NDM 2013‎ > ‎


In addition to increasing data volumes, future scientific collaborations require cooperative work at the extreme scale. As the number of multidisciplinary teams and experimental facilities increase, data sharing and resource coordination among distributed centers are becoming significant challenges every passing year.  In the age of extraordinary advances in communication technologies, there is a need for efficient use of the network infrastructure to address increasing data requirements of today’s applications.  

Traditional network and data management techniques are unlikely to scale to meet the needs of future collaborative data-intensive systems. We require novel data access mechanisms and intelligent network middleware to enable future design principles of network-aware data management. 

This workshop will seek contributions from academia, government, and industry to discuss emerging trends and new technological developments in dynamic resource provisioning, intelligent data-flow and resource coordination, end-to-end processing of data, network-aware application design issues, and cutting-edge network performance problems.

 Topics of interest include but are not limited to:

  • High-performance network protocols
  • Performance problems in networking applications
  • Network support for data-intensive computing
  • Network-aware data scheduling and resource brokering
  • Dynamic resource provisioning and network virtualization
  • Performance evaluation of network-aware data management
  • Tools and systems to support future collaborative science
  • Practical experiences and prototypes for large-scale data streaming
  • Requirements and Issues for Network Quality of Service (QoS)
  • Application pipelines and network-aware toolkits for data distribution
  • Data replication and re-configurable data-access frameworks 
  • Network Fault Tolerant Data Distribution for large scientific datasets
  • Optimization and development of data transfer protocols
  • Scalable services for network-aware applications
  • Data clouds, data scheduling, and data placement
  • Heterogeneous and distributed resource management
  • Performance evaluation of data intensive networking applications