Hailiang ZHAO @ ZJU.CS.CCNT

  


  Ph.D. Student, CCNT Lab
  AdvanCed Computing aNd SysTem Laboratory
  College of Computer Science and Technology
  Zhejiang University, Hangzhou 310027, China

  Supervisor: Prof. Shuiguang Deng
  Email: hliangzhao97 {AT} gmail {DOT} com
  Laboratory: Cao Guangbiao Sci-tech Building, Yuquan Campus of Zhejiang University

Biography

Currently I am a third-year Ph.D. student of College of Computer Science and Technology, Zhejiang University. Before my Ph.D. career, I was an undergraduate student from Wuhan University of Technology and received my B.Eng. degree in Computer Science and Technology on June, 2019. In September 2019, I was admitted to study for a Ph.D. degree in Zhejiang University under the supervision of Prof. Shuiguang Deng without entrance examination. [CV] [简历]

Github Profile: https://github.com/hliangzhao
Research Gate: http://dwz.win/uJY
Google Scholar: http://t.cn/AiWSETna

IMPORTANT NOTICE: Related documents, notes, and slides can be found at here. Technology blogs on related topics are available at hliangzhao.cn.

Research Interests

I am interested in Cloud & Edge Computing, Distributed Systems, and Network Management. Currently I am focusing on:

  • Dependent Task Scheduling & Online Resource Allocation
    Cluster schedulers are key to realizing improvements in resource utilization for the cloud-native apps. Current cluster schedulers rely on heuristics that prioritize generality, ease of understanding, and straightforward implementation over achieving the ideal performance on a specific workload. However, they ignore readily available information about job structure (i.e., internal dependencies) and efficient parallelism for jobs’ input sizes. How to design a better online resource allocation algorithm (mechanism) is of importance.
  • AI-driven Optimization
    In our Edge Intelligence paper, we divide Edge Intelligence into AI for edge and AI on edge. The former focuses on providing a more optimal solution to the key concerns in Edge Computing with the help of popular and resultful AI technologies while the latter studies how to carry out the entire process of AI models, i.e., model training and inference, on edge. What I put emphasis on is that how to apply AI-based models, especially reinforcement learning and graph neural networks, to improve the QoE.

Selected Publications

Awards & Honors & Contests

  • The Outstanding Postgraduates Award of CCNT Lab and College of Computer Science and Technology, Zhejiang University, Oct 2020.
  • The Doctoral Freshman Scholarship of Zhejiang University, Sep 2019.
  • The Best Student Paper Award of 2019 IEEE International Conference on Web Services, Jul 2019.
  • The Outstanding Graduates Award of Wuhan University of Technology, Apr 2019.
  • The Excellence Scholarship of Wuhan University of Technology (only 20 students per year), Nov 2018.

Professional Services

  • Reviewer for: IEEE Transactions on Services Computing, IEEE Communications Letters, Transactions on Emerging Telecommunications Technologies.
  • Sub-reviewer for: IEEE Internet of Things Journal, ACM Transactions on Internet Technology, IEEE Transactions on Cloud Computing, ICWS 2020, ICSOC 2020, SCC 2021.

Experiences

  • R&D Intern of Cloud BU, Huawei Technologies Co., Ltd, Nov 2020 ~ Jan 2021.

Correspondence

Email: hliangzhao97 {AT} gmail {DOT} com

Laboratory Address: Cao Guangbiao Sci-tech Building, Yuquan Campus of Zhejiang University.
中国浙江省杭州市西湖区浙大路38号, 浙江大学玉泉校区 310027.