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A Data Locality Aware Online Scheduling Approach for I/O-Intensive Jobs with File Sharing

  • Gaurav Khanna
  • Umit Catalyurek
  • Tahsin Kurc
  • P. Sadayappan
  • Joel Saltz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4376)

Abstract

Many scientific investigations have to deal with large amounts of data from simulations and experiments. Data analysis in such investigations typically involves extraction of subsets of data, followed by computations performed on extracted data. Scheduling in this context requires efficient utilization of the computational, storage and network resources to optimize response time. The data-intensive nature of such applications necessitates data-locality aware job scheduling algorithms. This paper proposes a hypergraph based dynamic scheduling heuristic for a stream of independent I/O intensive jobs with file sharing behavior. The proposed heuristic is based on an event-driven, run-time hypergraph modeling of the file sharing characteristics among jobs. Our experiments on a coupled compute/storage cluster show it performs better compared to previously proposed strategies, under a varying set of parameters for workloads from the application domain of biomedical image analysis.

Keywords

Execution Time Completion Time Average Response Time Storage Node Gantt Chart 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Gaurav Khanna
    • 1
  • Umit Catalyurek
    • 2
  • Tahsin Kurc
    • 2
  • P. Sadayappan
    • 1
  • Joel Saltz
    • 2
  1. 1.Dept. of Computer Science and Engineering 
  2. 2.Dept. of Biomedical Informatics, The Ohio State University 

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