Efficient Load Balancing in Cloud Environment Using Improved Spider Monkey Optimization

Abstract

Abstract Cloud computing provides a shared resource pool in a distributed environment. Users can access these resources anywhere anytime as per the provider’s policy. Whenever tasks are submitted on the cloud for execution, they needs to be scheduled appropriately in the cloud environment as many Virtual Machines (VM) are available at the backend. The performance of the entire system depends on the scheduling algorithm. A good scheduling algorithm distributes given tasks among all VMs so that the load of each VM is balanced. This problem is called a load balancing problem and comes under the category of NP-Hard problems. This paper uses the Spider Monkey Optimization algorithm for more efficient load balancing. The proposed optimization algorithm aims to enhance the performance by selecting the minimum loaded VM for balancing. Results of simulation show that proposed algorithm performance is improved compared to the existing algorithms with respect to load balancing, response time, make span, and resource utilization.