Mobile edge computing (MEC) paradigm supports cloud-like computing capabilities at the edge of the network and offers low-latency services. Proxy servers of MEC with mobility and limited computing, e.g., flying unmanned aerial vehicles (UAVs) have emerged as competitors in providing services. This work considers a task offloading problem for an UAV-assisted MEC system and designs an integrated cloud-edge network with multiple mobile users (MUs) and layered UAVs to improve MEC with a network of UAVs. In our system, edge UAVs (EUAVs) and the cloud collaborate to provide caching and computing services for MUs. We consider static and dynamic applications that support task offloading. Our proposed approach minimizes the weighted cost of latency and energy consumption by jointly optimizing caching and offloading, deployment of EUAVs, and allocation of computation resources. Simultaneously, this work also considers UAVs’ caching and computation capacities while meeting MUs’ latency and energy constraints. Thus, a constrained mixed integer nonlinear program for a layered UAV-assisted hybrid cloud-edge system is formulated. To solve it, this work designs a hybrid metaheuristic algorithm named adaptive and genetic simulated annealing (SA)-based particle swarm optimization (AGSP). Experimental results with a real-life dataset verify that the AGSP’s system energy consumption and task latency are reduced by at least 7.4% and 8.46%, respectively, compared with the state-of-the-art algorithms, thus proving that AGSP greatly enhances the energy and latency of the system.

中文翻译：

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利用分层无人机实现移动边缘计算中经济高效的任务卸载


移动边缘计算（MEC）范式支持网络边缘的类云计算能力，并提供低延迟服务。具有移动性和有限计算能力的MEC代理服务器，例如飞行的无人机（UAV）已经成为提供服务的竞争对手。这项工作考虑了无人机辅助 MEC 系统的任务卸载问题，并设计了一个具有多个移动用户 (MU) 和分层无人机的集成云边缘网络，以通过无人机网络改进 MEC。在我们的系统中，边缘无人机 (EUAV) 和云协作为 MU 提供缓存和计算服务。我们考虑支持任务卸载的静态和动态应用程序。我们提出的方法通过联合优化缓存和卸载、EUAV 的部署以及计算资源的分配，最大限度地减少延迟和能耗的加权成本。同时，这项工作还考虑了无人机的缓存和计算能力，同时满足移动设备的延迟和能量限制。因此，制定了分层无人机辅助混合云边系统的约束混合整数非线性程序。为了解决这个问题，这项工作设计了一种混合元启发式算法，称为基于自适应和遗传模拟退火（SA）的粒子群优化（AGSP）。真实数据集的实验结果验证了AGSP的系统能耗和任务延迟与最先进的算法相比分别降低了至少7.4%和8.46%，从而证明AGSP极大地提高了系统性能。系统的能量和延迟。