SALSA: Analyzing Logs as StAte Machines

SALSA examines system logs to derive state-machine views of the sytem's execution, along with control-flow, 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 statemachine views and statistics. Acknowledgements: This work is partially supported by the NSF CAREER Award CCR-0238381, NSF Award CCF-0621508, and the Army Research Office grant number DAAD19-02-1-0389 ("Perpetually Available and Secure Information Systems") to the Center for Computer and Communications Security at Carnegie Mellon University. 

• Type: Conference
• Year: 2008
• Where: OSDI
• Authors: Jiaqi Tan, Xinghao Pan, Soila Kavulya, Rajeev Gandhi, Priya Narasimhan
• Keywords: Hadoop Cluster, NSF Award CCF-0621508, NSF Career Award, Operating System, OSDI 2008