Papers Papers/2022 Papers Papers/2022



Paper Unlock

Authors: Sakil Barbhuiya 1 ; Zafeirios Papazachos 2 ; Peter Kilpatrick 2 and Dimitrios S. Nikolopoulos 2

Affiliations: 1 Queen's University of Belfast, United Kingdom ; 2 Queen’s University of Belfast, United Kingdom

Keyword(s): Anomaly Detection, Cloud Computing, Data Centres, Monitoring, Correlation.

Related Ontology Subjects/Areas/Topics: Big Data Cloud Services ; Cloud Application Architectures ; Cloud Application Scalability and Availability ; Cloud Applications Performance and Monitoring ; Cloud Computing ; Platforms and Applications

Abstract: Cloud data centres are critical business infrastructures and the fastest growing service providers. Detecting anomalies in Cloud data centre operation is vital. Given the vast complexity of the data centre system software stack, applications and workloads, anomaly detection is a challenging endeavour. Current tools for detecting anomalies often use machine learning techniques, application instance behaviours or system metrics distribution, which are complex to implement in Cloud computing environments as they require training, access to application-level data and complex processing. This paper presents LADT, a lightweight anomaly detection tool for Cloud data centres that uses rigorous correlation of system metrics, implemented by an efficient correlation algorithm without need for training or complex infrastructure set up. LADT is based on the hypothesis that, in an anomaly-free system, metrics from data centre host nodes and virtual machines (VMs) are strongly correlated. An anomal y is detected whenever correlation drops below a threshold value. We demonstrate and evaluate LADT using a Cloud environment, where it shows that the hosting node I/O operations per second (IOPS) are strongly correlated with the aggregated virtual machine IOPS, but this correlation vanishes when an application stresses the disk, indicating a node-level anomaly. (More)


Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Barbhuiya, S.; Papazachos, Z.; Kilpatrick, P. and S. Nikolopoulos, D. (2015). A Lightweight Tool for Anomaly Detection in Cloud Data Centres. In Proceedings of the 5th International Conference on Cloud Computing and Services Science - CLOSER; ISBN 978-989-758-104-5; ISSN 2184-5042, SciTePress, pages 343-351. DOI: 10.5220/0005453403430351

author={Sakil Barbhuiya. and Zafeirios Papazachos. and Peter Kilpatrick. and Dimitrios {S. Nikolopoulos}.},
title={A Lightweight Tool for Anomaly Detection in Cloud Data Centres},
booktitle={Proceedings of the 5th International Conference on Cloud Computing and Services Science - CLOSER},


JO - Proceedings of the 5th International Conference on Cloud Computing and Services Science - CLOSER
TI - A Lightweight Tool for Anomaly Detection in Cloud Data Centres
SN - 978-989-758-104-5
IS - 2184-5042
AU - Barbhuiya, S.
AU - Papazachos, Z.
AU - Kilpatrick, P.
AU - S. Nikolopoulos, D.
PY - 2015
SP - 343
EP - 351
DO - 10.5220/0005453403430351
PB - SciTePress