Optimizing Multitenancy: Adaptive Resource Allocation in Serverless Cloud Environments Using Reinforcement Learning

Abstract

The growing adoption of serverless computing has highlighted critical challenges in resource allocation, policy fairness, and energy efficiency within multitenancy cloud environments. This research proposes a reinforcement learning (RL)-based adaptive resource allocation framework to address these issues. The framework models resource allocation as a Markov Decision Process (MDP) with dynamic states that include latency, resource utilization, and energy consumption. A reward function is designed to optimize the throughput, latency, and energy efficiency while ensuring fairness among tenants. The proposed model demonstrates significant improvements over heuristic approaches, achieving a 50% reduction in latency (from 250 ms to 120 ms), a 38.9% increase in throughput (from 180 tasks/s to 250 tasks/s), and a 35% improvement in energy efficiency. Additionally, the model reduces operational costs by 40%, achieves SLA compliance rates above 98%, and enhances fairness by lowering the Gini coefficient from 0.25 to 0.10. Under burst loads, the system maintains a service level objective success rate of 94% with a time to scale of 6 s. These results underscore the potential of RL-based solutions for dynamic workload management, paving the way for more scalable, cost-effective, and sustainable serverless multitenancy systems.