The keystroke-level model for user performance time with interactive systems
Communications of the ACM
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Contextual Design: Defining Customer-Centered Systems
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MapReduce: simplified data processing on large clusters
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SCUBA: focus and context for real-time mesh network health diagnosis
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Mochi: visual log-analysis based tools for debugging hadoop
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Hunting for problems with Artemis
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X-trace: a pervasive network tracing framework
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
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
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New abstractions are simplifying the programming of large clusters, but diagnosis nontheless gets more and more challenging as cluster sizes grow: Debugging information increases linearly with cluster size, and the count of intercomponent relationships grows quadratically. Worse, the new abstractions which simplified programming can also obscure the relationships between high-level (application) and low-level (task/process/disk/CPU) information flows. In this paper we analyze the workflow of several users and systems administrators connected with a large academic cluster (based the popular Hadoop implementation of the MapReduce abstraction) and propose improvements to the diagnosis-relevant information displays. We also offer a preliminary analysis of the efficacy of the changes we propose that demonstrates a 40% reduction in the time taken to accomplish 5 representative diagnostic tasks as compared to the current system.