A REVIEW OF LOAD BALANCING IN CLOUD COMPUTING

Abstract

-Load balancing is a critical aspect of cloud computing that ensures efficient resource utilization and optimal performance. As cloud computing continues to gain prominence, load balancing algorithms and techniques have become a subject of extensive research. This paper presents an abstract of a comprehensive review of load balancing in cloud computing, focusing on various approaches and strategies proposed in the literature. The review provides an overview of the fundamental concepts and challenges associated with load balancing in cloud computing. It explores different types of load balancing algorithms, including static, dynamic, and hybrid approaches. The discusses the advantages and limitations of each algorithm, highlighting their suitability for different cloud computing scenarios. Furthermore, the review examines popular load balancing techniques such as round-robin, least connection, and weighted round-robin, as well as advanced methods like ant colony optimization, particle swarm optimization, genetic algorithms, and machine learning-based approaches. The discusses the underlying principles, advantages, and limitations of each technique, shedding light on their applicability and performance in load balancing scenarios. In addition to algorithmic approaches, the review investigates architectural and system-level load balancing mechanisms. These mechanisms include content-based routing, virtual machine migration, and task scheduling strategies. The examines their effectiveness in achieving load balancing and analyzes their impact on system performance and resource utilization. The review also discusses the challenges and open research issues in load balancing for cloud computing, such as dynamic workload fluctuations, resource heterogeneity, fault tolerance, scalability, and energy efficiency.