Department of Computer Science and Engineering, Beihang University, Beijing, People's Republic of China.
PLoS One. 2013;8(2):e57551. doi: 10.1371/journal.pone.0057551. Epub 2013 Feb 25.
Power and performance management problem in large scale computing systems like data centers has attracted a lot of interests from both enterprises and academic researchers as power saving has become more and more important in many fields. Because of the multiple objectives, multiple influential factors and hierarchical structure in the system, the problem is indeed complex and hard. In this paper, the problem will be investigated in a virtualized computing system. Specifically, it is formulated as a power optimization problem with some constraints on performance. Then, the adaptive controller based on least-square self-tuning regulator(LS-STR) is designed to track performance in the first step; and the resource solved by the controller is allocated in order to minimize the power consumption as the second step. Some simulations are designed to test the effectiveness of this method and to compare it with some other controllers. The simulation results show that the adaptive controller is generally effective: it is applicable for different performance metrics, for different workloads, and for single and multiple workloads; it can track the performance requirement effectively and save the power consumption significantly.
在数据中心等大规模计算系统中，电力和性能管理问题引起了企业和学术研究人员的极大兴趣，因为在许多领域，节能变得越来越重要。由于系统中存在多个目标、多个影响因素和层次结构，因此问题确实很复杂和困难。在本文中，将在虚拟化计算系统中研究该问题。具体来说，将其表述为具有一些性能约束的功率优化问题。然后，设计基于最小二乘自校正调节器（LS-STR）的自适应控制器来首先跟踪性能；并在第二步中分配控制器解决的资源，以最小化功耗。设计了一些仿真来测试该方法的有效性，并将其与其他一些控制器进行比较。仿真结果表明，自适应控制器通常是有效的：它适用于不同的性能指标、不同的工作负载以及单个和多个工作负载；它可以有效地跟踪性能要求并显著节省功耗。
