Zongshun Zhang(张宗舜)

I am a third-year CS Ph.D. student at Boston University, advised by Professor Abraham Matta. I am interested in researching cloud resource-orchestration methods, recently focusing on the efficient usage of serverless platforms for ML(Neural Network) algorithms. I received my BS in Computer Science from University of Minnesota, Twin City in 2019. Go Gophers!!

zhangzs AT bu DOT edu  /  CV  /  Google Scholar  /  LinkedIn  /  Github

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Research

  • I am studying general resource provisioning methods in the central cloud and edge environments. At this point, I am experimenting with machine learning techniques like NN or SVM to guide the reaction or prediction in resource demand for provisioning. The experiments were using the Kubernetes cluster with Docker containers.
  • We are also developing novel architectures for deep neural networks while taking privacy into consideration.
  • I have the details of my publications and projects in the following table.
Cloud Resource Provisioning

(ing...)Studying cloud resource provisioning techniques in general. Experimenting to enhance Docker container scheduling, scaling, and load balancing performance over k8s.

"It turns out that we don't really need a model that fits very well for systems also..."

Effcient NN Training Systems

  • (ing...)Researching novel architectures for efficient deep neural networks training considering model and data privacy.

  • Our new architecture combines the efficiency of federated learning and source data privacy of split learning. Furthermore, our new system has lower resource demand and processing time comparing to the states of the arts. Our experiments show that this improvement has a linear relationship with the number of clients. This work has been published at IEEE CLOUD2021 and a poster at CoNEXT2020.

  • Valeria Turina, Zongshun Zhang, Flavio Esposito, and Ibrahim Matta. Federated or Split? A Performance and Privacy Analysis of Hybrid Split and Federated Learning Architectures.In IEEE International Conference on Cloud Computing (IEEE CLOUD), September 2021.

  • Valeria Turina, Zongshun Zhang, Flavio Esposito, and Ibrahim Matta. Combining split and federated architectures for efficiency and privacy in deep learning. In Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies (CoNEXT 2020), Barcelona, Spain, December 2020.

LIBRA
  • By load balancing between FaaS and IaaS platforms with a low overhead statistical model, we are able to reduce 85% SLA violations and 20% cost for any general cloud application comparing to the states of the arts. We got the best paper award at IEEE IC2E for this project.

  • Ali Raza, Zongshun Zhang, Nabeel Akhtar, Vatche Isahagian, and Ibrahim Matta.2021. LIBRA: An Economical Hybrid Approach for Cloud Application with Strict SLAs (doi). In Proceedings of the 9th IEEE International Conference on Cloud Engineering (IEEE IC2E), October 2021. (Best Paper Award!)

Teaching

CS 655
Graduate Introduction to Computer Networks.
Fall 2021.
Teaching Fellow.

  • Office Hours
    • Wed 9:30am - 11:00am Boston Time
    • Thu 9:30am - 11:00am Boston Time
  • Lab Sections
    • A2 Fri 1:25pm - 2:15pm Boston Time
    • A4 Fri 6:30pm - 7:20pm Boston Time

CS 101
Introduction to Computing.
Fall 2019.
Teaching Fellow.



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