Changgang Zheng 郑长刚

DPhil Student, Computing Infrastructure Group
Department of Engineering Science & Jesus College, University of Oxford

Email: changgang.zheng@eng.ox.ac.uk
Office: ETB, Parks Road, Oxford, OX1 3PH

Publications  |  Google Scholar

Grau, teurer Freund, ist alle Theorie, Und grün des Lebens goldner Baum.
理论是灰色的，愿生命之树常青。    -- Johann Wolfgang von Goethe

I am a final-year DPhil student (the thesis has been submitted recently) in Engineering Science at the University of Oxford advised by Prof. Noa Zilberman. My research interests include networking, in-network computing, and machine learning. A specific interest of mine is utilizing in-network computing and machine learning techniques to solve networking problems.

I used to be a research assistant supervised by Prof. Junming Shao in Data Mining Lab between 2017 and 2020. I obtained first-class honour BEng in EEE from University of Glasgow and BEng in CE from UESTC in 2020.

Using programmable network devices to aid in-network machine learning has been the focus of significant research. This work presents Planter, an open-source, modular framework for mapping trained machine learning models to programmable devices. Planter supports a wide range of machine learning models, multiple targets and can be easily extended.

This work presents IIsy, which implements machine learning classification models in a hybrid fashion using off-the-shelf network devices. Besides a range of traditional and ensemble machine learning models, IIsy also supports hybrid classification, achieving near-optimal classification results, while significantly reducing latency and load on the servers.

In-network ML is a promising technology that provides ML inference services with high throughput, low latency and high power efficiency. This paper provides a holistic review of the fundamentals of in-network ML. It introduces its background and solutions, discusses the existing challenges and open issues, and provides insights for further research explorations.

Research has focused on enabling on-device functionality, with limited consideration to distributed in-network computing. This paper explores the applicability of distributed computing to in-network computing and presents DINC, a framework enabling distributed in-network computing, generating deployment strategies, overcoming resource constraints and providing functionality guarantees across a network.

Traffic load balancing is a long-time networking challenge. This work presents QCMP, a ReinforcementLearning based load balancing solution implemented within the data plane, providing dynamic policy adjustment with quick response to changes in traffic. Our results show that QCMP requires negligible resources, runs at line rate, and adapts quickly to changes in traffic patterns.

Using programmable network devices to aid in-network machine learning has been the focus of significant research. This work presents Planter, an open-source, modular framework for mapping trained machine learning models to programmable devices. Planter supports a wide range of machine learning models, multiple targets and can be easily extended.

This work presents IIsy, which implements machine learning classification models in a hybrid fashion using off-the-shelf network devices. Besides a range of traditional and ensemble machine learning models, IIsy also supports hybrid classification, achieving near-optimal classification results, while significantly reducing latency and load on the servers.

Data classification within the network significantly benefits reaction time, servers offload and power efficiency. This work designs an algorithm for efficient mapping of ensemble models, such as XGBoost and Random Forest, to programmable switches. The proposed method overlaps trees within match-action tables, achieves high accuracy and low resource overhead.