Measuring performance quality scenarios in big data analytics applications: a DevOps and domain-specific model approach
Measuring performance quality scenarios in big data analytics applications: a DevOps and domain-specific model approach
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DOI：
10.1145/3344948.3344986
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发表时间：
2019-09
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影响因子：
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通讯作者：
Camilo Castellanos;Carlos A. Varela;Darío Correal
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文献类型：
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作者：
Camilo Castellanos;Carlos A. Varela;Darío Correal
大数据分析（BDA）应用程序使用高级分析算法从大型、快速和异构的数据源中提取有价值的见解。这些复杂的BDA应用程序需要软件设计、开发和部署策略来处理数量、速度和多样性（3vs），同时保持预期的性能水平。BDA软件的复杂性经常导致延迟部署、更长的开发周期和具有挑战性的性能监控。本文提出了一种DevOps和特定领域模型（DSM）方法，用于设计，部署和监控BDA应用程序中的性能质量场景（QS）。这种方法使用高级抽象来描述部署策略和支持性能监视的QS。我们的实验比较了BDA应用程序的开发，部署和QS监控的工作与近空中碰撞（NMAC）检测的两个用例。这些用例包括不同的性能QS、处理模型和部署策略。我们的研究结果表明，更短的（重新）部署周期和实现的延迟和最后期限QS的微批处理和批处理。
Big data analytics (BDA) applications use advanced analysis algorithms to extract valuable insights from large, fast, and heterogeneous data sources. These complex BDA applications require software design, development, and deployment strategies to deal with volume, velocity, and variety (3vs) while sustaining expected performance levels. BDA software complexity frequently leads to delayed deployments, longer development cycles and challenging performance monitoring. This paper proposes a DevOps and Domain Specific Model (DSM) approach to design, deploy, and monitor performance Quality Scenarios (QS) in BDA applications. This approach uses high-level abstractions to describe deployment strategies and QS enabling performance monitoring. Our experimentation compares the effort of development, deployment and QS monitoring of BDA applications with two use cases of near mid-air collisions (NMAC) detection. The use cases include different performance QS, processing models, and deployment strategies. Our results show shorter (re)deployment cycles and the fulfillment of latency and deadline QS for micro-batch and batch processing.