Detection of SLA Violation for Big Data Analytics Applications in Cloud

Xuezhi Zeng, Saurabh Garg, Mutaz Barika, Sanat Bista, Deepak Puthal, Albert Y. Zomaya, Rajiv Ranjan

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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.

Original languageBritish English
Article number9097404
Pages (from-to)746-758
Number of pages13
JournalIEEE Transactions on Computers
Volume70
Issue number5
DOIs
StatePublished - 1 May 2021

Keywords

  • Big data
  • big data analytics application
  • machine learning
  • neural network
  • resampling
  • service layer
  • service level agreement
  • SLA violation

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