EdgeGym: A Reinforcement Learning Environment for Constraint-Aware NFV Resource Allocation
Jing Su, Suku Nair, Leo Popokh
Abstract
Optimizing resource allocation in Network Functions Virtualization (NFV) deployment remains a challenging problem due to the complex interactions between network functions and the limited resources available at the network edge. Deep reinforcement learning (DRL) has achieved impressive results in a variety of domains. This paper presents EdgeGym, a reinforcement learning environment to simulate the edge network contexts and constraints for NFV resource allocation. EdgeGym allows researchers and practitioners to evaluate and compare different reinforcement learning algorithms for optimizing the allocation of resources in NFV environments, taking into account various constraints such as affinity policies and maximum latency. We demonstrate the effectiveness of EdgeGym through extensive experiments on training and action masking efficiency. EdgeGym provides a reliable framework for advancing the DRL agent performance in NFV resource allocation and paves the way for further research in this area.