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:	Refereed Article
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
Research Division:	Information and Computing Sciences
Research Group:	Distributed computing and systems software
Research Field:	Cloud computing
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Computer systems
UTAS Author:	Garg, SK (Dr Saurabh Garg)
UTAS Author:	Barika, M (Mr Mutaz Barika)
ID Code:	142783
Year Published:	2020
Deposited By:	Information and Communication Technology
Deposited On:	2021-02-11
Last Modified:	2021-08-05
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