I am Ruitao Xie. Currently I am an assistant professor in College of Computer Science and Software Engineering, Shenzhen University, and I am a member of IoT Research Center. Previously I worked in SenseTime Group Limited as a researcher. Before that, I received my PhD in Computer Science from City University of Hong Kong, where I worked with Prof. Xiaohua Jia on  wireless sensor networks and cloud computing. Before then, I received my BEng degree from Beijing University of Posts and Telecommunications in 2008. My research interests include mobile computing, cloud computing and wireless sensor networks.

Email: lastname@szu.edu.cn

Office: Room 1034, CSSE building, SZU.

To prospective graduate students:  If you are highly motivated and have the similar research interests with me, then you are welcome to contact me.

Recent Research Projects

Research on Adaptive Congestion Control for Data Center Networks by Using Reinforcement Learning, Young Scientists Fund of the National Natural Science Foundation of China, Jan. 2019-Dec. 2021

Research on Adaptive Optimization Approaches for Data Center Networks by Using Reinforcement Learning, Tencent Rhino-Bird Young Faculty Research Fund, Jan. 2019-Dec.2020

Publications

Journal Papers

Ruitao Xie, Xiaohua Jia, Lu Wang and Kaishun Wu, "Energy Efficiency Enhancement for CNN-based Deep Mobile Sensing", IEEE Wireless Communications Magazine, 2019, accepted NEW! 

Ruitao Xie and Xiaohua Jia, “Data transfer scheduling for maximizing throughput of big-data computing in cloud systems,” IEEE Transactions on Cloud Computing, vol. 6, no. 1, pp. 87–98, Jan.–March 2018 [PDF]

Bo Zhang, Xiaohua Jia, Kan Yang, and Ruitao Xie, “Design of analytical model and algorithm for optimal roadside ap placement in vanets,” IEEE Transactions on Vehicular Technology, vol. 65, no. 9, pp. 7708–7718, Sep. 2016

Ruitao Xie, Yonggang Wen, Xiaohua Jia, and Haiyong Xie, “Supporting seamless virtual machine migration via named data networking in cloud data center,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 12, pp. 3485–3497, Dec. 2015 [PDF] [Code]

Ruitao Xie and Xiaohua Jia, “Transmission-efficient clustering method for wireless sensor networks using compressive sensing,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 3, pp. 806–815, Mar. 2014 [PDF] [Supplementary File] [Code]

Kan Yang, Xiaohua Jia, Kui Ren, Bo Zhang, and Ruitao Xie, “Dac-macs: Effective data access control for multiauthority cloud storage systems,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 11, pp. 1790–1801, Nov. 2013

Conference Papers

Ruitao Xie, Zuneera Umair, and Xiaohua Jia, “A wireless solution for SDN (software defned networking) in data center networks,” in GLOBECOM 2016

Kan Yang, Xiaohua Jia, Kui Ren, Ruitao Xie, and Liusheng Huang, “Enabling efcient access control with dynamic policy updating for big data in the cloud,” in IEEE INFOCOM 2014, Apr. 2014, pp. 2013–2021

Ruitao Xie and Xiaohua Jia, et al., “Energy saving virtual machine allocation in cloud computing,” in ICDCS 2013 workshop, pp. 132–137

Bo Zhang, Xiaohua Jia, Kan Yang, and Ruitao Xie, “Multi-path routing and stream scheduling with spatial multiplexing and interference cancellation in mimo networks,” in ICDCS 2013 Workshops, Jul. 2013, pp. 256–261

Ruitao Xie and Xiaohua Jia, “Minimum transmission data gathering trees for compressive sensing in wireless sensor networks,” in GLOBECOM 2011

Selected Research Projects

Energy Efficiency Enhancement for CNN-based Deep Mobile Sensing
Recently, deep learning is used to tackle mobile sensing problems, and the inference phase of deep learning is preferred to be run on mobile devices for speedy responses. However, mobile devices are resource-constrained platforms from both computation and power. Moreover, an inference task with deep learning involves tens of billions of mathematical operations and tens of millions of parameter reads. Thus, it is a critical issue to reduce the energy consumption of deep learning inference algorithms. In this article, we survey various of energy reduction approaches, and classify them into three categories: compressing neural network model, minimizing the data transfer required in computation and offloading workloads. Moreover, we simulate and compare three techniques of model compression, by applying them to an object recognition problem.
Ruitao Xie, Xiaohua Jia etc.
WCM 2019
Data Transfer Scheduling for Maximizing Throughput of Big-Data Computing in Cloud Systems
The normal pipeline of a big-data computing job is first reading data from storage nodes to computing nodes, then processing, and finally storing results back in storage nodes. Thus, the data transmission efficiency between storage and computing nodes is critical and impacts on job completion time. We propose a data scheduling approach to reduce the data retrieval time. This approach leverages multiple routing paths among storage and computing nodes, and multiple data replicas on storage. By balanced scheduling, potential congestion can be avoided and data retrieval time is reduced.
Ruitao Xie, Xiaohua Jia
TCC 2015
Supporting Seamless Virtual Machine Migration via Named Data Networking in Cloud Data Center
A great advantage of cloud computing is elastic service, where computing resources are allocated as required. The elasticity is supported by virtualization technology, where the resources of physical machines are partitioned into several virtual machines and the amount of resources for each VM can be dynamically adjusted. A challenging issue is how to keep service accessible  during VM migration from a physical machine to another. We propose an approach to avoid service interruption during VM migration. This approach leverages named data networking technology. Virtual machines are named and identified with the services they host. In this way, request routing can find a destination via a service name rather than an IP address bounded with physical machines.
Ruitao Xie, Yonggang Wen, Xiaohua Jia, Haiyong Xie
TPDS 2015
Transmission Efficient Clustering Method for Wireless Sensor Networks using Compressive Sensing
Energy is an important challenge for wireless sensor networks. Because sensor nodes are battery-constrained  and usually are very hard to recharge in most application scenarios. We propose an approach to reduce the energy consumption of wireless sensor nodes in data collection. This approach leverages compressive sensing, a coding method, to compress data and builds an optimal cluster topology to transmit data. The topology is optimally designed so that the total number of data transmissions required for successful data recovery is minimized. Our approach can achieve 60% energy reduction.
Ruitao Xie, Xiaohua Jia
TPDS 2014

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