AF: Small: Collaborative Research: Algorithmic Approaches to Energy-Efficient Computing

能源消耗现在正在成为计算机系统的主要性能指标。近年来，通过更好的硬件设计和软件工具的结合，在提高能源效率方面取得了重大进展。然而，未来节能计算机系统的设计最终将需要开发可用于指导实际解决方案的基本模型和算法工具。本项目旨在研究提高计算机系统中数据处理和存储的能效的算法方法。基本方法是用组合优化语言对各种系统部件的运行进行建模，以目标函数表示能量消耗，并使用精确或近似的有效算法来解决这些问题。其中许多问题可以从任务调度的角度来阐述，其目标是优化完成一组任务所需的CPU能源消耗，同时满足一些性能要求。其他例子包括通过优化功率级别和复杂的分页或高速缓存策略来最小化存储器系统的能量消耗，包括内部和外部存储器。除了解决一些特定的能源优化问题外，这项工作还有望产生新的算法技术，以及更深入地了解标准性能增强工具(如缓存和负载平衡)对于提高能源效率的充分性。对能量复杂性的研究也将有助于揭示计算和能量之间的关系。在研究过程中开发的一些算法将在基于FreeBSD的平台上进行实现和经验性测试，并提供给实践者。教育部分包括研究生和本科生的研究项目，以及开发一门关于可持续计算的课程。

Energy consumption is now emerging as a dominant performance measure in computer systems. In recent years, significant progress in improving energy efficiency has been accomplished by a combination of better hardware design and software tools. Yet the design of future energy-efficient computer systems will ultimately require the development of fundamental models and algorithmic tools that can be used to guide practical solutions.This project is to study algorithmic methods for improving energy efficiency of data processing and storage in computer systems. The basic approach is to model the operation of various system components in the language of combinatorial optimization, with the objective function representing energy consumption, and to solve these problems using exact or approximate efficient algorithms. Many of those problems can be formulated in terms of task scheduling, where the objective is to optimize the CPU energy consumption required to complete a collection of tasks, while meeting some performance requirements. Other examples include minimizing energy consumption of memory systems, both the internal and external memories, by optimizing power levels and sophisticated paging or caching strategies. In addition to addressing some specific energy optimization problems, this work is expected to produce new algorithmic techniques, as well as deeper understanding of the adequacy of standard performance enhancement tools, like caching and load balancing, for improving energy efficiency. The study on energy complexity will also shed some light on the relation between computation and energy.Some algorithms developed in the course of this research will be implemented, tested empirically on the FreeBSD-based platform, and made available to practitioners. The educational component includes research projects for graduate and undergraduate students, and developing a course on sustainable computing.