Dynamic Resource Allocation in Serverless Architectures via Reinforcement Learning

Abstract

Serverless computing architectures offer significant advantages in terms of scalability and cost-efficiency. However, traditional serverless resource allocation strategies often rely on static configurations or simple heuristics, which can lead to suboptimal performance and increased costs. This paper proposes a novel approach to dynamic resource allocation in serverless environments using reinforcement learning (RL). The core idea is to train an RL agent to intelligently allocate serverless function resources—specifically, memory (M) and CPU (C)—in response to fluctuating demand. The agent observes system metrics such as function invocation rates, execution times, and error rates, and uses this information to learn an optimal policy for resource allocation. The objective is to minimize the total cost while maintaining a desired level of performance. The system utilizes a Q-learning algorithm to learn the optimal Q-values, representing the expected cumulative reward for taking a specific action in a given state. The agent's policy is then derived from these learned Q-values. Simulation results demonstrate that the RL-based approach significantly outperforms traditional allocation methods in terms of both cost reduction and performance improvement. The research highlights the potential of RL for optimizing resource utilization in serverless systems and contributes to a more efficient and responsive cloud computing landscape.