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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A multiobjective migration algorithm as a resource consolidation strategy in cloud computing.

Danqing Feng1,2, Zhibo Wu1, DeCheng Zuo1

  • 1Computer Science and Technology, Harbin Institute of Technology, Harbin, China.

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|February 7, 2019
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Summary
This summary is machine-generated.

This study introduces a novel two-level scheduling algorithm for efficient cloud computing resource consolidation. It optimizes virtual machine migration to reduce costs and energy consumption in datacenters.

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Resource Management

Background:

  • Cloud computing demands efficient resource management for user satisfaction.
  • Virtualization is crucial for cloud resource consolidation.
  • Multiobjective resource consolidation presents significant challenges.

Purpose of the Study:

  • To develop an adaptive resource consolidation strategy for cloud datacenters.
  • To address the challenges of multiobjective optimization in resource consolidation.
  • To reduce operational costs and energy consumption through efficient virtual machine migration.

Main Methods:

  • A two-level scheduling algorithm combining Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to identify resource hotspots (CPU, memory).
  • A hybrid heuristic algorithm utilizing Particle Swarm Optimization (PSO) for rapid virtual machine migration decisions and Ant Colony Optimization (ACO) for optimal placement.
  • Adaptive consolidation of resources during dynamic allocation processes.

Main Results:

  • The proposed GRA and TOPSIS policy effectively identifies resource hotspots.
  • The hybrid PSO and ACO algorithm successfully consolidates resources, reducing costs and energy consumption.
  • The two-level scheduling algorithm demonstrates efficient performance in dynamic allocation scenarios.

Conclusions:

  • The novel two-level scheduling algorithm provides an efficient solution for adaptive resource consolidation in cloud datacenters.
  • This approach effectively balances multiple objectives, including cost and energy reduction.
  • The method enhances the overall efficiency of cloud computing resource management.