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A heuristic placement selection of live virtual machine migration for energy-saving in cloud computing environment.
Jia Zhao1, Liang Hu2, Yan Ding2
1College of Computer Science and Engineering, ChangChun University of Technology, Changchun, China; College of Computer Science and Technology, Jilin University, Changchun, China.
This study introduces PS-ES, a novel heuristic approach for energy-saving live virtual machine (VM) migration policies. PS-ES optimizes VM placement for reduced energy consumption and improved performance during live VM migration.
Area of Science:
- Cloud Computing
- Green Computing
- Energy-Aware Systems
Background:
- Live virtual machine (VM) migration is crucial for efficient cloud resource management.
- Optimizing VM placement during live migration is key to energy saving in green cloud computing.
- Existing methods lack long-term energy optimization and performance guarantees.
Purpose of the Study:
- To present a novel heuristic approach, PS-ES, for energy-efficient live VM migration policies.
- To enhance VM placement selection for long-term energy savings and performance preservation.
- To improve the effectiveness and value of live VM migration events.
Main Methods:
- Combines Particle Swarm Optimization (PSO) with Simulated Annealing (SA) for improved global search capability.
- Utilizes Probability Theory and Mathematical Statistics with SA for data processing and final solution derivation.
- Considers both current and future problem scenarios for holistic optimization.
Main Results:
- PS-ES significantly reduces total incremental energy consumption during live VM migration.
- The approach effectively protects the performance of VMs during running and migration phases.
- Outperforms random and optimal migration strategies in energy saving and performance.
Conclusions:
- The proposed PS-ES approach offers a valuable solution for energy-efficient live VM migration.
- PS-ES enhances the overall effectiveness and value of live VM migration events in cloud environments.
- Demonstrates a viable strategy for achieving long-term energy optimization in cloud computing.
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