自适应预测容器扩容延迟，提升系统响应速度与稳定性。
ADAPT: A Self-Calibrating Proactive Autoscaler for Container Orchestration
- 实时估算冷启动时间，动态调整扩容前瞻期。
- 使用MPC+LSTM策略，全负载下服务等级协议违规低于5%。
- 适合需要高可用、低延迟的云原生应用部署场景。
容器化工作负载的主动扩缩容依赖于对资源配置延迟（即从扩容决策到新实例就绪服务的时间）的准确预估。实际中，该冷启动时长在不同环境甚至连续扩缩事件间变化显著。我们提出ADAPT（自适应持续时长近似预测），一种在线指数加权移动平均（EWMA）估计器，用于运行时跟踪冷启动耗时。ADAPT将动态规划窗口FH-OPT输入模型预测控制（MPC）框架，优化滚动窗口内的副本数量。二者结合形成闭环主动扩缩设计，可依据实测资源延迟自适应调整前瞻能力。在三种策略（MPC+LSTM、MPC+Prophet、HPA）与六类工作负载原型上，经五次随机种子测试，MPC+LSTM在所有负载下均实现低于5%的SLA违规率，而传统反应式HPA为7-19%，MPC+Prophet在双峰流量下最高达28.7%。
原文摘要 · Abstract (English)
Proactive autoscaling for containerized workloads depends on knowing the provisioning delay, i.e., the time between a scaling decision and the moment new capacity is ready to serve traffic. In practice, this cold-start duration can vary substantially across environments and even across consecutive scale-out events. We present ADAPT (Adaptive Duration Approximation for Predictive Timing), an online EWMA estimator that tracks coldstart duration at runtime. ADAPT feeds a dynamic planning horizon, FH-OPT, into a Model Predictive Controller (MPC) that optimizes replica counts over a rolling window. Together, these components form a closed-loop proactive autoscaling design that adapts its lookahead based on measured provisioning delay. Evaluated across three policies (MPC+LSTM, MPC+Prophet, HPA) and six workload archetypes with five random seeds, MPC+LSTM achieves below 5% SLA violation on all workloads, compared with 7-19% for reactive HPA and up to 28.7% for MPC+Prophet on bimodal traffic.
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