I’m an assistant professor at the Department of Computer Science, Memorial University of Newfoundland (MUN). Before joining MUN, I worked as a Post Doctoral Fellow at the University of Victoria, Canada with Prof. Jianping Pan. I received my Ph.D. degree in the School of Information Science and Technology, Central South University, Changsha, China, in June 2019. During 2016–2018, I was a research assistant at the University of Victoria, Canada. My current research areas include distributed cloud/edge computing and storage networks, data center networks, and distributed machine learning, with a special focus on the analysis and optimization of data-intensive services. I’m a Senior Member of IEEE.

🔥 News

• 2024.07  🎉🎉 Dr. Liu has been elevated to IEEE Senior Member.
• 2024.04  🎉🎉 Our paper on sampling-based multi-job placement for heterogeneous deep learning clusters is accepted by IEEE Transactions on Parallel and Distributed Systems.
• 2024.04  🎉🎉 Dr. Liu has received the prestigious 5-Year NSERC Discovery Grants and the Discovery Launch Supplement Award.
• 2023.08  🎉🎉 Our paper on sampling-based caching in distributed coded storage system is accepted by IEEE Transactions on Services Computing.
• 2023.08  🎉🎉 Our paper on VM/NFV migration in data center networks is accepted by IEEE GLOBECOM’23.
• 2023.07  🎉🎉 Our paper on learning-based congestion control is accepted by IEEE ICDCS’23.
• 2022.04  🎉🎉 Our paper on adaptive and scalable caching in distributed coded storage system is accepted by IEEE Transactions on Cloud Computing.
• 2022.03  🎉🎉 Our paper on distributed energy management is accepted by Sustainable Energy, Grids and Networks.
• 2021.11  🎉🎉 Our paper on optimal caching in distributed coded storage system is accepted by IEEE/ACM Transactions on Networking.
• 2020.12  🎉🎉 I win the IEEE Technical Committee on Cloud Computing (TCCLD) Outstanding Ph.D. Thesis Award.
• 2020.12  🎉🎉 I win the Outstanding Ph.D. Thesis Award at Central South University.
• 2020.11  🎉🎉 Our paper on abnormal sound event detection is accepted by CCF Transactions on Networking.
• 2020.01  🎉🎉 Our paper on data replica placement in data center networks is accepted by IEEE Transactions on Parallel and Distributed Systems.
• 2019.11  🎉🎉 Our paper on mobile-to-mobile edge computing is accepted by IEEE Internet of Things Journal.
• 2019.09  🎉🎉 Our paper on data placement in data center networks is accepted by IEEE Transactions on Cloud Computing.
• 2019.03  🎉🎉 Our paper on wireless charging and mobile data collection in WSNs is accepted by IEEE Transactions on Vehicular Technology.
• 2018.07  🎉🎉 Our paper on learning-based abnormal sound events for city surveillance is accepted by IEEE IPCCC’18 (Acceptance Rate: 28.8%).
• 2018.07  🎉🎉 Our paper on learning-based data placement is accepted by IEEE LCN’18 and nominated as the best paper candidate (Acceptance Rate: under 30%).
• 2018.07  🎉🎉 Our paper on learning-based mobility management for mobile edge computing is accepted by IEEE GLOBECOM’18 (Acceptance Rate: 38%).

🔈 Open Positions

• For Prospective Students：I currently have open M.Sc./Ph.D. positions for highly self-motivated graduate students. Areas of research include Network Intelligence for Next-generation Cloud Computing, Protocols Design for Advanced Networking, and Distributed Machine Learning. If interested, please send me by email your CV, transcripts, sample publications (if any), and TOFEL/IELTS test results, with the email subject line “Prospective [M.Sc./Ph.D.] Student - [Your Name]”. Admitted Ph.D./M.Sc. students will be provided with full financial support. Admission to the Ph.D. or the M.Sc. program is mainly granted for the Fall intake semesters (application deadline: December 1st). Please refer to the application guides on the Faculty of Graduate Studies website for M.Sc. and Ph.D. programs. Visiting scholars/students are also welcomed worldwide.

🔈 Research

Research programs

• AI for Cloud Computing. We will explore how AI techniques can be applied and further improved in the network optimization domain, especially considering the heterogeneity and complexity of large-scale cloud data centers. The production systems rely on handcrafted heuristics, which are simple and easy to implement. However, the problem-specific heuristics rest on certain assumptions and predefined actions to certain events, which do not generalize well to changing environments. As a replacement for heuristics, existing clean-slate learning-based solutions have been proposed, which, however, raise new concerns such as high overhead, slow convergence, and bad performance over unforeseen conditions. We will combine traditional optimization strategies with machine learning and propose a set of practical and evolutionary learning-based solutions to achieve high throughput, low latency and overhead for next-generation cloud computing.

• Cloud Computing for AI. We will also examine how cloud data centers can be improved to better support AI applications in three dimensions: computation, communication, and network topology. As of today, AI training systems are predominantly built on top of traditional data center clusters due to their scalability, reliability, and cost-effectiveness. However, cloud computing was initially designed to achieve reliable resource sharing without optimization for specific applications. The network topology and the protocol stack used in cloud data centers need to be revolutionized to better support AI applications. We plan to co-optimize network topology, protocol, and parallelization strategy for accelerating the training of AI applications in cloud data centers.

Research Fundings

• NSERC (Discovery Grants and Discovery Launch Supplement)

• MUN (Dean of Science Research Start-Up Grant)

📝 Publications

Selected Peer-reviewed Journal Publications

• TPDS 2024 Sampling-based Multi-job Placement for Heterogeneous Deep Learning Clusters
  Kaiyang Liu, Jingrong Wang, Zhiming Huang, and Jianping Pan. IEEE Transactions on Parallel and Distributed Systems, vol. 35, no. 6, pp. 874-888, 2024.

• TSC 2023 Sampling-based Caching for Low Latency in Distributed Coded Storage Systems
  Kaiyang Liu, Jingrong Wang, Heng Li, Jun Peng, and Jianping Pan. IEEE Transactions on Services Computing, vol. 16, no. 6, 4275-4287, 2023.

• TCC 2023 Adaptive and scalable caching for low latency in distributed coded storage systems
  Kaiyang Liu, Jun Peng, Jingrong Wang, Zhiwu Huang, and Jianping Pan. IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp. 1840-1853, 2023.

• SEGN 2022 Distributed energy management scheme based on hybrid ADMM and extended optimization horizon for energy Internet
  Yijun Cheng, Jun Peng, Kaiyang Liu, Fu Jiang, Yue Wu, and Zhiwu Huang. Sustainable Energy, Grids and Networks, vol. 31, 2022.

• TCC 2022 A Learning-based Data Placement Framework for Low Latency in Data Center Networks
  Kaiyang Liu, Jun Peng, Jingrong Wang, Boyang Yu, Zhuofan Liao, Zhiwu Huang, and Jianping Pan. IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 146–157, 2022.

• ToN 2022 Optimal caching for low latency in distributed coded storage systems
  Kaiyang Liu, Jun Peng, Jingrong Wang, and Jianping Pan. IEEE/ACM Transactions on Networking, vol. 30, no. 3, pp. 1132–1145, 2022.

• TSC 2021 An Instance Reservation Framework for Cost Effective Services in Geo-Distributed Data Centers
  Kaiyang Liu, Jun Peng, Boyang Yu, Weirong Liu, Zhiwu Huang, and Jianping Pan. IEEE Transactions on Services Computing, vol. 14, no. 2, pp. 356–370, 2021.

• TPDS 2020 Scalable and Adaptive Data Replica Placement for Geo-distributed Cloud Storages
  Kaiyang Liu, Jun Peng, Jingrong Wang, Weirong Liu, Zhiwu Huang, and Jianping Pan. IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 7, pp. 1575–1587, 2020.

• IoT-J 2020 Online UAV-mounted Edge Server Dispatching for Mobile-to-Mobile Edge Computing
  Jingrong Wang, Kaiyang Liu, and Jianping Pan. IEEE Internet of Things Journal, vol. 7, no. 2, pp. 1375–1386, 2020.

• CCFToN 202 Cooperative abnormal sound event detection in end-edge-cloud orchestrated systems
  Jingrong Wang, Kaiyang Liu, and Jianping Pan. CCF Transactions on Networking, vol. 3, pp. 158–170, 2020.

• TVT 2019 An Active Mobile Charging and Data Collection Scheme for Clustered Sensor Networks
  Kaiyang Liu, Jun Peng, Liang He, Jianping Pan, Shuo Li, Ming Ling, and Zhiwu Huang. IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 5100–5113, 2019.

• FGCS 2016 Multi-device Task Offloading with Time-constraints for Energy Efficiency in Mobile Cloud Computing
  Kaiyang Liu, Jun Peng, Heng Li, Xiaoyong Zhang, and Weirong Liu. Future Generation Computer Systems, vol. 64, pp. 1–14, 2016.

Selected Peer-reviewed Conference Publications

• GLOBECOM 2023 Energy-aware Inter-Data Center VM Migration over Elastic Optical Networks
  Fatima S. Amri, Zhiming Huang, Kaiyang Liu, and Jianping Pan. IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023.

• ICDCS 2023End-to-End Congestion Control as Learning for Unknown Games with Bandit Feedback
  Zhiming Huang, Kaiyang Liu, and Jianping Pan. The 43rd IEEE International Conference on Distributed Computing Systems, Hong Kong, China, 2023.

• IPCCC 2018 Learning-based Cooperative Sound Event Detection with Edge Computing
  Jingrong Wang, Kaiyang Liu, George Tzanetakis, and Jianping Pan.
  The 37th IEEE International Performance Computing and Communications Conference, Orlando, Florida, USA, 2018.

• LCN 2018 Learning-based Adaptive Data Placement for Low Latency in Data Center Networks (Best Paper Candidate)
  Kaiyang Liu, Jingrong Wang, Zhuofan Liao, Boyang Yu, and Jianping Pan. The 43nd IEEE Conference on Local Computer Networks, Chicago, USA, 2018.

• GLOBECOM 2018Learning Based Mobility Management under Uncertainties for Mobile Edge Computing
  Jingrong Wang, Kaiyang Liu, Minming Ni, and Jianping Pan. IEEE Global Communications Conference, Abu Dhabi, United Arab Emirate (UAE), 2018.

• GLOBECOM 2016A Combinatorial Optimization for Energy-Efficient Mobile Cloud Offloading over Cellular Networks
  Kaiyang Liu, Jun Peng, Xiaoyong Zhang, and Zhiwu Huang. IEEE Global Communications Conference, Washington, DC, USA, 2016.

• GLOBECOM 2014Dynamic Resource Reservation via Broker Federation in Cloud Service: A Fine-grained Heuristic-based Approach
  Kaiyang Liu, Jun Peng, Weirong Liu, Pingping Yao, and Zhiwu Huang. IEEE Global Communications Conference, Austin, USA, 2014.

🎖 Honors and Awards

• 2021.01 Outstanding Ph.D. Thesis Award at Central South University
• 2020.12 IEEE Technical Committee on Cloud Computing(TCCLD) Outstanding Ph.D. Thesis Award
• 2018.10 IEEE Conference on Local Computer Networks (LCN) Best Paper Award Finalist
• 2016.12 - 2018.12 CSC-UVic Fellowship
• 2016.12 China National Scholarship for Graduate Students
• 2012.06 Outstanding Graduates at Central South University
• 2011.08 Champion of RoboCup Rescue League, Team CSU Yunlu, Robot Soccer World Cup

📖 Educations

• 2014.09 - 2019.06 Ph.D. in School of Information Science and Technology, Central South University, Changsha, China
• 2016.12 - 2018.12 Research Assistant in Computer Science, University of Victoria, Victoria, BC, Canada
• 2012.09 - 2014.08 MSc. in School of Information Science and Technology, Central South University, Changsha, China
• 2008.09 - 2012.06 B.Eng. in School of Information Science and Technology, Central South University, Changsha, China

📑 Teaching

• Fall 2024 COMP 4759 - Computer Networks
  • Instructor, Department of Computer Science, Memorial University of Newfoundland, Canada
• Fall 2023 COMP 4759/6777 - Computer Networks/Mobile Ad Hoc Networking
  • Instructor, Department of Computer Science, Memorial University of Newfoundland, Canada
• Fall 2022/Spring 2021 CSC 466/579 - Advanced Computer Networks
  • Co-Instructor, Department of Computer Science, University of Victoria, Canada
• Spring 2022 CSC 360 - Operating Systems
  • Tutorial Instructor, Department of Computer Science, University of Victoria, Canada
• Fall 2022 CSC 361 - Computer Communication and Networks
  • Guest Lecturer, Department of Computer Science, University of Victoria, Canada
• Spring 2020 CSC 466/579 - Advanced Computer Networks
  • Guest Lecturer, Department of Computer Science, University of Victoria, Canada