Computation-intensive mobile applications are explosively increasing and cause computation overload for smart mobile devices (SMDs). With the assistance of mobile edge computing and mobile cloud computing, SMDs can rent computation resources and offload the computation-intensive applications to edge clouds and remote clouds, which reduces the application completion delay and energy consumption of SMDs. In this paper, we consider the mobile applications with task call graphs and investigate the task offloading and resource scheduling problem in hybrid edge-cloud networks. Due to the interdependency of tasks, time-varying wireless channels, and stochastic available computation resources in the hybrid edge-cloud networks, it is challenging to make task offloading decisions and schedule computation frequencies to minimize the weighted sum of energy, time, and rent cost (ETRC). To address this issue, we propose two efficient algorithms under different conditions of system information. Specifically, with full system information, the task offloading and resource scheduling decisions are determined based on semidefinite relaxation and dual decomposition methods. With partial system information, we propose a deep reinforcement learning framework, where the future system information is inferred by long short-term memory networks. The discrete offloading decisions and continuous computation frequencies are learned by a modified deep deterministic policy gradient algorithm. Extensive simulations evaluate the convergence performance of ETRC with various system parameters. Simulation results also validate the superiority of the proposed task offloading and resource scheduling algorithms over baseline schemes.

中文翻译：

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混合边缘云网络中的任务卸载和资源调度


计算密集型移动应用程序呈爆炸式增长，导致智能移动设备 (SMD) 的计算过载。在移动边缘计算和移动云计算的帮助下，SMD可以租用计算资源，将计算密集型应用卸载到边缘云和远程云，从而降低SMD的应用完成延迟和能耗。在本文中，我们考虑具有任务调用图的移动应用程序，并研究混合边缘云网络中的任务卸载和资源调度问题。由于边缘云混合网络中任务、时变无线信道和随机可用计算资源的相互依赖性，做出任务卸载决策和调度计算频率以最小化能量、时间和租金的加权和具有挑战性成本（ETRC）。为了解决这个问题，我们提出了两种在不同系统信息条件下有效的算法。具体来说，利用完整的系统信息，基于半定松弛和对偶分解方法来确定任务卸载和资源调度决策。利用部分系统信息，我们提出了一个深度强化学习框架，通过长短期记忆网络推断未来的系统信息。离散卸载决策和连续计算频率是通过改进的深度确定性策略梯度算法学习的。广泛的仿真评估了 ETRC 与各种系统参数的收敛性能。仿真结果还验证了所提出的任务卸载和资源调度算法相对于基线方案的优越性。