Packet-Level Analytics in Software without Compromises

Authors: 

Oliver Michel, University of Colorado Boulder; John Sonchack, University of Pennsylvania; Eric Keller, University of Colorado Boulder; Jonathan M. Smith, University of Pennsylvania

Abstract: 

Traditionally, network monitoring and analytics systems rely on aggregation (e.g., flow records) or sampling to cope with the high data rates large-scale networks operate on. This has the downside that, in doing so, we lose data granularity and accuracy, and in general limit the possible network analytics we can perform. Recent proposals leveraging software-defined networking or programmable hardware provide more fine-grained, per-packet monitoring but still are based on the fundamental principle of data reduction before being processed. In this paper, we provide a first step towards a cloud-scale, packet-level monitoring and analytics system based on stream processing entirely in software. Software provides virtually unlimited programmability and makes modern (e.g., machine-learning) network analytics applications possible. We identify unique features of network analytics applications which enable the specialization of stream processing systems. As a result, an evaluation with our preliminary implementation shows that we can scale up to several million packets per second per core and together with load balancing and further optimizations, the vision of cloud-scale per-packet network analytics is possible.

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BibTeX
@inproceedings {216847,
author = {Oliver Michel and John Sonchack and Eric Keller and Jonathan M. Smith},
title = {Packet-Level Analytics in Software without Compromises},
booktitle = {10th {USENIX} Workshop on Hot Topics in Cloud Computing (HotCloud 18)},
year = {2018},
address = {Boston, MA},
url = {https://www.usenix.org/conference/hotcloud18/presentation/michel},
publisher = {{USENIX} Association},
}