Demo Data from EDBT 2026 / reviewers in the wild / expert
Minglong Dai
dblp:209/8662
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2Systems, architecture and hardware · 1
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
3 papers |
Cellular and mobile networks · 34% Wireless networking · 20% Network measurement and analytics · 19% |
Topics — the 5 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cellular and mobile networks
mobility management |
0.4 | 1 | 2019 | Wireless Network Instabilities in the Wild: Measurement, Applications (Non)Resilience, and OS Remedy · IEEE/ACM Trans. Netw. 2019 |
Network measurement and analytics
wireless network measurement |
0.4 | 1 | 2019 | Wireless Network Instabilities in the Wild: Measurement, Applications (Non)Resilience, and OS Remedy · IEEE/ACM Trans. Netw. 2019 |
Transport protocols and congestion control › multipath transport
multipath TCP |
0.3 | 1 | 2018 | STMS: Improving MPTCP Throughput Under Heterogeneous Networks · USENIX ATC 2018 |
Wireless networking › wireless link
heterogeneous wireless links |
0.1 | 1 | 2018 | STMS: Improving MPTCP Throughput Under Heterogeneous Networks · USENIX ATC 2018 |
Content delivery and video streaming
quality of experience |
0.1 | 1 | 2017 | Wireless network instabilities in the wild: Prevalence, App (non)resilience, and OS remedy · ICNP 2017 |
Methods — techniques the papers use, named apart from their topics
measurement study · 0.7prototype implementation · 0.4MPTCP congestion control · 0.3interface management · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Wireless Network Instabilities in the Wild: Measurement, Applications (Non)Resilience, and OS RemedyabstractWhile the bandwidth and latency improvement of both WiFi and cellular data networks in the past decades are plenty evident, the extent of signal strength fluctuation and network disruptions (unexpected switching or disconnections) experienced by mobile users in today's network deployment remains less clear. This paper makes three contributions. First, we conduct the first extensive measurement of network disruptions and significant signal strength fluctuations (together denoted as network instabilities) experienced by 2000 smartphones in the wild. Our results show that network disruptions and signal strength fluctuations remains prevalent as we moved into the 4G era. Second, we study how well popular mobile apps today handle such network instabilities. Our results show that even some of the most popular mobile apps do not implement any disruption-tolerant mechanisms. Third, we present Janus, an intelligent interface management framework that exploits the multiple interfaces on a handset to transparently handle network disruptions and satisfy apps' performance requirement. We have implemented a prototype of Janus and our evaluation using a set of popular apps shows that Janus can: 1) transparently and efficiently handle network disruptions; 2) reduce video stalls by 2.9 times and increase 31% of the time of good voice quality; 3) reduce traffic size by 26.4% and energy consumption by 16.3% compared to naive solutions. Yong Cui 0001, Zeqi Lai, Y. Charlie Hu, Kun Tan 0002, Minglong Dai, Kai Zheng 0003, Yi Li 0015 |
IEEE/ACM Trans. Netw. | 7 |
| 2018 | STMS: Improving MPTCP Throughput Under Heterogeneous Networks
Yong Cui 0001, Xin Wang 0001, Yuming Hu, Minglong Dai, Fanzhao Wang, Kai Zheng 0003 |
USENIX ATC | 5 |
| 2017 | Wireless network instabilities in the wild: Prevalence, App (non)resilience, and OS remedyabstractWhile the bandwidth and latency improvement of both WiFi and cellular data networks in the past decade are plenty evident, the extent of signal strength fluctuation and network disruptions (unexpected switching or disconnections) experienced by mobile users in today's network deployment remains less clear. This paper makes three contributions. First, we conduct the first extensive measurement of network disruptions and signal strength fluctuations (together denoted as instabilities) experienced by 2000 smartphones in the wild. Our results show that network disruptions and signal strength fluctuations remain prevalent as we moved into the 4G era. Second, we study how well popular mobile apps today handle such network instabilities. Our results show that even some of the most popular mobile apps do not implement any disruption-tolerant mechanisms. Third, we present JANUS, an intelligent interface management framework that exploits the multiple interfaces on a handset to transparently handle network disruptions and improve apps' QoE. We have implemented JANUS on Android and our evaluation using a set of popular apps shows that Janus can (1) transparently and efficiently handle network disruptions, (2) reduce video stalls by 2.9 times and increase 31% of the time of good voice quality compared to naive solutions. Zeqi Lai, Yong Cui 0001, Y. Charlie Hu, Kun Tan 0002, Minglong Dai, Kai Zheng 0003 |
ICNP | 7 |