@inproceedings{narayanan2003predictive,
author = {Narayanan, Dushyanth and Satyanarayanan, M.},
title = {Predictive Resource Management for Wearable Computing},
booktitle = {Proceedings of the the 1st International Conference on Mobile Systems, Applications, and Services (MobiSys '03)},
year = {2003},
month = {May},
abstract = {Achieving crisp interactive response in resource-intensive applications such as augmented reality, language translation, and speech recognition is a major challenge on resource-poor wearable hardware. In this paper we describe a solution based on multi-fidelity computation supported by predictive resource management. We show that such an approach can substantially reduce both the mean and the variance of response time. On a benchmark representative of augmented reality, we demonstrate a 60% reduction in mean latency and a 30% reduction in the coefficient of variation. We also show that a history-based approach to demand prediction is the key to this performance improvement: by applying simple machine learning techniques to logs of measured resource demand, we are able to accurately model resource demand as a function of fidelity.},
publisher = {USENIX},
url = {http://approjects.co.za/?big=en-us/research/publication/predictive-resource-management-for-wearable-computing/},
edition = {Proceedings of the the 1st International Conference on Mobile Systems, Applications, and Services (MobiSys '03)},
}