Message complexity of distributed algorithms revisited

Behnish Mann, Alex Arvavid
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引用次数: 1

Abstract

Distributed systems offer many features such as resource sharing, scalability, fault tolerance and reliability. Several distributed algorithms have been proposed in literature to solve fundamental problems such as mutual exclusion and leader election in distributed systems. When more than one algorithm is invented to solve the same problem particularly in asynchronous distributed systems, their performance is compared mostly based on the message complexity. This paper reviews the concept of message complexity and offers more clarity by studying the performance of the two most popular distributed algorithms - Ricart-Agrawala's algorithm and Raymond algorithm designed to solve the mutual exclusion problem. The paper has four main contributions (i) observes how the message complexity is understood and computed in the asynchronous distributed system so far and exposes its elusiveness; (ii) offers a more suitable definition of message complexity; (iii) briefly presents the simulator designed to study the performance of the distributed algorithms using the refined metric; and finally (iv) discusses about the simulation study to illustrate the significance and usefulness of the proposed metric.
重新审视分布式算法的消息复杂性
分布式系统提供了许多特性,如资源共享、可伸缩性、容错和可靠性。文献中提出了几种分布式算法来解决分布式系统中的互斥和领导者选举等基本问题。特别是在异步分布式系统中,当提出多个算法来解决同一个问题时,它们的性能比较主要基于消息复杂度。本文回顾了消息复杂性的概念,并通过研究解决互斥问题的两种最流行的分布式算法Ricart-Agrawala算法和Raymond算法的性能,提供了更多的清晰度。本文有四个主要贡献:(1)观察了异步分布式系统中消息复杂度是如何被理解和计算的,并揭示了其难以捉摸性;(ii)提供更合适的消息复杂性定义;(iii)简要介绍了使用改进度量来研究分布式算法性能的模拟器;最后(iv)讨论了仿真研究,以说明所提出度量的意义和有用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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