ENES: Energy-Efficient Scheduling

在这个项目中，我们将研究硬件环境中节能的算法技术，从而支持系统级大数据集的处理。我们将重点关注动态速度缩放技术，这是目前已知的微处理器系统中最有前途的节能方法。该项目的具体目标是使算法结果更接近实践。大多数先前的工作都是理想化的体系结构假设：处理器将具有连续的无界速度谱，并且可以在任何时间免费执行速度更改。我们将为处理器具有有限离散速度水平的现实场景设计和分析算法。为此，我们将采用已知的连续处理器速度谱策略，更重要的是，设计专门针对离散速度的更精确场景的新算法。上述理论导向的工作将辅以深入的实验，形成项目的算法工程部分。迄今为止，在算法速度缩放文献中开发的任何策略都没有在实际数据上实施和测试。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.