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MobiSys '03 Abstract
Predictive Resource Management for Wearable Computing
Dushyanth Narayanan, Carnegie Mellon University; M. Satyanarayanan, Carnegie Mellon University and Intel Research Pittsburgh
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.
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