HP-ICM: Curiosity-Driven Offloading Based on Hypergraph Partitioning

Computing power network (CPN) is a distributed network system designed to connect and integrate computing resources globally, enabling efficient sharing and utilization of computing power. In dependent task offloading, the dependency relationship between tasks is generally used to determine the ex-ecution order. However, the assignment phase often overlooks the relevance and sharing between tasks, leading to a waste of system resources in CPNs. In the learning process, agents frequently encounter the issue of sparse rewards, which results in slow learning and makes it challenging to develop effective strategies. To address the aforementioned issues, we design a dependent task offloading method based on hypergraph partitioning and an intrinsic curiosity module, i.e., HP-ICM, which offloads tasks with similar resource requirements or dependencies into the same partition and utilizes the ICM to enhance the speed and quality of learning. Simulation results show that HP-ICM can reduce latency by 22.8% and energy consumption by 25.7% compared to the PPO baseline during task offloading,