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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 paper, 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.

Item Type:	Article
Authors/Creators:	Zeng, X and Garg, SK and Barika, M and Bista, S and Puthal, D and Zomaya, A and Ranjan, R
Keywords:	big data, big data, analytics application, service level agreement, machine learning, resampling, service layer, SLA violation, neural network, cloud computing, feature extraction, web services, electronic mail, predictive models
Journal or Publication Title:	IEEE Transactions on Computers
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1557-9956
DOI / ID Number:	10.1109/TC.2020.2995881
Copyright Information:	Copyright 2020 IEEE
Related URLs:	Google Scholar: Garg, SK UTAS Profile: Garg, SK
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