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SLA management for big data analytical applications in clouds: a taxonomy study

Zeng, X, Garg, S ORCID: 0000-0003-3510-2464, Barika, M, Zomaya, AY, Wang, L, Villari, M, Chen, D and Ranjan, R 2020 , 'SLA management for big data analytical applications in clouds: a taxonomy study' , ACM Computing Surveys, vol. 53, no. 3 , pp. 1-40 , doi: 10.1145/3383464.

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Abstract

Recent years have witnessed the booming of big data analytical applications (BDAAs). This trend provides unrivaled opportunities to reveal the latent patterns and correlations embedded in the data, and thus productive decisions may be made. This was previously a grand challenge due to the notoriously high dimensionality and scale of big data, whereas the quality of service offered by providers is the first priority. As BDAAs are routinely deployed on Clouds with great complexities and uncertainties, it is a critical task to manage the service level agreements (SLAs) so that a high quality of service can then be guaranteed. This study performs a systematic literature review of the state of the art of SLA-specific management for Cloud-hosted BDAAs. The review surveys the challenges and contemporary approaches along this direction centering on SLA. A research taxonomy is proposed to formulate the results of the systematic literature review. A new conceptual SLA model is defined and a multi-dimensional categorization scheme is proposed on its basis to apply the SLA metrics for an in-depth understanding of managing SLAs and the motivation of trends for future research.

Item Type: Article
Authors/Creators:Zeng, X and Garg, S and Barika, M and Zomaya, AY and Wang, L and Villari, M and Chen, D and Ranjan, R
Keywords: general and reference, surveys and overviews, information systems, data analytics, online analytical processing, computer systems organization, cloud computing, big data, analytics application, service level agreement, service layer, SLA metrics, SLA
Journal or Publication Title: ACM Computing Surveys
Publisher: Assoc Computing Machinery
ISSN: 0360-0300
DOI / ID Number: 10.1145/3383464
Copyright Information:

© 2020 Association for Computing Machinery

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