Enabling Intelligent In-Network Computing for Cloud Systems

With the network infrastructure becoming highly programmable, it is time to rethink the role of networks in the cloud computing landscape beyond just packet delivery. The network itself emerges as a computing platform with a unique advantage of full network visibility. This project enables advanced approximate telemetry (e.g., sketches) with relevant applications on programmable networks (e.g., programmable switches, SmartNICs, and FPGAs) for cloud system management. Specifically, we will develop a cloud-native, approximate telemetry framework to offer low-overhead, fine-grained, real-time visibility into the underlying network traffic. Moving forward, this line of research with “intelligent” networking aims to provide insights and system abstractions to improve the performance, reliability, and security of cloud systems.

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Project Team
Principal Investigator: Alan (Zaoxing) Liu, Boston University
PhD Students: Peiqing Chen and Zeying Zhu

Undergraduate Student: Julia Hua

Repositories

• HYDRA: https://github.com/antonis-m/HYDRA_VLDB
• SketchLib: https://github.com/SketchLib

Presentations

• “Unleashing the Algorithmic Power of Approximate Computing Systems,” BU Systems Seminar, October 5, 2022
• “Enabling Intelligent In-Network Computing for Cloud Systems,” Red Hat Research Interest Group, April 5, 2022
• “Network Telemetry Systems,” BU systems seminar, Peiqing Chen, PhD talks, February 25, 2022
• “Approximate Graph Systems,” Rice University Computer Science Department Colloquium, 2022
• “On the Algorithmic Potential of Approximate Computing Systems,” Harvard University, Systems+Theory Seminar, 2022