Collaborative Research: Performance Analysis and Design of Systems with Interconnected Resources

协作研究：资源互联系统的性能分析与设计

基本信息

批准号：
1562065
负责人：
Atilla Eryilmaz
金额：
$ 25万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2020-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1562065&HistoricalAwards=false
关键词：
Collaborative Research Performance Analysis Design

项目摘要

Many information processing, service and manufacturing systems can be viewed as networks of interacting resources. Examples include data centers for cloud computing, online advertising systems, electronic chip manufacturing lines, the Internet, and health care service systems. Requests for services from such systems are processed by an interconnected set of resources such as computers, manufacturing stations, and human servers. In such applications, a common objective is to identify service policies (routing, service order, and server control algorithms) that minimize delays for customers using the system. Except in a few special cases, currently there are no mathematical tools available to compute performance metrics such as the delay experienced by the customers, especially when the system size is large. The goal of this project is to advance the mathematics tools needed to compute performance metrics of large systems of networked resources. These mathematical techniques will enable the design of good service policies for the myriad of applications mentioned earlier. The results from the project will be incorporated into courses. Outreach efforts will be made to include students from underrepresented groups and minorities in the project. Often the problem of optimal control of networks of interacting resources can be modeled as a Markov Decision Problem (MDP), but the state-space is prohibitively large to obtain optimal solutions. Therefore, it is common to study such problems (under some appropriate scaling) either as fluid control problems, or Brownian control problems, or large-deviations problems. The objective of this project is to enable an alternative approach, which involves studying the drift of appropriately-chosen Lyapunov functions, in transient and steady-state modes. The specific challenge involves developing lower-dimensional models for high-dimensional systems, and using the lower-dimensional models to study the optimality, or lack thereof, of specific architectures and algorithms. If successful, this project will result in (i) new analysis tools based on the drift-based arguments, which provide tight bounds on the steady-state performance of control and decision algorithms in large networks, and (ii) design of optimal or near-optimal algorithms that perform well at all traffic loads.

许多信息处理，服务和制造系统都可以视为交互资源的网络。示例包括用于云计算的数据中心，在线广告系统，电子芯片制造线，互联网和医疗保健服务系统。来自此类系统的服务请求由一组相互联系的资源（例如计算机，制造台和人类服务器）处理。在这样的应用程序中，一个常见的目标是确定服务策略（路由，服务顺序和服务器控制算法），以最大程度地减少使用系统的客户延迟。除了一些特殊情况下，目前尚无数学工具来计算性能指标，例如客户经历的延迟，尤其是当系统大小较大时。该项目的目的是推进计算大型网络资源系统性能指标所需的数学工具。这些数学技术将为前面提到的无数应用程序设计出良好的服务政策。该项目的结果将纳入课程中。将进行推广工作，以包括来自代表性不足的团体和少数民族的学生。通常，可以将交互资源网络网络的最佳控制问题建模为马尔可夫决策问题（MDP），但是状态空间在获得最佳解决方案方面非常大。因此，通常将这些问题（在某些适当的缩放下）作为流体控制问题，布朗控制问题或大问题问题是很常见的。该项目的目的是启用一种替代方法，该方法涉及研究适当选择的Lyapunov函数在瞬态和稳态模式下的漂移。具体的挑战涉及为高维系统开发较低维度的模型，并使用较低维模型来研究特定体系结构和算法的最优性或缺乏效果。如果成功，该项目将基于基于漂移的参数产生（i）新的分析工具，该工具为大型网络中控制和决策算法的稳态性能提供了紧密的界限，以及（ii）设计最佳或近乎最佳算法的设计，这些算法在所有交通负载下都表现良好。