LAUSR

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.