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oai:www.ideals.illinois.edu:2142/81779 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Abdelzaher, Tarek F. Huang, Chengdu 2015-09-25T20:20:25Z 2015-09-25T20:20:25Z 10000-01-01 2007 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. Made available in DSpace on 2015-09-25T20:20:25Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3290251.pdf: 5234503 bytes, checksum: b6f3f5ffb9e4d768c66a08ff9a2834ea (MD5) Previous issue date: 2007 Embargo set by: Seth Robbins for item 83060 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 156 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007. http://hdl.handle.net/2142/81779 (MiAaPQ)AAI3290251 eng Computer Science A Scalable Self -Diagnosing Content Distribution Service With Bounded Latency text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.