AI 中文总结
针对云数据中心时变功率约束，提出分层滚动时域控制器，分离服务器慢速重配置与快速节流，通过凸对偶分解和排名前缀搜索，在超大规模实例上实现无违规且低服务影响的近最优功率调制。
AI 中文摘要
云数据中心必须越来越多地根据时变的电网和基础设施约束来调制功率。我们将此问题建模为网络化混合动力系统的有限时域控制，其中数据中心功率和服务能力取决于服务器、工作线程和托管服务之间的交互。功率可以通过快速的连续工作线程节流来降低，该节流立即生效但会降低服务能力，以及缓慢的离散服务器状态转换，该转换提供更深的节能但存在延迟。协调这些机制产生了一个高维混合整数动态优化问题，在规模上难以求解。我们提出了一种分层滚动时域控制器，将缓慢的服务器重配置与快速的节流补救分开。对于固定的服务器状态，节流层简化为服务级别的凸补救问题，通过对偶分解高效求解。然后，服务器层使用排名前缀搜索，通过规划时域内的补救值评估候选配置。在超过15,000台服务器、200,000个工作线程和1,400个服务的真实实例上的实验表明，该控制器满足时变功率上限且无违规，并且比仅快速或仅缓慢的基线具有显著更低的服务影响。与标准优化求解器相比，我们的方法提供了显著的加速，在20秒的实时控制间隔内计算出接近最优的计划。
英文摘要
Cloud datacenters must increasingly modulate power in response to time-varying grid and infrastructure constraints. We study this problem as finite-horizon control of a networked hybrid dynamical system, where datacenter power and service capacity depend on interactions between servers, workers, and hosted services. Power can be reduced through fast continuous worker throttling, which acts immediately but degrades service capacity, and slow discrete server transitions, which provide deeper savings but evolve with delay. Coordinating these mechanisms yields a high-dimensional mixed-integer dynamic optimization problem which is intractable to solve at scale. We propose a hierarchical receding-horizon controller that separates slow server reconfiguration from fast throttling recourse. For fixed server states, the throttling layer reduces to a service-level convex recourse problem solved efficiently by dual decomposition. The server layer then uses a ranked-prefix search that evaluates candidate configurations through the recourse value over the planning horizon. Experiments on realistic instances with over 15,000 servers, 200,000 workers, and 1,400 services show that the controller satisfies time-varying power caps with no violations and substantially lower service impact than fast-only or slow-only baselines. Our method offers significant speedups compared to standard optimization solvers, computing near-optimal plans within a 20 second real-time control interval.
