I am a second-year Ph.D. student in the School of Computer Science and Engineering at the Beihang University (BUAA), advised by Prof. Shan Zhang and Hongbin Luo. My research interests are broadly in computer networks. I am a visiting student at KAUST since September 2025, advised by Prof. Marco Canini.
Measuring traffic metrics is indispensable in virtual networks as it is the basis for a wide range of applications, such as network diagnostics and performance evaluation of the network algorithms. However, existing measurement schemes fail to have all these excellent characteristics simultaneously: 1) fine-grained, i.e. to obtain per packet level information. 2) lightweight, namely low CPU and bandwidth overhead. 3) network-wide, which means obtaining metrics of the whole network, e.g. per packet path. 4) easy-to-deploy, which refers to deployment without additional modification of Maximum Transmission Units (MTUs). We design vNetRadar, a virtual network measurement system, which has these excellent characteristics simultaneously. Specifically, vNetRadar 1) identifies each packet without increasing the size of each packet, to obtain network-wide metrics without MTU modification, 2) allocates each packet an area in memory, called backpack, and carries metadata in it to largely reduce bandwidth overhead. vNetRadar is implemented based on the extended Berkeley Packet Filter (eBPF) and is mainly in kernel space, avoiding the CPU overhead of copying packets to user space when performing the fine-grained measurement. Evaluation results show that the easy-to-deploy vNetRadar can get fine-grained network-wide metrics with low CPU and bandwidth overhead.
@inproceedings{10000703,author={Ma, Tie and Zhang, Jin and Luo, Long and Yu, Hongfang and Sun, Gang and Sun, Jian},booktitle={IEEE Global Communications Conference (GLOBECOM)},title={vNetRadar: Lightweight and Network-Wide Traffic Measurement in Virtual Networks},year={2022},volume={},number={},pages={5741-5746},keywords={Performance evaluation;Area measurement;Bandwidth;Metadata;Size measurement;Global communication;Kernel},doi={10.1109/GLOBECOM48099.2022.10000703}}
IoTJ
Efficient Multisource Data Delivery in Edge Cloud With Rateless Parallel Push
Shouxi Luo , Tie Ma, Wei Shan , and 3 more authors
As the key infrastructure for emerging 5G and Internet-of-Things (IoT) applications, micro data centers would be widely deployed at network edges to provide high-bandwidth low-latency cloud service. In these systems, applications would deliver large-size data objects among servers for various purposes like service deployment, application scale-up, and data duplication on demand. Accordingly, reducing delivery time is crucial for the optimization of service delay and system utilization. To accelerate the delivery, this article proposes a multisource-aware adaptive data transmission solution, Parallel Push (PPUSH), by leveraging the fact that data objects in the cloud are generally replicated among servers by design. At the high level, PPUSH achieves efficient delivery of multisource data by launching multiple push flows in parallel; and at the low level, it decouples transfers from different sources by encoding data objects with rateless RaptorQ code, and further employing novel congestion controls to prioritize the bandwidth allocation of concurrent tasks respecting their remaining sizes. Fluid model analysis along with Mininet-based test and packet-level simulation shows that, unlike DCTCP and other proposals, push is robust to packet loss and achieves provable prioritized bandwidth allocation. Extensive simulation results imply that, with above advantages, PPUSH could achieve very efficient data delivery by making use of all available data sources: for instance, compared with the straightforward design of equal-size task split and fair bandwidth allocation, its adaptive task assignment and prioritized traffic scheduling reduce the average task completion time in a tested scenario by 1.495× and 1.329×, respectively, demonstrating a total improvement of 1.586×, when enabled at the same time.
@article{9098934,author={Luo, Shouxi and Ma, Tie and Shan, Wei and Fan, Pingzhi and Xing, Huanlai and Yu, Hongfang},journal={IEEE Internet of Things Journal (IoTJ)},title={Efficient Multisource Data Delivery in Edge Cloud With Rateless Parallel Push},year={2020},volume={7},number={10},pages={10495-10510},keywords={Peer-to-peer computing;Task analysis;Channel allocation;Cloud computing;Servers;Data centers;Switches;Congestion control;data delivery;edge cloud;prioritized bandwidth allocation},doi={10.1109/JIOT.2020.2996800}}