Autonomic Computing
Autonomic Computing studies techniques for making computing systems self-configuring, self-tuning, self-diagnosing, self-repairing, and self-protecting. Our current work in the area focuses on using machine learning techniques to allow computing systems to configure and tune themselves to adapt to changing workloads and to automatically acquire patterns for detecting and blocking Internet worms.
Peter Stone Professor pstone@cs.utexas.edu
Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study 2009
W. Bradley Knox and Ole Mengshoel
Autonomous Return on Investment Analysis of Additional Processing Resources 2007
Jonathan Wildstrom and Peter Stone and Emmett Witchel
Machine Learning for On-Line Hardware Reconfiguration 2007
Jonathan Wildstrom and Peter Stone and Emmett Witchel and Mike Dahlin
Adapting to Workload Changes Through On-The-Fly Reconfiguration 2006
Jonathan Wildstrom and Peter Stone and Emmett Witchel and Mike Dahlin
Fast and Effective Worm Fingerprinting via Machine Learning 2006
Stewart Yang, Jianping Song, Harish Rajamani, Taewon Cho, Yin Zhang and Raymond Mooney
Fast and Effective Worm Fingerprinting via Machine Learning 2006
Stewart Yang, Jianping Song, Harish Rajamani, Taewon Cho, Yin Zhang and Raymond Mooney
Towards Self-Configuring Hardware for Distributed Computer Systems 2005
Jonathan Wildstrom, Peter Stone, E. Witchel, Raymond Mooney and M. Dahlin
Adaptive Job Routing and Scheduling 2004
Shimon Whiteson and Peter Stone