Phronesis, a diagnosis and recovery tool for system administrators

Abstract

The LHCb experiment relies on the Online system, which includes a very large and heterogeneous computing cluster. Ensuring the proper behavior of the different tasks running on the more than 2000 servers represents a huge workload for the small operator team and is a 24/7 task. At CHEP 2012, we presented a prototype of a framework that we designed in order to support the experts. The main objective is to provide them with steadily improving diagnosis and recovery solutions in case of misbehavior of a service, without having to modify the original applications. Our framework is based on adapted principles of the Autonomic Computing model, on Reinforcement Learning algorithms, as well as innovative concepts such as Shared Experience. While the submission at CHEP 2012 showed the validity of our prototype on simulations, we here present an implementation with improved algorithms and manipulation tools, and report on the experience gained with running it in the LHCb Online system. © Published under licence by IOP Publishing Ltd.