A Mean Field Game Model for Timely Computation in Edge Computing Systems
arxiv(2024)
摘要
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 in this scenario is difficult due to the presence
of a large number of devices, we employ the mean-field game framework to reduce
the finite-agent game problem to a generic user's multi-objective optimization
problem, with a coupled consistency condition. By leveraging the novel age of
information (AoI) metric, we invoke techniques from stochastic hybrid systems
(SHS) theory and study the tradeoffs between increasing information freshness
and reducing power consumption. In numerical simulations, we validate that a
higher load at the ES may lead devices to upload their task to the ES less
often.
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