utilization Capacity Planning Queueing Network Queueing Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Betriebswirtschaftlicher Verlag Dr. Th. Gabler | GWV Fachverlage GmbH, Wiesbaden 2008

Personalised recommendations