Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud. (15th February 2019)

• Record Type: Journal Article
• Authors: Adhikari, Mainak; Nandy, Sudarshan; Amgoth, Tarachand
• Is Part Of: Journal of network and computer applications. Volume 128(2019)
• Page Start: 64
• Page End: 77
• Publication Date: 2019-02-15
• Subjects: IaaS cloud -- Load balancing -- Resource clustering -- Task assignment -- Resource utilization -- Makespan.; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2018.12.010
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: An Infrastructure- as -a-service (IaaS) cloud is a new paradigm which offers computing and storage services in form of virtual machines to the cloud users. Due to increasing demand and diversity of the applications, most of the servers are become overloaded or imbalanced and affects the performance of the cloud system. Most of the existing strategies have developed a series of algorithms to pick out the optimal target server to achieve the immediate load balancing. However, it is proved in the literature that immediate load balancing does not meet the high execution efficiency and resource utilization of the servers. In this paper, we propose a new load balancing mechanism for a long-term process referred as LB-RC (load balancing resource clustering). The meta-heuristic Bat-algorithm is applied to obtain optimal resource clustering and their cluster centers for faster convergence. We also propose a new dynamic task assignment policy to achieve the minimum makespan and execution cost within the given constraints. The proposed algorithm is tested and compared with the existing algorithms over various synthetic datasets and performance matrices to prove its superiority.