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Shekhar Gupta

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AAAI Conference 2013 Conference Paper

An Optimal Task Assignment Policy and Performance Diagnosis Strategy for Heterogeneous Hadoop Cluster

  • Shekhar Gupta

The goal of the proposed research is to improve the performance of Hadoop-based software running on a heterogeneous cluster. My approach lies in the intersection of machine learning, scheduling and diagnosis. We mainly focus on heterogeneous Hadoop clusters and try to improve the performance by implementing a more efficient scheduler for this class of cluster.

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AAAI Conference 2012 Conference Paper

Exploiting Shared Resource Dependencies in Spectrum Based Plan Diagnosis

  • Shekhar Gupta
  • Nico Roos
  • Cees Witteveen
  • Bob Price
  • Johan DeKleer

In case of a plan failure, plan-repair is a more promising solution than replanning from scratch. The effectiveness of plan-repair depends on knowledge of which plan action failed and why. Therefore, in this paper, we propose an Extended Spectrum Based Diagnosis approach that efficiently pinpoints failed actions. Unlike Model Based Diagnosis (MBD), it does not require the fault models and behavioral descriptions of actions. Our approach first computes the likelihood of an action being faulty and subsequently proposes optimal probe locations to refine the diagnosis. We also exploit knowledge of plan steps that are instances of the same plan operator to optimize the selection of the most informative diagnostic probes. In this paper, we only focus on diagnostic aspect of planrepair process.

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