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An Efficient Virtual Machine Consolidation Algorithm for Cloud Computing.

Ling Yuan1, Zhenjiang Wang1, Ping Sun2,3

  • 1Department of Computer Science, Huazhong University of Science and Technology, Wuhan 430074, China.

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Summary
This summary is machine-generated.

This study introduces a novel virtual machine consolidation (VMC) algorithm using load forecasting to enhance energy efficiency in blockchain cloud computing. The new method improves VM selection and migration strategies for better performance.

Keywords:
blockchainload predictionvirtual machine consolidation modelvirtual machine migration

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Area of Science:

  • Cloud Computing
  • Blockchain Technology
  • Internet of Things (IoT)

Background:

  • Virtual Machine Consolidation (VMC) is crucial for energy efficiency and service quality in blockchain cloud computing.
  • Current VMC algorithms lack effectiveness due to not treating virtual machine (VM) load as a time series.
  • Integration of blockchain and IoT necessitates advanced VMC strategies.

Purpose of the Study:

  • To propose an improved VMC algorithm leveraging load forecasting for enhanced efficiency in blockchain environments.
  • To address the limitations of existing VMC methods by incorporating time-series analysis of VM loads.
  • To optimize energy consumption and service quality in cloud computing platforms.

Main Methods:

  • Developed a VM selection strategy (LIP) based on load increment prediction to identify overloaded physical machines (PMs).
  • Introduced a VM migration point selection strategy (SIR) using load sequence prediction to merge VMs with complementary loads.
  • Integrated LIP and SIR into a novel VMC algorithm for predictive load management.

Main Results:

  • The proposed LIP strategy improves the accuracy of selecting VMs from overloaded PMs.
  • The SIR strategy enhances PM load stability by merging complementary VM loads, reducing service level agreement violations (SLAV) and resource competition.
  • The combined VMC algorithm demonstrates significant improvements in energy efficiency.

Conclusions:

  • The proposed VMC algorithm based on load prediction effectively enhances energy efficiency in blockchain cloud computing.
  • Incorporating load forecasting into VMC strategies offers a promising approach to optimize cloud resource management.
  • The LIP and SIR strategies contribute to more stable PM loads and reduced service disruptions.