INSPIRE: Stochastic Processing Calculus: A New Methodology for Advanced Semiconductor Manufacturing and Data Center Networking

INSPIRE：随机处理微积分：先进半导体制造和数据中心网络的新方法

• 批准号: 1248117
• 负责人: Bill Lin
• 金额: $ 75万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1248117&HistoricalAwards=false
• 关键词: INSPIRE; Stochastic; Processing; Calculus; New

This INSPIRE award is partially funded by Research in Networking Technology and Systems Program in the Division of Computer and Network Systems in the Directorate for Computer and Information Science and Engineering, the Manufacturing Enterprise Systems Program in the Division of Civil, Mechanical and Manufacturing in the Directorate for Engineering, and the Communications and Information Foundations Program in the Division of Computing and Communications Foundations in the Directorate for Computer and Information Science and Engineering. This project addresses common problems across two traditionally separate disciplines of manufacturing and networking by focusing on the following applications in the two fields-advanced semiconductor manufacturing and virtualized data center networking. Research in each field has heretofore focused on different problems, with semiconductor manufacturing largely focused on throughput maximization, mean cycle time minimization and system stability and cloud networking on network performance guarantees. However, the two areas would benefit from sharing a common integrated focus and a unifying stochastic processing network model for modeling and analyzing problems. This project contributes this new mathematical foundation along with a set of practical service disciplines and scheduling algorithms to enable reasoning about and to provide performance guarantees in stochastic processing networks. In particular, this project will concurrently investigate the problems of delivery guarantees in semiconductor manufacturing and network performance guarantees in virtualized data centers so that the two application domains can inform each other and by doing so develop new solutions that might not otherwise be imagined. The central contribution of the proposed work will be a new mathematical foundation, which the principal investigators call Stochastic Processing Calculus that will allow researchers and practitioners to reason about and provide performance guarantees for diverse range of applications that can be modeled as stochastic processing networks. Scientific discoveries often happen at the intersection of two disciplines. This project involves very competent and accomplished researchers (in the areas of networking, network theory, and industrial systems engineering and operations research) and crosses diverse disciplines with the intension of looking at a set of intersections as it explores and advances Stochastic Processing Calculus. The contributions from this effort include exploring this new area of mathematics and in its potentially transformational application to each of the two research areas. Broader Impact: This INSPIRE project is transformational in that it promises to deliver a new rigorous modeling and analytical framework that can encompass a broad range of emerging networking problems. New analytical and algorithmic results that will be developed for the general abstraction of stochastic processing network are expected to have broad applications in a diverse range of fields. More broadly speaking, the proposed work is transformational in that it will contribute to a larger body of 'Network Science'. Network Science is being recognized as an emerging field in its own right in that many of the mathematical foundations and network algorithmics developed in the networking community are finding wide applications in many other fields.

该INSPIRE奖部分由计算机和信息科学与工程局计算机和网络系统司的网络技术和系统计划研究，工程局土木，机械和制造司的制造企业系统计划，以及计算机和信息科学与工程局计算和通信基础处的通信和信息基础计划。该项目通过关注先进半导体制造和虚拟化数据中心网络这两个领域中的以下应用，解决了制造和网络这两个传统上独立的学科中的常见问题。迄今为止，每个领域的研究都集中在不同的问题上，半导体制造主要集中在吞吐量最大化，平均周期时间最小化和系统稳定性以及网络性能保证的云网络。 然而，这两个领域将受益于共享一个共同的综合重点和一个统一的随机处理网络模型建模和分析问题。这个项目贡献了这个新的数学基础沿着一套实用的服务学科和调度算法，使推理和随机处理网络提供性能保证。特别是，本项目将同时研究半导体制造中的交付保证问题和虚拟化数据中心中的网络性能保证问题，以便两个应用领域可以相互通知，并通过这样做开发可能无法想象的新解决方案。拟议工作的核心贡献将是一个新的数学基础，主要研究人员称之为随机处理演算，这将使研究人员和从业者能够推理并为各种可以建模为随机处理网络的应用提供性能保证。 科学发现往往发生在两个学科的交叉点上。该项目涉及非常有能力和有成就的研究人员（在网络，网络理论，工业系统工程和运筹学领域），并跨越不同的学科，旨在探索和推进随机处理演算。 这项工作的贡献包括探索这一新的数学领域，并在其潜在的转型应用到两个研究领域。 更广泛的影响：这个INSPIRE项目是变革性的，因为它承诺提供一个新的严格的建模和分析框架，可以涵盖广泛的新兴网络问题。 新的分析和算法的结果，将开发的随机处理网络的一般抽象，预计将有广泛的应用在不同的领域。 更广泛地说，拟议的工作是转型，因为它将有助于更大的“网络科学”的机构。 网络科学本身被认为是一个新兴的领域，因为在网络社区中开发的许多数学基础和网络算法在许多其他领域中得到了广泛的应用。