Value is King: The MECForge Deep Reinforcement Learning Solution for Resource Management in 5G and Beyond

Abstract

Multi-access edge computing (MEC) is a key enabler to fulfill the promises of a new generation of immersive and low-latency services in 5G and Beyond networks. MEC represents a defining function of 5G, offering significant computational power at a reduced latency, allowing to augment the capabilities of user equipments while preserving their battery life. However, the demands generated by a plethora of innovative and concurrent IT services requiring high quality of service and quality of experience levels will likely overwhelm the—albeit considerable—resources available in 5G and Beyond scenarios. To take full advantage of its potential, MEC needs to be paired with innovative resource management solutions capable of effectively addressing the highly dynamic aspects of the scenario and of properly considering the heterogeneous and ever-changing nature of next generation IT services, prioritizing the assignment of resources in a highly dynamic and contextual fashion. This calls for the adoption of Artificial Intelligence based tools, implementing self-* approaches capable of learning the best resource management strategy to adapt to the ever changing conditions. In this paper, we present MECForge, a novel solution based on deep reinforcement learning that considers the maximization of total value-of-information delivered to end-user as a coherent and comprehensive resource management criterion. The experimental evaluation we conducted in a simulated but realistic environment shows how the Deep Q-Network based algorithm implemented by MECForge is capable of learning effective autonomous resource management policies that allocate service components to maximize the overall value delivered to the end-users.