Role of Load Balancers in High Availability and Fault Tolerance of Enterprise Applications

Venkat Marella

doi:10.32628/CSEIT2425171

Authors

Venkat Marella Independent Researcher, USA Author

DOI:

https://doi.org/10.32628/CSEIT2425171

Keywords:

Virtual Machines (VMs), Carbon Emission, Large-Scale, Load Unbalancing, Cloud Computing, Scalability, Energy Consumption, Techniques

Abstract

A multi-variant, multi-constraint issue that reduces computing resource performance and efficiency is load unbalancing. Overloading and under loading are two undesired aspects of load unbalancing that load balancing solutions address. To the best of our knowledge, there is no thorough, detailed, systematic, and hierarchical categorization of the load balancing strategies that are now in use, despite their significance. A new paradigm for large-scale distributed computing is emerging: cloud computing. It is a framework for making a shared pool of computer resources accessible via a network whenever needed. One of the biggest problems with cloud computing is load balancing, which is necessary to divide the dynamic workload across many nodes so that no one node is overloaded. For effective scheduling, load balancing—that is, dividing up the duties evenly across all of the virtual machines (VMs)—is essential. By efficiently using the resources and meeting the needs of the end users, load balancing may enable server-less computing, volunteer computing, software-defined computing, etc. In order to significantly decrease energy consumption and carbon emission rates—two critical requirements of cloud computing—load balancing aims to minimize resource utilization. For energy-efficient load balancing in cloud computing, this establishes the need of new measurements, energy usage, and carbon emissions. This study examines the various load balancing strategies now used in cloud computing and compares them according to a number of factors that are taken into account by various strategies, such as performance, scalability, related overhead, etc. It goes on to examine these methods from the standpoint of energy use and carbon emissions.

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