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.

翻译：无服务器计算通过引入细粒度、事件驱动的执行模型，将基础设施管理抽象化，从而改变了云应用程序的部署方式。其按需特性使其对延迟敏感和突发性工作负载尤其具有吸引力。然而，冷启动问题——即平台在配置新容器时产生显著延迟——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性无服务器调度框架，以主动缓解冷启动，从而改善端到端响应时间。通过预测未来的函数调用，控制器联合优化容器预热和请求调度，在降低延迟的同时最小化资源开销。我们在基于Kubernetes的测试平台上，于Apache OpenWhisk中实现了我们的方法。使用真实函数追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方法，实现了高达85%的尾部延迟降低和34%的资源使用减少。