Partitioning or Not? Hierarchical Task Offloading Optimization in Collaborative Satellite Edge Computing Networks

Yan Chen, Haiquan Wang, Jun Liu, Xiaolin Jia, Jiejie Zhao, Han Qiu

Abstract: As a promising paradigm, Satellite Edge Computing (SEC) enables new opportunities for facilitating intelligent processing onboard, crucial for the timely execution of mission-critical tasks. These tasks typically involve high data capture rates and rely on compute-intensive Deep Neural Network (DNN) models. However, a single satellite struggles to handle these tasks promptly due to its limited computational capabilities. Thus, effective collaboration within the SEC network is urgently needed to adapt to diverse capture rates, optimize resource utilization, and ensure real-time responses. Motivated by the fact that partitioning a DNN model can accelerate task inference and make better use of idle resources by simultaneous sub-task execution and reduced transmitted data, we propose HiO2, a hierarchical task offloading framework that maximizes system throughput by effective collaboration among satellites and ground stations to process the partitioned sub-tasks. This highlights the challenge of designing effective task partitioning and offloading strategies in dynamic, resource-constrained networks. HiO2 addresses this challenge with two key methods. First, it adopts a distributed swarm-level task offloading strategy that assigns tasks to swarms based on their optimal quantity. Second, HiO2 introduces a distributed node-level partitioning and offloading scheme, which dynamically identifies efficient cut-points according to workload and network dynamics, then offloads sub-tasks by collaboration among nodes in each swarm. Extensive data-driven evaluations demonstrate that, compared to the state-of-the-art baselines, HiO2 improves throughput to 1.19×, reduces average task completion time to 69.7%, and consistently meets task deadlines.

External IDs:dblp:conf/icdcs/ChenWLJZQ25