Fully Decentralized Task Offloading in Multi-Access Edge Computing Systems

We consider the problem of task offloading in multi-access edge computing (MEC) systems constituting N devices assisted by an edge server (ES), where the devices can split task execution between a local processor and the ES. Since the local task execution and communication with the ES both consume power, each device must judiciously choose between the two. We model the problem as a large population non-cooperative game among the N devices. Since computation of an equilibrium policy in this large-device scenario can be extremely difficult, and can incur significant communication overhead, we employ the mean-field game framework to compute fully decentralized low complexity solutions for each device. By leveraging the novel age of information (AoI) metric, we invoke techniques from stochastic hybrid systems (SHS) theory to study the tradeoffs between increasing information freshness and reducing power consumption. In numerical results, we verify that a higher load at the ES may lead devices to push the tasks to the ES less often.