You are here

ADVANCED DIGITAL NETWORK TECHNOLOGIES AND MIDDLEWARE SERVICES

Description:

 Please Note that a Letter of Intent is due Tuesday, September 06, 2016

Program Area Overview

Office of Advanced Scientific Computing Research

 The primary mission of the Advanced Scientific Computing Research (ASCR) program is to discover, develop, and deploy computational and networking capabilities to analyze, model, simulate, and predict complex phenomena important to the Department of Energy. A particular challenge of this program is fulfilling the science potential of emerging computing systems and other novel computing architectures, which will require numerous significant modifications to today's tools and techniques to deliver on the promise of exascale science. To accomplish this mission, ASCR funds research at public and private institutions and at DOE laboratories to foster and support fundamental research in applied mathematics, computer science, and high-performance networks. In addition, ASCR supports multidisciplinary science activities under a computational science partnership program involving technical programs within the Office of Science and throughout the Department of Energy.

 ASCR also operates high-performance computing (HPC) centers and related facilities, and maintains a high-speed network infrastructure (ESnet) at Lawrence Berkeley National Laboratory (LBNL) to support computational science research activities. The HPC facilities include the Oak Ridge Leadership Computing Facility (OLCF) at Oak Ridge National Laboratory (ORNL), the Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory (ANL), and the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory (LBNL).

  ASCR supports research on applied computational sciences in the following areas:

 - Applied and Computational Mathematics - to develop the mathematical algorithms, tools, and libraries to model complex physical and biological systems.

 - High-performance Computing Science - to develop scalable systems software and programming models, and to enable computational scientists to effectively utilize petascale computers to advance science in areas important to the DOE mission.

 - Distributed Network Environment - to develop integrated software tools and advanced network services to enable large-scale scientific collaboration and make effective use of distributed computing and science facilities in support of the DOE science mission.

 - Applied Computational Sciences Partnership - to achieve breakthroughs in scientific advances via computer simulation technologies that are impossible without interdisciplinary effort.

 For additional information regarding the Office of Advanced Scientific Computing Research priorities, click here.

 

 1. ADVANCED DIGITAL NETWORK TECHNOLOGIES AND MIDDLEWARE SERVICES

 

Maximum Phase I Award Amount: $225,000

Maximum Phase II Award Amount: $1,500,000

Accepting SBIR Applications: YES

Accepting STTR Applications: YES

  Advanced digital network technologies and middleware services play a significant role in the way DOE scientists communicate with peers and collect/process data. Optical networks operating at rates of more than 100 Gbps support the transfer of petabytes of data per day. These networks also peer with commercial networks allowing scientists remote access to instruments and facilities while also allowing citizens access to the data and knowledge that has been produced. Improvements in the tools and services used to manage and operate this infrastructure are needed to meet the needs of both network operators and users.

 Scientific instruments and supercomputer facilities generate, consume, process, and store both raw and analyzed data enabling the discovery of new knowledge. Efforts are underway to scale these computers to support extreme-scale computationally intensive science applications and to deal with increasing volumes and velocities of experimental and observational data. This topic addresses the need for higher level middleware services and analysis tools that are needed to turn raw data into actionable knowledge

 This topic solicits proposals that address issues related to developing tools and services that analyze network operations data in a manner suitable for network engineers or application users and the hardening of middleware tools and services that deal with Big Data.

 a. Network Analysis Tools and Services

 Network operations staff collect a wide variety of data from the network itself. This includes, but is not limited to, SNMP based network interface counter data, NetFlow/SFlow aggregate based flow data, perfSONAR based delay, loss, and throughput data, and packet trace data. Routers and switches may also export exception or error messages back to a log host to inform operations staffs of significant changes or faults. Finally, IDS systems and other security appliances also generate data that impacts the status and performance of the network. Making sense of all this data is a daunting challenge that requires advanced analysis tools and services.

 Grant applications are sought to improve the usability and scalability of network analysis tools and services. Analysis tools may operate in real-time, accepting data from links operating at 100 Gbps or greater speeds or they may provide post-hoc analysis capabilities from stored data archives. Tools may correlate data from multiple input sources or they may deeply analyze a single input data stream. Tools should use widely available data formats and visualization systems to display results. Proposals to develop new data collections tools or complete Network Management Systems are out of scope for this topic.

 Questions – contact Richard Carlson, richard.carlson@science.doe.gov

 b. Big Data Technologies

  This sub-topic focuses on complex data management technologies that go beyond traditional relational database management systems. The efficient and cost-effective technologies to collect, manage, and analyze distributed BigData is a challenge to many organizations including the scientific community. Database management technologies based traditional relational and hierarchical database systems are proving to be inadequate to deal with BigData complexities (volume, variety, veracity, and velocity), especially when applied to BigData systems in science and engineering. While the primary focus is on the development of tools and services to support complex scientific and engineering data, all sources of complex data are in-scope for this sub-topic. The focus of this sub-topic is on the development of cost-/time-effective commercial grade technologies in the following categories:  

  • BigData management software-enabling technologies – this includes but are not limited to the development of software tools, algorithms, and turnkey solutions for complex data management such as NOSQL/graph databases to deal with unstructured data in new ways; visualization and data processing tools for unstructured multi-dimensional data, robust tools to test, validate, and remove defects in large unstructured data sets; tools to manage and analyze hybrid structured and unstructured data; BigData security and privacy solutions; BigData as a service systems; high-speed data hardware/software data encryption and reduction systems; and online management and analysis of streaming and text data from instruments or embedded systems 

  • BigData Network-aware middleware technologies – This includes high-speed network and middleware technologies that enable the collection, archiving, and movement of massive amounts of data within datacenters, data cloud systems, and over Wide Area Networks (WANS). This may include but are not limited to hardware subsystems such high-performance data servers and data transfer nodes, high-speed storage area network (SAN) technologies; network-optimized data cloud services such as virtual storage technologies; and other distributed BigData solutions

    Grant applications must ensure the following: a) that proposed work is based on concrete BigData owned by the company or readily accessible and b) that the proposed work goes beyond traditional data management system technologies.

    Questions – contact Thomas Ndousse, thomas.ndousse-fetter@science.doe.gov

  • c. Other

    In addition to the specific subtopics listed above, the Department invites grant applications in other areas that fall within the scope of the topic description above.

    Questions – contact: Richard Carlson, richard.carlson@science.doe.gov

  • References: Subtopic a:

 1. Kanuparthy, P., et al., 2013, Pythia: Detection, Localization, and Diagnosis of Performance Problems, Communications Magazine, IEEE, Vol. 51, Issue 11, p. 55-62.

 (http://www.cc.gatech.edu/~dlee399/files/kanuparthy.pdf)

 2. Calyam, P., Pu, J., Mandrawa, W., & Krishnamurthy, A., 2010, Ontimedetect: Dynamic Network Anomaly Notification in PerfSONAR Deployments, In Proceedings - 18th Annual IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2010, IEEE, pp. 328-337.

 (https://uncch.pure.elsevier.com/en/publications/ontimedetect-dynamic-network-anomaly-notification-in-perfsonar-de)

 3. Sampaio, L., Koga, I., Costa, R., et al., 2007, Implementing and Deploying Network Monitoring Service Oriented Architectures: Brazilian National Education and Research Network Measurement Experiments, Proceedings of the 5th Latin American Network Operations and Management Symposium (LANOMS 2007). Rio de Janeiro, Brazil. September 10-12. p. 28-37.

 (http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4362457&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4362457)

 References: Subtopic b:

 1. Hey, T., Tansley, S., Tolle, K., 2009, The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research, Redmond, Washington, p. 284.

 (https://www.amazon.com/Fourth-Paradigm-Data-Intensive-Scientific-Discovery/dp/0982544200)

 2. Ahrens, J., et al., 2011, Data-intensive Science in the U.S. DOE: Case Studies and Future Challenges, Computing Science and Engineering, Vol. 13, Issue 6, IEEE, p. 14-24.

 (http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5999634&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5999634)

 3. Bryant, R.E., 2011, Data-intensive Scalable Computing for Scientific Applications, Computing Science and Engineering, Vol. 13, Issue 6, p. 25-33.

 (http://www.computer.org/csdl/mags/cs/2011/06/mcs2011060025-abs.html)

 4. Szalay, A., 2011, Extreme Data-intensive Scientific Computing, Computing Science and Engineering, Vol. 13, Issue 6, p. 34-41.

 (https://www.computer.org/csdl/mags/cs/2011/06/mcs2011060034-abs.html)

 5. Manyika, J., Chui, M., Brown, B., et al., 2011, Big data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global Institutes, p. 156.

 (http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation)

 6. Berkeley Lab, Scientific Data Management Research Group, 2016, FastBit: An Efficient Compressed Bitmap Index Technology.

 (https://sdm.lbl.gov/fastbit/)

 7. ESnet, Engineering Services, OSCARS: On-Demand Secure Circuits and Advance Reservation System.

 (https://www.es.net/engineering-services/oscars/)

 8. University of Chicago, Nimbus: An open source toolkit for Infrastructure-as-a-Service for clouds, Homepage.

 (http://www.nimbusproject.org/)

 9. Department of Energy, VACET, The Visualization and Analytics Center for Enabling Technologies (VACET), Homepage.

 (http://www.vacet.org/about.html)

 10. Department of Energy, SciDAC, 2007, Visualization & Data Management.

 (http://www.scidac.gov/viz/viz.html)

 11. Department of Energy, SciDAC, Monroe, D., From Data to Discovery, SciDAC Data Management Center.

 (http://www.scidacreview.org/0602/html/data.html)

 12. The Apache Software Foundation, 2016, Welcome to Apache Hadoop!, Homepage.

 (http://hadoop.apache.org/)

 13. Google, E-Center: End-to-end enterprise network monitoring.

 (http://code.google.com/p/ecenter/  

US Flag An Official Website of the United States Government