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Leveraging Resource Prediction for Anticipatory Dynamic Configuration

Vahe Poladian, David Garlan, Mary Shaw, Bradley Schmerl, João Sousa and Mahadev Satyanarayanan.


In Proceedings of the First IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO-2007, Pages 214-223, 8-11 July 2007.

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Abstract
Self-adapting systems based on multiple concurrent applications must decide how to allocate scarce resources to applications and how to set the quality parameters of each application to best satisfy the user. Past work has made those decisions with analytic models that used current resource availability information: they react to recent changes in resource availability as they occur, rather than anticipating future availability. These reactive techniques may model each local decision optimally, but the accumulation of decisions over time nearly always becomes less than optimal. In this paper, we propose an approach to self adaptation, called anticipatory configuration that leverages predictions of future resource availability to improve utility for the user over the duration of the task. The approach solves the following technical challenges: (1) how to express resource availability prediction, (2) how to combine prediction from multiple sources, and (3) how to leverage predictions continuously while improving utility to the user. Our experiments show that when certain adaptation operations are costly, anticipatory configuration provides better utility to the user than reactive configuration, while being comparable in resource demand.

Keywords: Aura, Dynamic Configuration, Resource Aware Computing, Service Composition, Ubiquitous Computing.  
    Created: 2007-04-03 10:18:50     Modified: 2007-09-12 16:02:42
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