Finding External Indicators of Load on a Web Server via Analysis of Black-Box Performance Measurements

Traditional methods for system performance analysis have long relied on a mix of queuing theory, detailed system knowledge, intuition, and trial-anderror. These approaches often require construction of incomplete gray-box models that can be costly to build and difficult to scale or generalize. In this thesis, we present a black-box analysis method to discover the amount of load on a web server with minimal knowledge of its internal mechanisms. In contrast to white-box analysis, where a system’s internal mechanisms can help to explain its behavior, black-box analysis relies on external measurements of a system’s reactions to well-understood inputs. The primary advantages of black-box analysis are its relative independence from specific architectures,its applicability to opaque environments (e.g., closed-source systems), and its scalability. In this thesis, we show that statistical analyses of web server response times can be used to discover which server resources are stressed by particular workloads. We also show that under certain conditions, the settling period of server response times after resource perturbation correlates positively with the degree of perturbation. Finally, we use the two-sample KolmogorovSmirnov (KS) test to measure statistical equality of multiple samples drawn from response times of a server under various steady-state load conditions. We show that in specific circumstances, the number of samples that test as statistically equal can serve as an imprecise indicator of the amount of load on a server. All of these contributions will aid performance analysis in new environments such as cloud computing, where internal server mechanisms and configurations change dynamically and structural information is hidden from users.