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Babel: A Generic Benchmarking Platform for Big Data Architectures
Big Data Research ( IF 2.673 ) Pub Date : 2021-01-07 , DOI: 10.1016/j.bdr.2021.100186
Lilia Sfaxi; Mohamed Mehdi Ben Aissa

In this era of big and fast data, software architects tend to find it really hard to make consistent decisions about which architecture and technologies are ideal for a certain business need. It is even harder to make them while dealing with the scarcity of clear methodologies, best practices and reference architectures. In this prospect, architecture evaluation through benchmarking can be of great interest, as it enables the detection of performance anomalies or bottlenecks as you go. The problem when talking about Big Data benchmarking, is that existing solutions remain technology-related, and do not deal with the heterogeneous aspect of complex architectures. In addition to that, businesses are in general dealing with multi-layered complex systems, involving various technologies, paradigms and micro-architectures. This means that the benchmarking solution must be able to give fine-grained insights about each of the layers. A successful benchmarking system must also be seamless, easy to use, scalable, and preferably cloud native. To satisfy these requirements, we designed and implemented Babel, a generic Big Data benchmarking platform, that insures an end-to-end performance evaluation and monitoring. We present in this paper the principles, architecture, integration and deployment steps of Babel.



中文翻译:

Babel:大数据架构的通用基准测试平台

在这个庞大而快速的数据时代,软件架构师往往很难做出一致的决定,即哪种架构和技术最适合特定业务需求。在应对缺乏清晰的方法论,最佳实践和参考体系结构的情况下,制作它们变得更加困难。在这种情况下,通过基准测试进行体系结构评估可能会引起人们极大的兴趣,因为它使您可以随时检测性能异常或瓶颈。在谈论大数据基准测试时,问题在于现有解决方案仍与技术相关,并且无法处理复杂体系结构的异构方面。除此之外,企业通常会处理涉及各种技术,范例和微体系结构的多层复杂系统。这意味着基准测试解决方案必须能够提供有关每个层的细粒度见解。成功的基准测试系统还必须是无缝的,易于使用的,可伸缩的,并且最好是云原生的。为了满足这些要求,我们设计并实现了Babel,这是一个通用的大数据基准测试平台,可确保端到端的性能评估和监控。我们在本文中介绍Babel的原理,体系结构,集成和部署步骤。

更新日期:2021-01-13
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