With the advance of emerging digital twin (DT) technology, the combination of DT with edge intelligence brings great potential for high-fidelity, delay-sensitive inference services at the network edge. Due to the training data drift over time, the accuracy of an inference model degrades dramatically. To maintain and/or enhance its accuracy, the service model requires to be continuously retrained using newly generated update data. However, model retraining and inference services compete with each other for the limited computing resource in a mobile edge computing network (MEC), which may decrease user satisfaction with services due to unbearable service delays caused by insufficient resource supplies. Therefore, to ensure high fidelity of service models by choosing models for retraining while maximizing user satisfaction, it becomes a great challenge to allocate the limited computing resource in an MEC to both model retraining and inference services. In this paper, we investigate fidelity-aware, delay-sensitive services in a DT-assisted MEC network over a given time horizon. We study a novel user satisfaction maximization problem with the aim to maximize the long-term user satisfaction on services. We first formulate an integer linear programming (ILP) solution to its offline version. We then devise an online algorithm for the problem with a bounded expected cumulative regret, by leveraging an efficient prediction mechanism and a multi-armed bandit (MAB) based resource allocation strategy. Finally, we evaluate the performance of the proposed algorithm via simulations. Simulation results demonstrate that the proposed algorithm is promising, outperforming the comparison benchmarks.

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.

The Digital Twin (DT) technique enables seamless integrations between the physical and virtual worlds. By continuously synchronizing DTs with their physical counterparts, DTs can provide accurate reflections of physical objects and facilitate high-fidelity inference services based on service models. Orthogonal to the DT technology, Mobile Edge Computing (MEC) has been envisioning as a promising paradigm for providing intelligent services to users while meeting stringent delay and accuracy requirements. In this paper, we investigate fidelity-aware inference services in a DT-assisted MEC network where there are multiple source DTs providing new updated training data to service models often. We jointly schedule mobile devices to upload their update data to their DTs, and choose service models for retraining using their updated source DT data over a given time horizon. We further assume that the previous version of each service model can still serve its users during its retraining period, while a retrained service model can provide high-fidelity services to its users. To this end, we first formulate two novel optimization problems: the model instance placement problem that assigns model instances to cloudlets in an MEC network so that the total placement cost of all service models is minimized, and the cumulative utility maximization problem to maximize the cumulative fidelity of all service models over a given time horizon, by jointly scheduling mobile devices to upload their update data to their DTs and service models to be trained using their updated source DT data at each time slot. We then formulate an integer linear programming (ILP) solution for the model instance placement problem when the problem size is small; otherwise we develop an approximate solution to the problem, at the expense of moderate resource violations. We also devise an efficient online algorithm for the cumulative utility maximization problem. We finally evaluate the performance of the proposed algorithms via simulations, and the simulation results demonstrate that the proposed algorithms are promising.