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A Pebble Game for Internet-Based Computing

  • Grzegorz Malewicz
  • Arnold L. Rosenberg
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3895)

Abstract

Advances in technology have rendered the Internet a viable medium for employing multiple independent computers collaboratively in the solution of a single computational problem, leading to the new genre of collaborative computing that we term Internet-based computing (IC). Scheduling a computation for IC presents challenges that were not encountered with earlier modalities of collaborative computing, especially when the computation’s constituent tasks have interdependencies that constrain their order of execution. This paper surveys an ongoing study of (an abstraction of) the scheduling problem for such computations for IC. The work employs a “pebble game on computation-dags,” that abstracts the process of allocating a computation’s interdependent tasks to participating remote computers. The goal of a schedule, motivated by two related scheduling challenges, is to maximize the production rate of tasks that are eligible for execution. First, in many modalities of IC, remote computers become available at unpredictable times. Always having a maximal number of execution-eligible tasks enhances the utilization of available resources. Second, the fact that remote computers are often not dedicated to this IC computation, hence, may be more dilatory than anticipated, can lead to a type of “gridlock” that results when a computation stalls because (due to dependencies) all execution-eligible tasks are already allocated to remote computers. These motivating challenges raise the hope that the optimality results presented here within an abstract IC setting have the potential of improving efficiency and fault-tolerance in real IC settings.

Keywords

Schedule Problem Optimal Schedule Schedule Strategy Priority Relation Remote Computer 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Grzegorz Malewicz
    • 1
  • Arnold L. Rosenberg
    • 2
  1. 1.Google, Inc. 
  2. 2.Dept. of Computer ScienceUniversity of MassachusettsAmherstUSA

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