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Kai Bu

Researcher at Zhejiang University

Publications -  69
Citations -  1150

Kai Bu is an academic researcher from Zhejiang University. The author has contributed to research in topics: Network packet & Software-defined networking. The author has an hindex of 19, co-authored 64 publications receiving 926 citations. Previous affiliations of Kai Bu include Nanjing University of Posts and Telecommunications & Hong Kong Polytechnic University.

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Journal ArticleDOI

Unknown Tag Identification in Large RFID Systems: An Efficient and Complete Solution

TL;DR: This paper develops several novel techniques to quickly deactivate already identified tags and prevent them from replying during the interrogation of unknown tags, which avoids re-identification of these tags and consequently improves time efficiency.
Proceedings ArticleDOI

Is every flow on the right track?: Inspect SDN forwarding with RuleScope

TL;DR: RuleScope is presented, a more comprehensive solution for inspecting SDN forwarding with accurate and efficient forwarding inspection with limited bandwidth and packet-switching overhead and various techniques for enhancing algorithm efficiency without sacrificing inspection accuracy.
Proceedings ArticleDOI

RuleTris: Minimizing Rule Update Latency for TCAM-Based SDN Switches

TL;DR: RuleTris is presented, the hrst SDN update optimization framework that minimizes rule update latency for TCAM-based switches, and it is proved that RuleTris generates TCAM updates with the minimum number of TCAM entry moves.
Journal ArticleDOI

Fault Management in Software-Defined Networking: A Survey

TL;DR: This paper applies a systematic classification of SDN faults, compares and analyze existing SDN fault management solutions in the literature, and conducts a gap analysis between solutions developed in an academic research context and practical deployments.
Proceedings ArticleDOI

ENDA: embracing network inconsistency for dynamic application offloading in mobile cloud computing

TL;DR: This work proposes ENDA, a three-tier architecture that leverages user track prediction, realtime network performance and server loads to optimize offloading decisions and outperforms existing offloading techniques that do not consider user mobility and server workload balance management.