Scientific
applications and experimental facilities generate large amounts of
data. In addition to increasing data volumes and computational
requirements, today’s major science requires cooperative work in
globally distributed multidisciplinary teams. In the age of
extraordinary advances in communication technologies, there is a need
for efficient use of the network infrastructure to address increasing
data and compute requirements of large-scale applications. Since the
amount of data and the size of scientific projects are continuously
growing, traditional data management techniques are unlikely to support
future collaboration systems at the extreme scale. Network-aware data
management services for dynamic resource provisioning, end-to-end
processing of data, intelligent data-flow and resource coordination are
highly desirable. This workshop will seek contribution from academia,
government, and industry to discuss emerging trends in use of networking
for data management, novel techniques for data representation,
simplification of end-to-end data flow, resource coordination, and
network-aware tools for the scientific applications.
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Topics of interest include but are not limited to:
- High-bandwidth networks/protocols and middleware
- Network support for data-intensive computing
- Scalable services for network-aware applications
- Network-aware data scheduling and resource brokering
- Dynamic resource provisioning mechanisms
- Performance evaluation of network-aware data management
- Cloud/Grid management systems
- Tools and systems to support future collaborative science
- Practical experiences and prototypes for large-scale data streaming
- Performance modeling/ Quality of Service (QoS) issues
- Application pipelines and workflow management
- Network-aware toolkits for data distribution
- Data replication and metadata management
- Heterogeneous resource management
- Recovery from network failures
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