Mobile edge computing (MEC) has been regarded as a promising approach to deal with explosive computation requirements by enabling cloud computing capabilities at the edge of networks. Existing models of MEC impose some strong assumptions on the known processing cycles and unintermittent communications. However, practical MEC systems are constrained by various uncertainties and intermittent communications, rendering these assumptions impractical. In view of this, we investigate how to schedule task offloading in MEC systems with uncertainties. First, we derive a closed-form expression of the average offloading success probability in a device-to-device (D2D) assisted MEC system with uncertain computation processing cycles and intermittent communications. Then, we formulate a task offloading maximization problem (TOMP), and prove that the problem is NP-hard. For problem solving, if the problem instance exhibits a symmetric structure, we propose a task scheduling algorithm based on dynamic programming (TSDP). By solving this problem instance, we derive a bound to benchmark sub-optimal algorithm. For general scenarios, by reformulating the problem, we propose a repeated matching algorithm (RMA). Finally, in performance evaluations, we validate the accuracy of the closed-form expression of the average offloading success probability by Monte Carlo simulations, as well as the effectiveness of the proposed algorithms.

翻译：移动边缘计算（MEC）通过在网络边缘提供云计算能力，被视为应对爆炸式计算需求的一种有前景方法。现有MEC模型对已知处理周期和无间歇通信做出了较强假设。然而，实际MEC系统受各种不确定性和间歇通信的制约，使得这些假设不切实际。鉴于此，我们研究了如何在不确定性条件下调度MEC系统中的任务卸载。首先，我们推导出在计算处理周期不确定和通信间歇情形下，设备到设备（D2D）辅助MEC系统中平均卸载成功概率的闭式表达式。然后，我们构建了任务卸载最大化问题（TOMP），并证明该问题为NP难问题。对于问题求解，若问题实例具有对称结构，我们提出一种基于动态规划的任务调度算法（TSDP）。通过求解此类问题实例，我们获得一个用于基准测试次优算法的界。针对一般场景，通过重构问题，我们提出一种重复匹配算法（RMA）。最后，在性能评估中，我们通过蒙特卡洛仿真验证了平均卸载成功概率闭式表达式的准确性，以及所提算法的有效性。