USITS '03 Abstract
Using Fault Injection and Modeling to Evaluate the Performability of Cluster-Based Services
Kiran Nagaraja, Xiaoyan Li, Ricardo Bianchini, Richard P. Martin, and Thu D. Nguyen, Rutgers University
We propose a two-phase methodology for quantifying the performability (performance and availability) of cluster-based Internet services. In the first phase, evaluators use a fault-injection infrastructure to measure the impact of faults on the server's performance. In the second phase, evaluators use an analytical model to combine an expected fault load with measurements from the first phase to assess the server's performability. Using this model, evaluators can study the server's sensitivity to different design decisions, fault rates, and environmental factors. To demonstrate our methodology, we study the performability of 4 versions of the PRESS Web server against 5 classes of faults, quantifying the effects of different design decisions on performance and availability. Finally, to further show the utility of our model, we also quantify the impact of two hypothetical changes, reduced human operator response time and the use of RAIDs.
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