Ruzicka Indexive Throttled Deep Neural Learning for Resource-Efficient Load Balancing in a Cloud Environment

Abstract

Cloud Computing (CC) is a prominent technology that permits users as well as organizations to access services based on their requirements. This computing method presents storage, deployment platforms, as well as suitable access to web services over the internet. Load balancing is a crucial factor for optimizing computing and storage. It aims to dispense workload across every virtual machine in a reasonable manner. Several load balancing techniques have been conventionally developed and are available in the literature. However, achieving efficient load balancing with minimal makespan and improved throughput remains a challenging issue. To enhance load balancing efficiency, a novel technique called Ruzicka Indexive Throttle Load Balanced Deep Neural Learning (RITLBDNL) is designed. The primary objective of RITLBDNL is to enhance throughput and minimize the makespan in the cloud. In the RITLBDNL technique, a deep neural learning model contains one input layer, two hidden layers, as well as one output layer to enhance load balancing performance. In the input layer, the number of cloud user tasks is collected and sent to hidden layer 1. In that layer, the load balancer in the cloud server analyzes the virtual machine resource status depending on energy, bandwidth, memory, and CPU using the Ruzicka Similarity Index. Then, it is classified VMs as overloaded, less loaded, or balanced. The analysis results are then transmitted to hidden layer 2, where Throttled Load Balancing is performed to dispense the workload of weighty loaded virtual machines to minimum loaded ones. The cloud server efficiently balances the workload between the virtual machines in higher throughput and lower response time and makespan for handling a huge number of incoming tasks. To evaluate experiments, the proposed technique is compared with other existing load balancing methods. The result shows that the proposed RITLBDNL provides better performance of higher load balancing efficiency of 7%, throughput of 46% lesser makespan of 41%, and response time of 28% than compared to conventional methods.