Collaborative Research: Framework: Software: CINES: A Scalable Cyberinfrastructure for Sustained Innovation in Network Engineering and Science

合作研究：框架：软件：CINES：用于网络工程和科学持续创新的可扩展网络基础设施

• 批准号: 1916805
• 负责人: Madhav Marathe
• 金额: $ 288万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-11-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916805&HistoricalAwards=false
• 关键词: Collaborative; Research; Framework; Software; CINES

Networks are ubiquitous and are a part of our common vocabulary. Network science and engineering has emerged as a formal field over the last twenty years and has seen explosive growth. Ideas from network science are central to companies such as Akamai, Twitter, Google, Facebook, and LinkedIn. The concepts have also been used to address fundamental problems in diverse fields (e.g., biology, economics, social sciences, psychology, power systems, telecommunications, public health and marketing), and are now part of most university curricula. Ideas and techniques from network science are widely used in making scientific progress in the disciplines mentioned above. Networks are now part of the public vocabulary, with news articles and magazines frequently using the term "networks" to refer to interconnected entities. Yet, resources for effective use of techniques from network science are largely dispersed and stand-alone, of small scale, home-grown for personal use, and/or do not cover the broad range of operations that need to be performed on networks. Compositions of these diverse capabilities are rare. Furthermore, many researchers who study networks are not computer scientists. As a result, they do not have easy access to computing and data resources; this creates a barrier for researchers. This project will develop a sophisticated cyberinfrastructure that brings together various resources to provide a unifying ecosystem for network science that is greater than the sum of its parts. The resulting cyberinfrastructure will benefit researchers and students from various disciplines by facilitating access to various tools for synthesizing and analyzing large networks, and by providing access points for contributors of new software and data. An important benefit of the system is that it can be readily used even by researchers who have no formal training in computer programming. The cyberinfrastructure resulting from this work will foster multi-disciplinary and multi-university research and teaching collaborations. As part of this project, comprehensive education and outreach programs will be launched by the participating institutions, spanning educators and K-12 students. These programs will include network science courses with students from minority and under-represented groups, and students at smaller institutions who do not have easy access to high performance computing resources.Resources for doing network science are largely dispersed and stand-alone (in silos of isolated tools), of small scale, or home-grown for personal use. What is needed is a cyberinfrastructure to bring together various resources, to provide a unifying ecosystem for network science that is greater than the sum of its parts. The primary goal of this proposal is to build self-sustaining cyberinfrastructure (CI) named CINES (Cyberinfrastructure for Sustained Innovation in Network Engineering and Science) that will be a community resource for network science. CINES will be an extensible and sustainable platform for producers and consumers of network science data, information, and software. CINES will have: (1) a layered architecture that systematically modularizes and isolates messaging, infrastructure services, common services, a digital library, and APIs for change-out and updates; (2) a robust and reliable infrastructure that---for applications (apps)---is designed to accommodate technological advances in methods, programming languages, and computing models; (3) a resource manager to enable jobs to run on target machines for which they are best suited; (4) an engine to enable users to create new workflows by composing available components and to distribute the resulting workload across computing resources; (5) orchestration among system components to provide CI-as-a-service (CIaaS) that scales under high system load to networks with a billion or more vertices; (6) a digital library with 100,000+ networks of various kinds that allows rich services for storing, searching, annotating, and browsing; (7) structural methods (e.g., centrality, paths, cuts, etc.) and dynamical models of various contagion processes; (8) new methods to acquire data, build networks, and augment them using machine learning techniques; (9) a suite of industry- recognized tools such as SNAP, NetworkX, and R-studio that make it easier for researchers, educators, and analysts to do network science and engineering; (10) a suite of APIs that allows developers to add new web-apps and services, based on an app-store model, and allows access to CINES from third party software; and (11) metrics and a Stack Overflow model, among other features, for producers and consumers to interact (in real-time) and guide the evolution of CINES. CINES will enable fundamental changes in the way researchers study and teach complex networks. The use of state-of-the-art high-performance computing (HPC) resources to synthesize, analyze, and reason about large networks will enable researchers and educators to study networks in novel ways. CINES will allow scientists to address fundamental scientific questions---e.g., biologists can use network methods to reason about genomics data that is now available in large quantities due to fast and effective sequencing and the NIH Microbiome Program. It will enable educators to harness HPC technologies to teach Network Science to students spanning various academic levels, disciplines, and institutions. CINES, which will be useful to researchers supported by many NSF directorates and divisions, will be designed for scalability, usability, extensibility, and sustainability. This project will also advance the fields of digital libraries and cloud computing by stretching them to address challenges related to Network Science. Given the multidisciplinary nature of the field, CINES will provide a collaborative space for scientists from different disciplines, leading to important cross fertilization of ideas.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

网络无处不在，是我们常见词汇的一部分。在过去的二十年中，网络科学和工程已经成为正式领域，并看到了爆炸性的增长。 网络科学的想法对Akamai，Twitter，Google，Facebook和LinkedIn等公司来说是核心。 这些概念还用于解决不同领域（例如生物学，经济学，社会科学，心理学，电力系统，电信，公共卫生和营销）的基本问题，现在已成为大多数大学课程的一部分。网络科学的思想和技术被广泛用于在上述学科中取得科学进步。 现在，网络已成为公共词汇的一部分，新闻文章和杂志经常使用“网络”一词来指代相互联系的实体。 然而，有效利用网络科学技术的资源在很大程度上分散和独立，小规模，本地供个人使用和/或不涵盖需要在网络上执行的广泛操作。 这些不同功能的组成很少见。 此外，许多研究网络的研究人员不是计算机科学家。 结果，它们不容易访问计算和数据资源。这为研究人员造成了障碍。该项目将开发一个复杂的网络基础架构，该基础架构将各种资源汇集在一起​​，为网络科学提供统一的生态系统，该生态系统大于其部分的总和。由此产生的网络基础设施将通过促进访问各种工具来综合和分析大型网络，并为新软件和数据的贡献者提供访问点，从而使来自各个学科的研究人员和学生受益。该系统的一个重要好处是，即使没有在计算机编程中进行正规培训的研究人员也可以很容易地使用它。 这项工作产生的网络基础设施将促进多学科和多大学研究和教学合作。作为该项目的一部分，跨越教育者和K-12学生将启动综合教育和外展计划。这些课程将包括来自少数群体和代表性不足的团体的学生的网络科学课程，以及不容易访问高性能计算资源的较小机构的学生。进行网络科学的资源在很大程度上分散和独立（在隔离工具的孤岛中​​），小规模或本土用于个人使用。 需要的是网络基础设施，以汇集各种资源，为网络科学提供一个统一的生态系统，该系统大于其部分的总和。该提案的主要目标是建立名为Cines的自我维持的网络基础设施（CI）（网络工程和科学中持续创新的Cyber​​inFrastructure），这将是网络科学的社区资源。 对于网络科学数据，信息和软件的生产者和消费者来说，CINS将是一个可扩展且可持续的平台。 Cines将有：（1）一个分层体系结构，该体系结构系统地模块化并隔离了消息传递，基础架构服务，通用服务，数字库以及用于更改和更新的API； （2）用于应用程序（应用程序）的强大而可靠的基础架构 - 旨在适应方法，编程语言和计算模型的技术进步； （3）资源经理使作业能够在最适合其的目标机器上运行； （4）引擎使用户能够通过组成可用组件并在计算资源中分配所得的工作负载来创建新的工作流； （5）系统组件之间的编排，以提供CI-AS-A-Service（CIAAS），该服务在高系统负载下向具有十亿或更多顶点的网络缩放； （6）具有100,000多种各种网络的数字图书馆，允许丰富的服务用于存储，搜索，注释和浏览； （7）结构方法（例如，中心性，路径，切割等）和各种传染过程的动态模型； （8）使用机器学习技术获取数据，构建网络并增强它们的新方法； （9）一套行业认可的工具，例如SNAP，NetworkX和R-Studio，使研究人员，教育者和分析师更容易地进行网络科学和工程； （10）一套API套件，允许开发人员根据应用商店模型添加新的Web应用程序和服务，并允许从第三方软件访问CINES； （11）指标和堆栈溢出模型以及其他功能，供生产者和消费者实时互动并指导CINES的演变。 Cines将在研究人员研究和教授复杂网络的方式上进行根本性的变化。 最先进的高性能计算（HPC）资源来综合，分析和有关大型网络的理由将使研究人员和教育工作者能够以新颖的方式研究网络。 CINE将允许科学家解决基本科学问题 - 例如，生物学家可以使用网络方法来理解基因组学数据，这些数据由于快速有效的测序以及NIH微生物组计划而大量可用。 它将使教育工作者能够利用HPC技术向跨越各种学术水平，学科和机构的学生教授网络科学。 西内斯（Cines）将对许多NSF局和部门支持的研究人员有用，它将用于可扩展性，可用性，可扩展性和可持续性。该项目还将通过拉伸数字图书馆和云计算来推动它们的领域，以应对与网络科学相关的挑战。 鉴于该领域的多学科性质，Cines将为来自不同学科的科学家提供一个协作空间，从而导致了重要的思想交叉施肥。该奖项反映了NSF的法定使命，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来评估的支持。

项目成果

A Simulation-based Approach for Large-scale Evacuation Planning

基于仿真的大规模疏散规划方法

• DOI: 10.1109/bigdata50022.2020.9377794
• 发表时间: 2020
• 期刊: IEEE International Conference on Big Data
• 作者: Islam, Kazi Ashik;Marathe, Madhav;Mortveit, Henning;Swarup, Samarth;Vullikanti, Anil
• 通讯作者: Vullikanti, Anil

CSonNet: An Agent-Based Modeling Software System for Discrete Time Simulation

• DOI: 10.1109/wsc52266.2021.9715287
• 发表时间: 2021-12
• 期刊: 2021 Winter Simulation Conference (WSC)
• 作者: Joshua D. Priest;Aparna Kishore;Lucas Machi;C. Kuhlman;D. Machi;Sujith Ravi

Identifying Complicated Contagion Scenarios from Cascade Data

• DOI: 10.1145/3580305.3599841
• 发表时间: 2023-08
• 期刊: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Galen Harrison;Amro Alabsi Aljundi;Jiangzhuo Chen;Sujith Ravi;A. Vullikanti;M. Marathe;Abhijin Adiga

Effective Social Network-Based Allocation of COVID-19 Vaccines

基于社交网络的有效 COVID-19 疫苗分配

• DOI: 10.1145/3534678.3542673
• 发表时间: 2022
• 期刊: Proceedings of the KDD Health Day (2022
• 作者: Chen, Jiangzhuo;Hoops, Stefan;Marathe, Achla;Mortveit, Henning;Lewis, Bryan;Venkatramanan, Srinivasan;Haddadan, Arash;Bhattacharya, Parantapa;Adiga, Abhijin;Vullikanti, Anil
• 通讯作者: Vullikanti, Anil

Asymptomatic individuals can increase the final epidemic size under adaptive human behavior.

• DOI: 10.1038/s41598-021-98999-2
• 发表时间: 2021-10-05
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Espinoza B;Marathe M;Swarup S;Thakur M
• 通讯作者: Thakur M