The 7U Evaluation Method: Evaluating Software Systems via Runtime Fault-Injection and Reliability, Availability and Serviceability (RAS) Metrics and Models

The 7U Evaluation Method: Evaluating Software Systems via Runtime Fault-Injection and Reliability, Availability and Serviceability (RAS) Metrics and Models Rean Griffith Renewed interest in developing computing systems that meet additional non-functional requirements such as reliability, high availability and ease-of-management/self-management (serviceability) has fueled research into developing systems that exhibit enhanced reliability, availability and serviceability (RAS) capabilities. This research focus on enhancing the RAS capabilities of computing systems impacts not only the legacy/existing systems we have today, but also has implications for the design and development of next generation (selfmanaging/self-*) systems, which are expected to meet these non-functional requirements with minimal human intervention. To reason about the RAS capabilities of the systems of today or the self-* systems of tomorrow, there are three evaluation-related challenges to address. First, developing (or identifying) practical fault-injection tools that can be used to study the failure behavior of computing systems and exercise any (remediation) mechanisms the system has available for mitigating or resolving problems. Second, identifying techniques that can be used to quantify RAS deficiencies in computing systems and reason about the efficacy of individual or combined RAS-enhancing mechanisms (at design-time or after system deployment). Third, developing an evaluation methodology that can be used to objectively compare systems based on the (expected or actual) benefits of RAS-enhancing mechanisms. This thesis addresses these three challenges by introducing the 7U Evaluation Methodology, a complementary approach to traditional performance-centric evaluations that identifies criteria for comparing and analyzing existing (or yet-to-be-added) RAS-enhancing mechanisms, is able to evaluate and reason about combinations of mechanisms, exposes under-performing mechanisms and highlights the lack of mechanisms in a rigorous, objective and quantitative manner. The development of the 7U Evaluation Methodology is based on the following three hypotheses. First, that runtime adaptation provides a platform for implementing efficient and flexible fault-injection tools capable of in-situ and in-vivo interactions with computing systems. Second, that mathematical models such as Markov chains, Markov reward networks and Control theory models can successfully be used to create simple, reusable templates for describing specific failure scenarios and scoring the system’s responses, i.e., studying the failure-behavior of systems, and the various facets of its remediation mechanisms and their impact on system operation. Third, that combining practical fault-injection tools with mathematical modeling techniques based on Markov Chains, Markov Reward Networks and Control Theory can be used to develop a benchmarking methodology for evaluating and comparing the reliability, availability and serviceability (RAS) characteristics of computing systems. This thesis demonstrates how the 7U Evaluation Method can be used to evaluate the RAS capabilities of real-world computing systems and in so doing makes three contributions. First, a suite of runtime fault-injection tools (Kheiron tools) able to work in a variety of execution environments is developed. Second, analytical tools that can be used to construct mathematical models (RAS models) to evaluate and quantify RAS capabilities using appropriate metrics are discussed. Finally, the results and insights gained from conducting fault-injection experiments on real-world systems and modeling the system responses (or lack thereof) using RASmodels are presented. In conducting 7U Evaluations of real-world systems, this thesis highlights the similarities and differences between traditional performance-oriented evaluations and RAS-oriented evaluations and outlines a general framework for conducting RAS evaluations.