CC* Integration Enhancement and Deployment of LDM7 for Scientific Data Distribution

Funding Agency: National Science Foundation (NSF)

Award: $1,000,000
Dates: 01-APR-2017 through 30–SEP-2021

The University Corporation for Atmospheric Research (UCAR) distributes scientific weather data from instruments and simulations to its 250 consortium member institutions via the Internet Data Distribution (IDD) system. The software program used for this data distribution is called Local Data Manager (LDM). LDM6, the current deployed version, has been in use since 2003. This project seeks to create a higher performing solution for real-time scientific data distribution by leveraging Software Defined Networking and OpenFlow technologies.This project seeks to (i) deploy LDM7 on multiple campuses after provisioningVLAN's across campuses and regionals, and connecting them to a multipoint VLAN across Internet2's network, (ii) add new features for security and control-plane to LDM7, (iii) integrate LDM7 with an SDN controller (OESS) client for automated addition/deletion of VLAN segments to a multipoint VLAN, (iv) collect information, and develop tools for simplifying VLAN provisioning and testing L2 path connectivity and performance using perfSONAR and other tools, and (v) run extensive tests of LDM7 for performance and reliability.

Selected Publications

  1. Yuanlong Tan, Malathi Veeraraghavan, Hwajung Lee, Steve Emmerson, Jack W. Davidson. A Trail Deployment of a Reliable Network-Multicast Application across Internet2, Proceedings of the 2020 IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS ‘20), November 2020, pp. 22–32. (Selected as Best INDIS 2020 Paper).

  2. Yuanlong Tan, Malathi Veeraraghavan, Hwajung Lee, Steve Emmerson, Jack W. Davidson. High-Performance Reliable Network-Multicast over a Trial Deployment (accepted for publication), Cluster Computing: The Journal of Networks, Software Tools and Applications.

  3. Yujia Mu, Yuanlong Tan, Malathi Veeraraghavan, Cong Shen. A Machine Learning Approach for Rate Prediction in Multicast File-stream Distribution Networks. Proceedings of the 2021 IEEE Global Communications Conference, Madrid, Spain. December 2021. (PDF)


  • Yuanlong Tan is a sixth year graduate student.

  • Hongying Dong is a fourth year graduate student.

  • Yujia Mu is a fourth year graduate student.

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