基于云计算的mapreduce云分布式控制框架

Tzu-Chi Huang, Kuo-Chih Chu, Guo-Hao Huang, Yan-Chen Shen, C. Shieh
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引用次数: 1

摘要

MapReduce云成为当今云计算成功的关键。然而,MapReduce云使用单个Master节点作为大脑来管理分布在Slave节点上的任务,以控制应用程序执行的整个进程。因此,MapReduce云在运行时很容易让从节点发送的报告使主节点过载,从而影响性能。此外,MapReduce云使Master节点成为单个故障点,在Master节点无法工作时暂停应用程序的执行。MapReduce云可以使用本文提出的分布式控制框架(DCF)来提高性能和容错性,因为DCF将主节点的大部分工作转移到每个从节点中共存的DCF主代理上,并允许从节点在运行时加入或离开云,而不会中断应用程序的执行。根据本文对各种应用的实验观察,与原生MapReduce云相比,使用DCF的MapReduce云具有更好的性能和容错能力。
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Distributed control framework for mapreduce cloud on cloud computing
A MapReduce cloud becomes a key to the success of cloud computing today. However, a MapReduce cloud uses a single Master node as the brain to manage tasks distributed over Slave nodes for controlling the entire progress of the application execution. Accordingly, a MapReduce cloud easily overloads the Master node with reports sent from Slave nodes at run time to harm performance. Besides, a MapReduce cloud makes the Master node a single failure point to suspend the application execution when the Master node cannot work. A MapReduce cloud can use the Distributed Control Framework (DCF) proposed in this paper to improve both performance and fault tolerance, because DCF shifts most works of a Master node to a DCF Master Agent coexisting in each Slave node and allows Slave nodes to join or leave a cloud at run time without interrupting the application execution. According to observations on experiments with various applications in this paper, a MapReduce cloud can use DCF to have better performance and fault tolerance in comparison to a native MapReduce cloud.
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