An efficient artificial intelligence based mechanism for energy optimization in cloud computing

Abstract

The proposed research work introduces three new AI-based optimization methods for cloud computing: ARL-ACO aimed at dynamic assignment of VMs and scheduling of tasks with migration; DRL-ABCQ aimed at intelligent load balancing in task scheduling applications; and CSGJO with Fuzzy Logic and Cosine Similarity to allocate the best task based on previous defined quality size. The processes described advance task scheduling models far beyond earlier algorithms by adding adaptive cloud resource management, scalability, and a sustainable aspect; thus, cloud resource management can be claimed improvements over earlier models in dynamic or complex adaptive systems.