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Towards a non-intrusive recognition of anomalous system behavior in data centers

Towards a non-intrusive recognition of anomalous system behavior in data centers
Towards a non-intrusive recognition of anomalous system behavior in data centers
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.
Springer
Lombardi, Federico
78e41297-64c9-4c1e-9515-8eb59334a795
Lombardi, Federico
78e41297-64c9-4c1e-9515-8eb59334a795

Lombardi, Federico (2014) Towards a non-intrusive recognition of anomalous system behavior in data centers. In Lecture Notes in Computer Science: International Conference on Computer Safety, Reliability, and Security. vol. 8696, Springer.. (doi:10.1007/978-3-319-10557-4_38).

Record type: Conference or Workshop Item (Paper)

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.

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Towards a Non-intrusive Recognition of Anomalous System Behavior in Data Centers - Accepted Manuscript
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Published date: 2014

Identifiers

Local EPrints ID: 431255
URI: http://eprints.soton.ac.uk/id/eprint/431255
PURE UUID: 1ce98805-6c96-40ec-85f8-14427556d25b
ORCID for Federico Lombardi: ORCID iD orcid.org/0000-0001-6463-8722

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Date deposited: 28 May 2019 16:30
Last modified: 21 Nov 2021 16:37

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Author: Federico Lombardi ORCID iD

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