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.

中文翻译：

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云中大数据分析应用的 SLA 管理

近年来见证了大数据分析应用（BDAAs）的蓬勃发展。这种趋势为揭示数据中嵌入的潜在模式和相关性提供了无与伦比的机会，从而可以做出富有成效的决策。由于众所周知的大数据的高维和规模，这在以前是一个巨大的挑战，而供应商提供的服务质量是第一要务。由于 BDAA 经常部署在具有极大复杂性和不确定性的云上，因此管理服务水平协议 (SLA) 以保证高质量的服务是一项关键任务。本研究对云托管的 BDAA 的 SLA 特定管理的最新技术进行了系统的文献回顾。该评论以 SLA 为中心，调查了沿着这个方向的挑战和当代方法。提出了一种研究分类法来制定系统文献综述的结果。定义了一个新的概念 SLA 模型，并在此基础上提出了一个多维分类方案，以应用 SLA 度量来深入理解管理 SLA 和未来研究趋势的动机。