AHP Aided Decision-Making in Virtual Machine Migration for Green Cloud

In this study, an analytical hierarchy process based model is proposed to perform the decision-making for virtual machine migration towards green cloud computing. The virtual machine migration evaluation index system is established based on the process of constructing hierarchies for evaluation of virtual machine migration, and selection of task usage. A comparative judgment of two hierarchies has been conducted. In the experimental study, five-point rating scale has been adopted to map the raw data to the scaled rating score; this rating method is used to analyze the performance of each virtual machine and its task usage data. The results show a significant improvement in the decision-making process for the virtual machine migration. The deduced results are useful for the system administrators to migrate the exact virtual machine, and then switch on the power of physical machine that the migrated virtual machine resides on. Thus the proposed method contributes to the green cloud computing environment

Energy Efficient Scheduling in Heterogeneous Systems with a Parallel Multiobjective Local Search

This article introduces ME-MLS, an e cient multithreading local search algorithm for solving the multiobjective scheduling problem in heterogeneous com- puting systems. We consider the minimization of both the makespan and energy consumption objectives. The proposed method follows a fully multiobjective ap- proach, applying a Pareto-based dominance search that is executed in parallel by using several threads. The experimental analysis demonstrates that the new multi- threading algorithm outperforms a set of fast and accurate two-phases deterministic heuristics based on the traditional MinMin. The new ME-MLS method is able to achieve signi cant improvements in both makespan and energy consumption objec- tives in reduced execution times for a large set of testbed instances, while exhibiting a near linear speedup behavior when using up to 24 threads