Continuous edge inference necessitates not merely low per-timeslot latency, but sustained timeliness guarantees in the presence of time-varying channels, fluctuating edge workloads, and coupled bandwidth-computing resource constraints. Existing studies predominantly optimize instantaneous delay or per-timeslot utility, while largely overlooking the regulation of cross-time deadline violation dynamics in continuous services. To address this, we propose AEGIS, a prediction-empowered risk-budgeted online scheduling framework for continuous edge inference. AEGIS models deadline-violation tendency as an updatable cross-time control state through dynamic user-level risk budgets, so that online scheduling accounts for both instantaneous efficiency and long-term service stability. To support proactive decision making, AEGIS leverages LSTM-based short-term state prediction to construct a smooth deadline-violation risk surrogate, and formulates the resulting time-wise resource competition as a potential-aligned game under coupled feasibility constraints. An asynchronous online algorithm is then developed with finite-step convergence. Experiments demonstrate that AEGIS improves the timely inference ratio, reduces average violation risk and violation burst length, and achieves a favorable delay--risk--convergence trade-off over representative baselines.

翻译：连续边缘推理不仅需要低单次时隙延迟，更要求在时变信道、波动边缘负载以及带宽-计算资源耦合约束下，提供持续的时效性保障。现有研究主要优化瞬时延迟或单时隙效用，却鲜少关注连续服务中跨时间截止期违背动态的调控。为此，我们提出AEGIS—一种预测增强的风险预算化在线调度框架，用于连续边缘推理。AEGIS通过动态用户级风险预算，将截止期违背倾向建模为可更新的跨时间控制状态，使在线调度同时兼顾瞬时效率与长期服务稳定性。为支持前瞻性决策，AEGIS利用基于LSTM的短期状态预测构建平滑的截止期违背风险代理，并将由此产生的时域资源竞争建模为耦合可行性约束下的势对齐博弈。进而开发了一种具有有限步收敛性的异步在线算法。实验表明，AEGIS提升了及时推理比例，降低了平均违背风险与违背突发长度，并在代表性基线方法中实现了延迟-风险-收敛性的良好权衡。