节能数据中心的最优异步动态策略
Optimal Asynchronous Dynamic Policies in Energy-Efficient Data Centers
摘要：
在本文中，我们使用马尔可夫决策过程，以找到最佳的节能数据中心的异步策略，两组异构服务器，有限的缓冲区，并在睡眠状态下的快速设置过程。组1中的服务器始终工作。组2中的服务器可以工作或睡眠，当服务器的状态从睡眠更改为工作时，会发生快速设置过程。在这样的数据中心中，异步动态策略被设计为两个子策略：设置策略和睡眠策略，其确定组2中的服务器在工作和睡眠状态之间的切换规则。为了分析最优异步动态策略，我们应用马尔可夫决策过程建立了一个基于策略的Poisson方程，并通过RG分解给出了性能势的唯一解。在此基础上，我们可以刻画数据中心的长期平均利润的单调性和最优性相对于异步动态策略在不同的服务价格。此外，我们证明了bang-bang控制总是最优的优化问题，并支持阈值型动态控制的节能数据中心。我们希望本文的方法和结果可以为更一般的节能数据中心的研究提供参考。
英文摘要：
In this paper, we use a Markov decision process to find optimal asynchronous policy of an energy-efficient data center with two groups of heterogeneous servers, a finite buffer, and a fast setup process at sleep state. Servers in Group 1 always work. Servers in Group 2 may either work or sleep, and a fast setup process occurs when server's states are changed from sleep to work. In such a data center, an asynchronous dynamic policy is designed as two sub-policies: The setup policy and the sleep policy, which determine the switch rule between the work and sleep states for the servers in Group 2. To analyze the optimal asynchronous dynamic policy, we apply the Markov decision process to establish a policy-based Poisson equation, which provides expression for the unique solution of the performance potential by means of the RG-factorization. Based on this, we can characterize the monotonicity and optimality of the long-run average profit of the data center with respect to the asynchronous dynamic policy under different service prices. Furthermore, we prove that the bang-bang control is always optimal for this optimization problem, and supports a threshold-type dynamic control in the energy-efficient data center. We hope that the methodology and results derived in this paper can shed light to the study of more general energy-efficient data centers.
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