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Research Article | Open Access
Volume 14 2022 | .
STUDY ON JOB SCHEDULING AND RESOURCE PROVISIONING MODELS FOR EFFICIENT MAPREDUCE PROGRAMMING FOR BIG DATA PROCESSING
G Suhasini, Dr.C D Kumawat
Pages: 2659-2663
Abstract
Many companies are increasingly using MapReduce for efficient large scale data processing such as personalized advertising, spam detection, and different data mining tasks. Cloud computing offers an attractive option for businesses to rent a suitable size Hadoop cluster, consume resources as a service, and pay only for resources that were utilized. One of the open questions in such environments is the amount of resources that a user should lease from the service provider. Often, a user targets specific performance goals and the application needs to complete data processing by a certain time deadline. However, currently, the task of estimating required resources to meet application performance goals is solely the users’ responsibility. In this work, we introduce a novel framework and technique to address this problem and to offer a new resource sizing and provisioning service in MapReduce environments. For a MapReduce job that needs to be completed within a certain time, the job profile is built from the job past executions or by executing the application on a smaller data set using an automated profiling tool.
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