Learning application models for utility resource planning

Published

Journal Article

Shared computing utilities allocate compute, network, and storage resources to competing applications on demand. An awareness of the demands and behaviors of the hosted applications can help the system to manage its resources more effectively. This paper proposes an active learning approach that analyzes performance histories to build predictive models of frequently used applications; the histories consist of measures gathered from noninvasive instrumentation on previous runs with varying assignments of compute, network, and storage resources. An initial prototype uses linear regression to predict application interactions with candidate resources, and combines them to forecast completion time for a candidate resource assignment. Experimental results from the prototype show that the mean forecasting errors range from 1% to 11% for a set of batch tasks captured from a production cluster. Examples illustrate how a system can use the learned models to guide task placement and data staging. © 2006 IEEE.

Duke Authors

Cited Authors

  • Shivam, P; Babu, S; Chase, JS

Published Date

  • December 1, 2006

Published In

  • Proceedings 3rd International Conference on Autonomic Computing, Icac 2006

Volume / Issue

  • 2006 /

Start / End Page

  • 255 - 264

Citation Source

  • Scopus