ENES: Energy-Efficient Scheduling

在这个项目中，我们将研究在硬件环境中节省能源的算法技术，从而支持在系统层面上处理大数据集。我们将集中在动态速度缩放的技术，目前已知的最有前途的方法，以节省能源的微处理器系统。该项目的具体目标是使算法结果更接近实践。大部分的工作都是基于理想化的架构假设：处理器将具有连续的无限速度谱，并且速度变化可以在任何时间进行，而无需任何成本。我们将设计和分析处理器具有有限的离散速度水平的现实场景的算法。为此，我们将采用已知的连续处理器速度谱的策略，更重要的是，设计新的算法，专门针对具有离散速度的更准确的场景。上述理论导向的工作将通过彻底的实验来补充，形成该项目的算法工程部分。到目前为止，在算法速度缩放文献中开发的策略都没有在真实世界的数据上实现和测试。我们希望克服这一缺陷。具体来说，我们将建立一个数据集库，并实现/测试所提出的算法。

In this project we will study algorithmic techniques for energy savings in hardware environments, thereby supporting the processing of big data sets on the systems level. We will focus on the technique of dynamic speed scaling as the most promising approach currently known to conserve energy in microprocessor systems. The specific goal of this project is bring algorithmic results closer to practice. Most of the prior work makes idealized architectural assumptions: A processor would have a continuous unbounded speed spectrum, and speed changes could be performed at any time at no cost.We will design and analyze algorithms for the realistic scenario that a processor has a finite set of discrete speed levels. To this end we will adapt strategies known for a continuous processor speed spectrum and, more importantly, design new algorithms that are specifically tailored to the more accurate scenario with discrete speeds. The above theoretically oriented work will be complemented by thorough experiments, forming an algorithms engineering part of the project. To date none of the strategies developed in the algorithmic speed scaling literature has been implemented and tested on real-world data. We wish to overcome this deficiency. Specifically, we will build up a library of data sets and implement / test the proposed algorithms.