Towards Understanding Cloud Performance Tradeoffs Using Statistical Workload Analysis and Replay

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Provided by: University of California San Francisco
Topic: Cloud
Format: PDF
Cloud computing has given rise to a variety of distributed applications that rely on the ability to harness commodity resources for large scale computations. The inherent performance variability in these applications' workload coupled with the system's heterogeneity render ineffective heuristics-based design decisions such as system configuration, application partitioning and placement, and job scheduling. Furthermore, the cloud operator's objective to maximize utilization conflicts with cloud application developers' goals of minimizing latency, necessitating systematic approaches to tradeoff these optimization angles.
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