Detection of SLA Violation for Big Data Analytics Applications in Cloud

• Published in: IEEE transactions on computers Vol. 70; no. 5; pp. 746 - 758
• Main Authors: Zeng, Xuezhi, Garg, Saurabh, Barika, Mutaz, Bista, Sanat, Puthal, Deepak, Zomaya, Albert Y., Ranjan, Rajiv
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Big Data; big data analytics application; Cloud computing; Customer services; Data analysis; Electronic mail; Feature extraction; Machine learning; neural network; Predictive models; Resampling; service layer; service level agreement; SLA violation; Violations; Web services
• ISSN: 0018-9340; 1557-9956
• DOI: 10.1109/TC.2020.2995881

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.