标题： 在线协作资源分配与任务卸载用于多接入边缘计算 显示英文标题

标题： Online Collaborative Resource Allocation and Task Offloading for Multi-access Edge Computing

摘要： 多接入边缘计算（MEC）正作为一种有前景的范式出现，以在用户设备（UDs）附近提供灵活的计算服务。 然而，满足UDs对计算需求高和延迟敏感的要求面临诸多挑战，包括MEC服务器的资源限制、MEC系统中的固有动态和复杂特性，以及处理时间耦合和决策耦合优化的困难。 在本工作中，我们首先提出一种边缘-云协同的MEC架构，其中MEC服务器和云协同为UDs提供卸载服务。 此外，我们制定一个节能且延迟感知的优化问题（EEDAOP），在任务截止时间和长期队列延迟的约束下最小化UDs的能量消耗。 由于该问题被证明是非凸混合整数非线性规划（MINLP），我们提出一种在线联合通信资源分配和任务卸载方法（OJCTA）。 具体来说，我们通过采用Lyapunov优化框架将EEDAOP转化为实时优化问题。 然后，为了求解实时优化问题，我们通过采用Tammer分解机制、凸优化方法、双边匹配机制和依赖舍入方法提出一种通信资源分配和任务卸载优化方法。 仿真结果表明，所提出的OJCTA相比基准方法可以实现更优越的系统性能。 显示英文摘要

摘要： Multi-access edge computing (MEC) is emerging as a promising paradigm to provide flexible computing services close to user devices (UDs). However, meeting the computation-hungry and delay-sensitive demands of UDs faces several challenges, including the resource constraints of MEC servers, inherent dynamic and complex features in the MEC system, and difficulty in dealing with the time-coupled and decision-coupled optimization. In this work, we first present an edge-cloud collaborative MEC architecture, where the MEC servers and cloud collaboratively provide offloading services for UDs. Moreover, we formulate an energy-efficient and delay-aware optimization problem (EEDAOP) to minimize the energy consumption of UDs under the constraints of task deadlines and long-term queuing delays. Since the problem is proved to be non-convex mixed integer nonlinear programming (MINLP), we propose an online joint communication resource allocation and task offloading approach (OJCTA). Specifically, we transform EEDAOP into a real-time optimization problem by employing the Lyapunov optimization framework. Then, to solve the real-time optimization problem, we propose a communication resource allocation and task offloading optimization method by employing the Tammer decomposition mechanism, convex optimization method, bilateral matching mechanism, and dependent rounding method. Simulation results demonstrate that the proposed OJCTA can achieve superior system performance compared to the benchmark approaches.