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Lookup NU author(s): Dr Mutaz Barika, Dr Deepak PuthalORCiD, Professor Raj Ranjan
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2021.
For re-use rights please refer to the publisher's terms and conditions.
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.
Author(s): Zeng X, Garg S, Barika M, Bista S, Puthal D, Zomaya A, Ranjan R
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Computers
Year: 2021
Volume: 70
Issue: 5
Pages: 746-758
Print publication date: 01/05/2021
Online publication date: 20/05/2020
Acceptance date: 10/05/2020
Date deposited: 11/02/2021
ISSN (print): 0018-9340
ISSN (electronic): 1557-9956
Publisher: IEEE
URL: https://doi.org/10.1109/TC.2020.2995881
DOI: 10.1109/TC.2020.2995881
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