Deep Learning based Energy-Efficient Task Scheduling in Cloud Computing

A growing number of manufacturing businesses are becoming more concerned with energy efficiency because of rising energy costs and environmental consciousness. The enormous shift of an organization's resource requirements from on-site technology to cloud-based systems is resulting in a significant rise in the expenses that cloud reviews face when it comes to building, maintaining, and providing hardware for servers, storage, networks, and processing. Because cloud reviews operate on such a massive scale, even a little decrease in performance can result in significant increases in energy or resource usage expenses. Numerous studies have put forth techniques designed to optimize throughput and resource consumption while accounting for varied cloud settings. This review's scope is reviewing several survey studies on cloud offloading methods based on GGCN and discussing the advantages and drawbacks of each. An exhaustive and scientific examination of the latest GGCN methods related to cloud unloading. Subsequently, the energy efficiency studies are examined. Lastly, a few recommendations are made for further research.