On the Exploration of Equal Length Cellular Automata Rules Targeting a MapReduce Design in Cloud

Introduction

Modern days’ many of our daily life applications are based on Cloud Computing. Cloud computing is described as “computing as a service over the Internet” at “pay-for-use basis” (in “http://searchcloudcomputing.techtarget.com/definition/MapReduce”).

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  JobTracker (JT): A node (server) is responsible for managing all jobs and resources in a cluster;

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  TaskTrackers (TT): Agents positioned at each node (client) in cluster to execute map and trim down tasks;

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  JobHistoryServer (JHS): A component that keeps track of completed modules of job; typically implemented as a separate function or along with JT.

Fault-tolerance is an essential criterion for MapReduced computing; each node periodically updates its condition to a master node; otherwise, master node re-assigns job to other available computing units. Applications of MapReduce (Zhouet al., 2011; Koundinya et al, 2012; Padhy et al., 2013; and Yang et al., 2014), ‘Hadoop’ (Yamamoto et al., 2012; Yang et al., 2012; Lin et al., 2013; Liu et al., 2015; and Río et al., 2015) were described for big data processing in clouds. ‘Hadoop’ is a popular third-party deployment of MapReduce in distributed computing. ‘Hadoop’ provides a distributed computing architecture for big data. “Hadoop Distributed File System” (HDFS) is provided to store multiple copies of data blocks in available clusters of computing nodes. A file in HDFS is decomposed into chunks; each chunk is assigned to different working machine. Equal load distribution is an important criterion in MapReduced computation. Hence a cost-effective approach towards equally load distributed MapReduce may be beneficial in clouds.