Making time-stepped applications tick in the cloud

  • Authors:

  • Tao Zou;Guozhang Wang;Marcos Vaz Salles;David Bindel;Alan Demers;Johannes Gehrke;Walker White

  • Affiliations:

  • Cornell University Ithaca, NY;Cornell University Ithaca, NY;University of Copenhagen Copenhagen, Denmark;Cornell University Ithaca, NY;Cornell University Ithaca, NY;Cornell University Ithaca, NY;Cornell University Ithaca, NY

  • Venue:
  • Proceedings of the 2nd ACM Symposium on Cloud Computing
  • Year:
  • 2011

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Abstract

Scientists are currently evaluating the cloud as a new platform. Many important scientific applications, however, perform poorly in the cloud. These applications proceed in highly parallel discrete time-steps or "ticks," using logical synchronization barriers at tick boundaries. We observe that network jitter in the cloud can severely increase the time required for communication in these applications, significantly increasing overall running time. In this paper, we propose a general parallel framework to process time-stepped applications in the cloud. Our framework exposes a high-level, data-centric programming model which represents application state as tables and dependencies between states as queries over these tables. We design a jitter-tolerant runtime that uses these data dependencies to absorb latency spikes by (1) carefully scheduling computation and (2) replicating data and computation. Our data-driven approach is transparent to the scientist and requires little additional code. Our experiments show that our methods improve performance up to a factor of three for several typical time-stepped applications.