Carbon-aware Cloud Resource Scheduling Using Reinforcement Learning for Energy Efficiency Data Centers

Abstract

Due to the rapid development of cloud computing services, today's data centers are consuming much more energy and emitting much higher levels of carbon dioxide into the atmosphere compared to earlier years, thus requiring the development of sustainable solutions for resource management in the cloud. In this paper, an approach called Carbon Aware Cloud Resource Scheduling based on the use of Reinforcement Learning is presented. The proposed approach includes workload demand, resource utilization, and carbon intensity values in its decision making process in order to allocate workloads efficiently and minimize environmental impact caused by cloud computing services. The reinforcement learning algorithm trains the RL agent to find the optimal strategies for workload scheduling that involve execution of workloads, postponing their execution, migrating them from one location to another and re-allocation of resources. The results from experimental evaluation indicate that the suggested framework successfully reduces the carbon emission level up to 30% and energy cost level to around 24%, respectively, in comparison with traditional scheduling methods, without lowering the SLA compliance rate down to 98.4% and workload starvation level up to 0.3%. The analysis proves that the combination of carbon awareness and reinforcement learning helps to create an intelligent, adaptive, and ecologically sustainable system for managing cloud resources.