Ítem
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Cutting Latency Tail: Analyzing and Validating Replication without Canceling
Título de la revista
Autores
Qiu Z.
Pérez, Juan F.
Birke R.
Chen L.
Harrison P.G.
Fecha
2017
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IEEE Computer Society
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Abstract
Response time variability in software applications can severely degrade the quality of the user experience. To reduce this variability, request replication emerges as an effective solution by spawning multiple copies of each request and using the result of the first one to complete. Most previous studies have mainly focused on the mean latency for systems implementing replica cancellation, i.e., all replicas of a request are canceled once the first one finishes. Instead, we develop models to obtain the response-time distribution for systems where replica cancellation may be too expensive or infeasible to implement, as in 'fast' systems, such as web services, or in legacy systems. Furthermore, we introduce a novel service model to explicitly consider correlation in the processing times of the request replicas, and design an efficient algorithm to parameterize the model from real data. Extensive evaluations on a MATLAB benchmark and a three-tier web application (MediaWiki) show remarkable accuracy, e.g., 7 (4 percent) average error on the 99th percentile response time for the benchmark (respectively, MediaWiki), the requests of which execute in the order of seconds (respectively, milliseconds). Insights into optimal replication levels are thereby gained from this precise quantitative analysis, under a wide variety of system scenarios. © 2017 IEEE.
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Keywords
Application programs , Benchmarking , Computer software , Computer software selection and evaluation , Legacy systems , MATLAB , Web services , Effective solution , Matrix analytic methods , Response time distribution , Response time variability , Service time , Software applications , Software quality engineering , Speculative computing , Response time (computer systems) , Correlated service times , Matrix analytic methods , Software quality engineering , Speculative computing
