Mining web logs to debug distant connectivity problems
Proceedings of the 2006 SIGCOMM workshop on Mining network data
HOTOS'05 Proceedings of the 10th conference on Hot Topics in Operating Systems - Volume 10
Towards design for self-healing
Fourth international workshop on Software quality assurance: in conjunction with the 6th ESEC/FSE joint meeting
Failure Detection in Large-Scale Internet Services by Principal Subspace Mapping
IEEE Transactions on Knowledge and Data Engineering
Adaptive quality of service management for enterprise services
ACM Transactions on the Web (TWEB)
IEEE Transactions on Knowledge and Data Engineering
Why did my pc suddenly slow down?
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
Dustminer: troubleshooting interactive complexity bugs in sensor networks
Proceedings of the 6th ACM conference on Embedded network sensor systems
Finding Symbolic Bug Patterns in Sensor Networks
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
A survey of online failure prediction methods
ACM Computing Surveys (CSUR)
SNTS: sensor network troubleshooting suite
DCOSS'07 Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems
DS'07 Proceedings of the 10th international conference on Discovery science
A study of dynamic meta-learning for failure prediction in large-scale systems
Journal of Parallel and Distributed Computing
A statistical approach to detect application-level failures in internet services
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Mochi: visual log-analysis based tools for debugging hadoop
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Empirical comparison of techniques for automated failure diagnosis
SysML'08 Proceedings of the Third conference on Tackling computer systems problems with machine learning techniques
Detecting user-visible failures in AJAX web applications by analyzing users' interaction behaviors
Proceedings of the IEEE/ACM international conference on Automated software engineering
Visualizing windows system traces
Proceedings of the 5th international symposium on Software visualization
Analyzing web logs to detect user-visible failures
SLAML'10 Proceedings of the 2010 workshop on Managing systems via log analysis and machine learning techniques
The SCADS director: scaling a distributed storage system under stringent performance requirements
FAST'11 Proceedings of the 9th USENIX conference on File and stroage technologies
Clustering performance anomalies in web applications based on root causes
Proceedings of the 8th ACM international conference on Autonomic computing
Web analytics and the art of data summarization
SLAML '11 Managing Large-scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques
I-queue: smart queues for service management
ICSOC'06 Proceedings of the 4th international conference on Service-Oriented Computing
Theia: visual signatures for problem diagnosis in large hadoop clusters
lisa'12 Proceedings of the 26th international conference on Large Installation System Administration: strategies, tools, and techniques
Troubleshooting interactive complexity bugs in wireless sensor networks using data mining techniques
ACM Transactions on Sensor Networks (TOSN)
Generating profile-based signatures for online intrusion and failure detection
Information and Software Technology
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Web applications suffer from software and configuration faults that lower their availability. Recovering from failure is dominated by the time interval between when these faults appear and when they are detected by site operators. We introduce a set of tools that augment the ability of operators to perceive the presence of failure: an automatic anomaly detector scours HTTP access logs to find changes in user behavior that are indicative of site failures, and a visualizer helps operators rapidly detect and diagnose problems. Visualization addresses a key question of autonomic computing of how to win operatorsý confidence so that new tools will be embraced. Evaluation performed using HTTP logs from Ebates.com demonstrates that these tools can enhance the detection of failure as well as shorten detection time. Our approach is application-generic and can be applied to any Web application without the need for instrumentation.