Making Decisions at Data Plane Speeds
Pages 88 - 90
Abstract
Feedback control loops to implement self-driving networks constitute data collection to sense the network, and control algorithms to make decisions driving the network. Highquality data is necessary for smart decisions. Yet, highquality data is hard to obtain from the network data plane, due to insufficient visibility and large data volumes stemming from high packet rates. This paper distills principles to collect high-quality data arising from our own research experience: (i) filter and aggregate data as close to the source as possible; (ii) identify broad families of statistics that are measurable with bounded inaccuracy; (iii) don't assume lowlevel data plane software is easy to instrument, but instead (iv) apportion software flexibility by the time scales of the computation; and (v) prefer in-band approaches where possible for timely and efficient reactivity. We call the community to act upon these principles to leverage emerging opportunities using safely-extensible network stacks.
References
[1]
Prateesh Goyal et al. Elasticity detection: A building block for internet congestion control. In SIGCOMM, 2022.
[2]
Akshay Narayan et al. Restructuring endpoint congestion control. In SIGCOMM, 2018.
[3]
Srinivas Narayana et al. Language-directed hardware design for network performance monitoring. In SIGCOMM, 2017.
[4]
Bhavana Vannarth Shobhana et al. Load balancers need in-band feedback control. In ACM HotNets, 2022.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 02 October 2023
Published in SIGMETRICS Volume 51, Issue 2
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