CSR-PDOS: Optimizing the Client/Server Environment Subject to User Satisfaction

CSR-PDOS：根据用户满意度优化客户端/服务器环境

• 批准号: 0720691
• 负责人: Peter Dinda
• 金额: $ 72.5万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0720691&HistoricalAwards=false
• 关键词: CSR; PDOS; Optimizing; Client; Server

This project reexamines systems problems in interactive client/server computing through the concept of having systems software optimize directly for the expressed satisfaction of the individual user. Satisfaction with a particular configuration or choice of parameters at the system software level varies dramatically from user to user. The project tests the hypothesis that a thin, nonintrusive user interface, in some cases combined with learning algorithms, can convey the individual user's satisfaction level, or other guidance, directly to the systems software with sufficient detail to allow the systems software to effectively customize its decisions to that individual user. To gather evidence about the hypothesis, the project is applying the concept to several systems problems that arise in contexts such as thin clients, desktop replacement systems, and modern web applications. These include: dynamic voltage and frequency scaling algorithms in power management; speculation in remote display systems; thread, process, and VM scheduling; and thread assignment/migration in multicore processors. The work in these areas is unified through: (1) the design, implementation, and evaluation (through user studies) of effective user interfaces to systems software, (2) seeking to determine an appropriate mix of explicit and implicit user feedback, and (3) determining the role of learning to minimize the amount of explicit feedback. If the hypothesis holds, it will illustrate a new way to build systems that are more satisfying for individual users.

该项目通过直接优化系统软件以满足单个用户的满意度的概念，重新审视交互式客户端/服务器计算中的系统问题。对于系统软件级别的特定配置或参数选择的满意度因用户而异。该项目测试了这样的假设：在某些情况下，与学习算法相结合的薄型非侵入式用户界面可以将个人用户的满意度或其他指导直接传达给系统软件，并提供足够的细节，以便系统软件能够有效地定制其个人用户的决定。为了收集有关该假设的证据，该项目正在将该概念应用于瘦客户端、桌面替换系统和现代 Web 应用程序等环境中出现的几个系统问题。其中包括：电源管理中的动态电压和频率缩放算法；对远程显示系统的猜测；线程、进程和VM调度；以及多核处理器中的线程分配/迁移。这些领域的工作通过以下方式统一起来：（1）系统软件有效用户界面的设计、实现和评估（通过用户研究），（2）寻求确定显式和隐式用户反馈的适当组合，以及（ 3）确定学习的角色，尽量减少显式反馈的数量。如果这个假设成立，它将说明一种构建更令个人用户满意的系统的新方法。