标题： 驯服冷启动：使用模型预测控制的主动无服务器调度 显示英文标题

标题： Taming Cold Starts: Proactive Serverless Scheduling with Model Predictive Control

摘要： 无服务器计算通过引入细粒度、事件驱动的执行模型，抽象了基础设施管理，从而改变了云应用的部署方式。 其按需特性使其特别适合对延迟敏感和突发的工作负载。 然而，冷启动问题，即平台在分配新容器时会遇到显著延迟，仍然是此类平台的致命弱点。 本文提出了一种基于模型预测控制的预测无服务器调度框架，以主动缓解冷启动问题，从而提高端到端响应时间。 通过预测未来的调用，控制器联合优化容器预热和请求分发，在降低资源开销的同时提高延迟。 我们在基于Kubernetes的测试平台上实现了该方法。 使用真实世界函数跟踪和合成工作负载的实验结果表明，我们的方法显著优于最先进的基线，实现了尾部延迟降低高达85%，资源使用减少34%。 显示英文摘要

摘要： Serverless computing has transformed cloud application deployment by introducing a fine-grained, event-driven execution model that abstracts away infrastructure management. Its on-demand nature makes it especially appealing for latency-sensitive and bursty workloads. However, the cold start problem, i.e., where the platform incurs significant delay when provisioning new containers, remains the Achilles' heel of such platforms. This paper presents a predictive serverless scheduling framework based on Model Predictive Control to proactively mitigate cold starts, thereby improving end-to-end response time. By forecasting future invocations, the controller jointly optimizes container prewarming and request dispatching, improving latency while minimizing resource overhead. We implement our approach on Apache OpenWhisk, deployed on a Kubernetes-based testbed. Experimental results using real-world function traces and synthetic workloads demonstrate that our method significantly outperforms state-of-the-art baselines, achieving up to 85% lower tail latency and a 34% reduction in resource usage.