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 second-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

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.

Publications

• Cheng Zhang, Shuiguang Deng, Hailiang Zhao, Zhengzhe Xiang, Jianwei Yin, and Schahram Dustdar, Joint Energy Consumption and Weighted Flow Time Minimization in Edge Networks. Under Review.
• Haowei Chen, Shuiguang Deng, Hongze Zhu, Hailiang Zhao, Schahram Dustdar, and Albert Y. Zomaya, Mobility-aware Offloading and Resource Allocation for Distributed Services Collaboration. Under Review.
• Shuiguang Deng, Hailiang Zhao, Zhengzhe Xiang, Cheng Zhang, Jianwei Yin, Schahram Dustdar, and Albert Y. Zomaya, Dependent Function Embedding for Distributed Serverless Edge Computing. Under Review. [Slide]
• Zhengzhe Xiang, Shuiguang Deng, Yuhang Zheng, Dongjing Wang, Hailiang Zhao, Javid Taheri, and Zengwei Zheng, Cost-effective and Robust AI Service Provisioning under Multi-Access Edge Computing Environment. Under Review.
• 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 Transactions on Mobile Computing (TMC), doi: 10.1109/TMC.2021.3074934. [Slide] (Core A*, CCF A)
• 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，接收。
• 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 Transactions on Services Computing (TSC), doi: 10.1109/TSC.2020.3013600. (Core A*, CCF B)
• Shuiguang Deng, Guanjie Cheng, Hailiang Zhao, Honghao Gao, and Jianwei Yin, Incentive-driven Computation Offloading in Blockchain-enabled E-commerce. In: ACM Transactions on Internet Technology (TOIT), doi: https://doi.org/10.1145/3397160. (Core B, CCF B)
• 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, doi: 10.1109/JIOT.2020.2984887. (SCI 1区, JCR Q1)
• 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] (Core A, CCF B)
• 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. (Core A, CCF B, short paper)
• Wei Du, Tao Lei, Qiang He, Wei Liu, Qiwang Lei, Hailiang Zhao, and Wei Wang, Service Capacity Enhanced Task Offloading and Resource Allocation in Multi-Server Edge Computing Environment. In: Proceedings of the 17th IEEE International Conference on Web Services (ICWS '19), Milan, Italy, 2019. (Core A, CCF B)
• Wei Du, Qiwang Lei, Qiang He, Wei Liu, Feifei Chen, Lei Pan, Tao Lei, and Hailiang Zhao, Multiple Energy Harvesting Devices Enabled Joint Computation Offloading and Dynamic Resource Allocation for Mobile-Edge Computing Systems. In: Proceedings of the 17th IEEE International Conference on Web Services (ICWS '19), Milan, Italy, 2019. (Core A, CCF B, short paper)
• Cheng Zhang, Hailiang Zhao, and Shuiguang Deng, A Density-based Offloading Strategy for IoT Devices in Edge Computing Systems. In: IEEE Access, doi: 10.1109/ACCESS.2018.2882452.
• 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] (Core B, CCF C)

Documents & Notes

• 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。
• 最优化简介
  这篇文档给出了最优化理论的基本知识，包含优化问题的分类、解的形式、收敛速度等，Jun 2021。
• 深度学习理论速查清单
  这篇文档给出了深度学习模型的理论原理，Oct 2020。
• 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. 21.06.22)
  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。
• MEC业务系统架构
  这篇笔记总结了多接入边缘计算的业务架构，May 2020。
• 比特币与区块链总结 [lightChain]
  这篇笔记总结了我在理解比特币和区块链概念时遇到的一些问题，Nov 2019。
• 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。
• 线性代数中的一些基本观念
  这篇文档对线性代数中的一些基本观念给出了有趣的阐释，Oct 2018。

Discussions & Slides

• Dependent Function Embedding for Distributed Serverless Edge Computing
  This slide is a report on my preprint Placement is not Enough: Embedding with Proactive Stream Mapping on the Heterogenous Edge. This talk was held in the PhD Symposium, CCF ICSS '21, May 2021.
• DPoS: Decentralized, Privacy-Preserving, and Low-Complexity Online Slicing for Multi-Tenant Networks
  This slide is a report on my paper DPoS: Decentralized, Privacy-Preserving, and Low-Complexity Online Slicing for Multi-Tenant Networks, published in IEEE TMC. This talk was held in Apr 2021.
• Park: An Open Platform for Learning-Augmented Computer Systems
  This slide is a report on Hongzi Mao’s paper Park: An Open Platform for Learning Augmented Computer Systems, published in NeurIPS '19. This paper won the ICML Workshop Best Paper Award. This talk was held in Jan 2021.
• Review the Transport Layer: TCP and UDP
  This slide is a reminder for chapter 3 of the book Computer Networking: A Top-Down Approach, Sep 2020.
• Review the Application Layer
  This slide is a reminder for chapter 2 of the book Computer Networking: A Top-Down Approach, Sep 2020.
• Computer Networks and the Internet: A Comprehensive View
  This slide is a reminder for chapter 1 of the book Computer Networking: A Top-Down Approach, Sep 2020.
• How Should I Slice My Network?
  This slide is a report on the paper How Should I Slice My Network? A Multi-Service Empirical Evaluation of Resource Sharing Efficiency, published in MobiCom '18. This talk was held in Dec 2019.
• CrowdBC: A Blockchain-Based Decentralized Framework for Crowdsourcing
  This slide is a report on the paper CrowdBC: A Blockchain-Based Decentralized Framework for Crowdsourcing, published in IEEE TPDS, Jun 2019.
• Edge Intelligence: the Confluence of Edge Computing and Artificial Intelligence
  This slide is a report on our preprint Edge Intelligence: the Confluence of Edge Computing and Artificial Intelligence, Sep 2019.
• Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
  This slide is a report on the paper Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing, preprinted on arXiv, May 2019.
• On Mobile Edge Computing: Game Theory, Edge AI and Other New Ideas
  The slide reviews some important concepts and methods in Game Theory and Edge Intelligence. Especially their utilization in wireless communication and networks. The talk was held in Jan 2019.
• Lyapunov Optimization: An Introduction
  The slide is a simplified version of the monograph Lyapunov Optimization: An Introduction.
• Application of Optimization Methods and Edge AI
  In this slide, we review the methods used in MEC and discuss how to design a better system model with methods embedded naturally, Nov 2018.
• Towards an Intelligence Edge: Wireless Communication Meets Machine Learning
  In this slide, we study the learning-driven communication scheme, proposed in Towards an Intelligence Edge: Wireless Communication Meets Machine Learning, Sep 2018.
• Edge Intelligence: On-demand Deep Learning Model Co-inference with Device-edge Synergy
  In this slide, we study the Edgent framework, published in MECOMM'18.
• Models and Methods in Mobile Edge Computing Systems
  In this slide, we review the classic models and methods proposed in edge computing systems up to right now, Aug 2018.

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, 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.