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A uto P lacer : Scalable Self-Tuning Data Placement in Distributed Key-Value Stores

A uto P lacer : Scalable Self-Tuning Data Placement in Distributed Key-Value Stores AUTOPLACER: Scalable Self-Tuning Data Placement in Distributed Key-Value Stores ~ JOAO PAIVA, INESC-ID, Instituto Superior T´ cnico, Universidade de Lisboa, Portugal e PEDRO RUIVO, Red Hat, Inc. PAOLO ROMANO and LU´S RODRIGUES, INESC-ID, Instituto Superior T´ cnico, Universidade I e de Lisboa, Portugal This article addresses the problem of self-tuning the data placement in replicated key-value stores. The goal is to automatically optimize replica placement in a way that leverages locality patterns in data accesses, such that internode communication is minimized. To do this efficiently is extremely challenging, as one needs not only to find lightweight and scalable ways to identify the right assignment of data replicas to nodes but also to preserve fast data lookup. The article introduces new techniques that address these challenges. The first challenge is addressed by optimizing, in a decentralized way, the placement of the objects generating the largest number of remote operations for each node. The second challenge is addressed by combining the usage of consistent hashing with a novel data structure, which provides efficient probabilistic data placement. These techniques have been integrated in a popular open-source key-value store. The performance results show that the throughput of the optimized system can be http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

A uto P lacer : Scalable Self-Tuning Data Placement in Distributed Key-Value Stores

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2014 by ACM Inc.
ISSN
1556-4665
DOI
10.1145/2641573
Publisher site
See Article on Publisher Site

Abstract

AUTOPLACER: Scalable Self-Tuning Data Placement in Distributed Key-Value Stores ~ JOAO PAIVA, INESC-ID, Instituto Superior T´ cnico, Universidade de Lisboa, Portugal e PEDRO RUIVO, Red Hat, Inc. PAOLO ROMANO and LU´S RODRIGUES, INESC-ID, Instituto Superior T´ cnico, Universidade I e de Lisboa, Portugal This article addresses the problem of self-tuning the data placement in replicated key-value stores. The goal is to automatically optimize replica placement in a way that leverages locality patterns in data accesses, such that internode communication is minimized. To do this efficiently is extremely challenging, as one needs not only to find lightweight and scalable ways to identify the right assignment of data replicas to nodes but also to preserve fast data lookup. The article introduces new techniques that address these challenges. The first challenge is addressed by optimizing, in a decentralized way, the placement of the objects generating the largest number of remote operations for each node. The second challenge is addressed by combining the usage of consistent hashing with a novel data structure, which provides efficient probabilistic data placement. These techniques have been integrated in a popular open-source key-value store. The performance results show that the throughput of the optimized system can be

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS)Association for Computing Machinery

Published: Dec 8, 2014

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