Virtual Machine Consolidation Framework for Energy and Performance Efficient Cloud Data Centers

With the rapid development of integration in blockchain and IoT, virtual machine consolidation (VMC) has become a heated topic because it can effectively improve the energy efficiency and service quality of cloud computing in the blockchain. The current VMC algorithm is not effective enough because it does not regard the load of the virtual machine (VM) as an analyzed time series. Therefore, we proposed a VMC algorithm based on load forecast to improve efficiency. First, we proposed a migration VM selection strategy based on load increment prediction called LIP. Combined with the current load and load increment, this strategy can effectively improve the accuracy of selecting VM from the overloaded physical machines (PMs). Then, we proposed a VM migration point selection strategy based on the load sequence prediction called SIR. We merged VMs with complementary load series into the same PM, effectively improving the stability of the PM load, thereby reducing the service level agreement violation (SLAV) and the number of VM migrations due to the resource competition of the PM. Finally, we proposed a better virtual machine consolidation (VMC) algorithm based on the load prediction of LIP and SIR. The experimental results show that our VMC algorithm can effectively improve energy efficiency.

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Virtual Machine Consolidation Framework for Energy and Performance Efficient Cloud Data Centers

Cited by 2 publications

References 18 publications

A Study on Energy-Aware Virtual Machine Consolidation Policies in Cloud Data Centers Using Cloudsim Toolkit

A Study on Energy-Aware Virtual Machine Consolidation Policies in Cloud Data Centers Using Cloudsim Toolkit

An Efficient Virtual Machine Consolidation Algorithm for Cloud Computing

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