Main Reference PaperOn Power-Peak-Aware Scheduling for Large-Scale Shared Clusters, IEEE Transactions on Big Data, 2018[Python/Hadoop]
  • To reduce the power peak in large-scale big data analytics, the proposed scheduling framework mitigates the power peaks via a lexicographic minimization operation. In focusing on batch job arrivals, the proposed method also capable of handling dynamic job arrivals and checks that the feasibility of new job arrivals and rejects the jobs that lead to scheduling infeasibility.

Description
  • To reduce the power peak in large-scale big data analytics, the proposed scheduling framework mitigates the power peaks via a lexicographic minimization operation. In focusing on batch job arrivals, the proposed method also capable of handling dynamic job arrivals and checks that the feasibility of new job arrivals and rejects the jobs that lead to scheduling infeasibility.

  • To reduce the total energy consumption of the cluster.

  • Reducing the power fluctuations.

Aim & Objectives
  • To reduce the total energy consumption of the cluster.

  • Reducing the power fluctuations.

  • Proposed System is improved further through the scalability.

Contribution
  • Proposed System is improved further through the scalability.

  • 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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