Empirical Study on the Dynamic Load Balancing Approach in Cloud Computing

Computing in the cloud has recently emerged as an emerging front-end technology. It delivers the primary attention for many different marketplaces and technological organizations. Clients benefitted from this solution's security and expandable storage capacity features. Client, server, data center, virtual machine (VM), and userbase are all system components. The requests come from the client side and are routed via the user base before being sent to the data center. From there, they are sent to the processor for further processing. However, the fundamental challenge that cloud computing must overcome is the processing power of every machine, which is limited. Sometimes, many requests may be produced at a single data center, which can cause a processor to become overloaded. Using a load-balancing algorithm enables data centers to circumvent the aforementioned computational overheads. We have attempted to study a variety of various load balancing techniques that are currently accessible in this research paper. These consist of the algorithms Round Robin (RR), Shortest Job First (SJF), Throttled, Genetic Algorithm (GA), and M-Throttled. These algorithms are executed in a Cloud SIM environment with a set and optimized data center. Response time and processing time were utilized as the search parameters to focus the search for a successful load-balancing method that can be used in the cloud computing environment.