Elastic AI: system support for adaptive machine learning in pervasive computing systems

Abstract

AbstractArtificial intelligence (AI) is an important part of today’s pervasive computing systems. Still, there is no end-to-end system platform that allows to deploy, update, manage and execute AI models in pervasive systems. We propose such a system platform in this paper. Most importantly, we reuse concepts and techniques from twenty years of pervasive computing research on how to enable runtime adaptation and apply it to AI. This allows to specify adaptive AI models that are able to react to a multitude of dynamic changes, e.g. with respect to available devices, networking conditions, but also application requirements and sensor data sources. Developers can optimise their applications iteratively, starting with a generic setup and refining it step by step towards their specific pervasive computing scenario. To show the applicability of our platform, we apply it to two pervasive use cases and evaluate them, achieving up to four times faster inference and three times lower energy consumption compared to a classical AI deployment.