Optimal Load Balancing in Cloud using ANFIS and MGWO based Polynomial Neural Network

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. Here providing an appropriate service is considered a major challenge in the heterogeneous environment. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. In order to provide optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. In addition to this, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. In addition to this, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.