数据中心鲁棒控制的最优阈值策略
Optimal Threshold Policies for Robust Data Center Control
摘要：
随着能源成本和对云计算需求的同时上升，对数据中心的有效控制变得至关重要。在数据中心控制问题中，需要计划在每个时间步打开或关闭多少服务器，以满足随机的作业到达，同时尽量减少电力消耗。当服务器可以是各种类型并且来自不同类别的作业只能由某些类型的服务器提供服务时，这个问题变得特别具有挑战性，因为在实际数据中心中通常是这种情况。我们将此问题建模为鲁棒马尔可夫决策过程（即，不假定转移函数是精确已知的）。我们给出了充分的条件（这似乎是合理的，并在实践中得到满足），保证存在一个最优的阈值政策。这个属性，然后可以利用在设计一个有效的解决方法，我们提供。最后，我们提出了一些实验结果证明我们的方法的实用性，并与以前的相关方法的基础上模型预测控制。
英文摘要：
With the simultaneous rise of energy costs and demand for cloud computing, efficient control of data centers becomes crucial. In the data center control problem, one needs to plan at every time step how many servers to switch on or off in order to meet stochastic job arrivals while trying to minimize electricity consumption. This problem becomes particularly challenging when servers can be of various types and jobs from different classes can only be served by certain types of server, as it is often the case in real data centers. We model this problem as a robust Markov Decision Process (i.e., the transition function is not assumed to be known precisely). We give sufficient conditions (which seem to be reasonable and satisfied in practice) guaranteeing that an optimal threshold policy exists. This property can then be exploited in the design of an efficient solving method, which we provide. Finally, we present some experimental results demonstrating the practicability of our approach and compare with a previous related approach based on model predictive control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
