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A generic API for load balancing in distributed systems for big data management

机译：用于大数据管理的分布式系统中的负载平衡的通用API

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Distributed systems for big data management very often face the problem of load imbalance among nodes. To address this issue, there exist almost as many load balancing strategies as there are different systems. When designing a scalable distributed system geared towards handling large amounts of information, it is often not so easy to anticipate which kind of strategy will be the most efficient to maintain adequate performance regarding response time, scalability, and reliability at any time. Based on this observation, we describe a generic API to implement and experiment any strategy independently from the rest of the code, prior to a definitive choice for instance. We then show how existing load balancing strategies used by famous systems could be implemented with this API. We also present how this work has helped us implement load balancing on our distributed system and modify the behavior of our strategy in a few lines of code. This led us to easily perform various experiments to determine the most efficient scheme for our system. This paper is an extension to our work presented at Workshop on Parallel and Distributed Computing for Big Data Applications (WPBA) 2014. We detail here more experiments and extend the use of the API to a broad class of big data storage systems. Copyright © 2015 John Wiley & Sons, Ltd.
机译：用于大数据管理的分布式系统通常会面临节点之间负载不平衡的问题。为了解决此问题，几乎存在与不同系统一样多的负载平衡策略。在设计用于处理大量信息的可伸缩分布式系统时，通常很难轻易地预料哪种策略最有效地在任何时候都保持足够的性能，包括响应时间，可伸缩性和可靠性。基于这种观察，我们将介绍一种通用API，以在例如进行最终选择之前独立于其余代码来实施和试验任何策略。然后，我们展示如何使用此API来实现著名系统使用的现有负载平衡策略。我们还将介绍这项工作如何帮助我们在分布式系统上实现负载平衡，并通过几行代码来修改策略的行为。这使我们可以轻松地执行各种实验，以确定最有效的系统方案。本文是对我们在2014年大数据应用程序并行和分布式计算研讨会（WPBA）上提出的工作的扩展。我们在此详细介绍更多实验，并将API的使用扩展到大类大数据存储系统。版权所有©2015 John Wiley＆Sons，Ltd.

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