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Towards a Non-intrusive Recognition of Anomalous System Behavior in Data Centers
- Source :
- Lecture Notes in Computer Science ISBN: 9783319105567, SAFECOMP Workshops
- Publication Year :
- 2014
- Publisher :
- Springer International Publishing, 2014.
-
Abstract
- In this paper we propose a monitoring system of a data center that is able to infer when the data center is getting into an anomalous behavior by analyzing the power consumption at each server and the data center network traffic. The monitoring system is non-intrusive in the sense that there is no need to install software on the data center servers. The monitoring architecture embeds two Elman Recurrent Networks (RNNs) to predict power consumed by each data center component starting from data center network traffic and viceversa. Results obtained along six mounts of experiments, within a data center, show that the architecture is able to classify anomalous system behaviors and normal ones by analyzing the error between the actual values of power consumption and network traffic and the ones inferred by the two RNNs.
- Subjects :
- Engineering
black box
critical infrastructure
data centers
dependability
failure prediction
monitoring
network traffic
non-intrusive
power consumption
Computer Science (all)
Theoretical Computer Science
business.industry
Real-time computing
Critical infrastructure
Power (physics)
Software
Black box
Component (UML)
Server
Dependability
Data center
business
Subjects
Details
- ISBN :
- 978-3-319-10556-7
- ISBNs :
- 9783319105567
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Computer Science ISBN: 9783319105567, SAFECOMP Workshops
- Accession number :
- edsair.doi.dedup.....b8d753d2e1a59039802b7fa042b9c1b2