Scalable Load Balancing Algorithms for Cloud Infrastructures

Abstract

Cloud computing has become a paradigm of providing dynamically scalable and on-demand computing in the Internet. Effective load balancing is one of the most significant issues in cloud environment that guarantees optimal resource use, low response time, high availability, and quality of service (QoS). Traditional methods of load balancing are inadequate as cloud infrastructures increase due to scale and complexity because of the dynamism and heterogeneity in their workloads. As a result, there is a need to have scalable and flexible load balancing strategies that would efficiently distribute workloads in large-scale cloud data centres. The paper provides a detailed research of the scalable load-balancing algorithms to the cloud infrastructures in terms of their architectural principles, performance indicators, scaling attributes, and fault-tolerance. Some of the classical and state of the artload balancing algorithms, such as, are statical, dynamic, heuristic, and nature inspired strategies that are reviewed in the paper. It is also suggested that a new approach to scaling hybrid load balancing methodology should be offered because it combines distributed decision-making with predictive estimation of workload. The suggested solution is intended to increase the throughput of the system, reduce the response time, and optimize the use of resources in the presence of highly changing workloads. A long analysis assessment and comparative report is carried out to reveal the efficiency of the scalable load balancing strategies at the large-scale cloud environment. The findings show that adaptive algorithm and hybrid algorithm is better in scalability, robustness and the overall performance of the system compared to the traditional centralized algorithms. The results of this paper can be helpful to researchers and practitioners during the development of the next-generation cloud load balancing mechanisms.