NDLI: Mining Invariants from SaaS Application Logs (Practical Experience Report)

Content Provider	IEEE Xplore Digital Library
Author	Sarkar, S. Ganesan, R. Cinque, M. Frattini, F. Russo, S. Savignano, A.
Copyright Year	2014
Description	Author affiliation: Infosys Labs., Bangalore, India (Sarkar, S.; Ganesan, R.) \|\| Univ. degli Studi di Napoli Federico II, Naples, Italy (Cinque, M.; Frattini, F.; Russo, S.; Savignano, A.)
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.
Starting Page	50
Ending Page	57
File Size	938447
Page Count	8
File Format	PDF
ISBN	9781479938049
DOI	10.1109/EDCC.2014.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-13
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	time coalescence Software as a service Time series analysis SaaS invariant mining Data mining Personnel Transient analysis Monitoring failure detection
Content Type	Text
Resource Type	Article

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