Multi-Objective Virtual Machine Placement using Improved Teaching Learning Based Optimization in Cloud Data Centers

摘要

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 multi-objective resource management and the simulation based result validate the effectiveness of TLBO compared to First Fit (FF), Best Fit (BF) and Genetic Algorithm (GA).