On Energy Conservation in Data Centers
On Energy Conservation in Data Centers
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DOI：
10.1145/3364210
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
S. Albers
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文献类型：
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作者：
S. Albers
我们制定和研究的优化问题，出现在数据中心的能源管理，更普遍的是，多处理器环境。数据中心托管大量异构服务器。每个服务器都有一个活动状态和几个待机/睡眠状态，具有各自的功耗率。对计算能力的需求随时间而变化。空闲服务器可以转换到低功率模式，以便适当地调整活动服务器池的大小。我们的目标是找到一个状态转换时间表的服务器，最大限度地减少总的能源消耗。在小规模上，在芯片上具有异构处理器的多核架构中也会出现同样的问题。必须确定核心的活动和空闲周期，以便保证一定的服务并最小化消耗的能量。对于这个电源/容量管理问题，我们开发了两个主要的结果。我们使用数据中心设置的术语。首先，我们研究的情况下，每个服务器有两个状态：活动状态和睡眠状态。我们表明，一个最佳的解决方案，最大限度地减少能源消耗，可以计算在多项式时间的组合算法。该算法采用单商品最小费用流计算。其次，我们研究每个服务器都有一个活动状态和多个备用/睡眠状态的一般场景。我们设计了一个τ-近似算法，依赖于两个商品的最小成本流计算。这里，τ是不同服务器类型的数量。数据中心有大量的机器，但只有相对较少的不同服务器架构。此外，在优化中，可以将具有可比能耗的服务器分配到同一类。从技术上讲，我们的算法都涉及非平凡的流修改程序。特别是，给出了一个分数的两种商品流，我们的算法执行先进的舍入和流包装例程。
We formulate and study an optimization problem that arises in the energy management of data centers and, more generally, multiprocessor environments. Data centers host a large number of heterogeneous servers. Each server has an active state and several standby/sleep states with individual power consumption rates. The demand for computing capacity varies over time. Idle servers may be transitioned to low-power modes so as to rightsize the pool of active servers. The goal is to find a state transition schedule for the servers that minimizes the total energy consumed. On a small scale, the same problem arises in multicore architectures with heterogeneous processors on a chip. One has to determine active and idle periods for the cores so as to guarantee a certain service and minimize the consumed energy. For this power/capacity management problem, we develop two main results. We use the terminology of the data center setting. First, we investigate the scenario that each server has two states: an active state and a sleep state. We show that an optimal solution, minimizing energy consumption, can be computed in polynomial time by a combinatorial algorithm. The algorithm resorts to a single-commodity minimum-cost flow computation. Second, we study the general scenario that each server has an active state and multiple standby/sleep states. We devise a τ-approximation algorithm that relies on a two-commodity minimum-cost flow computation. Here, τ is the number of different server types. A data center has a large collection of machines but only a relatively small number of different server architectures. Moreover, in the optimization, one can assign servers with comparable energy consumption to the same class. Technically, both of our algorithms involve nontrivial flow modification procedures. In particular, given a fractional two-commodity flow, our algorithm executes advanced rounding and flow packing routines.