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Award Detail

Doing Business As Name:Rutgers University New Brunswick
  • Dario Pompili
  • (848) 445-8533
Award Date:01/17/2008
Estimated Total Award Amount: $ 212,000
Funds Obligated to Date: $ 402,999
  • FY 2011=$53,000
  • FY 2012=$69,000
  • FY 2009=$65,999
  • FY 2008=$162,000
  • FY 2010=$53,000
Start Date:01/15/2008
End Date:12/31/2014
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research: Center for Cloud and Autonomic Computing
Federal Award ID Number:0758566
DUNS ID:001912864
Parent DUNS ID:001912864
Program:IUCRC-Indust-Univ Coop Res Ctr
Program Officer:
  • Thyagarajan Nandagopal
  • (703) 292-4550

Awardee Location

Street:33 Knightsbridge Road
Awardee Cong. District:06

Primary Place of Performance

Organization Name:Rutgers University New Brunswick
Street:33 Knightsbridge Road
Cong. District:06

Abstract at Time of Award

This award establishes the Industry/University Cooperative Research Center (I/UCRC) for Autonomic Computing at the University of Florida, University of Arizona and Rutgers University. The I/UCRC will focus on multi university research on improving the design and engineering systems that are capable of funning themselves, adapting their resources and operations to current workloads and anticipating the needs of their users. The center will work on improving hardware, networks and storage, middleware, service and information layers used by modern industry. The research performed at this center is important for U.S. industry to help maintain its lead in the information technology field. This I/UCRC will have a broad impact on the participating students and faculty through involvement with the industrial members. This center has the potential to develop new knowledge in this area that will increase US industrial competitiveness.

Publications Produced as a Result of this Research

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N. Jiang and M. Parashar "In-network Data Estimation Mechanisms for Sensor-driven Scientific Applications" Proceedings of the 15th IEEE International Conference on High Performance Computing (HiPC 2008), v., 2008, p..

A. Quiroz, N. Gnanasambandam, M. Parashar and N. Sharma "Robust clustering analysis for the management of self-monitoring distributed systems" Cluster Computing, v.12(1), 2009, p.73-85.

E. K. Lee, I. S. Kulkarni, D. Pompili, and M. Parashar "Proactive Thermal Management in Green Datacenters" Journal of Supercomputing (Springer), v.60, no., 2012, p.165.

H. Viswanathan, E. K. Lee, and D. Pompili "Self-organizing Sensing Infrastructure for Autonomic Management of Green Datacenters" IEEE Network Magazine, v.25(4), 2011, p.3440.

Moustafa AbdelBaky, Manish Parashar, Kirk Jordan, Hyunjoo Kim, Hani Jamjoom, Zon-Yin Shae, Gergina Pencheva, Vipin Sachdeva, James Sexton, Mary Wheeler "Enabling High Performance Computing as a Service" IEEE computer Society Digital Library, v., 2012, p..

A. Quiroz, N. Gnanasambandam, M. Parashar, and N. Sharma "Robust Clustering Analysis for the Management of Self-Monitoring Distributed Systems" The Journal of Networks, Software Tools, and Applications, v., 2008, p..

A. Quiroz, M. Parashar, N. Gnanasambandam and N. Sharma "Clustering Analysis for the Management of Self-Monitoring Device Networks" Proceedings of the 5th IEEE International Conference on Autonomic Computing (ICAC 2008), v., 2008, p..

H. Liu, E. K. Lee, D. Pompili, and X. Kong "Thermal Camera Networks for Large Datacenters using Real-Time Thermal Monitoring Mechanism" Journal of Supercomputing (Springer),, v., 2012, p..

H. Viswanathan, B. Chen, and D. Pompili "Research Challenges in Computation, Communication, and Context Awareness for Ubiquitous Healthcare" IEEE Communication Magazine, v.50, no., 2012, p.92.

Pierre St. Juste, David Wolinsky, P. Oscar Boykin, Michael J. Covington, Renato J. Figueiredo "SocialVPN: Enabling Wide-Area Collaboration with Integrated Social and Overlay Networks" Computer Networks, v.54, 2010, p.1926.

H. Viswanathan, E. K. Lee, I. Rodero, D. Pompili, M. Parashar, and M. Gamell "Energy-Aware Application-Centric VM Allocation for HPC" High Performance Grid and Cloud Computing Workshop (HPGC), Anchorage, AK, v., 2011, p..

X. Qi, D. Wang, I. Rodero, J. DiazMontes, R. H. Gensure, F. Xing, H. Zhong, L. Goodell, M. Parashar, D. J. Foran and L. Yang "Content-based Histopathology Image Retrieval Using CometCloud" BMC Bioinformatics, v., 2014, p.. doi:10.1186/1471210515287 

C. Docan, F. Zhang, T. Jin, H. Bui, Q. Sun, J. Cummings, N. Podhorszki, S. Klasky, and M.Parashar "ActiveSpaces: Exploring Dynamic Code Deployment for Extreme Scale Data Processing" Concurrency and Computation: Practice and Experience, v., 2014, p.. doi:10.1002/cpe.3407 

Rodero, E. K. Lee, D. Pompili, M. Parashar, M. Gamell, and R. J. Figueiredo "Towards Energy-Efficient Reactive Thermal Management in Instrumented Datacenters" Proc. of IEEE/ACM International Conference on Energy Efficient Grids, Clouds and Clusters Workshop (E2GC2), Brussels, Belgium, v., 2010, p..

H. Liu, E. K. Lee, D. Pompili, and X. Kong "Thermal Camera Networks for Large Datacenters using Real-Time Thermal Monitoring Mechanism" Journal of Supercomputing (Springer), v.64, 2013, p..

F. Zhang, S. Lasluisa, T. Jin, I. Rodero, H. Bui, M. Parashar "In-situ Feature-based Objects Tracking for Data-Intensive Scientific and Enterprise Analytics Workflows. Cluster Computing" Cluster Computing: The Journal of Networks, Software Tools, and Applications, v., 2014, p..

J. Diaz-Montes, Y. Xie, I. Rodero, J. Zola, B. Ganapathysubramanian, and M. Parashar "Federated Computing for the Masses ? Aggregating Resources to Tackle Large-Scale Engineering Problems" IEEE Computing in Science and Engineering (CiSE) Magazine, v., 2014, p.. doi:10.1109/MCSE.2013.134 

H. Viswanathan, E. K. Lee, and D. Pompili "SILENCE: Distributed Adaptive Sampling for Sensor-based Autonomic Systems" Proc. of IEEE International Conference on Autonomic Computing (ICAC), Karlsruhe, Germany, v., 2011, p..

Project Outcomes Report


This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The I/UCRC Center for Cloud and Autonomic Computing (CAC) developed a comprehensive research and education program, in close collaboration with industry and government members, to address issues of design, use and management of cloud computing, IT systems and IT application complexity through autonomic approaches. Autonomic computing approaches enable systems and applications to manage themselves, making them more reliable, more secure, and more efficient. The center conducted scientific and engineering research and development on methods, architectures and technologies for the design, implementation, integration and evaluation of special- and general-purpose computing systems, components and applications that are provisioned by IT clouds and/or are capable of autonomously achieving desired behaviors. The center sites, distributed across four institutions (University of Florida, University of Arizona, Rutgers University, and Mississippi State University), also educated students in the interdisciplinary fields of cloud and autonomic computing. Specifically, the center addressed research in the domains of cybersecurity, management of virtualized systems (virtual machines, virtual storage, and virtual networks), energy consumption in data centers, and high-performance distributed computing applications.  Outcomes of the research activities included papers in journals and conferences, technology transfer to industry, and open-source software. Highlighted outcomes of center projects include the following. A startup company, AVIRTEK, was founded to transition the University of Arizona autonomic management and cybersecurity solutions to the marketplace, and Cybersecurity technologies developed at the University of Arizona CAC site have been transitioned to this company. The company has started deploying commercially network appliances with automated and integrated management for small and medium size networks. Power consumption is an increasingly significant percentage of the cost of operating large data centers, which are used by banks, investment firms, IT service providers and other large enterprises. A project at the University of Florida researched virtualization-based autonomic computing  approaches to monitor, model and predict workloads associated with individual services; model and predict global resource demand; dynamically allocate and de-allocate virtual machines to physical machines; devise methods based on control theory and/or market-based approaches to use the above-described mechanisms to minimize the cost of providing individual services while globally minimizing power consumption and delivering contracted service levels. Research also considered self-organizing virtual networks and efforts to connect personal devices of a user and across their social network, leading to development of an open-source infrastructure that allows users to create Virtual Private Networks (VPNs) connecting resources from different sites securely, without the cost and complexity associated with setup and management of typical approaches. Contributions of this infrastructure include techniques associated with self-configuration of virtual networks for the simple deployment of collaborative environments. The research projects at the Rutgers University site have demonstrated how autonomic computing can effectively optimize various aspects of computing systems and applications, including efficiency, performance, adherence to Service Level Agreements (SLAs), fault tolerance and cost-effectiveness. Also, Rutgers explored ways to take actions to reduce energy consumption at the server side in a large machine room before performing costly migrations of Virtual Machines (VMs). Specifically, Rutgers focused on exploiting VM-based configurations which are complementary to other techniques at the physical server layer, such as low-...

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