Performance Extrapolation using Load Testing Results

Load testing of IT applications is fraught with the challenges of time to market, quality of results, high cost of commercial tools, and accurately representing production like scenarios. It would help IT projects to be able to test with a small number of users and extrapolate to scenarios with much larger number of users. This in turn will cut down cycle times and costs and allow for a variety of extrapolations closer to production. In this paper, we present a simple extrapolation technique based on statistical empirical modelling that analyses application workload based on its service demand on various system resources. The proposed strategy is also applied to predict the performance of mixture workloads before the system encounters any hardware resource bottleneck. The strategy is found to be more than 90% accurate for a range of applications running across a number of hardware servers. The technique has currently been validated for scenarios where the hardware is the bottleneck and is extensible to a wider range of scenarios as well.