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Schooling NOOBs with eBPF

Published:10 September 2023Publication History

ABSTRACT

While networks have evolved in profound ways, the tools to measure them from end hosts have not kept pace. State-of-the-art tools are ill-suited for elucidating observed network performance impairments and path dynamics, and are susceptible to operational policies of the network. Consequently, the semantic gap between the application-view of network performance vs. actual conditions has resulted in network oblivious (NOOB) systems and applications.

To address this NOOB problem, we examine the Extended Berkeley Packet Filter (eBPF) as a new way to improve the practice of gathering fine-grained network telemetry from the edge. More specifically, by leveraging the safe and efficient in-kernel programming mechanism of eBPF, we design a high-performance telemetry framework called nooBpf with two tools---namely noobprobe and noobflow---to quantify the actual network performance from end hosts and offer unprecedented insights into the flow-level performance, including in-network queuing and routing-induced delays. We illustrate the potential of these two tools to address the NOOB problem through a variety of experiments. The results of our experiments strongly suggest eBPF as a promising foundation for high-performance telemetry and for addressing the NOOB problem.

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                • Published in

                  cover image ACM Conferences
                  eBPF '23: Proceedings of the 1st Workshop on eBPF and Kernel Extensions
                  September 2023
                  96 pages
                  ISBN:9798400702938
                  DOI:10.1145/3609021

                  Copyright © 2023 ACM

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                  Publication History

                  • Published: 10 September 2023

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                  eBPF '23 Paper Acceptance Rate12of21submissions,57%Overall Acceptance Rate12of21submissions,57%
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