Demo Data from EDBT 2026 / reviewers in the wild / expert
Ruobin Zheng
dblp:79/946
· DBLP profile ↗
3ranked-venue papers
1as first author
2since 2021 · last 2025
0009-0004-7160-8141ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 3 · 1 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Routing and switching · 61% Software-defined and programmable networks · 39% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Routing and switching
multicast routing |
0.9 | 1 | 2025 | Stateless and Proactive Routing for Dynamic Multicast With Deep Reinforcement Learning · IEEE Trans. Netw. 2025 |
Software-defined and programmable networks › SDN traffic engineering
multicast traffic engineering |
0.9 | 1 | 2025 | Stateless and Proactive Routing for Dynamic Multicast With Deep Reinforcement Learning · IEEE Trans. Netw. 2025 |
Routing and switching
traffic engineering |
0.9 | 1 | 2025 | Stateless and Proactive Routing for Dynamic Multicast With Deep Reinforcement Learning · IEEE Trans. Netw. 2025 |
Software-defined and programmable networks
SDN control plane |
0.3 | 1 | 2025 | Stateless and Proactive Routing for Dynamic Multicast With Deep Reinforcement Learning · IEEE Trans. Netw. 2025 |
Methods — techniques the papers use, named apart from their topics
temporal convolutional network · 0.9source aggregation · 0.9deep reinforcement learning · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Stateless and Proactive Routing for Dynamic Multicast With Deep Reinforcement LearningabstractStateful multicast protocols manage multicast group memberships by maintaining state information about active groups and their members. They have seen limited adoption in the modern internet due to lack of scalability, simplicity, and flexibility. Although stateless multicast protocols, like BIER, eliminate extensive state management, they still face complex tree computation and limited scalability for concurrent requests. In this paper, we propose Hawkeye, a stateless multicast mechanism with deep reinforcement learning (DRL) for real-time responses to dynamic multicast requests with near-optimal multicast TE performance. This mechanism is suited for Software-Defined Networking (SDN) environment where the controller has a global view of the network and supports flexible configuration of network resources for traffic engineering. For real-time responses to multicast requests, we leverage DRL enhanced by a temporal convolutional network (TCN) to model the sequential feature of dynamic group membership, and thus are able to build multicast trees proactively for upcoming requests. We develop a novel source aggregation mechanism to facilitate the convergence of the DRL agent under high volume of multicast requests. Moreover, to improve the practicality and robustness of Hawkeye, we design incremental deployment and single failure handling mechanisms, which take advantages of source aggregation and fit well with multicast routing. Evaluation with real-world topologies and multicast requests demonstrates that Hawkeye responds effectively to dynamic multicast requests. Itoffers rapid routing decisions, e.g., making routing decisions in under 5ms on a tested topology, and reduces path latency variation by up to 89.5%, with less than a 10% increase in bandwidth consumption compared to the offline theoretical minimum. Qing Li 0006, Lie Lu, Dan Zhao 0003, Zeyu Luan, Yuan Yang 0001, Yong Jiang 0001, Jingpu Duan, Ruobin Zheng, Shaoteng Liu, Dingding Chen |
IEEE Trans. Netw. | 8 |
| 2024 | MATE: When multi-agent Deep Reinforcement Learning meets Traffic Engineering in multi-domain networks
Zeyu Luan, Qing Li 0006, Yong Jiang 0001, Jingpu Duan, Ruobin Zheng, Dingding Chen, Shaoteng Liu |
Comput. Networks | 5 |
| 2005 | Scalable multiple description coding and distributed video streaming in 3G mobile communicationsabstractAbstract This paper proposes a distributed multimedia delivery mobile network for video streaming in 3rd generation (3G) mobile communications. The joint design of layered coding (LC) and multiple description coding (MDC) is employed to address the bandwidth fluctuations and packet loss problems in the wireless network and to further enhance the error resilience tools in MPEG‐4. A new Internet protocol (IP) differentiated services (DiffServ) video marking algorithm is presented to support an unequal error protection of the LC components. Both intra‐RAN (radio access network) handoff and inter‐RAN handoff procedures are discussed, which provide path diversity to combat streaming video outage due to handoff in the universal mobile telecommunications system (UMTS). Computer simulation results demonstrate that: (1) the newly proposed IP DiffServ video marking algorithm is more suitable for video streaming in an IP mobile network as compared with the previously proposed algorithm, and (2) the proposed handoff procedures have better performance in terms of handoff latency, end‐to‐end delay and handoff scalability than that in UMTS. Copyright © 2005 John Wiley & Sons, Ltd. Ruobin Zheng, Weihua Zhuang, Hai Jiang 0001 |
Wirel. Commun. Mob. Comput. | 1 |