Improved Virus Optimization algorithm for two-objective tasks Scheduling in Cloud Environment

Abstract

Cloud computing is increasingly recognized as a new way to use on-demand, computing, storage and network services in a transparent and efficient way. The development of applications in cloud environments is faced with the need to efficiently schedule a large number of tasks and resources. However, in the most of the time, the resources in cloud are not efficiently utilized due to inadequate scheduling task algorithm in virtual machines. Therefore, task scheduling is one of the most challenging issues in cloud computing. In this paper, we propose two-objective virus optimization algorithm of the makespan and the cost, for mapping tasks to virtual machines in order to meet the needs of cloud service quality and proper assignment of resources. Thus, based on genetic algorithm some parameters of Virus optimization algorithm are redefined to strengthen sorting ability between virus infection strategies. Our combined methods aims to improve the performance of scheduling algorithms. It outperforms some existing approaches for task scheduling in Cloud computing.

Analytical and Comparative Study for Optimization Problems

“…The algorithm is then modified to incorporate an archive and target selection approach to estimate Pareto optimum front for MOPs. [14] K. B. Bey…”

Comparative Study: Defuzzification Functions and Their Effect on the Performance of the OFNbee Optimization Algorithm

“…It is intuitively known that the appropriate selection of methods can improve the performance of optimization algorithms based on OFN. Until then [1], [5], the impact of the fuzzyfication function on the results of OFNBee has been examined, while the following text will present the most popular defuzzyfication methods and their impact on the results of the algorithm. Algorithms based on the behavior of insects or animals are an attempt to use natural mechanisms for optimization [6].…”