NDLI: ESTIMA: extrapolating scalability of in-memory applications

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ESTIMA: extrapolating scalability of in-memory applications

Content Provider	ACM Digital Library
Author	Guerraoui, Rachid Chatzopoulos, Georgios Dragojević, Aleksandar
Abstract	This paper 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 various events, such as cache misses or waiting on a lock). 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 effectively used to predict the scalability of in-memory applications. 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 on a large number of in-memory benchmarks shows that ESTIMA has generally low prediction errors.
Starting Page	1
Ending Page	11
Page Count	11
File Format	PDF
ISBN	9781450340922
DOI	10.1145/2851141.2851159
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-02-27
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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