Enhancing Cloud Computing Performance: A Novel Approach for Optimizing Energy Efficiency through AI- Based Load Balancing Algorithm

Due to vast computing resources, workloads processed by cloud infrastructure are heterogeneous, diversified, and dynamic in nature. Virtual Machines (VMs) linked to different physical servers need to be utilized optimally to achieve desired Quality of Service (QoS) by cloud infrastructure. Towards this end, balancing workload is very important to ensure that cloud infrastructure works optimally and consumers are satisfied by honoring Service Level Agreements (SLAs). Traditional approaches for load balancing are based on heuristics. However, given user workloads in large scale, it is important to have Artificial Intelligence (AI) enabled approach. The rationale behind this is that deep learning techniques are learning based and their decisions are based on highly dynamic runtime situations and resources rather than traditional heuristics. Therefore, in this paper, proposed a framework known as AI Enabled Load Balancing Framework (AI-LBF). Empirical study with simulations revealed that LbDLD outperforms existing methods in terms of makespan and cost.