Abstract

In this study, numerous cloud computing load balancing algorithms are examined, and important difficulties that must be considered while building new load balancing algorithms are also explored. Various measurement criteria, such as performance, scalability, throughput, resource usage, fault tolerance, reaction time, and others, are studied in the literature to compare current static and dynamic load balancing methods. Cloud computation offloading and work schedules have been the subject of some research. Degrading cloud computing performance is a major concern for cloud enterprises of the huge amount of data and varied sources inside this cloud that must be managed. To maximize system performance and reduce carbon emissions, load balancing aims to use resources in the most efficient way possible while minimizing the number of resources used. This paper focuses on cloud computing load balancing strategies. Several qualitative factors, including throughput, reliability, energy-saving features, performance, scalability, and associated overhead, are considered to examine, and compare the present techniques of load balancing.

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