Developers deploying web applications in the cloud often need to determine how changes to service tiers or runtime load may affect user-perceived page load time. We devise and evaluate a systematic methodology for exploring such \u201cwhat-if\u201d questions when a web application is deployed. Given a website, a web request, and \u201cwhatif\u201d scenario, with a hypothetical configuration and runtime conditions, our methodology, embedded in a system called WebPerf, can estimate a distribution of cloud latency of the request under the \u201cwhat-if\u201d scenario. In achieving this goal, WebPerf makes three contributions: (1) automated instrumentation of websites written in an increasingly popular task asynchronous paradigm, to capture causal dependencies of various computation and asynchronous I\/O calls; (2) an algorithm to use the call dependencies, together with onlineand offline-profiled models of various I\/O calls to estimate a distribution of end-to-end latency of the request; and (3) an algorithm to find the optimal measurements to take within a limited time to minimize modeling errors. We have implemented WebPerf for Microsoft Azure. In experiments with six real websites and six scenarios, the WebPerf\u2019s median estimation error is within 7% in all experiments.<\/p>\n","protected":false},"excerpt":{"rendered":"
Developers deploying web applications in the cloud often need to determine how changes to service tiers or runtime load may affect user-perceived page load time. We devise and evaluate a systematic methodology for exploring such \u201cwhat-if\u201d questions when a web application is deployed. Given a website, a web request, and \u201cwhatif\u201d scenario, with a hypothetical 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