Multiaccess edge computing (MEC) systems provide low-latency computing services for Internet of Things (IoT) applications by processing IoT data on edge servers. In the era of heterogeneous IoT environments, the success of IoT applications hinges on the processing of diversified IoT data. To leverage MEC systems to enable timely IoT services, we characterize IoT applications as cotasks, where each cotask is completed only if all its constituent subtasks (e.g., IoT data processing) are finished. Existing works have been devoted to the design of task offloading and scheduling decisions for MEC-enabled IoT applications, but they mostly neglect the cotask feature. In this article, we investigate the problem of cotask processing in MEC systems, and we formulate it as a nonlinear program (NLP) to minimize total cotask completion time (TCCT). In the light of uncertain communication latency, we transform the NLP to a parameterized and unconstrained version, based on which we propose the deep dual learning (DDL) method, where the learner keeps updating primal and dual variables based on randomly perturbed samples. Furthermore, we provide the duality gap and time complexity analyses for the DDL method. Our simulation results demonstrate that the proposed solution can gradually converge over iterations, and its TCCT performance outperforms other comparison schemes under various system settings.

中文翻译：

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多访问边缘计算系统中基于深度双学习的协同任务处理


多路访问边缘计算 (MEC) 系统通过在边缘服务器上处理物联网数据，为物联网 (IoT) 应用提供低延迟计算服务。在异构物联网环境时代，物联网应用的成功取决于多样化物联网数据的处理。为了利用MEC系统来实现及时的物联网服务，我们将物联网应用程序描述为协同任务，其中每个协同任务只有在其所有组成的子任务（例如物联网数据处理）完成后才完成。现有的工作一直致力于为支持 MEC 的物联网应用程序设计任务卸载和调度决策，但它们大多忽略了协同任务功能。在本文中，我们研究了 MEC 系统中的协同任务处理问题，并将其制定为非线性程序 (NLP)，以最小化总协同任务完成时间 (TCCT)。鉴于通信延迟的不确定性，我们将 NLP 转换为参数化且无约束的版本，在此基础上我们提出了深度对偶学习（DDL）方法，其中学习器根据随机扰动样本不断更新原始变量和对偶变量。此外，我们还提供了 DDL 方法的对偶间隙和时间复杂度分析。我们的仿真结果表明，所提出的解决方案可以通过迭代逐渐收敛，并且其 TCCT 性能在各种系统设置下优于其他比较方案。