Efficient Resource Management for Edge Computing Systems Using Reinforcement Learning Techniques

Abstract

Edge computing has emerged as a foundational paradigm for latency-sensitive, bandwidth-intensive, and data-sovereignty-conscious applications by moving computation closer to users, devices, and data sources. However, edge infrastructures are characterized by high resource heterogeneity, constrained energy budgets, dynamic workload arrival, multi-tenant competition, and fragmented administrative control. These conditions make static resource management heuristics and conventional optimization approaches increasingly inadequate. Reinforcement learning offers a promising alternative by enabling systems to learn adaptive sequential decision policies from operational experience without requiring complete models of workload behavior or infrastructure dynamics. This paper provides a system-level examination of reinforcement learning for resource management in edge computing environments. It analyzes architectural integration, structural trade-offs, governance requirements, deployment constraints, robustness, fairness, and sustainability. The discussion extends beyond algorithmic performance to consider how learning-based controllers interact with existing orchestration layers, telemetry systems, security boundaries, and policy frameworks. It argues that successful deployment depends on treating reinforcement learning as part of a larger socio-technical infrastructure rather than as an isolated optimization component. The paper draws on cross-domain illustrations from vehicular networks, content delivery, cloud scheduling, and collective resource governance to identify forward-looking research directions and policy implications for adaptive edge systems.