Software systems are growing rapidly in size and complexity, and becoming
more and more difficult and expensive to maintain exclusively by human operators.
These systems are expected to be highly available, and failure in these systems
is expensive. To meet availability and performance requirements within budget,
automated and efficient approaches for systems monitoring are highly desirable.
Autonomic computing is an effort in this direction, which promises systems that
self-monitor, thus alleviating the burden of detailed operation oversight from human
administrators. In particular, a solution is to develop automated monitoring
systems that continuously collect monitoring data from target systems, analyze the
data, detect errors and diagnose faults automatically. In this dissertation, we survey
work based on management metrics and describe the common features of these
current solutions. Based on observations of the advantages and drawbacks of these
solutions, we present a general solution framework in four separate steps: metric
modeling, system-health signature generation, system-state checking, and fault
localization. Within our framework, we present two specific solutions for error detection
and fault diagnosis in the system, one based on improved linear-regression
modeling and the second based on summarizing the system state by an informationtheoretic
measurement. We evaluate our monitoring solutions with fault-injection
experiments in a J2EE benchmark and show the effectiveness and efficiency of our
solutions.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/6341 |
| Date | January 2011 |
| Creators | Jiang, Miao |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
Page generated in 0.0019 seconds