Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach

arXiv:2312.00279v2 Announce Type: replace
Abstract: With the rapid development of Mobile Edge Computing (MEC), various real-time applications have been deployed to benefit people's daily lives. The performance of these applications relies heavily on the freshness of collected environmental information, which can be quantified by its Age of Information (AoI). In the traditional definition of AoI, it is assumed that the status information can be actively sampled and directly used. However, for many MEC-enabled applications, the desired status information is updated …

abstract age applications arxiv benefit computing cs.lg cs.ni daily development edge edge computing environmental information mobile mobile edge computing people performance real-time real-time applications reinforcement reinforcement learning scheduling type