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

该INSPIRE奖的部分资金来自计算机与信息科学与工程理事会计算机与网络系统部门的网络技术与系统研究项目、工程理事会土木、机械与制造部门的制造企业系统项目、以及计算机与信息科学与工程理事会计算与通信基础部门的通信与信息基础项目。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的以下应用，解决了制造和网络这两个传统上独立学科的常见问题。到目前为止，每个领域的研究都集中在不同的问题上，半导体制造主要关注吞吐量最大化、平均周期时间最小化和系统稳定性，而云网络主要关注网络性能保证。然而，这两个领域将受益于共享一个共同的集成焦点和一个统一的随机处理网络模型来建模和分析问题。该项目提供了这一新的数学基础，以及一套实用的服务学科和调度算法，以实现对随机处理网络的推理并提供性能保证。特别是，该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，以便这两个应用程序领域能够相互通知，并通过这样做开发可能无法想象的新解决方案。这项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微积分，这将使研究人员和实践者能够推理并为各种各样的应用程序提供性能保证，这些应用程序可以建模为随机处理网络。科学发现往往发生在两个学科的交叉点。该项目涉及非常有能力和成就的研究人员（在网络、网络理论、工业系统工程和运筹学领域），并跨越不同学科，在探索和推进随机处理微积分的过程中，着眼于一系列交叉领域。这项工作的贡献包括探索这一新的数学领域，以及在这两个研究领域中潜在的转化应用。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，可以涵盖广泛的新出现的网络问题。对于随机处理网络的一般抽象，新的分析和算法结果有望在不同的领域得到广泛的应用。更广泛地说，拟议的工作是变革性的，因为它将为更大的“网络科学”做出贡献。网络科学被认为是一个新兴的领域，因为网络社区开发的许多数学基础和网络算法在许多其他领域得到了广泛的应用。

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.