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Autonomic Computing Systems

To operate distributed computing systems effectively in an uncertain and dynamic environment, multiple performance-related parameters such as resource provisioning to applications, relative priority between applications, and application operational modes, must be dynamically tuned to match time-varying application requirements and operating conditions. To cope with their growing scale and complexity, these systems must become autonomic, i.e., capable of managing themselves given high-level objectives from administrators.

This project aims to develop the theory and practice of designing autonomic computing systems. The specific research objectives are to: (1) Develop a decentralized framework, using concepts from model-predictive and optimal control, to manage large-scale computing systems; (2) Analyze the performance of the control algorithms in terms of stability and robustness properties; and (3) Validate the control techniques using trace-based simulations and actual implementations on a Blade-server cluster.

The expected outcome of this project is a set of technologies to convert a significant number of system management tasks into systematic processes using proven control-theoretic techniques. This will reduce system operating costs while ensuring the smooth operation of applications supporting the business, science, and engineering communities.

Acknowledgement

This project is supported by a CAREER award from the National Science Foundation under Grant No. CNS-0643888.

Publications

Acceptance rates for conference papers, when known, are provided.

Journals

  • R. Wang and N. Kandasamy, "Workload Consolidation in Virtualized Computing Systems via Hierarchical Control," Intel Technology Journal: Exploring Control and Autonomic Computing, vol. 19, no. 2, pp. 208-221, June 2012.
  • D. Kusic, N. Kandasamy, and G. Jiang, "Combined Power and Performance Management of Virtualized Computing Environments Serving Session-based Workloads," IEEE Transactions on Network and Service Management, vol. 8, no. 3, September 2011. (download from IEEE)
  • S. Abdelwahed, J. Bai, R. Su, and N. Kandasamy, "On the Application of Predictive Control Techniques for Adaptive Performance Management of Computing Systems," IEEE Transactions on Network and Service Management, vol. 6, no. 4, pp. 212-225, December 2009. (download from IEEE)

  • D. Kusic, J. Kephart, J. Hanson, N. Kandasamy, and G. Jiang, "Power and Performance Management of Virtualized Computing Environments via Lookahead Control," Cluster Computing: Special Issue on Autonomic Computing, Springer, vol. 12, no.1, pp. 1-15, March 2009. (download from Springer)

  • D. Kusic, N. Kandasamy, and G. Jiang, "Approximation Modeling for the Online Performance Management of Distributed Computing Systems," IEEE Transactions on Systems, Man, and Cybernetics: Part B, vol. 38, no. 5, pp. 1221-33, October 2008. (download from IEEE)

  • D. Kusic and N. Kandasamy, "Risk-Aware Limited Lookahead Control for Dynamic Resource Provisioning in Enterprise Computing Systems," Cluster Computing: The Journal of Networks, Software Tools, and Applications, Special Issue on Autonomic Computing, Kluwer Academic Publishers, vol. 10, no. 4, pp. 395-408, December 2007. (download from Springer)

  • V. Bhat, M. Parashar, H. Liu, M. Khandekar, N. Kandasamy, S. Abdelwahed, and S. Klasky, "An Autonomic Data Streaming Service," Cluster Computing: The Journal of Networks, Software Tools, and Applications, Special Issue on Autonomic Computing, Kluwer Academic Publishers, vol. 10, no. 4, pp. 365-383, December 2007. (download from Springer)

  • M. Wang, N. Kandasamy, A. Guez, and M. Kam, "Adaptive Performance Control of Computing Systems via Distributed Cooperative Control: Application to Power Management in Computing Clusters," IEEE Internet C omputing, vol. 11, no.1, pp. 31-39, January/February 2007. (download from IEEE)

  • M. Khandekar, N. Kandasamy, S. Abdelwahed, and G. Sharp, "A Control-Based Framework for Self-Managing Computing Systems," Multiagent and Grid Systems: An International Journal, vol. 1, no. 2, pp. 63-72, 2006.

Book Chapters 

  • S. Abdelwahed and N. Kandasamy, "A Control-Based Approach to Autonomic Performance Management in Computing Systems," CRC Handbook on Autonomic Computing: Concepts, Infrastructure, and Applications, M. Parashar and S. Hariri (Eds.), CRC Press, Aug. 2006. (Link to the book)

  • V. Bhat, M. Parashar, and N. Kandasamy, "Autonomic Data Streaming for High Performance Scientific Applications," CRC Handbook on Autonomic Computing: Concepts, Infrastructure, and Applications, M. Parashar and S, Hariri (Eds.), CRC Press, August 2006.

  • N. Kandasamy, S. Abdelwahed, G. Sharp, and J. P. Hayes, "An Online Control Framework for Designing Self-Optimizing Computing Systems: Application to Power Management," Self-Star Properties in Complex Information Systems, O. Babaoglu et al., (Eds.), Lecture Notes  in Computer Science, vol. 3460, Springer-Verlag, pp.174-189, 2005.

Conferences 

  • S. DeCelles and N. Kandasamy, "Entropy-based Detection of Incipient Faults in Software Systems," Proc. 18th IEEE Pacific Rim International Symposium on Dependable Computing  (PRDC), November 2012. Best student paper award.
  • T. Huang, N. Kandasamy, and H. Sethu, "Evaluating Compressive Sampling Strategies for Performance Monitoring of Data Centers," Proc. 9th IEEE/ACM Conf. Autonomic Computing  (ICAC), September 2012. Acceptance rate: 24%. Download from ACM.
  • R. Wang and N. Kandasamy, "On the Design of Decentralized Control Architectures for Workload Consolidation in Large-Scale Server Clusters," Proc. 9th IEEE/ACM Conf. Autonomic Computing (ICAC), September 2012. Acceptance rate: 24%. Download from ACM.
  • S. Spatari, N. Kandasamy, D. Kusic, E. V. Ellis, "Energy and Locational Workload Management in Data Centers," Proc. IEEE Int'l Symp. Sustainable Systems & Technology (ISSST), May 2011.
  • S. Spatari, N. Kandasamy, D. Kusic, E. V. Ellis, and J. Wen, "Energy and Environmental Aspects of Data Centers,'' EEE-Energy & Environmental Aspects of Data Centers, World Renewable Energy Congress, May 2011.
  • R. Wang, D. Kusic, and N. Kandasamy, "A Distributed Control Framework for Performance Management of Virtualized Computing Environments," Proc. 6th IEEE Int'l Conf. Autonomic Computing, June 2010. Acceptance rate: 25%. Download from ACM.
  • D. Kusic, J. Kephart, J. Hanson, N. Kandasamy, and G. Jiang, "Power and Performance Management of Virtualized Computing Environments Via Lookahead Control," Proc. 5th IEEE Int'l Conf. Autonomic Computing, June 2008. Best student paper award. Acceptance rate: 25%. Download from IEEE.
  • V. Bhat, M. Parashar, M. Khandekar, N. Kandasamy, and S. Klasky, "A Self-Managing Wide-Area Data Streaming Service using Model-based Online Control," Proc. 7th IEEE/ACM Intl Conf. Grid Computing (GRID), pp. 176-183, September 2006. Acceptance rate: 18%. Download from IEEE.
  • N. Kandasamy, M. Khandekar, and S. Abdelwahed, "A Hierarchical Optimization Framework for Autonomic Performance Management of Distributed Computing Systems," Proc. 26th IEEE Int'l Conf. Distributed Computing Systems (ICDCS), July 2006. Acceptance rate: 14%. Download from IEEE.
  • M. Wang, N. Kandasamy, A. Guez, and M. Kam, "Adaptive Performance Control of Computing Systems via Distributed Cooperative Control: Application to Power Management in Computing Clusters," Proc. 3rd IEEE Int'l Conf. Autonomic Computing (ICAC), pp. 164-173, June 2006. Acceptance rate: 21%. Download from IEEE.
  • D. Kusic and N. Kandasamy, "Risk-Aware Limited Lookahead Control for Dynamic Resource Provisioning in Enterprise Computing Systems," Proc. 3rd IEEE Int'l Conf. Autonomic ComputingICAC), pp. 74-83, June 2006. Best paper award. Acceptance rate: 21%. Download from IEEE.
  • V. Bhat, M. Parashar, M. Khandekar, N. Kandasamy, and S. Abdelwahed, "Enabling Self-Managing Applications using Model-based Online Control Strategies," Proc. 3rd IEEE Int'l Conf. Autonomic Computing (ICAC), pp. 15-24, June 2006. Acceptance rate: 21%. Download from IEEE.
  • N. Kandasamy, S. Abdelwahed, and J. P. Hayes, "Self-Optimization in Computer Systems via Online Control: Application to Power Management," Proc. IEEE Conf. Autonomic ComputingICAC), pp. 54-61, 2004. Download from IEEE.
  • S. Abdelwahed, N. Kandasamy, and S. Neema, "Online Control for Self-Management in Computing Systems," Proc. 10th IEEE Real-Time and Embedded Tech. & Applications Symp. (RTAS), pp. 368-375, 2004. Download from IEEE.

Posters and Short Papers

  • T. Huang, N. Kandasamy, and H. Sethu, "Evaluating Compressive Sampling Strategies for Performance Monitoring of Data Centers," Proc. IEEE/IFIP Network Operations & Management (NOMS), April 2012. Download from IEEE.
  • S. Spatari, N. Kandasamy, D. Kusic, and E.V. Ellis, "Average and Marginal Reductions in Greenhouse Gas Emissions from Data Center Power Optimization," LCA X Conference, 2010.
  • D. Kusic, N. Kandasamy, and G. Jiang, "Approximation Modeling for the Online Performance Management of Distributed Computing Systems," Proc. 4th IEEE Int'l Conf. Autonomic Computing (ICAC), pp. 23-24, June 2007. Acceptance rate: 17%. Download from IEEE.
  • S. Abdelwahed and N. Kandasamy, "Fault-Adaptive Control for Robust Performance Management of Computing Systems," Proc. 4th IEEE Int'l Conf. Autonomic Computing (ICAC), pp. 20-22, June 2007. Acceptance rate: 17%. Download from IEEE.

Workshops 

  • R. Wang, N. Kandasamy, and C. Nwankpa, "Data Centers as Demand Response Resources in the Electricity Market: Some Preliminary Results," 7th Int'l Workshop on Feedback Computing, 2012.
  • R. Wang and N. Kandasamy, "A Distributed Control Framework for Performance Management of Virtualized Computing Environments: Some Preliminary Results," Workshop on Automated Control for Datacenters and Clouds (ACDC), Barcelona, June 2009.

  • D. Kusic, N. Kandasamy, S. Abdelwahed, and G. Jiang, "Towards Fault-Adaptive Control of Enterprise Computing Systems---A Position Paper," Int'l Workshop on Feedback Control Implementation and Design in Computing Systems and Networks (FeBID), June 2008.

  • S. Abdelwahed, N. Kandasamy, and S. Neema, "A Control-Based Framework for Self-Managing Distributed Computing Systems," Proc. ACM Workshop Self-Managing Systems (WOSS), 2004. (pdf)

Technical Reports 

  • M. Wang, N. Kandasamy, and M. Kam, "Autonomic Performance Management via Distributed Cooperative Control: Application to Power Management in Computing Clusters," Technical Report ACL-2005-01, Electrical and Computer Engineering Department, Drexel University, December 2005. (pdf)

  • N. Kandasamy and S. Adbelwahed, "Designing Self-Managing Distributed Systems using Online Predictive Control," Technical Report ISIS-03-404, Institute for Software Integrated Systems, Vanderbilt University, December 2003.