Understanding and Improving the Diagnostic Workflow of MapReduce Users

ACM Symposium on Computer Human Interaction for Management of Information Technology (CHIMIT), Boston, MA, December 2011.

Jason D. Campbell**, Arun B. Ganesan, Ben Gotow, Soila P. Kavulya, James Mulholland, Priya Narasimhan, Sriram Ramasubramanian, Mark Shuster, Jiaqi Tan*

School of Computer Science & Electrical and Computer Engineering
Carnegie Mellon University
Pittsburgh, PA 15213

*DSO National Laboratories Singapore
**Intel Labs Pittsburgh


New abstractions are simplifying the programming of large clusters, but diagnosis nonetheless gets more and more challenging as cluster sizes grow: Debugging information increases linearly with cluster size, and the count of inter-component 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.

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