@article{liang2019the,
author = {Liang, Chieh-Jan Mike and Xue, Hui and Yang, Mao and Zhou, Lidong},
title = {The Case for Learning-and-System Co-design},
year = {2019},
month = {July},
abstract = {While decision-makings in systems are commonly solved with explicit rules and heuristics, machine learning (ML) and deep learning (DL) have been driving a paradigm shift in modern system design. Based on our decade of experience in operationalizing a large production cloud system, Web Search, learning fills the gap in comprehending and taming the system design and operation complexity. However, rather than just improving specific ML/DL algorithms or system features, we posit that the key to unlocking the full potential of learning-augmented systems is a principled methodology promoting learning-and-system co-design. On this basis, we present the AutoSys, a common framework for the development of learning-augmented systems.},
url = {http://approjects.co.za/?big=en-us/research/publication/the-case-for-learning-and-system-co-design/},
pages = {68-74},
journal = {ACM SIGOPS Operating Systems Review},
volume = {53},
number = {1},
}