Load Models by : R . Scott Barber Load Models for Performance Testing with Incomplete Empirical Data

For all that it is a common topic of conversation and concern among people and organizations that build or depend on web-based applications, testing a website in such a way that the test can reliably predict performance is still often more of an art than a science. More than a few brilliant minds have dedicated their careers to this complex topic. Several have published detailed and mathematically sound methods to plan for, predict and model performance characteristics very accurately... as long as there is sufficient empirical data to work from. In this paper, we will first briefly explore the concepts, strengths and applications of Connie Smith’s Software Performance Engineering (SPE) methods for creating well performing web-based applications. Then we’ll look at Alberto Savoia’s approach to creating representative load models from the analysis of existing patterns to then be applied by load generation tools. Next, we’ll look at Daniel Menasce’s techniques for building load models of existing sites that both predicts bottlenecks before they happen and generate the data necessary to prevent or push out that bottleneck before the real load gets great enough to show any symptoms of the bottleneck. Then, we’ll look at how J.D. Meier ties these methods together into a single end-toend approach. As you will see, these are all very powerful tools that performance testers and analysts have at our disposal. After reviewing these methods, you will also see that all of these approaches depend on a fairly significant amount of empirical data that is rarely available to the individuals who are doing most of the performance testing and analysis of web-based applications in the industry today – consultants. That is where the insight in this paper actually begins, with the question of “How do I predict actual performance, when I don’t have enough data to model the load accurately?” We will explore various methods that have proven valuable both individually and collectively for some of the top performance test/analysis consultants in the world. Finally, we will tie it all back together by demonstrating how parts and concepts from each of these methods can be blended together when there is not enough data to apply them exactly as described by their authors, based on the context of the task at hand, to Create Effective Load Models for Performance Testing with Incomplete Empirical Data.