Abstract.As the complexity of large-scale enterprise applications increases, providing performance verification through staging becomes an important part of reducing business risks associated with violating sophisticated service-level agreement (SLA). Currently, performance verification during the staging process is accomplished through either an expensive, cumbersome manual approach or ad hoc automation. This paper describes an automation approach as part of the Elba project supporting monitoring and performance analysis of distributed multi-tiered applications that helps in bottleneck detection. We use machinelearning to determine service-level objectives (SLOs) satisfaction and locate bottlenecks in candidate deployment scenarios. We evaluate our tools with TPC-W, an on-line bookstore, and RUBiS, an on-line auction site.
Abstract. The complexity of today's large-scale enterprise applications demands system administrators to monitor enormous amounts of metrics, and reconfigure their hardware as well as software at run-time without thorough understanding of monitoring results. The Elba project is designed to achieve an automated iterative staging to mitigate the risk of violating Service Level Objectives (SLOs). As part of Elba we undertake performance characterization of system to detect bottlenecks in their configurations. In this paper, we introduce our concrete bottleneck detection approach used in Elba, and then show its robustness and accuracy in various configurations scenarios. We utilize a wellknown benchmark application, RUBiS (Rice University Bidding System), to evaluate the classifier with respect to successful identification of different bottlenecks.
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