Main Reference PaperOptimization for Speculative Execution in Big Data Processing Clusters, IEEE Transactions on Parallel and Distributed Systems, 2016 [Java/Hadoop].
  • This paper proposes a technique to optimize the two performance metrics such as the total job delay, response time and the computation cost by defining a utility function. In order to optimize the Speculative Execution, this system proposes a Smart Cloning Algorithm (SCA), which serves as an approximate solution to the optimization framework in a lightly-load cluster. It also proposes the Enhanced Speculative Execution (ESE) algorithm for a heavily loaded cluster.

+ Description
  • This paper proposes a technique to optimize the two performance metrics such as the total job delay, response time and the computation cost by defining a utility function. In order to optimize the Speculative Execution, this system proposes a Smart Cloning Algorithm (SCA), which serves as an approximate solution to the optimization framework in a lightly-load cluster. It also proposes the Enhanced Speculative Execution (ESE) algorithm for a heavily loaded cluster.

  • To maximize the overall system utility.

  • Reducing job delay and computation costs.

+ Aim & Objectives
  • To maximize the overall system utility.

  • Reducing job delay and computation costs.

  • This work may contributes in design of speculative execution schemes for more complex jobs which can have additional task-dependency constraints.

+ Contribution
  • This work may contributes in design of speculative execution schemes for more complex jobs which can have additional task-dependency constraints.

  • Java JDK 1.8, Hadoop 1.2.1.

  • Netbeans 8.0.1, J2SE, Hadoop.

+ Software Tools & Technologies
  • Java JDK 1.8, Hadoop 1.2.1.

  • Netbeans 8.0.1, J2SE, Hadoop.

  • B.E/B.Tech/M.E/M.Tech

+ Project Recommended For
  • B.E/B.Tech/M.E/M.Tech

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