<i>c2mec</i> : Cooperative Multi-split and Multi-hop Edge Computing Based on Deep Reinforcement Learning

Recent research highlights a critical gap between the computing capabilities of modern IoT devices and the computational demands of Artificial Intelligent (AI) applications. The edge computing paradigm offers a promising solution by providing reliable and fast computing services close to the data source. Due to their inherent resource constraints, a single edge server often cannot handle the heavy computational load from nearby IoT devices, requiring multi-server collaboration. However, achieving efficient cooperation is challenging due to dynamic workload fluctuations and uneven data distribution. To address these issues, this paper presents a novel solution that involves optimizing task execution paths and resource management to enhance the performance of edge servers, particularly in scenarios with unbalanced data or uneven distribution of IoT devices. Our approach not only deploys multiple edge servers, but also focuses on the intelligent allocation and management of computing tasks. Specifically, we propose c2mec , a cooperative multi-split and multi-hop edge computing framework. The proposed c2mec framework uses problem decomposition to efficiently decouple the variables that need to be optimized. In addition, c2mec employs a multi-agent Deep Reinforcement Learning (DRL) based algorithm to mitigate the negative impact of decoupling and provides a flexible data splitting strategy. Finally, c2mec designs an energy-aware training method for IoT devices to reduce their long-term training cost. Through our comprehensive experimental results, we demonstrate how c2mec achieves notable improvement in energy saving across different scenarios compared to existing solutions, such as full and partial task offloading.