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

• 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]
• 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)
• 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)

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.