Load balancing in cloud computing via intelligent PSO-based feedback controller

Abstract

Load balancing effectively distributes network load and balances the load during the scheduling and allocation process. Hence various load balancing techniques in task scheduling and resource allocation along with VM migration has been presented previously but they have a heavy load on some VM and violate cloud service level agreement with a single point of failure. Therefore, a novel Intelligent PSO-based Feedback Controller has been proposed with regulated Scheduling, Allocation, and VM migration to perform optimal load balancing. In this proposed technique, a novel Intelligent Weighted filtering based PSO Approach is used to reduce computation time during task scheduling and resource allocation. This approach uses a multi-objective PSO algorithm with Pareto dominance to achieve high quality of service, throughput, scalability, low response time, and optimal bilateral transposed conv filtering. Moreover, during VM migration existing techniques result in service level agreement violations owing to inefficient VM placement among PMs. To overcome these issues, a Double Deep Q proximal model with a feedback controller has been proposed. The double weight set in the offline and online updating process in the decision model maintains a smooth service level agreement with the cloud. Also, centralized and decentralized controller algorithm fails with a single point of failure and coordination issue in complicated situations with instruction mixing of processes. Finally, the conditional GAN feedback controller has been used to eliminate a single point of failure with high fault tolerance, low energy consumption and migration time.