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Research Spending & Results

Award Detail

Awardee:MISSISSIPPI STATE UNIVERSITY
Doing Business As Name:Mississippi State University
PD/PI:
  • Ioana Banicescu
  • (662) 325-2756
  • ioana@cse.msstate.edu
Co-PD(s)/co-PI(s):
  • Sherif Abdelwahed
Award Date:07/20/2010
Estimated Total Award Amount: $ 274,788
Funds Obligated to Date: $ 566,506
  • FY 2010=$102,469
  • FY 2012=$129,723
  • FY 2013=$107,967
  • FY 2011=$126,392
  • FY 2014=$99,955
Start Date:08/01/2010
End Date:07/31/2017
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:I/UCRC CGI: Center for Cloud and Autonomic Computing at Mississippi State University
Federal Award ID Number:1034897
DUNS ID:075461814
Parent DUNS ID:075461814
Program:IUCRC-Indust-Univ Coop Res Ctr
Program Officer:
  • Dmitri Perkins
  • (703) 292-0000
  • dperkins@nsf.gov

Awardee Location

Street:PO Box 6156
City:MISSISSIPPI STATE
State:MS
ZIP:39762-9662
County:Mississippi State
Country:US
Awardee Cong. District:03

Primary Place of Performance

Organization Name:Mississippi State University
Street:PO Box 6156
City:MISSISSIPPI STATE
State:MS
ZIP:39762-9662
County:Mississippi State
Country:US
Cong. District:03

Abstract at Time of Award

Mississippi State University (MSU) is planning to join the Industry/University Cooperative Research Center (I/UCRC) entitled "Center for Autonomic Computing (CAC)" which currently is a multi- university center comprised of the University of Florida (lead institution), the University of Arizona and Rutgers, The State University of New Jersey. The mission of the CAC at MSU is to engage academia and industrial partners in joint efforts to accelerate both the understanding of the fundamentals of autonomic computing, and the transfer of these fundamentals into industry solutions. MSU will bring to the existing CAC much needed complementary capabilities in the areas of model-based autonomic computing, and resource management and scheduling in parallel and distributed systems. The proposed site will closely collaborate with the industry to identify generally applicable approaches to IT management, based on scientific principles that guide the selection, modification and integration of these techniques into demonstratively efficient solutions. The properties targeted by autonomic computing include power reduction, performance optimization, increased security, fault tolerance, operational efficiency and usability, and low cost. The proposed site will lead the research to investigate and develop model-based techniques and mechanisms for self-management and self-healing. The planned research has the potential to significantly to reduce the cost of operating large-sale distributed computing systems and improve the reliability and efficiency of the services hosted on these systems. The proposed site will bring a contribution to the existing benefits arising from the ongoing collaboration efforts that pool resources and leverage synergies of all participants in accordance to policies and procedures specified in the CAC membership agreement. For broadening participation, the PIs plan to investigate partnership opportunities with local and national outreach programs including the Increasing Minority Access to Graduate Education program.

Publications Produced as a Result of this Research

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Iannucci, Stefano, and Sherif Abdelwahed. ""Towards Autonomic Intrusion Response Systems."" Autonomic Computing (ICAC), 2016 IEEE International Conference on., v., 2016, p..

Srishti Srivastava, Ioana Banicescu "Towards Robust Resource Allocations via Performance Modeling with Stochastic Process Algebra" 18th {IEEE} International Conference on Computational Science and Engineering, {CSE} 2015, v., 2015, p.270. doi:978-1-4673-8297-7 

Iannucci, Stefano, and Sherif Abdelwahed. ""A probabilistic approach to autonomic security management."" Autonomic Computing (ICAC), 2016 IEEE International Conference on., v., 2016, p..

Boulmier, A., I. Banicescu, F. Ciorba and N. Abdenadher, ""An Autonomic Approach for the Selection of Robust Dynamic Loop Scheduling Techniques"," In Proceedings of the 16th International Symposium on Parallel and Distributed Computing (ISPDC 2017), Innsbruck, Austria, July 3-6, 2017., v., 2017, p..

Iannucci, Stefano, Qian Chen, and Sherif Abdelwahed. ""High-Performance Intrusion Response Planning on Many-Core Architectures."" Computer Communication and Networks (ICCCN), 2016 25th International Conference on. IEEE,, v., 2016, p..

Rajat Mehrotra, Srishti Srivastava, Ioana Banicescu, Sherif Abdelwahed "Towards an autonomic performance management approach for a cloud broker environment using a decomposition-coordination based methodology" Future Generation Computing Systems, v.54, 2016, p.195. doi:10.1016/j.future.2015.03.020 

R. Mehrotra, S. Srivastava, I. Banicescu, S. Abdelwahed, "Towards Autonomic Performance Management in Cloud Broker Environment Using Interaction Balance Approach" Future Generation Computer Systems, v., 2015, p..

R. Mehrotra, S. Srivastava, I. Banicescu, S. Abdelwahed. ""Towards an Autonomic Performance Management Approach for a Cloud Broker Environment Using a Decomposition-Coordination Based Methodology"," Future Generation Computer Systems - Elsevier,, v.54, 2016, p.195.

S. Srivastava and I. Banicescu, ""Robust resource allocations through performance modeling with stochastic process algebra"," Concurrency and Computation, Practice and Experience, John Wiley & Sons, Ltd., v., 2016, p..


Project Outcomes Report

Disclaimer

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.

This project established an NSF Industry/University Cooperative Research Center site for Autonomic Computing (CAC) at Mississippi State University (MSU). The mission of this center is to advance the scientific foundation of autonomic computing and lead the research efforts to design and engineer systems that are capable of running themselves, are able to adapt their resources and operations to current workloads, and to anticipate the needs of their users.

 

Intellectual merit: The main goal of the CAC at MSU is to advance the theory and practice of designing a reliable fault-adaptive autonomic execution environment for distributed computing systems. The key innovation of the research conducted at CAC at MSU is in the development of a decentralized performance management technology that integrates control, diagnosis, and fault recovery modules into a common model-based framework that enables healthy computing systems adapt efficiently to variations in the operating environment, and enables faulty systems recover and maintain functionality.

 

Broader Impact: The projects conducted as part of the CAC at MSU has demonstrated the potential of the developed technology to significantly reduce the cost of operating large-scale distributed computing systems and improve the reliability and efficiency of the services hosted on these systems. These broader impacts flow from the project main goals of identifying  opportunities to introduce adaptive self-managing behavior in such computing applications, and developing novel solutions to improve the system performance based on theoretically sound techniques. Using a system-theoretic basis for self-management allows managers and system administrators to formally reason about application performance and Quality of Service guarantees. This will, in turn, impact a wide range of distributed applications in science, engineering, business, and the society at large.

 


Last Modified: 10/12/2017
Modified by: Ioana Banicescu

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