Machine Learning for Adaptive and Highly Reliable Networked Computer Systems, NSF DMS0624849 ChengZh

1
Machine Learning for Adaptive and Highly Reliable
Networked Computer Systems, NSF
DMS-0624849Cheng-Zhong Xu, George G. Yin, and Le
Yi WangWayne State University, Detroit, MI
Research Objectives Design of adaptive, highly
reliable, and self-manageable networked computer
systems via machine learning.
Approaches Aggregated reinforcement learning,
statistical scheduling and optimization, and
autonomic resource management.
Significant Results
Fig. 1 QoS assurance for premium clients on the
Internet when the requests of WorldCup98 was
replayed. Its adaptive to change of network
conditions and server load.

• Proved it feasible to use on-line feedback
  control approaches to provide
• guaranteed quality of services on Internet
  servers. Previous studies were
• limited to quality control of simple static
  web pages. Our eQoS approach
• advances the technology to the level of
  control over more practical dynamic
• multi-objects web pages 1.
• Proved it feasible to use statistical learning
  techniques to predict systems failures.
• Previous studies were focused on modeling and
  analysis of temporal correlations of
• failures. Our hPredictor approach quantifies
  additional spatial correlations between
• processing nodes by using time-efficient
  aggregate stochastic models 2.
• Developed regime-switching models in stochastic
  optimization. We have shown that under
• simple conditions, a continuous-time
  interpolation of the iterates of the recursive
  algorithm
• converges weakly to a system of ODEs with
  regime switching and that suitably scaled
• sequence of the tracking errors converges to a
  system of switching diffusion 3. This paves
• a way to the use of stochastic approximation
  for autonomic resource management in large scale
  distributed computer systems.

Fir.2 hPredictor has been in operation online to
predict node failures of WSU campus grid of high
perf. clusters since April 6, 2006.
Broader Impacts Enhance computer systems
sustainable performance and reliability in real
applications advance mathematical models and
theories in new applications Motivate students
to participate in interdisciplinary research in
computer and mathematical sciences.
1. IEEE Trans. on Computers, 2006 (in press) 2.
IEEE Trans. on DSC, to be submitted (2006) 3.
SIAM J. Optim. (2004)
http//www.ece.eng.wayne.edu/czxu/siloam.html