Hailiang ZHAO @ ZJU.CS.CCNT

  


  4th-Year 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 (ZJU)
  Email: hliangzhao97 {AT} gmail {DOT} com
  Laboratory: Cao Guangbiao Sci-tech Building, Yuquan Campus of Zhejiang University

Biography

Currently I am a fourth-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]

From Sep. 2022 to Aug. 2023, I am a joint PhD student at Nanyang Technological University (NTU), Singapore, under the supervision of Prof. Xueyan Tang.

Github Profile: https://github.com/hliangzhao
Research Gate: https://www.researchgate.net/profile/Hailiang-Zhao-4
Google Scholar: https://scholar.google.com/citations?hl=en&user=wMj2rFsAAAAJ

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

Research Interests

Currently I am focusing on Cloud & Edge Computing, Distributed Computing & Systems, and Optimization Algorithm Design & Analysis. Recently, I pay great attention to the following topics:

  • Resource Allocation & Job Scheduling Algorithms with Theoretical Guarantees
    Scheduling algorithms are key to realizing improvements in resource utilization for the cloud-native applications. I am interested with the design and analysis of scheduling algorithms for large-scale clusters, DCs, cluster federations, etc. Currently I put special effort on deep learning workload scheduling in GPU clusters.
  • Ready-for-Use Cluster Schedulers in K8s-Native Systems
    K8s (Kubernetes) is an open-source system for automating deployment, scaling, and management of containerized applications. Kube-scheduler is the default component responsible for the scheduling of pods (a set of inter-related containers). By leveraging the power of CRD (Custom Resource Definition), customer schedulers can be designed. I am interested with the R&D of custom schedulers for various batch & elastic workloads.

Selected Publications

Awards & Honors & Contests

  • The CSC Scholarship, Aug 2022.
  • The Outstanding Postgraduates Award of College of CS, Zhejiang University, 2020 & 2021.
  • 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 Trans. on Services Computing
  • IEEE Internet of Things Journal
  • IEEE Trans. on Green Communication and Networking
  • Mobile Networks & Applications
  • IEEE Communications Letters
  • Trans. on Emerging Telecommunications Technologies
Sub-reviewer for:
  • ICSOC 2022
  • SCC 2021
  • ICSOC 2020
  • ICWS 2020

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.