Main Reference PaperLocality-aware and load-balanced static task scheduling for Mapreduce, Future Generation Computer Systems, 2018 [Java/Hadoop].
  • A modified MapReduce Framework model is proposed for simultaneous scheduling the map and reduce tasks, in order to improve data locality and balance the processor’s loads in both map and reduce phases. The data locality reduces the amount of data transfer in the shuffle phase and balancing of computations leads to faster task execution.

Description
  • A modified MapReduce Framework model is proposed for simultaneous scheduling the map and reduce tasks, in order to improve data locality and balance the processor’s loads in both map and reduce phases. The data locality reduces the amount of data transfer in the shuffle phase and balancing of computations leads to faster task execution.

  • To improve the data locality and balances the load between Map and Reduce tasks.

  • Reducing the job execution time.

Aim & Objectives
  • To improve the data locality and balances the load between Map and Reduce tasks.

  • Reducing the job execution time.

  • A fault tolerance issue is handled in MapReduce Framework.

Contribution
  • A fault tolerance issue is handled in MapReduce Framework.

  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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