An Integrated Optimization-Learning Framework for Online Combinatorial Computation Offloading in MEC Networks

Deep Reinforcement Learning-Based Online Resource Management for UAV-Assisted Edge Computing With Dual Connectivity

“…Indeed, using DNNs in optimization is a model-free approach in which the solver learns from experience (i.e., driven by the training data) to construct an optimal mapping policy, rather than relying on complex mathematical models that might not always be accurate and readily available. However, purely relying on the model-free solution has been reported to lead to unstable performance and suffer from slow convergence or even divergence [23], [25], [26]. A proper approach could be letting the DNN take part of the optimization (e.g., for optimizing binary variables) while still using conventional modelbased methods for the rest.…”

“…A proper approach could be letting the DNN take part of the optimization (e.g., for optimizing binary variables) while still using conventional modelbased methods for the rest. Indeed, the integration of datadriven and conventional model-based methods has improved the robustness and convergence of the DRL framework via online training [23]- [26].…”

“…The other challenge in optimizing MEC networks is the preference for long-term key performance indicators (KPIs) of dynamic queueing systems, which refer to time-evolving queueing networks where decisions made in a time slot affect the optimization in subsequent slots [26], [28]. A typical example could be minimizing the long-term average power consumption, subject to queue stability constraints and given the randomness of the environment (e.g., channel gains and task arrival).…”

“…The framework can be used to transform a multi-state problem into deterministic per-time slot subproblems while providing a theoretical guarantee to long-term system stability. The combination of the two robust tools, DRL and Lyapunov framework, thus is a promising approach to solving the MINLP problem of network resource management while monitoring the long-term KPIs, especially in large-scale multi-user, multi-server MEC systems [26].…”

Edge Computing Server Placement Strategy Based on SPEA2 in Power Internet of Things

“…In addition, centralizing data in the cloud platform can cause a burden on network communication and computing resources, resulting in transmission interruption or link congestion. erefore, it is difficult for the cloud computing architecture to meet the service requirements of terminal equipment in the Power Internet of ings [4,5].…”