Load-balancing algorithms in cloud computing: A survey. (15th June 2017)

• Record Type: Journal Article
• Authors: Jafarnejad Ghomi, Einollah; Masoud Rahmani, Amir; Nasih Qader, Nooruldeen
• Is Part Of: Journal of network and computer applications. Volume 88(2017)
• Page Start: 50
• Page End: 71
• Publication Date: 2017-06-15
• Subjects: Cloud computing -- Load balancing -- Task scheduling -- Hadoop MapReduce; 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.2017.04.007
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Cloud computing is a modern paradigm to provide services through the Internet. Load balancing is a key aspect of cloud computing and avoids the situation in which some nodes become overloaded while the others are idle or have little work to do. Load balancing can improve the Quality of Service (QoS) metrics, including response time, cost, throughput, performance and resource utilization. In this paper, we study the literature on the task scheduling and load-balancing algorithms and present a new classification of such algorithms, for example, Hadoop MapReduce load balancing category, Natural Phenomena-based load balancing category, Agent-based load balancing category, General load balancing category, application-oriented category, network-aware category, and workflow specific category. Furthermore, we provide a review in each of these seven categories. Also. We provide insights into the identification of open issues and guidelines for future research.