Energy-efficient strategy for virtual machine consolidation in cloud environment

An important issue of energy efficiency in cloud environment is to perform more jobs while consuming less amount of power. Virtual machine consolidation remains the most deployed strategy to manage both performance and energy consumption. Most of existing energy efficiency techniques save energy aga...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2020-10, Vol.24 (19), p.14845-14859
Main Authors: Saadi, Youssef, El Kafhali, Said
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Carbon
Computational Intelligence
Computer centers
Consolidation
Control
Energy consumption
Energy efficiency
Engineering
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Overloading
Performance degradation
Robotics
Thresholds
Virtual environments
Workload
Workloads
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Summary:An important issue of energy efficiency in cloud environment is to perform more jobs while consuming less amount of power. Virtual machine consolidation remains the most deployed strategy to manage both performance and energy consumption. Most of existing energy efficiency techniques save energy against the cost on performance degradation. Consolidation techniques leverage thresholds to detect overloaded and underloaded hosts that could be vacated to achieve optimal balance between host utilization and energy consumption. In this research, we propose an energy-efficient strategy (EES) to consolidate virtual machines in cloud environment with an aim of reducing the energy consumption while completing more tasks with the highest throughput. Our proposal makes use of the performance-to-power ratio to set upper thresholds for overload detection. In addition, EES considers the overall data center workload utilization to set lower thresholds, which can reduce the number of virtual machine migrations. The simulation results show that EES leads to energy-efficient workload consolidation with the minimal number of migrations and less energy consumption. The results conclude that EES saves energy consumption without compromising user’s workload requirement.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-020-04839-2