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Dynamic Performance Profiling of Cloud Caches by Hjortur Bjornsson; Gregory Chockler; Trausti Saemundsson; Ymir Vigfusson is a scholarly article available to read on EtoBox.

What is Dynamic Performance Profiling of Cloud Caches about?

In-memory object caches, such as memcached, are critical to the success of popular web sites, such as Facebook [3], by reducing database load and improving scalability [2]. The prominence of caches implies that configuring their ideal memory size has the potential for significant savings on computation resources and energy costs, but unfortunately cache configuration is poorly understood. The modern practice of manually tweaking live caching systems takes significant effort and may both increase the variance for client request latencies and impose high load on the database backend. Contributions. We provide an efficient online algorithm to estimate how an LRU cache would perform using a different memory allocation, continually exposing a hit rate curve as a function of space (Figure 1). Our method is lock-free and compatible with modern multithreaded cache servers, such as memcached. Approach. For a cache of size n, the challenge is to generate a hit rate curve for cache sizes ranging from 0 to 2n. First, to predict how a cache would perform beyond the current allocation of n, we track metadata for n additional dataless elements, so-called "ghosts" [1]. While technically a cache mi

Author

Hjortur Bjornsson; Gregory Chockler; Trausti Saemundsson; Ymir Vigfusson

Publisher

ACM

Published

2013

Language

EN