Cognitive Systems Research Group
[ Description | Meeting
Schedule | Paper Readings]
Autonomic computing has tremendous potential to advance computer
systems research by simplifying the design, deployment, configuration,
and support of large-scale systems. To realize such potential in
practice, however, a number of research challenges must be addressed
to integrate machine learning with computer systems. The goal of the
proposed research is to obtain a fundamental understanding of such
challenges and develop effective techniques to address them in the
most general way possible. To this end, we will develop prototypes
that integrate machine learning and systems for a broad range of very
different system applications that span distributed systems, software
support, operating systems, networking, and security. By leveraging
our expertise across levels of computer systems and across types of
machine learning, our research promises to significantly advance the
state of the art in all these systems by making them self-tuning,
self-correcting, self-reporting, self-managing, and self-protecting.
In addition, our effort will help broaden the foundation of machine
learning by both developing new techniques and adapting existing
techniques to better fulfill the requirements of these real-world
autonomic systems. Finally, the techniques that we develop and the
lessons learned from our experience will help both ourselves and
others to make progress towards fully integrating machine learning and
Our own experience, and the experience of others, shows that machine
learning cannot be integrated into systems as a simple black box.
This proposal is motivated by the recognition that to realize the
goals of autonomic computing, we will need to achieve a much tighter
coupling between systems and machine learning in which system designs
are adapted to facilitate machine-learning-based control, and in which
machine learning techniques are advanced to meet the demands of
In order to tightly couple systems and machine learning, this project
addresses two classes of research challenges: those relating to
defining the systems/AI interface, and those pertaining to tailoring
machine learning towards autonomic computing.
By careful definition of the systems/AI interface we aim to develop
general techniques for designing systems that better support autonomic
operation. The key enablers of autonomic operation are (i) to develop
expressive representations of system behavior that are efficient to
measure; and (ii) to develop techniques to use machine learning models
to improve system feedback and interoperability; and (iii) to ensure
safe system control so users can trust autonomic operation.
The project aims to advance the state of the art in machine learning
by developing new techniques to meet the demands of autonomic
computing. In order for machine learning techniques to meet the
challenge of autonomic systems: (i) machine learning algorithms need
to be modified to address privacy and security issues, and (ii) new
machine learning algorithms need to be developed including new
reinforcement learning algorithms for sequential decision-making
problems and ensemble methods designed to exploit hierarchical feature
Our goal is to learn how to build autonomic systems. To make
autonomic computing practical and widely applicable we must build
several different systems that integrate machine learning and systems.
Machine learning and computer systems are large and varied fields: the
deep lessons of how to make them work together will become clear only
if our case studies contain a reasonable sampling from each field. We
choose three case studies that cover a wide variety of systems and
employ many different types of learning algorithms. The first system
does adaptive resource management for performance tuning of
distributed systems, particularly a web server with database back-end.
The second provides improved software support by classifying program
behavior. The final system can automate detection, diagnosis and
reaction to changing network conditions.
This project is supported by the NSF grant CNS-0615104.
The seed research on this project was sponsored by an IBM faculty award to Peter Stone.
- Jungwoo Ha, Christopher J. Rossbach, Jason V. Davis, Indrajit Roy, Hany E. Ramadan, Donald E. Porter, David L. Chen, Emmett Witchel.
Improved Error Reporting for Software that Uses Black Box Components.
In Proceedings of the ACM SIGPLAN 2007 Conference on Programming Language Design and Implementation, San Deigo, CA June 2007.
- Jonathan Wildstrom, Peter Stone, Emmett Witchel, and Mike Dahlin.
Machine Learning for On-Line Hardware Reconfiguration.
In The Twentieth International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India Jan 2007.
- Jason V. Davis, Jungwoo Ha, Christopher J. Rossbach, Hany E. Ramadan, and Emmett Witchel.
Cost-Sensitive Decision Tree Learning for Forensic Classification.
In Proceedings of the The 17th European Conference on Machine Learning, Berlin, Germany September 2006.
Fall 2007 Meeting Schedule
- Starting September 11, meetings will be held on a bi-weekly basis at
11am in ACES 2.404B
Crispin Cowan, Seth Arnold, Steve Beattie, Chris Wright, and John Viega
Defcon Capture the Flag: Defending Vulnerable Code from Intense Attack,
DARPA DISCEX III
Secil Ugurel, Robert Krovetz, C. Lee Giles, David M. Pennock, Eric J. Glover, and Hongyuan Zha What's the Code? Automatic Classification of Source Code Archives, KDD 2002
Murali Haran, Alan Karr, Alessandro Orso, Adam Porter, and Ashish Sanil Applying Classification Techniques to RemotelyCollected Program Execution Data.FSE, 2005
George K. Baah, Alexander Gray, and Mary Jean Harrold Online Anomaly Detection of Deployed Software: A Statistical Machine Learning Approach FSE, 2006
A. Zheng, M. I. Jordan, B. Liblit, M. Nayur, and A. Aiken Statistical debugging: Simultaneous identification of multiple bugs. ICML, 2006
H. Liu, V. Bhat, M. Parashar and S. Klasky An Autonomic Service Architecture for Self-Managing Grid Applications , IEEE Computer Society Press, 2005
Andrej Bratko, Bogdan Filipic, Gordon V. Cormack, Thomas R. Lynam, Blaz Zupan Spam Filtering Using Statistical Data Compression Models JMLR 2006
P. Ruth, J. Rhee, D. Xu , R. Kennell and S. Goasguen Autonomic Live Adaptation of Virtual Computational Environments in a Multi-domain Infrastructure , ICAC 2006
R. Tibshirani and T. Hastie Margin Trees for High-dimensional Classification , JMLR, 2007
Umut A. Acar, Guy Blelloch, Matthias Blume, Kanat Tangwongsan, An Experimental Analysis of Self-Adjusting Computation , PLDI 2006
Sandeep Uttamchandani, Kaladhar Voruganti, Sudarshan M. Srinivasan, John Palmer, David Pease Polus: Growing Storage QoS Management Beyond a "4-Year Old Kid" , FAST 2004
Lin Qiao, Balakrishna R. Iyer, Divyakant Agrawal, Amr El Abbadi, Sandeep Uttamchandani PulStore: Automated Storage Management with QoS Guarantee in Large-scale Virtualized Storage Systems , ICAC 2005.
Miscellaneous Topics in Privacy Preserving (Collection of papers)
If you find a certain paper interesting and would like to recommend
reading, please feel free to let us know during the meeting or
mail Indrajit Roy.
Mary Jean Harrold, James A. Jones, Tongyu Li, Donglin Liang,
Alessandro Orso, Maikel Pennings, Saurabh Sinha, Steven Spoon.
Test Selection for
Shan Lu et. al.
MUVI: Automatically Inferring Multi-Variable Access Correlations and
Detecting Related Semantic and Concurrency Bugs, SOSP 2007
Secil Ugurel, Robert Krovetz, C. Lee Giles, David M. Pennock, Eric J.
Glover, and Hongyuan Zha
What's the Code? Automatic Classification of
Source Code Archives, KDD 2002
- George Kofi Baah, Alexander Gray, and Mary Jean Harrold
Online Anomaly Detection of Deployed Software: A Statistical Machine Learning Approach SOQUA, 2006
- Lin Tan, Ding Yuan, and Yuanyuan Zhou
/* iComment: Bugs or Bad Comments? */ SOSP, 2007
- Greg Hamerly, Erez Perelman, Jeremy Lau, Brad Calder, Timothy Sherwood Using Machine Learning to Guide Architecture Simulation , JMLR 2006.
- James Newsome, Brad Karp, Dawn Song Paragraph: Thwarting Signature Learning By Training Maliciously , RAID 2006
- Jonathan Wildstrom, Peter Stone, Emmett Witchel, and Mike Dahlin. Machine learning for on-line hardware reconfiguration , IJCAI 2007
- Varun Aggarwal, Wesley O. Jim, Una-May O'Reilly Filter Approximation Using Explicit Time and Frequency Domain Specifications , GECCO 2006
- Bianca Schroeder, Adam Wierman and Mor Harchol-Balter Open Versus Closed: A Cautionary Tale, NSDI 2006
- G. Tesauro, R. Das, N. Jong and M. Bennani A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation , ICAC 2006
- Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken. Statistical Debugging: Simultaneous Identification of Multiple Bugs , ICML 2006
- Sandeep Uttamchandani, Li Yin, Guillermo Alvarez, John Palmer, Gul Agha Chameleon: a self-evolving, fully-adaptive resource arbitrator for storage systems , USENIX 2005
- Benjamin J. Kuipers, Alex X. Liu, Aashin Gautam, and Mohamed G. Gouda.
Zmail: Zero-sum free market control of spam, ASDN 2005
- V. Yegneswaran, J.T Giffin, P Barford and S JhaAn Architecture for Generating Semantics-Aware Signatures, USENIX Security, 2005
- Blum, Dwork, McSherry, Nissim.Practical Privacy: The SuLQ Framework
- J.Semke, J.Mahdavi, M.Mathis, Automatic TCP Buffer Tuning , ACM SIGCOMM '98
- Lindell, Pinkas.Privacy Preserving Data Mining, Lecture Notes in Computer Science 2000
- Evfimievski A., Gehrke J., Srikant R. Limiting Privacy Breaches in Privacy Preserving Data Mining , PODS 2003
- P. Broadwell, M. Harren and Naveen Sastry , Scrash: A System for Generating Secure Crash Information , Usenix Security 2003
- Calder, Grunwald, Jones, Lindsay, Martin, Mozer, and Zorn Evidence-based Static Branch Prediction using Machine Learning
- Helen J. Wang, John C. Platt, Yu Chen, Ruyun Zhang and Yi-Min Wang Automatic Misconfiguration Troubleshooting with PeerPressure
- Ira Cohen, S. Zhang, M. Goldszmidt, J. Symons, T. Kelly, A. Fox Capturing, Indexing, Clustering, and Retrieving System History
- D.A. Menasce, R. Dodge and D. Barbara Preserving QoS of E-commerce Sites Through Self-Tuning: A Performance Model Approach
- Li Zhuang, Feng Zhou, and J. D. Tygar Keyboard Acoustic Emanations Revisited
- C. Liu, X. Yan, H. Yu, J. Han & P. S. Yu Mining Behavior Graphs for "Backtrace" of Noncrashing Bugs
- D. T. McWherter, B. Schroeder, A Ailamaki & M. Harchol-Balter Priority Mechanisms for OLTP and Transactional Web Applications
- L. Ertoz, E. Eilertson,A. Lazarevic, P. Tan, J. Srivastava, V. Kumar, P. Dokas The MINDS - Minnesota Intrusion Detection System, "Next Generation Data Mining MIT Press, 2004
- J. Z. Kolter & M. A. Maloof Learning to Detect Malicious Executables in the Wild. KDD04
- J. Newsome, B. Karp & D. Song Polygraph: Automatically Generating Signatures for polymorphic worms SPS 05
- G. Tesauro et. al. Decompositional Reinforcement Learning and Workload Management
- S. Forrest, J. Balthrop, M. Glickman & D. Ackley Computation in the Wild
- J. Balthrop, F. Esponda, S. Forrest & M. Glickman Coverage and Generalization in an Artificial Immune System
- A. Fox, E. Kiciman & D. Patterson. Combining Statistical Monitoring and Predictable Recovery for Self-Management WOSS 04
- A. Fern, R. Givan, B. Falsafi & T. N. VijayKumar.
Dynamic Feature Selection for Hardware Prediction 2004
- A. V. Mirgorodskiy & B. P. Miller. Autonomous Analysis of Interactive Systems with Self-Propelled Instrumentation MCNC 2005
- IBM white paper An architectural blueprint for Autonomic Computing
- B. Liblit, A. Aiken, A. X. Zheng & M. I. Jordan.Bug Isolation via Remote Program SamplingPLDI 2003
- I. Cohen, M. Glodszmidt, T. Kelly, J. Symons &s; J. Chase. Correlating instrumentation
data to system states: a building block for automated diagnosis and control OSDI 04
- A. B. Brown, J. Hellerstein, M. Hogstrom, T. Lau, S. Lightstone, P.
Shum & M. Peterson. Benchmarking
Autonomic Capabilities: Promises and Pitfalls. ICAC 2004
- Y. Diao, J.L. Hellerstein, S. Parekh, & J.P. Bigus. Managing Web Server
Performance with AutoTune Agent. IBM Systems Journal, Vol 42, No. 1, 2003.
- M. Y. Chen, E. Kiciman, E. Fratkin, A. Fox & E. Brewer Pinpoint:
Problem Determination in Large, Dynamic Internet Services . DSN02
- P. Barham, R. Isaacs, R. Mortier, & D. Narayanan Magpie:
real-time modelling and performance-aware systems . HotOS03
- A. Brown, G. Kar & A. Keller. An
Active Approach to Characterizing Dynamic Dependencies for Problem
Determination in a Distributed Application Environment. IM01
- Y.H. Chang, T. Ho, & L. P. Kaelbling. Mobilized
Ad-Hoc Networks: A Reinforcement Learning Approach. AIM03.
- M. K. Aguilera, J. C. Mogul, J. L. Wiener, P. Reynolds & A.
Debugging for Distributed Systems of Black Boxes. SOOP03
- W. E. Walsh, G. Tesauro, J. O. Kephart & R. Das. Utility
Functions in Autonomic Systems. ICAC04
- J. L. Hellerstein, F. Zhang & P. Shahabuddin. Characterizing Normal operation
of a web server: Application to workload forecasting and problem
detection. CMG 98
- G. Aggarwal, M. Datar, N. Mishra & R. Motwani On
Identifying Stable Ways to Configure Systems ICAC04
- M. Mesnier, E. Thereska, D. Ellard, G. R. Ganger & M. Seltzer.
File Classification in
Self-* Storage Systems.ICAC04
- S. Whiteson & P. Stone Towards
Autonomic Computing: Adaptive Network Routing and Scheduling. IAAI04
- T. Abdelzaher, K. G. Shin, N. Bhatti. Performance Guarantees for
Web Server End-Systems: A Control-Theoretical Approach. PDS02
- M. Chen, A. X. Zheng, J. Lloyd, M. I. Jordan & E. Brewer Failure Diagnosis Using Decision
- F. Gomez, D. Burger & R. Miikkulainen
A Neuroevolution Method for Dynamic Resource Allocation on a Chip Multiprocessor.
- J.O. Kephart & D. M. Chess The Vision of Autonomic Computing IEEE Computer, 36(1):41--50. IEEE, January 2003
- R.J. Brachman Systems That Know What They're Doing IEEE Intelligent Systems, 17(6), Nov-Dec, 2002, pages67-71
For more information, please
contact Indrajit Roy