Communication-Dependent Computing Resource Management for Concurrent Task Orchestration in IoT Systems

Recent advances in distributed machine learning and wireless network technologies are bringing new opportunities for Internet of Things (IoT) systems, where smart devices are often wirelessly connected to collaborate, jointly completing tasks known as communication-dependent computing (CDC) tasks. However, due to the dependence of computing on communication and the presence of concurrent tasks, it remains a challenge to optimize the CDC task performance and efficiency while fulfilling multi-dimensional requirements, particularly with incomplete system information and dynamic environmental impacts. To overcome these challenges, we present a concurrent CDC task framework to facilitate tasks running efficiently in resource-limited IoT systems. To fulfill task requirements, we formulate a task orchestration and resource management problem for optimizing the overall utility of CDC tasks, where each task's utility is designed as a joint metric including deviated computing result and time efficiency. We then employ auxiliary graphs to capture the topological information of tasks and resources, and update weights based on the utility in dynamic environments. Subsequently, a multi-agent reinforcement learning algorithm is leveraged to make distributed decisions with incomplete information. Experiments demonstrate that the proposed approach outperforms baselines in terms of task performance and efficiency, indicating our solution holds great potential in IoT systems.