Mining Invariants from SaaS Application Logs (Practical Experience Report)

Abstract

The increasing popularity of Software as a Service (SaaS) stresses the need of solutions to predict failures and avoid service interruptions, which invariably result in SLA violations and severe loss of revenue. A promising approach to continuously monitor the correct functioning of the system is to check the execution conformance to a set of invariants, i.e., properties that must hold when the system is deemed to run correctly. In this paper we propose a framework and a tool to automatically discover invariants from application logs and to online detect their violation. The framework has been applied on 9 months of log events from a real-world SaaS application. Results show that the proposed tool is able to automatically select 12 invariants with a stringent goodness of fit criteria out of more than 500 potential relationships. We also show the usefulness of our approach to detect runtime issues from logs in the form of violations of selected invariants, corresponding to silent errors that usually go unnoticed by the system maintenance personnel, even if they could represent symptoms of upcoming service failures.