While per se, not a novel concept-previous incarnations of the data-center-at-the-edge concept, such as Cloudlets, go as far back as the early 00’s-MEC represents the first widely available and commercially viable implementation of edge computing on public infrastructure. In fact, together with Non Orthogonal Multiple Access (NOMA) which significantly improves network density and spectrum efficiency, MEC arguably represents the defining feature of 5G. To this end, applications can take advantage of the functions provided by Multi-access Edge Computing (MEC) to run software components in relatively resource rich servers that communicate with mobile devices at very low latency (1–10 msec). The deployment of 5G communications is opening up new computing scenarios that enable the next generation of immersive applications leveraging distributed computing resources in proximity to end-users. 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.
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. 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.
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.
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. 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.
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.
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