(Submitted on 7 Apr 2020)

Abstract: Diagnosing problems in Internet-scale services remains particularly difficult
and costly for both content providers and ISPs. Because the Internet is
decentralized, the cause of such problems might lie anywhere between an
end-user’s device and the service datacenters. Further, the set of possible
problems and causes is not known in advance, making it impossible in practice
to train a classifier with all combinations of problems, causes and locations.
In this paper, we explore how different machine learning techniques can be used
for Internet-scale root cause analysis using measurements taken from end-user
devices. We show how to build generic models that (i) are agnostic to the
underlying network topology, (ii) do not require to define the full set of
possible causes during training, and (iii) can be quickly adapted to diagnose
new services. Our solution, DiagNet, adapts concepts from image processing
research to handle network and system metrics. We evaluate DiagNet with a
multi-cloud deployment of online services with injected faults and emulated
clients with automated browsers. We demonstrate promising root cause analysis
capabilities, with a recall of 73.9% including causes only being introduced at
inference time.