Detection of SLA Violation for Big Data Analytics Applications in Cloud

Zeng, X; Garg, S; Barika, M; Bista, S; Puthal, D; Zomaya, AY; Ranjan, R

Detection of SLA Violation for Big Data Analytics Applications in Cloud

Zeng, X Garg, S Barika, M Bista, S Puthal, D

Zomaya, AY Ranjan, R

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Computers, 2021, 70, (5), pp. 746-758
Issue Date:: 2021-05-01

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Field	Value	Language
dc.contributor.author	Zeng, X
dc.contributor.author	Garg, S
dc.contributor.author	Barika, M
dc.contributor.author	Bista, S
dc.contributor.author	Puthal, D https://orcid.org/0000-0002-8332-278X
dc.contributor.author	Zomaya, AY
dc.contributor.author	Ranjan, R
dc.date.accessioned	2022-04-14T03:10:37Z
dc.date.available	2022-04-14T03:10:37Z
dc.date.issued	2021-05-01
dc.identifier.citation	IEEE Transactions on Computers, 2021, 70, (5), pp. 746-758
dc.identifier.issn	0018-9340
dc.identifier.issn	1557-9956
dc.identifier.uri	http://hdl.handle.net/10453/156245
dc.description.abstract	SLA violations do happen in real world. An SLA violation represents the failure of guaranteeing a service, which leads to unwanted consequences such as penalty payments, profit margin reduction, reputation degradation, customer churn and service interruptions. Hence, in the context of cloud-hosted big data analytics applications (BDAAs), it is paramount for providers to predict and prevent SLA violations. While machine learning-based techniques have been applied to detect SLA violations for web service or general cloud service, the study on detecting SLA violations dedicated for cloud-hosted BDAAs is still lacking. In this article, we propose four machine learning techniques and integrate 12 resampling methods to detect SLA violations for batch-based BDAAs in the cloud. We evaluate the efficiency of the proposed techniques in comparison with ideal and baseline classifiers based on a real-world trace dataset (Alibaba). Our work not only helps providers to choose the best performing prediction technique, but also provides them capabilities to uncover the hidden pattern of multiple configurations of BDAAs across layers.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Computers
dc.relation.isbasedon	10.1109/TC.2020.2995881
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 1006 Computer Hardware
dc.subject.classification	Computer Hardware & Architecture
dc.title	Detection of SLA Violation for Big Data Analytics Applications in Cloud
dc.type	Journal Article
utslib.citation.volume	70
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	1006 Computer Hardware
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-04-14T03:10:35Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	70
utslib.citation.issue	5

Abstract:

SLA violations do happen in real world. An SLA violation represents the failure of guaranteeing a service, which leads to unwanted consequences such as penalty payments, profit margin reduction, reputation degradation, customer churn and service interruptions. Hence, in the context of cloud-hosted big data analytics applications (BDAAs), it is paramount for providers to predict and prevent SLA violations. While machine learning-based techniques have been applied to detect SLA violations for web service or general cloud service, the study on detecting SLA violations dedicated for cloud-hosted BDAAs is still lacking. In this article, we propose four machine learning techniques and integrate 12 resampling methods to detect SLA violations for batch-based BDAAs in the cloud. We evaluate the efficiency of the proposed techniques in comparison with ideal and baseline classifiers based on a real-world trace dataset (Alibaba). Our work not only helps providers to choose the best performing prediction technique, but also provides them capabilities to uncover the hidden pattern of multiple configurations of BDAAs across layers.

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http://hdl.handle.net/10453/156245