ESTIMA: Extrapolating ScalabiliTy of In-Memory Applications

This article presents ESTIMA, an easy-to-use tool for extrapolating the scalability of in-memory applications. ESTIMA is designed to perform a simple yet important task: Given the performance of an application on a small machine with a handful of cores, ESTIMA extrapolates its scalability to a larger machine with more cores, while requiring minimum input from the user. The key idea underlying ESTIMA is the use of stalled cycles (e.g., cycles that the processor spends waiting for missed cache line fetches or busy locks). ESTIMA measures stalled cycles on a few cores and extrapolates them to more cores, estimating the amount of waiting in the system. ESTIMA can be e ectively used to predict the scalability of in-memory applications for bigger execution machines. For instance, using measurements of memcached and SQLite on a desktop machine, we obtain accurate predictions of their scalability on a server. Our extensive evaluation shows the effectiveness of ESTIMA on a large number of in-memory benchmarks.


Published in:
ACM Transactions on Parallel Computing, 4, 2, 10:1-28
Year:
2017
ISSN:
2329-4949
Keywords:
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 Record created 2017-09-04, last modified 2018-12-03

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