Freshness-Aware Inference Services in Edge Computing via Offloading or Local Processing

The collaboration between end devices and edge servers has been extensively investigated to enable adaptive service provisioning, particularly in scenarios requiring trade-offs between differential model accuracies and heterogeneous resource consumption costs. In this paper, we propose freshness-aware hierarchical inference service provisioning in a Mobile Edge Computing (MEC) network, where inference models are trained and maintained in edge servers to address resource limitations on devices. Devices dynamically download updated models to maintain local inference fidelity, mitigating performance degradation caused by model staleness. We formulate a freshness and cost minimization problem that maximizes overall inference fidelity while minimizing total costs, subject to long-term average energy budgets on devices and computing capacities on cloudlets. We design an online algorithm with provable competitive ratio, by leveraging Lyapunov optimization and randomized rounding techniques. We conduct simulations to evaluate the performance of the proposed online algorithm. Simulation results demonstrate that the proposed algorithm is promising.