Collaborative Research: CC-NIE Integration: Developing Applications with Networking Capabilities via End-to-End SDN (DANCES)

  • Nekrutenko, Anton (PI)

Project: Research project

Project Details

Description

The DANCES project team of network engineers, application developers, and research scientists is implementing a software-defined networking (SDN)-enabled end-to-end environment to optimize support for scientific data transfer. DANCES accomplishes this optimization by integrating high performance computing job scheduling, network control capabilities offered by SDN along with data movement applications in an end-to-end network infrastructure. This integration provides access to control mechanisms for managing network bandwidth. The control of network resources enabled by SDN enhances application stability, predictability and performance, thereby improving overall network utilization. Motivation for the DANCES project is to apply the advantages of advanced network services to the problem of congested metropolitan and campus networks. DANCES uses XSEDENet across Internet2 in conjunction with OpenFlow-enabled network switches installed at the collaborating sites as the end-to-end hardware and software substrate.

Knowledge gained through DANCES is being disseminated through educational programs offered by the participating institutions and at existing community workshops, meetings, and conferences. The insights and experience obtained through DANCES will promote a better understanding of the technical requirements for supporting end-to-end SDN across wide area and campus cyberinfrastructure. The resulting SDN-enabled applications will make the request and configuration of high bandwidth connections easily accessible to end users and improve network performance and predictability for supporting a wide range of applications.

StatusFinished
Effective start/end date1/1/1412/31/16

Funding

  • National Science Foundation: $125,000.00

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.