Model-driven placement of compute tasks and data in a networked utility

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Shivam, P; Iamnitchi, A; Yumerefendi, AR; Chase, JS

Published Date

  • December 1, 2005

Published In

  • Proceedings Second International Conference on Autonomic Computing, Icac 2005

Volume / Issue

  • 2005 /

Start / End Page

  • 344 - 345

Digital Object Identifier (DOI)

  • 10.1109/ICAC.2005.41

Citation Source

  • Scopus