Computational offloading, as an effective way to extend the capability of resource-limited edge devices in Internet of Things (IoT), is considered as a promising emerging paradigm for coping with delay-sensitive services. However, on one hand, applications commonly include several subtasks with dependent relations and on the other hand, the dynamic changes in network environments make offloading decision-making become a coupling and complex NP-hard problem, difficult to address. This paper proposes an intelligent Computational Offloading scheme for Dependent IoT Application (CODIA), which decouples the performance enhancement problem into two processes: scheduling and offloading. First, a prioritized scheduling strategy is designed and its complexity is analyzed. Then, an offloading algorithm with offline training and online deployment is introduced. Due to the temporal continuity between subtasks, the dependency relation is transformed into a transition of device state, and the overhead for the whole application is considered to be the long-term benefit. CODIA leverages an Actor-Critic-based solution, where the IoT devices are able to deploy intelligent models and dynamically adjust the offloading strategy to achieve low latency, while controlling energy consumption. Finally, a series of experiments are conducted to verify the robustness and efficiency of the proposed solution in terms of convergence, latency, and energy consumption.

中文翻译：

---

边缘智能：依赖物联网应用的计算任务卸载方案


计算卸载作为扩展物联网（IoT）中资源有限的边缘设备能力的有效方法，被认为是应对延迟敏感服务的有前途的新兴范例。然而，一方面，应用程序通常包含多个具有依赖关系的子任务，另一方面，网络环境的动态变化使得卸载决策成为一个耦合且复杂的NP-hard问题，难以解决。本文提出了一种针对相关物联网应用（CODIA）的智能计算卸载方案，该方案将性能增强问题解耦为两个过程：调度和卸载。首先设计了优先级调度策略并分析了其复杂度。然后，介绍了一种离线训练和在线部署的卸载算法。由于子任务之间的时间连续性，依赖关系转化为设备状态的转换，整个应用程序的开销被认为是长期利益。 CODIA 利用基于 Actor-Critic 的解决方案，其中物联网设备能够部署智能模型并动态调整卸载策略以实现低延迟，同时控制能耗。最后，进行了一系列实验，以验证所提出的解决方案在收敛性、延迟和能耗方面的鲁棒性和效率。