11/08/2020

Scouts: Improving the diagnosis process through domain-customized incident routing

Jiaqi Gao, Nofel Yaseen, Robert MacDavid, Felipe Vieira Frujeri, Vincent Liu, Ricardo Bianchini, Ramaswamy Aditya, Xiaohang Wang, Henry Lee, David Maltz, Minlan Yu, Behnaz Arzani

Keywords: Data center networks, Machine learning, Diagnosis

Abstract: Incident routing is critical for maintaining service level objectives in the cloud: the time-to-diagnosis can increase by 10x due to mis-routings. Properly routing incidents is challenging because of the complexity of today’s data center (DC) applications and their dependencies. For instance, an application running on a VM might rely on a functioning host-server, remote-storage service, and virtual and physical network components. It is hard for any one team, rule-based system, or even machine learning solution to fully learn the complexity and solve the incident routing problem. We propose a different approach using per-team Scouts. Each teams’ Scout acts as its gate-keeper — it routes relevant incidents to the team and routes-away unrelated ones. We solve the problem through a collection of these Scouts. Our PhyNet Scout alone — currently deployed in production — reduces the time-to-mitigation of 65

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