Main Reference PaperAn Energy‐Efficient Dynamic Resource Management Approach Based on Clustering and Meta-Heuristic Algorithms in Cloud Computing IaaS Platforms, Wireless Personal Communications, 2018[Java/CloudSim].
  • In this proposal, the KMGA technique is designed to reduce the total server power consumption in the cloud Datacenter as much as possible by improving resource utilization. For this purpose, apply a particularly micro-genetic algorithm for improving dynamic consolidation to achieve minimum energy consumption and k-means clustering technique to allocate the cluster of independent tasks to the cluster resources effectively by keeping the Make-span at minimal value. At the same time, KMGA is aimed at balancing the trade-off between the negotiated SLAs and energy consumption of hosting servers on cloud-computing platforms.

Description
  • In this proposal, the KMGA technique is designed to reduce the total server power consumption in the cloud Datacenter as much as possible by improving resource utilization. For this purpose, apply a particularly micro-genetic algorithm for improving dynamic consolidation to achieve minimum energy consumption and k-means clustering technique to allocate the cluster of independent tasks to the cluster resources effectively by keeping the Make-span at minimal value. At the same time, KMGA is aimed at balancing the trade-off between the negotiated SLAs and energy consumption of hosting servers on cloud-computing platforms.

  • To reduce the energy consumption of Datacenters and sustained quality of service.

  • To minimize the number of virtual machine migrations and makespan.

Aim & Objectives
  • To reduce the energy consumption of Datacenters and sustained quality of service.

  • To minimize the number of virtual machine migrations and makespan.

  • Applying dynamic scheduling algorithms for workflow tasks, in order to map them efficiently to cloud computing resources.

Contribution
  • Applying dynamic scheduling algorithms for workflow tasks, in order to map them efficiently to cloud computing resources.

  • 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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