5th-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 fifth-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. From Sep. 2022 to Sep. 2023, I was a visiting PhD student at Nanyang Technological University (NTU), Singapore, under the supervision of Prof. Xueyan Tang. [CV] [简历]

NOTICE: The webpage https://hliangzhao.cn is closed. I may re-open it or migrate the content to another URL.

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

Research Interests

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

• Resource Allocation & Job Scheduling Algorithms with Theoretical Guarantees
  Efficient scheduling is a key to the performance of applications in distributed systems. 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 combinatorial algorithm design and analysis for online problems.
• Ready-for-Use Cluster Schedulers in K8s-Native Systems
  K8s (Kubernetes) is an open-source operating system for automating the 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

• Shuiguang Deng, Hailiang Zhao, Bingbing Huang, Cheng Zhang, Feiyi Chen, Yinuo Deng, Jianwei Yin, Schahram Dustdar, and Albert Y. Zomaya, Cloud-Native Computing: A Survey from the Perspective of Services. In: TechRxiv preprints.
• Hailiang Zhao, Shuiguang Deng*, Zhengzhe Xiang, Xueqiang Yan, Jianwei Yin, Schahram Dustdar, and Albert Y. Zomaya, Scheduling Multi-Server Jobs with Sublinear Regrets via Online Learning. In: IEEE Trans. on Services Computing (TSC) , doi: 10.1109/TSC.2023.3303344.
• Hailiang Zhao, Shuiguang Deng*, Jianwei Yin, Schahram Dustdar, and Albert Y. Zomaya, Theoretically Guaranteed Online Workload Dispatching for Deadline-Aware Multi-Server Jobs. In: arXiv preprints.
• Haowei Chen, Shuiguang Deng, Hongze Zhu, Hailiang Zhao, Rong Jiang, Schahram Dustdar, and Albert Y. Zomaya, Mobility-aware Offloading and Resource Allocation for Distributed Services Collaboration. In: IEEE Trans. on Parallel and Distributed Systems (TPDS) , vol. 33, no. 10, pp. 2428-2443, 1 Oct. 2022, doi: 10.1109/TPDS.2022.3142314.
• Hailiang Zhao, Shuiguang Deng*, Feiyi Chen, Jianwei Yin, Schahram Dustdar, and Albert Y. Zomaya, Learning to Schedule Multi-Server Jobs with Fluctuated Processing Speeds. In: IEEE Trans. on Parallel and Distributed Systems (TPDS) , vol. 34, no. 1, pp. 234-245, 1 Jan. 2023, doi: 10.1109/TPDS.2022.3215947. [Slide]
• Shuiguang Deng, Hailiang Zhao, Zhengzhe Xiang, Cheng Zhang, Rong Jiang, Ying Li*, Jianwei Yin, Schahram Dustdar, and Albert Y. Zomaya, Dependent Function Embedding for Distributed Serverless Edge Computing. In: IEEE Trans. on Parallel and Distributed Systems (TPDS) , vol. 33, no. 10, pp. 2346-2357, 1 Oct. 2022, doi: 10.1109/TPDS.2021.3137380. [Slide]
• Hailiang Zhao, Shuiguang Deng*, Zijie Liu, Zhengzhe Xiang, Jianwei Yin, Schahram Dustdar, and Albert Y. Zomaya, DPoS: Decentralized, Privacy-Preserving, and Low-Complexity Online Slicing for Multi-Tenant Networks. In: IEEE Trans. on Mobile Computing (TMC) , vol. 21, no. 12, pp. 4296-4309, 1 Dec. 2022, doi: 10.1109/TMC.2021.3074934. [Slide]
• 陈昊崴，邓水光 *， 赵海亮 ，尹建伟， 面向移动边缘的组合服务选择及优化 ， 计算机学报 (Chinese Journal of Computers) ，doi: 10.11897/SP.J.1016.2022.00082。
• Hailiang Zhao, Shuiguang Deng, Zijie Liu, Zhengzhe Xiang, and Jianwei Yin, Placement is not Enough: Embedding with Proactive Stream Mapping on the Heterogenous Edge. In: arXiv preprints . [Code] (the revised version is Dependent Function Embedding)
• Hailiang Zhao, Shuiguang Deng*, Zijie Liu, Jianwei Yin, and Schahram Dustdar, Distributed Redundant Placement for Microservice-based Applications at the Edge. In: IEEE Trans. on Services Computing (TSC) , vol. 15, no. 3, pp. 1732-1745, 1 May-June 2022, doi: 10.1109/TSC.2020.3013600. [ESI Highly Cited Paper]
• Shuiguang Deng, Guanjie Cheng, Hailiang Zhao, Honghao Gao, and Jianwei Yin, Incentive-driven Computation Offloading in Blockchain-enabled E-commerce. In: ACM Trans. on Internet Technology (TOIT) , 21, 1, Article 9 (February 2021), 19 pages, doi: https://doi.org/10.1145/3397160.
• Shuiguang Deng, Hailiang Zhao, Weijia Fang*, Jianwei Yin, Schahram Dustdar, and Albert Y. Zomaya, Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence. In: IEEE Internet of Things Journal (IOTJ) , vol. 7, no. 8, pp. 7457-7469, Aug. 2020, doi: 10.1109/JIOT.2020.2984887. [ESI Highly Cited Paper] [ESI Hot Paper]
• Hailiang Zhao, Shuiguang Deng*, Cheng Zhang, Wei Du, Qiang He, and Jianwei Yin, A Mobility-aware Cross-edge Computation Offloading Framework for Partitionable Applications. In: Proceedings of the 17th IEEE International Conference on Web Services (ICWS '19) , Milan, Italy, 2019. [Slide] [Theoretical Analysis] [Best Student Paper]
• Yishan Chen, Shuiguang Deng*, Hailiang Zhao, Qiang He, Yin Li, and Honghao Gao, Data-intensive Application Deployment at Edge: A Deep Reinforcement Learning Approach. In: Proceedings of the 17th IEEE International Conference on Web Services (ICWS '19) , Milan, Italy, 2019. [Short Paper]
• Hailiang Zhao, Wei Du, Wei Liu, Tao Lei, and Qiwang Lei, QoE Aware and Cell Capacity Enhanced Computation Offloading for Multi-Server Mobile Edge Computing Systems with Energy Harvesting Devices. In: Proceedings of the 15th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC '18) , Guangzhou, China, 2018. [Code]

Selected Documents & Slides & Blogs

Here's some great stuff you might be interested with. A full list of documents, notes, and slides of related topics is available here. Technology blogs are available at hliangzhao.cn.

• 分布式训练的自动并行策略 · OSDI '22
  这篇博文对论文 Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning 进行了深入分析，Dec 2022。
• Everything about ADMM
  If you want to learn the past and present of the alternating direction method of multipliers (ADMM), you should not miss this slide. Nov 2022.
• ADMM: The Variational Inequality Perspective
  This slide demonstrates the ADMM technique from the Variational Inequality's perspective, Oct 2022.
• An Overview of Kubernetes Scheduling
  This slide is a report on the Kubernetes scheduling background and research actuality, Oct 2022.
• 优化算法复杂度分析
  这篇文档给出了优化算法的复杂度分析，Oct 2022。
• 如何设计一个 DRL 调度器？ · TPDS '21
  这篇博文对强化学习理论做了一个系统地回顾，并对论文 DL2: A Deep Learning-Driven Scheduler for Deep Learning Clusters 进行了深入分析，Aug 2022。
• CPU 和 MEM 也很重要！ · OSDI '22
  这篇博文对论文 Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters 进行了深入分析，Aug 2022。
• 自定义资源对象与控制器的实现 · K8S 实践 04
  这篇博文以一个具体项目为例，深入介绍了如何在 Kubernetes 中基于 Operator 模式实现自定义资源对象和控制器，Dec 2021。
• 面向深度学习的互适应调度 · OSDI '21
  这篇博文对论文 Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning 进行了深入分析，Sep 2021。
• 最优化理论基础
  这篇文档给出了最优化理论的数学基础，包含范数、梯度、海瑟矩阵、Lipschitz 连续、闭函数、凸集、仿射集、超平面、凸函数的定义与保凸运算、共轭函数等内容，Jul 2021。
• 理解共轭梯度法
  这篇文档给出了共轭梯度法的设计原理、算法实现以及收敛性分析，Jun 2020。
• 需要熟练掌握的算法理论基础 (ver. 22.01.28)
  这篇文档从 “为什么要这样” 的角度罗列并分析了概率论、数理统计、微积分、优化理论以及线性代数中一些广泛应用的话题，May 2020。
• 里亚普洛夫优化导论
  这篇文档给出了里亚普洛夫优化技术的使用方法及性能分析，Nov 2018。

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 (20 students per year), Nov 2018.

Professional Services

Reviewer for:

• IEEE Transactions on Services Computing
• IEEE Internet of Things Journal
• IEEE Transactions on Green Communication and Networking
• Mobile Networks & Applications
• IEEE Communications Letters
• Transactions on Emerging Telecommunications Technologies
• International Journal on Communication Systems

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.