A Scalable Self -Diagnosing Content Distribution Service With Bounded Latency

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Huang, Chengdu

Contributors dc:contributor

• Abdelzaher, Tarek F.

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3290251

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81779