Zhaoning Kong

dblp:250/0923 · DBLP profile ↗
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4ranked-venue papers
0as first author
2since 2021 · last 2022
0009-0009-8887-1698ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 2 · 2 since 2021Computer networks · 1Databases, data management, data science and information retrieval · 1
YearPublicationVenuePosition
2022 Can 5G mmWave Support Multi-user AR?
Moinak Ghoshal, Pranab Dash, Zhaoning Kong, Qiang Xu 0006, Y. Charlie Hu, Dimitrios Koutsonikolas, Yuanjie Li
PAM3
2021 Throughput Prediction on 60 GHz Mobile Devices for High-Bandwidth, Latency-Sensitive Applications
Shivang Aggarwal, Zhaoning Kong, Moinak Ghoshal, Y. Charlie Hu, Dimitrios Koutsonikolas
PAM2
2020 Physical Visualization Design
abstract
We demonstrate PVD, a system that visualization designers can use to co-design the interface and system architecture of scalable and expressive visualization.
Lana Ramjit, Zhaoning Kong, Ravi Netravali, Eugene Wu 0002
SIGMOD Conference2
2019 Distributed Dataset Synchronization in Disruptive Networks
abstract
Disruptive network scenarios with ad hoc, intermittent connectivity and mobility create unique challenges to supporting distributed applications. In this paper, we propose Distributed Dataset Synchronization over disruptive Networks (DDSN), a protocol which provides resilient multi-party communication in adverse communication environments. DDSN is designed to work on top of the Named-Data Networking protocol and utilizes semantically named, and secured, packets to achieve distributed dataset synchronization through an asynchronous communication model. A unique design feature of DDSN is letting individual entities exchange their dataset states directly, instead of using some compressed form of the states. We have implemented a DDSN prototype and evaluated its performance through simulation experimentation under various packet loss rates. Our results show that, compared to an epidemic routing based data dissemination solution, DDSN achieves 33-56% lower data retrieval delays and 40-44% lower overheads, with up to 20% packet losses. When compared to the existing NDN dataset synchronization protocols, DDSN can lower the state and data synchronization delays from one-third to two-third, and lower the protocol overhead by up to one-third, with the performance difference becoming more pronounced as network loss rates go up.
Zhaoning Kong, Spyridon Mastorakis, Lixia Zhang 0001
MASS2