Optimizing Load Balancing using Deep Learning in a Cloud Environment

In the context of cloud computing, this research emphasizes how deep learning methods have been used into load balancing. Traditionally, load balancing methods have been developed using hybrid approaches, such as Predict-Earliest-Finish Time with Ant Colony Optimization (ACO) for low-utilized VM optimization and the hybrid Bat technique for overflow VM optimization. However, there has been limited exploration of deep learning approaches in load-balancing research until recently. The Dynamic Virtual Machine Provisioning using Deep Learning (DVMPDL) framework has been proposed and implemented to address this gap. This framework leverages deep learning methodologies to make precise and effective resource allocation decisions, aiming to optimize quality of service (QoS) metrics like throughput, resource utilization, latency, response times, and costs in cloud environments. Specifically, DVMPDL utilizes deep learning techniques to determine the best schedule for virtual machines, taking into account the nature of genome workflow tasks. By intelligently allocating resources based on workload characteristics and system constraints, DVMPDL aims to improve system reliability and performance. The results of the proposed DVMPDL framework are evaluated in terms of make span and cost, comparing them with previously suggested load balancing techniques such as the deadline-constrained, energetic VM provision, and load balance methods. By integrating deep learning into load balancing research, this work aims to enhance the efficiency and effectiveness of resource allocation in cloud environments, ultimately improving overall system performance and user experience.