Yong Cui
Tsinghua University
241Publications
22H-index
1,665Citations
Publications 241
Newest
Published on Apr 29, 2019
Jinlong E (Nanyang Technological University), Yong Cui22
Estimated H-index: 22
(Tsinghua University)
+ 2 AuthorsEnnan Zhai8
Estimated H-index: 8
(Yale University)
Published on Feb 1, 2019in IEEE ACM Transactions on Networking 3.11
Yong Cui22
Estimated H-index: 22
(Tsinghua University),
Yimin Jiang (Tsinghua University)+ 6 AuthorsYi Li
While 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 instab...
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Published on Jan 1, 2019in IEEE Network 7.20
Ziyi Wang , Yong Cui22
Estimated H-index: 22
,
Zeqi Lai4
Estimated H-index: 4
Artificial intelligence (AI) technology makes mobile devices become intelligent objects which can learn and act automatically. Although AI will bring great opportunities for mobile applications, little work has focused on the architecture and the interaction with the cloud. In this article, we present three existing architectures of mobile intelligence in detail and introduce its broad application prospects. Furthermore, we conduct a series of experiments to evaluate the performance of the preva...
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Published on Jul 1, 2018
Yutao Huang1
Estimated H-index: 1
(Simon Fraser University),
Yifei Zhu1
Estimated H-index: 1
(Simon Fraser University)
+ 5 AuthorsYong Cui22
Estimated H-index: 22
(Tsinghua University)
Deep learning has been applied in many recent advanced applications in the field of transportation, finance and medicine. These applications require significant computation resources and large-scale training samples. Cloud becomes a natural choice for conducting these learning tasks due to its abundant resources. However, deeper penetration of deep learning techniques in mission critical applications, like driverless car, calls for stricter time requirement to guarantee its interaction and large...
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Published on Sep 1, 2018in IEEE Transactions on Big Data
Yong Cui22
Estimated H-index: 22
(Tsinghua University),
Jian Song3
Estimated H-index: 3
(Tsinghua University)
+ 3 AuthorsXuejun Cai1
Estimated H-index: 1
(Ericsson)
Cooperative cache has become a promising technique to optimize the traffic by caching big data in networks. However, controlling distributed cache nodes to update cached contents synergistically is still challenging in designing cooperative cache systems. This paper proposes an SDN-based Cooperative Cache Network (SCCN) for ISP networks, aiming to minimize the content transmission latency while reducing the inter-ISP traffic. Based on the proposed increment recording mechanism, the SCCN Controll...
4 Citations Source Cite
Published on Feb 1, 2018in IEEE ACM Transactions on Networking 3.11
Yong Cui22
Estimated H-index: 22
(Tsinghua University),
Shihan Xiao5
Estimated H-index: 5
(Tsinghua University)
+ 5 AuthorsNing Ge10
Estimated H-index: 10
(Tsinghua University)
The introduction of wireless transmissions into the data center has shown to be promising in improving cost effectiveness of data center networks (DCNs). For high transmission flexibility and performance, a fundamental challenge is to increase the wireless availability and enable fully hybrid and seamless transmissions over both wired and wireless DCN components. Rather than limiting the number of wireless radios by the size of top-of-rack switches, we propose a novel DCN architecture, Diamond ,...
8 Citations Source Cite
Published on Apr 1, 2018 in International Conference on Computer Communications
Yuchi Chen2
Estimated H-index: 2
(Simon Fraser University),
Wei Gong9
Estimated H-index: 9
(Simon Fraser University)
+ 1 AuthorsYong Cui22
Estimated H-index: 22
(Tsinghua University)
Recent years have seen various acoustic applications on mobile devices, e.g. range finding, gesture recognition, and device-to-device data transport, which use near-ultrasound signals at frequencies around 18–24 kHz. Due to the fixed low sound sample rate and hardware limitation, the highest detectable sound frequency on commercial-off-the-shelf (COTS) mobile devices is capped at 24 kHz, presenting a daunting barrier that prevents high-frequency ultrasounds from benefiting acoustic applications....
1 Citations Source Cite
Published on Aug 7, 2018 in ACM Special Interest Group on Data Communication
Zeqi Lai (Tsinghua University), Yong Cui22
Estimated H-index: 22
(Tsinghua University)
+ 1 AuthorsXiaoyu Hu (Tsinghua University)
Immersive computing (IC) technologies such as virtual reality and augmented reality are gaining tremendous popularity. In this poster, we present CoIC, a Cooperative framework for mobile Immersive Computing. The design of CoIC is based on a key insight that IC tasks among different applications or users might be similar or redundant. CoIC enhances the performance of mobile IC applications by caching and sharing computation-intensive IC results on the edge. Our preliminary evaluation results on a...
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Jinlong E1
Estimated H-index: 1
(Tsinghua University),
Yong Cui22
Estimated H-index: 22
(Tsinghua University)
+ 2 AuthorsChaokun Zhang (Tsinghua University)
Cloud storage services such as Dropbox have been widely used for file collaboration among multiple users. However, this desirable functionality is yet restricted to the “walled-garden” of each service. At present, the only feasible approach to cross-cloud file collaboration seems to be using web APIs, whose performance is known to be highly unstable and unpredictable. Now that using inefficient web APIs is inevitable, in this paper we attempt to achieve sound user-perceived performance for cross...
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Published on Mar 1, 2018in IEEE Cloud Computing
Zhan Qin8
Estimated H-index: 8
(University of Texas at San Antonio),
Jian Weng20
Estimated H-index: 20
(Jinan University)
+ 1 AuthorsKui Ren56
Estimated H-index: 56
(University at Buffalo)
Millions of private images are generated in various digital devices every day. The consequent massive computational workload makes people turn to cloud computing platforms for their economical computation resources. Meanwhile, the privacy concerns over the sensitive information contained in outsourced image data arise in public. In fact, once uploaded to cloud, the security and privacy of the image content can only presume upon the reliability of the cloud service providers. Lack of assuring sec...
1 Citations Source Cite
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