Alan Zaoxing Liu

  Alan Zaoxing Liu

      Assistant Professor

Scalable Learning from Distributed Data for Wireless Network Management

The transition to 5G is expected to witness not only an emergence of new applications such as mobile augmented and virtual reality, but also opens up the attack surface to both known, and previously unknown threats. Thus, wireless networks of the future will need better control and management at different temporal and traffic aggregation granularities (e.g., how to allocate spectrum, how to quarantine distributed attacks etc.). This project aims to develop scalable, machine learning based analytics on the data from a large set of geographically distributed wireless core network entities such as base stations. The research will enable new approaches for: (a) compressing the raw data via novel summaries and sketches, that reduce overhead while simultaneously enabling highly accurate scalable analytics (b) scalable yet highly flexible distributed learning approaches that are built upon the emerging federated learning paradigm and (c) flexible allocation of bandwidth to support the control plane analytics that minimizes the impact on the data plane.

The proposed research outcomes will be systems, algorithms, and data analytics workflows that will inform the design and management of next generation critical wireless infrastructures. The approaches developed will enable ISPs to better apportion resources and enable better performance for emerging augmented reality applications for societal benefit (e.g., disaster response and management). In addition, the approaches can enable the discovery and profiling of new threats, which will have significant implications on national security. The proposed education activities are expected to provide students with a comprehensive training in networking, security, system building, and data science. Thus, there is significant potential for broader impact in terms of contributions to workforce development in an area of national need.

Funding Source: NSF CNS-2106946, 10/1/2021 to 9/30/2025

People

  • BU PI: Alan (Zaoxing) Liu
  • All PIs: Srikanth Krishnamurthy (Lead, UCI), Evangelos Papalexakis (UCI), Vladimir Braverman (Rice), Vyas Sekar (CMU)
  • PhD Students: Yajie Zhou (BU), Antonis Manousis (CMU)
  • Ugrad Students: Nengneng Yu, Haoming Yi

Publications

[NSDI] Sketchovsky: Enabling Ensembles of Sketches on Programmable Switches
Hun Namkung, Zaoxing Liu, Daehyeok Kim, Vyas Sekar, Peter Steenkiste
in USENIX NSDI’23

[VLDB] Panakos: Chasing the Tails for Multidimensional Data Streams
Fuheng Zhao, Punnal Ismail Khan, Divyakant Agrawal, Amr El Abbadi, Arpit Gupta, Zaoxing Liu
in VLDB’23

[VLDB] Enabling Efficient and General Subpopulation Analytics In Multidimensional Data Streams
Antonis Manousis, Zhuo Cheng, Ran Ben Basat, Zaoxing Liu, Vyas Sekar
in VLDB’22

[NSDI] SketchLib: Enabling Efficient Sketch-based Monitoring on Programmable Switches
Hun Namkung, Zaoxing Liu, Daehyeok Kim, Vyas Sekar, Peter Steenkiste
in USENIX NSDI’22

[IMC] Precise Error Estimation for Sketch-based Flow Measurement
Peiqing Chen, Yuhan Wu, Tong Yang, Junchen Jiang, Zaoxing Liu
in ACM/SIGCOMM IMC’21

Software

[Hydra] Github

[Sketchovsky] Github