Model-Driven Placement of Compute Tasks and Data in a Networked Utility

  • Authors:
  • Adriana Iamnitchi;Aydan R. Yumerefendi;Jeffrey S. Chase

  • Affiliations:
  • Durham NC;Durham NC;Durham NC

  • Venue:
  • ICAC '05 Proceedings of the Second International Conference on Automatic Computing
  • Year:
  • 2005

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Abstract

An important problem in resource management for networked resource-sharing systems is the simultaneous allocation of multiple resources to an application. Self-optimizing systems must co-allocate resources in a way that reconciles competing demands and maximizes global system objectives under dynamic conditions. We propose a simple model-driven approach to estimate the performance of a candidate assignment of resources, and select the best candidate to meet local or global goals. In this work, we address the placement of batch compute tasks and data in a network of compute and storage sites. We use the model to select placements for a set of synthetic benchmarks and a functional MRI processing application. Our experiments show that the model predicts the performance of candidate assignments within 10% of the empirical values.