Multi-objective Energy Efficient Resource Allocation Using Virus Colony Search (VCS) Algorithm

Optimizing energy-efficient resource allocation in a cloud computing environment, which is a non-linear multi-objective NP-hard problem, plays a vital role in decreasing energy consumption, and increasing Quality of Service (QoS). In the area of resource allocation, Virtual Machine Placement (VMP) i...

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Bibliographic Details
Published in:	2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) pp. 766 - 773
Main Authors:	Jayasena, Kudamaduwage Pubudu Nuwanthika, Li, Lin, Abd Elaziz, Mohamed, Xiong, Shengwu
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-06-2018
Subjects:	Cloud computing Energy consumption Mathematical model MOVCS Multi-objective optimization Optimization Power demand Resource management SLAV Virtual machine monitors VMM VMP
Online Access:	Get full text
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Summary:	Optimizing energy-efficient resource allocation in a cloud computing environment, which is a non-linear multi-objective NP-hard problem, plays a vital role in decreasing energy consumption, and increasing Quality of Service (QoS). In the area of resource allocation, Virtual Machine Placement (VMP) is one of the most vital problems to discuss with various possible formulations and a large number of optimization methods. Considering different objectives of cloud service providers, multi-objective VMP model is built to minimize energy consumption, Service Level Agreements Violation (SLAV) and number of Virtual Machine Migration (VMM). The multi-objective Virus Colony Search (MOVCS) algorithm is proposed to address this problem. We evaluate the performance of our algorithm by comparing two multi-objective algorithms, namely, Multi-Objective Evolutionary Algorithm based on Decomposition (MOEAD) and Non-dominated Sorting Genetic Algorithm (NSGAII). We conduct experiments to verify the effectiveness of the MOVCS algorithm. The performance of the MOVCS algorithm is comparing with MOEAD and NSGA-II on the quality of the pareto optimal solution set with different objectives. The simulation results illustrate that MOVCS find better solutions than others considering these objectives and with less iteration.
DOI:	10.1109/HPCC/SmartCity/DSS.2018.00130