Multi-Objective Virtual Machine Placement using Improved Teaching Learning Based Optimization in Cloud Data Centers
نویسنده
چکیده
The energy consumption of a data center is the critical research issue, i.e. Virtual Machine (VM) placements to satisfy the resource requirements with minimum energy consumptions and active servers. The Multi-Objective Virtual Machine Placement (MOVMP) is a representation of a kind of combinatorial optimization problem. In this paper, Teaching Learning Based Optimization (TLBO) is used to solve the MOVMP problem. Our approach accounts for the multiobjective resource management and the simulation based result validate the effectiveness of TLBO compared to First Fit (FF), Best Fit (BF) and Genetic Algorithm (GA).
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