Performance Analysis Of Load Balancing Algorithms Based On Swarm Intelligence In Cloud Computing
The demand for storage and data retrieval over the internet changes the perception of technology. Cloud computing facilitates on-demand services based on users’ requirements. The pool of cloud computing services is based on various factors such as resource allocation, virtualization and management of user requests. The computing process provides a job scheduler for the processing of user requests. The nature of the job schedular is static and dynamic for the allocation of resources. The static job allocation is the conventional approach to handle the user requests. On the other hand, dynamic job schedulers play an essential role in capacity improvements in cloud computing—the dynamic scheduling process extent in the form of cloud load balancing. The ways of load balancing in cloud computing transform the storage mechanism and allocation process. Various research scholars and developers proposed dynamic load balancing algorithms based on swarm intelligence. The dynamic characteristics of swarm intelligence influence the performance of cloud computing. This paper analyzes the performance of various swarm algorithms applied for load balancing. The foremost swarm intelligence-based algorithms are particle swarm optimization, ant colony optimization, glowworm optimization and derived swarm intelligence algorithms. The process of analysis use cloud sim simulator and test different user groups with the different data center. The performance of algorithms indicates the utilization of swarm intelligence in cloud computing environments for load balancing.