Job Scheduling for Multi-User MapReduce Clusters

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Skip to Main Content. A not-for-profit organization, IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In. Each user requires to run one or more complex jobs that consist of multiple interconnected tasks. A job is considered finished when all its corresponding tasks have been executed in the system. Tasks can have different resource requirements.

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We propose an offline algorithm that computes optimal UDRF allocation while the scheduling process can be to be decentralize across multiple schedulers.

Due to the advent of new technologies, devices, and communication tools such as social networking sites, the amount of data produced by mankind is growing rapidly every year. Big data is a collection of large datasets that cannot be processed using traditional computing techniques. MapReduce has been introduced to solve large-data computational problems.

It is specifically designed to run on commodity hardware, and it depends on dividing and conquering principles. Nowadays, the focus of researchers has shifted towards Hadoop MapReduce. One of the most outstanding characteristics of MapReduce is data locality-aware scheduling.

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MAPREDUCE SCHEDULER: A DEGREE VIEW. Rajdeep Das1 The matchmaking scheduling main idea jobs, proposed online and offline both type of.

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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Sharing a MapReduce cluster between users is attractive because it enables statistical multiplexing lowering costs and allows users to share a common large data set. However, we find that traditional scheduling algorithms can perform very poorly in MapReduce due to two aspects of the MapReduce setting: the need for data locality running computation where the data is and the dependence between map and reduce tasks.

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HybSMRP: a hybrid scheduling algorithm in Hadoop MapReduce framework

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Empirical Study of Job Scheduling Algorithms in Hadoop MapReduce

This application is related to U. The present invention is directed generally to a scheduler for a grid computer network and specifically to a scheduler that does not need to keep track of the real-time state of the properties of each resource on the grid computer network. Recently, companies have begun to explore the possibilities of using grid computing networks grids to increase the companies’ productivity.

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