{"id":483,"date":"2020-10-11T06:41:40","date_gmt":"2020-10-11T06:41:40","guid":{"rendered":"http:\/\/supercomputing.caltech.edu\/?p=483"},"modified":"2020-10-11T06:41:40","modified_gmt":"2020-10-11T06:41:40","slug":"sc19-network-research-exhibition-nre-022-toward-unified-resource-discovery-and-programming-in-multi-domain-networks","status":"publish","type":"post","link":"https:\/\/supercomputing.caltech.edu\/index.php\/2020\/10\/11\/sc19-network-research-exhibition-nre-022-toward-unified-resource-discovery-and-programming-in-multi-domain-networks\/","title":{"rendered":"SC19 Network Research Exhibition NRE-022 Toward Unified Resource Discovery and Programming in Multi-Domain Networks"},"content":{"rendered":"\n

Submitted on behalf of the team by: Harvey Newman, Caltech, newman@hep.caltech.edu, Qiao Xiang and Jensen Zhang, Yale University, {qiao.xiang, jingxuang.zhang}@yale.edu<\/p>\n\n\n\n

Original NRE-022 Paper<\/a><\/p>\n\n\n\n

Abstract<\/strong><\/p>\n\n\n\n

The Yale, IBM, ESNet and Caltech team will demonstrate a novel, unified multi-domain resource discovery and programming system for data-intensive collaborative sciences. Specifically, this system provides three key components: (1) a fine-grained, accurate, highly-efficient multi-domain multi-resource discovery framework (a substantial extension of the team’s SC’18 Mercator paper), (2) a strong machine learning component to provide accurate performance prediction for dynamic, reactive science workflows, and (3) a high- level resource programming and composition framework. This demonstration will include: (1) efficient discovery of multiple available resources in a multi-domain wide-area collaborative science network connecting Los Angeles and Denver, (2) real-time, accurate performance prediction for dynamic, reactive science workflows in this wide-area network, and (3) high-level resource programming and composition in science network with automatic resource orchestration update.<\/p>\n\n\n\n

Goals<\/strong><\/p>\n\n\n\n

  1. Demonstrate how the proposed framework can discover fine-grained, global multi-resource information across networks while preserving the privacy of different networks [1-3];<\/li>
  2. Demonstrate how machine learning techniques can be utilized to provide accurate performance prediction for dynamic, reactive science workflows [4];<\/li>
  3. Demonstrate how a high-level resource programming language simplifies the resource orchestration in science networks [5]<\/li><\/ol>\n\n\n\n

    Resources<\/strong><\/p>\n\n\n\n

    This demo is composed of three domains. In particular, we will use 2-4 data transfer nodes (DTNs) and 2 switches in the Caltech booth at SC19 exhibit floor to form one network. This network will be connected to the Caltech SDN testbed located at Pasadena, California via a 100 Gbps WAN circuit, provided by SCinet, CenturyLink and CENIC Los Angeles. In the SDN testbed, several switches and DTNs will be used to form two other domains.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Involved Parties<\/strong><\/p>\n\n\n\n