Continuous and reliable service support is crucial for emerging latency-sensitive and computation-intensive applications in UAV-assisted edge networks (UENs) due to operational dynamics and environmental uncertainty. Although conventional designs can improve coverage and computing efficiency, they often rely on instantaneous resource optimization or reactive handover, rendering ongoing services vulnerable to non-negligible interruptions when the serving UAV degrades due to mobility, energy depletion, or channel dynamics. To avoid such post-failure recovery, a promising approach is to prepare a successor UAV in advance, i.e., a standby UAV that reserves minimal resources and synchronizes service context for possible takeover. Thus, we consider a dynamic UEN architecture where each mobile user carries an ongoing computing mission requiring persistent service support, while UAVs provide wireless access and computing services under time-varying network dynamics and stringent onboard energy constraints. To facilitate proactive and continuous service provisioning, we propose a forecasting-driven proactive reservation-based continuous service scheduling framework, termed Fresco. In Fresco, an LSTM-based module is first used to predict short-term disruption risks of ongoing missions from historical network observations. Guided by these predictions, an online risk-aware successor matching scheme selects suitable standby UAVs for high-risk missions under delay, resource, and energy constraints, while incorporating minimal communication/computation reservation and lightweight service-context synchronization for efficient takeover preparation. Experiments show that Fresco significantly reduces service interruptions and improves mission continuity over reactive and non-predictive baselines, with only modest reservation overhead.

翻译：持续可靠的服务支持对于新兴的延迟敏感型和计算密集型应用在无人机辅助边缘网络（UENs）中至关重要，这源于运行动态性和环境不确定性。尽管传统设计能够改善覆盖和计算效率，但它们往往依赖于瞬时资源优化或反应式切换，导致当服务无人机因移动性、能量消耗或信道动态而性能下降时，正在进行的服务容易遭受不可忽视的中断。为了避免这种故障后恢复，一种有前景的方法是提前准备后继无人机，即待命无人机，它预留最少资源并同步服务上下文以供可能的接管。因此，我们考虑一种动态的UEN架构，其中每个移动用户携带一个需要持续服务支持的持续计算任务，而无人机在时变网络动态和严格机载能量约束下提供无线接入和计算服务。为了促进主动且连续的服务提供，我们提出了一种基于预测的主动预留式连续服务调度框架，称为Fresco。在Fresco中，首先使用基于LSTM的模块从历史网络观测中预测持续任务短期中断风险。在这些预测指导下，一种在线风险感知后继匹配方案在延迟、资源和能量约束下为高风险任务选择合适的待命无人机，同时结合最小通信/计算预留和轻量级服务上下文同步以实现高效的接管准备。实验表明，与反应式和非预测基线相比，Fresco显著减少了服务中断并提高了任务连续性，且仅带来适度的预留开销。