Pinpoint: Problem Determination in Large, Dynamic Internet Services
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Performance debugging for distributed systems of black boxes
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
BlueGene/L Failure Analysis and Prediction Models
DSN '06 Proceedings of the International Conference on Dependable Systems and Networks
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Using magpie for request extraction and workload modelling
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
What Supercomputers Say: A Study of Five System Logs
DSN '07 Proceedings of the 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks
Bad Words: Finding Faults in Spirit's Syslogs
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
Discovering actionable patterns in event data
IBM Systems Journal
A new metric for probability distributions
IEEE Transactions on Information Theory
Detecting large-scale system problems by mining console logs
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Ganesha: blackBox diagnosis of MapReduce systems
ACM SIGMETRICS Performance Evaluation Review
An Analysis of Traces from a Production MapReduce Cluster
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Mochi: visual log-analysis based tools for debugging hadoop
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Mining invariants from console logs for system problem detection
USENIXATC'10 Proceedings of the 2010 USENIX conference on USENIX annual technical conference
Synoptic: summarizing system logs with refinement
SLAML'10 Proceedings of the 2010 workshop on Managing systems via log analysis and machine learning techniques
A graphical representation for identifier structure in logs
SLAML'10 Proceedings of the 2010 workshop on Managing systems via log analysis and machine learning techniques
ASDF: an automated, online framework for diagnosing performance problems
Architecting dependable systems VII
Rake: semantics assisted network-based tracing framework
Proceedings of the Nineteenth International Workshop on Quality of Service
Otus: resource attribution in data-intensive clusters
Proceedings of the second international workshop on MapReduce and its applications
In-situ MapReduce for log processing
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
Leveraging existing instrumentation to automatically infer invariant-constrained models
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Mining temporal invariants from partially ordered logs
SLAML '11 Managing Large-scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques
Mining temporal invariants from partially ordered logs
ACM SIGOPS Operating Systems Review
In-situ MapReduce for log processing
HotCloud'11 Proceedings of the 3rd USENIX conference on Hot topics in cloud computing
MalPEFinder: fast and retrospective assessment of data breaches in malware attacks
Security and Communication Networks
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
Assisting developers of big data analytics applications when deploying on hadoop clouds
Proceedings of the 2013 International Conference on Software Engineering
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SALSA examines system logs to derive state-machine views of the sytem's execution, along with controlflow, data-flow models and related statistics. Exploiting SALSA's derived views and statistics, we can effectively construct higher-level useful analyses. We demonstrate SALSA's approach by analyzing system logs generated in a Hadoop cluster, and then illustrate SALSA's value by developing visualization and failure-diagnosis techniques, for three different Hadoop workloads, based on our derived state-machine views and statistics.