Main Reference PaperTowards Max-Min Fair Resource Allocation for Stream Big Data Analytics in Shared Clouds, IEEE Transactions on Big Data, 2018 [Java/Apache Storm]
  • The resource provisioning in stream processing platforms where both the topology heterogeneity and the node heterogeneity are captured. This work proposes an efficient resource allocation scheme for a heterogeneous shared stream big data analytics cluster shared by multiple topologies, in order to achieve max-min fairness in the utilities of the throughput for all the topologies.

Description
  • The resource provisioning in stream processing platforms where both the topology heterogeneity and the node heterogeneity are captured. This work proposes an efficient resource allocation scheme for a heterogeneous shared stream big data analytics cluster shared by multiple topologies, in order to achieve max-min fairness in the utilities of the throughput for all the topologies.

  • To achieve resource allocation for stream data.

  • To improve the scalability.

Aim & Objectives
  • To achieve resource allocation for stream data.

  • To improve the scalability.

  • The proposed work contributes to implementing the proposed scheme into the real – world stream big data analytics clusters.

Contribution
  • The proposed work contributes to implementing the proposed scheme into the real – world stream big data analytics clusters.

  • 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.

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

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