Non-Intrusive Handover Strategy Optimization for Model-Partitioned DNN Inference in Satellite Edge Computing

Abstract

As Satellite Edge Computing (SEC) emerges as a computing backbone of 6 G IoT, its feasibility is threatened by frequent handovers that disrupt task continuity and cause deadline violations in AI-driven workloads. Conventional communication-centric handover strategies overlook the urgency of computational tasks, further exacerbating these issues. To address this gap, we propose the <bold>C</bold>omputation <bold>A</bold>ware <bold>C</bold>onditional <bold>H</bold>andover <bold>O</bold>ptimization (CACHO) framework, a non-intrusive solution fully compatible with 3GPP Conditional Handover (CHO). The framework integrates model partition driven delayed execution to enable seamless migration via inter-satellite tensor transmission, timeout-risk predictive proactive triggering to protect at-risk tasks from overloaded satellites, and task-load-aware target selection to balance computational workloads and service popularity. These modules are orchestrated within standardized CHO workflows without altering the core logic, ensuring deployability in existing satellite network systems. Simulations based on real Starlink constellation data demonstrate that our approach reduces task timeout rates by 29.95–51.02<inline-formula><tex-math notation="LaTeX">$\%$</tex-math><alternatives><mml:math><mml:mo>%</mml:mo></mml:math><inline-graphic xlink:href="xu-ieq1-3674471.gif"/></alternatives></inline-formula> compared to the 3GPP standard CHO and several state-of-the-art baselines, while maintaining handover success rates and minimizing extra handovers needed. Beyond performance gains, this work provides the first blueprint for embedding task life-cycle awareness into handover workflows, bridging mobility management and computation guarantees, and advancing the feasibility of robust SEC for mission-critical 6G IoT applications.