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Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform.

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Summary
This summary is machine-generated.

This study introduces an efficient algorithm (AESVMP) for virtual machine scheduling to reduce energy consumption and service level agreement violations in cloud data centers. The proposed method significantly lowers migration count, violations, and energy usage compared to existing approaches.

Keywords:
AHPCloud computing platformsQoSVirtual machine placement

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Artificial Intelligence

Background:

  • Cloud data centers face significant energy consumption and service level agreement (SLA) violations.
  • Dynamic virtual machine consolidation requires efficient scheduling and resource allocation mechanisms.
  • Improving Quality of Service (QoS) is crucial for cloud data center operations.

Purpose of the Study:

  • To propose an efficient algorithm (AESVMP) for virtual machine scheduling in dynamic consolidation.
  • To reduce energy consumption and SLA violations while improving QoS in cloud data centers.
  • To enhance virtual machine scheduling using the Analytic Hierarchy Process (AHP).

Main Methods:

  • Developed an algorithm (AESVMP) based on the Analytic Hierarchy Process (AHP).
  • Considered host power consumption, available resources, and resource allocation balance ratio as key criteria.
  • Calculated resource allocation balance ratio based on three-dimensional resources (CPU, RAM, BW).
  • Utilized multi-criteria decision-making techniques for virtual machine placement decisions.

Main Results:

  • The AESVMP algorithm reduced the number of migrations by an average of 51.76%.
  • Service Level Agreement Violations (SLAV) were reduced by an average of 67.4%.
  • Aggregate energy consumption (ESV) decreased by an average of 67.6% compared to LBVMP.
  • Extensive experiments were conducted using CloudSim emulator with PlanetLab workloads.

Conclusions:

  • The proposed AESVMP algorithm effectively alleviates energy consumption and SLA violations in cloud data centers.
  • AESVMP demonstrates superior performance in virtual machine scheduling compared to the cutting-edge LBVMP method.
  • The approach validates the effectiveness of AHP in multi-criteria decision-making for cloud resource management.