NDM 2015‎ > ‎


Today’s data centric environment both in industry and scientific domains depends on the underlying network infrastructure and its performance, to deliver highly distributed extreme- scale application workloads. As current technology enables faster storage devices and larger interconnect bandwidth, there is a substantial need for novel system design and middleware architecture to address increasing latency and scalability requirements. Furthermore, limitations in end-system architecture and system software design play an important role in many-core platforms. Traditional network and data management techniques are unlikely to scale to meet the needs of future data-intensive systems. We require new collaborations between data management and networking communities to develop intelligent networking middleware and efficient data management infrastructure. This workshop seeks contributions from academia, government, and industry to discuss future design principles of network- aware data management. We focus on emerging trends in resource coordination, data-aware scheduling, storage technologies, end-to-end performance, network-aware workflow management, high-performance networking, and network virtualization.

Topics of interest include but are not limited to the following list:

• Practical experiences and prototypes for network-aware data management

• Workload characterization for latency/throughput sensitive applications

• Operating system and virtualization support for networking

• Network-aware data management tools and systems

• Requirements and issues for network quality of service (QoS)

• Optimization of data transfer protocols, development of non-TCP protocols

• Applications of software defined networking for large data flows

• Data center networking and network management for Cloud environment

• Performance evaluation and experimental results from network applications

• Design, implementation, and analysis of high-performance networking

• Network virtualization for large-scale data management

• Experiences with emerging storage technologies for network applications

• Network fault tolerant data distribution for large scientific datasets

• Heterogeneous and distributed resource coordination and workflow management