ENHANCED TASK SCHEDULING PARTICLE SWARM OPTIMIZATION ALGORITHM (TSPSOA) BASED ON LOAD BALANCING FOR VIRTUAL MACHINE MIGRATIONS IN CLOUD COMPUTING

Abstract

In the contemporary data centers, virtual machine migration is a crucial technique for gaining scalability through optimum resource use. The majority of earlier publications focused on how to move virtual machines in Cloud environments with high traffic needs. It mainly concentrated on the internet activity between virtual computers and the energy required by those machines. None of the preceding algorithms gave consideration to the client's experience, such as the wait time they experienced to have their services handled. Among the most significant problems that Cloud technology is now experiencing is load balancing. All the nodes should receive an equitable distribution of the load. In dynamic and diverse contexts, dynamic algorithms produce superior outcomes. This research suggests an enhanced particle swarm optimization approach for Cloud Computing job scheduling optimization. First, a scheduling model utilizing an upgraded particle swarm technique is suggested based on the Cloud Computing scheduling algorithm concept to prevent the optimization approach from devolving into local optimization. Three load balancing algorithms are used for comparison: the Honeybee Foraging Behavior Load Balancing Algorithm, the Throttled Load Balancing Algorithm, and the ESCE (Equally Spread Current Execution) Task Allocation Algorithm. It is demonstrated through the simulation results that the suggested TSPSOA is superior to the other state-of-the-art algorithms.