Volume 17, No. 1, 2020
Hybrid of Meta Heuristic Firefly and Genetic Algorithm for Optimization Approach in the Cloud Environment
C. Vinothini, P. Balasubramanie and J. Priya
Abstract
Clouds are strengthening upcoming-generation storage systems as a virtual processing communication infrastructure. Cloud infrastructure resource allocation of processes is an essential component of allocating resources from either a cloud server. Utilization and distribution of resources will be as per Service Level Agreement (SLA) Quality of Service (QoS) to significantly reduce energy consumption. Demands for the cloud system services are simultaneous and efficient. Convenient utilization of resources is a basic requirement in the cloud server and can be carried out by introducing an automated and efficient optimization algorithm. Optimization is a scientific principle for finding the best solution to the problems through possible solutions. Therefore, plenty of the suggested algorithms concentrate on the quest for estimated VM scheduling algorithms solutions. Methodology, universe-heuristic and hybrid optimization strategy increasing in popularity as finding optimized solutions to critical issues in a timely manner. A hybrid meta-heuristic algorithm is recommended employing modified Firefly Optimization Algorithm (MOFA) and Genetic Algorithm techniques inspired by the natural. Behavior and performance result indicates through introduced solution tested on the cloudsim simulation platform.
Pages: 297-305
DOI: 10.14704/WEB/V17I1/WEB17005
Keywords: Cloud Computing, Hybrid Optimization, Load Balancing, Genetic Algorithm, Modified Firefly Optimization Algorithm (MFOA), Waiting Time, QoS