In recent years, the issue of energy consumption in parallel and distributed computing systems has attracted a great deal of attention. In response to this, many energy-aware scheduling algorithms have been developed primarily using the dynamic voltage-frequency scaling (DVFS) capability which has been incorporated into recent commodity processors. Majority of these algorithms involve two passes: schedule generation and slack reclamation. The former pass involves the redistribution of tasks among DVFS-enabled processors based on a given cost function that includes makespan and energy consumption; and, while the latter pass is typically achieved by executing individual tasks with slacks at a lower processor frequency. In this paper, a new slack reclamation algorithm is proposed by approaching the energy reduction problem from a different angle. Firstly, the problem of task slack reclamation by using combinations of processors' frequencies is formulated. Secondly, several proofs are provided to show that (1) if the working frequency set of processor is assumed to be continues, the optimal energy will be always achieved by using only one frequency, (2) for real processors with a discrete set of working frequencies, the optimal energy is always achieved by using at most two frequencies, and (3) these two frequencies are adjacent/neighbouring when processor energy consumption is a convex function of frequency. Thirdly, a novel algorithm to find the best combination of frequencies to result the optimal energy is presented. The presented algorithm has been evaluated based on results obtained from experiments with three different sets of task graphs: 3000 randomly generated task graphs, and 600 task graphs for two popular applications (Gauss-Jordan and LU decomposition). The results show the superiority of the proposed algorithm in comparison with other techniques.
PowerTracer: Tracing requests in multi-tier services to save cluster power consumption
随着能量比例计算逐渐将DVFS（Dynamic Voltage and Frequency Scaling）的成功扩展到整个系统，DVFS控制算法将在降低服务器集群功耗方面发挥关键作用。本文的重点是提供准确的集群级DVFS控制的服务器集群中的节能。为了实现这一目标，我们提出了一个请求跟踪方法，在线分类的主要因果路径模式的多层服务，并监测其性能数据作为指导，准确的DVFS控制。请求跟踪的方法显着减少了时间成本的性能分析实验，旨在建立经验的性能模型。此外，它降低了控制器的复杂性，使我们可以引入一个更简单的反馈控制器，它只依赖于单节点DVFS调制的时间，而不是同时改变多个CPU的频率。基于请求跟踪方法，我们提出了一种混合DVFS控制系统，它结合了经验的性能模型，在不同的负载水平和一个简单的反馈控制器的自适应快速调制。我们实现了一个原型的建议系统，称为PowerTracer，并进行了广泛的实验上的3层平台。我们的实验结果表明，PowerTracer优于其同行在节能和系统性能。
