A Scalable Self -Diagnosing Content Distribution Service With Bounded Latency

156 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.The self-diagnosing capability of our service comes from the scalable learning-based performance problem diagnosis techniques we propose. The increasing complexity of systems has motivated design of machine learning approaches to automate some system management tasks. However, with increase in scale, current approaches suffer from serious scalability issues. We present two scalable learning-based techniques that automatically identify probable causes of performance problems in large server systems with multiple tiers and replicated sites. By incorporating a large number of diagnostic information sources using a temporal segmentation mechanism and applying transfer learning techniques, we achieve both scalability and improved diagnosis accuracy.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD