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

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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

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About Me

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.

Selected News
Accepted "Planter: Rapid Prototyping of In-Network Machine Learning Inference", 2024
Changgang Zheng, Mingyuan Zang, Xinpeng Hong, Liam Perreault, Riyad Bensoussane, Shay Vargaftik, Yaniv Ben-Itzhak, and Noa Zilberman
ACM SIGCOMM Computer Communication Review (CCR), 2024 [Acceptance rates comparable to ACM SIGCOMM conference, i.e., under 15%]
PDF | arXiv (2022) | BibTex | Code | Doc

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.

Contributed "SmartEdge - Design of Dynamic and Secure Swarm Networking", 2024
..., Changgang Zheng, ... Noa Zilberman, et al. (sort in alphabetical order)
D4.1 Design of Dynamic & Secure Swarm Networking, EU Semantic Low-code Programming Tools for Edge Intelligence (SmartEdge) horizon project, GA 101092908
PDF | Link | X (Twitter)

Deliverable 4.1 is the first iteration of the Dynamic Swarm Networking technical Work Package. The Work Package combines three tasks: (1) Automatic discovery and dynamic network swarm formation (2) Embedded network security and isolation and (3) Hardware accelerated in-network operations.

Accepted "IIsy: Hybrid In-Network Classification Using Programmable Switches", 2024
Changgang Zheng, Zhaoqi Xiong, Thanh T Bui, Siim Kaupmees, Riyad Bensoussane, Antoine Bernabeu, Shay Vargaftik, Yaniv Ben-Itzhak, and Noa Zilberman
IEEE/ACM Transactions on Networking, 2024 [CCF A]
PDF | arXiv (2022) | BibTex | Code

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.

Accepted "In-Network Machine Learning Using Programmable Network Devices: A Survey" on the 7th of December, 2023.
Changgang Zheng, Xinpeng Hong, Damu Ding, Shay Vargaftik, Yaniv Ben-Itzhak, and Noa Zilberman
IEEE Communications Surveys and Tutorials [Impact Factor=35.6]
Paper | Link | BibTex

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.

Accepted "DINC: Toward Distributed In-Network Computing"
Changgang Zheng, Haoyue Tang, Mingyuan Zang, Xinpeng Hong, Aosong Feng, Leandros Tassiulas, and Noa Zilberman
ACM CoNEXT'23 & Proceedings of the ACM on Networking, 2023
[Acceptance Rate: 24/129=18.6%] Paper | BibTex | Code

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.

Accepted "QCMP: Load Balancing via In-network Reinforcement Learning", 2023
Changgang Zheng, Benjamin Rienecker, and Noa Zilberman
Proceedings of the ACM SIGCOMM Workshop on Future of Internet Routing & Addressing, 2023
Paper | BibTex | Code

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.

Visiting at the Yale University, 2022

I joined Graduate School of Arts and Sciences & Yale Institute for Network Science (YINS), Yale University, as a full-time visiting Ph.D. student supervised by Prof. Leandros Tassiulas. During the visiting, I was primarily focused on in-networking computing, machine learning, intrusion detection, and network modeling.

Jesus College Graduate Scholar
Award by the Governing Body of Jesus College, University of Oxford, 2022

Up to ten graduate students are awarded. Graduate Scholars are allowed to dine at High Table in Scholar's gown.

arXiv "Automating In-Network Machine Learning", 2022
Changgang Zheng, Mingyuan Zang, Xinpeng Hong, Riyad Bensoussane, Shay Vargaftik, Yaniv Ben-Itzhak, and Noa Zilberman
arXiv, 2022
Paper | BibTex | Code

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.

arXiv "IIsy: Practical In-Network Classification", 2022
Changgang Zheng, Zhaoqi Xiong, Thanh T Bui, Siim Kaupmees, Riyad Bensoussane, Antoine Bernabeu, Shay Vargaftik, Yaniv Ben-Itzhak, and Noa Zilberman
arXiv, 2022
Paper | BibTex

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.

Published "Planter: Seeding Trees Within Switches", 2021
Changgang Zheng, Noa Zilberman
Proceedings of the SIGCOMM'21 Poster and Demo Sessions
Paper | Short Video | Slides | BibTex | Poster | Code

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.

Joined the University of Oxford, 2020

I joined the University of Oxford as a DPhil student and was affiliated with Jesus College and the Departemnt of Engineering Science. I am a member of the Computing Infrastructure Group, under the direction of Prof. Noa Zilberman, aiming at In-Network Machine Learning research. My Resume | 我的简历 | My CV

Graduated from the University of Glasgow, 2020

I was graduated from the University of Glasgow and awarded first-class honour BEng in Electronics & Electrical Engineering. In UoG, I was affiliated with James Watt School of Engineering, Glasgow College, UESTC, a double degree programme that combines the strengths of two world-class university systems.

Graduated from the University of Electronic Science and Technology of China, 2020

I was graduated from the University of Electronic Science and Technology of China and received BEng in Communication Engineering. In UESTC, I was affiliated with Glasgow College, a double degree programme that combines the strengths of two world-class university systems.

Visiting at the University of Hong Kong, 2020

I joined Li Ka Shing Faculty of Medicine, University of Hong Kong, as a research assistant. During the visiting, I was primarily focused on medical image processing and reconstruction.

Awarded National Scholarship, 2019
Award by Ministry of Education of the People’s Republic of China

As the highest honour, the National Scholarship program is a program funded by the central government to reward outstanding undergraduate students. National Scholarships has the most rigorous and standardized selection process. Approximately 27 million students compete annually for 60,000 national scholarships, with only 0.2% winning.

Awarded National Scholarship, 2018
Award by Ministry of Education of the People’s Republic of China

As the highest honour, the National Scholarship program is a program funded by the central government to reward outstanding undergraduate students. National Scholarships has the most rigorous and standardized selection process. Approximately 27 million students compete annually for 60,000 national scholarships, with only 0.2% winning.

Joined the Data Mining Lab, 2017

I joined Data Mining Lab in the School of Computer Science and Engineering, University of Electronic Science and Technology of China, as a research assistant supervised by Prof. Junming Shao. My research interest was mainly focused on multi-task learning and medical image processing.