Minimizing Network Traffic for Distributed Joins Using Lightweight Locality-Aware Scheduling
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Autores:
Long Cheng, John Murphy, Qingzhi Liu, Chunliang Hao, Georgios Theodoropoulos
Publicado en:
Euro-Par 2018: Parallel Processing, 2018, Página(s) 293-305
Editor:
Springer International Publishing
DOI:
10.1007/978-3-319-96983-1_21
Learning Process Models in IoT Edge
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Autores:
Long Cheng, Cong Liu, Qingzhi Liu, Yucong Duan, John Murphy
Publicado en:
2019 IEEE World Congress on Services (SERVICES), 2019, Página(s) 147-150, ISBN 978-1-7281-3851-0
Editor:
IEEE
DOI:
10.1109/services.2019.00043
Resilient Neural Network Training for Accelerators with Computing Errors
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Autores:
Dawen Xu, Kouzi Xing, Cheng Liu, Ying Wang, Yulin Dai, Long Cheng, Huawei Li, Lei Zhang
Publicado en:
2019 IEEE 30th International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2019, Página(s) 99-102, ISBN 978-1-7281-1601-3
Editor:
IEEE
DOI:
10.1109/asap.2019.00-23
FlowCon - Elastic Flow Configuration for Containerized Deep Learning Applications
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Autores:
Wenjia Zheng, Michael Tynes, Henry Gorelick, Ying Mao, Long Cheng, Yantian Hou
Publicado en:
Proceedings of the 48th International Conference on Parallel Processing - ICPP 2019, 2019, Página(s) 1-10, ISBN 9781-450362955
Editor:
ACM Press
DOI:
10.1145/3337821.3337868
Deep Reinforcement Learning for IoT Network Dynamic Clustering in Edge Computing
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Autores:
Qingzhi Liu, Long Cheng, Tanir Ozcelebi, John Murphy, Johan Lukkien
Publicado en:
2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2019, Página(s) 600-603, ISBN 978-1-7281-0912-1
Editor:
IEEE
DOI:
10.1109/ccgrid.2019.00077
