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Helastic: On combining threshold-based and Serverless elasticity approaches for optimizing the execution of bioinformatics applications
Journal of Computational Science ( IF 3.1 ) Pub Date : 2021-06-17 , DOI: 10.1016/j.jocs.2021.101407
Mateus Rauback Aubin , Rodrigo da Rosa Righi , Victor Hugo Valiati , Cristiano André da Costa , Rodolfo Stoffel Antunes , Guilherme Galante

Recent advances in Next Generation Gene Sequencing (NGS) technologies brought an abundance of bioinformatics and phylogenetics data. The available datasets create new opportunities for studies about the genetic relationships among organisms, which previously relied mainly on manual observations. The state-of-the-art shows that the software employed in this area is based on technologically outdated solutions with ample space for adopting modern computing techniques such as cloud resource elasticity and dynamic load balancing. This article aims to fill this gap with the proposal of Helastic, a model to explore cloud elasticity on jModelTest. The latter is a widely used software for performing statistical selection of nucleotide replacement models in phylogenetic analyzes. Helastic’s contributions appear in a dual elasticity layer that combines the traditional threshold-based, reactive approach with Serverless (also referred to in the literature as Function-as-a-Service, or FaaS). Design decisions include interoperability as a requirement, enabling existing jModelTest applications to benefit from Helastic without significant code changes. We evaluate our proposal through a prototype, which was tested on both elastic and non-elastic scenarios. Data regarding execution time and resource usage are presented in this article. Results demonstrate our solution’s feasibility and the benefits of working with a dual-elasticity approach rather than a single resource rearrangement technique.



中文翻译:

Helastic:结合基于阈值和无服务器的弹性方法来优化生物信息学应用程序的执行

下一代基因测序 (NGS) 技术的最新进展带来了大量的生物信息学和系统发育数据。可用的数据集为研究生物之间的遗传关系创造了新的机会,这些关系以前主要依赖于人工观察。最先进的技术表明,该领域采用的软件基于技术过时的解决方案,有足够的空间采用云资源弹性和动态负载平衡等现代计算技术。本文旨在通过 Helastic 的提议填补这一空白,Helastic 是一种在 jModelTest 上探索云弹性的模型。后者是一种广泛使用的软件,用于在系统发育分析中对核苷酸替换模型进行统计选择。Helastic 的贡献出现在双重弹性层中,该层将传统的基于阈值的反应式方法与无服务器(在文献中也称为功能即服务或 FaaS)相结合。设计决策包括互操作性作为一项要求,使现有的 jModelTest 应用程序能够从 Helastic 中受益,而无需对代码进行重大更改。我们通过原型评估我们的提案,该原型在弹性和非弹性场景中进行了测试。本文提供了有关执行时间和资源使用情况的数据。结果证明了我们的解决方案的可行性以及使用双弹性方法而不是单一资源重排技术的好处。设计决策包括互操作性作为一项要求,使现有的 jModelTest 应用程序能够从 Helastic 中受益,而无需对代码进行重大更改。我们通过原型评估我们的提案,该原型在弹性和非弹性场景中进行了测试。本文提供了有关执行时间和资源使用情况的数据。结果证明了我们的解决方案的可行性以及使用双弹性方法而不是单一资源重排技术的好处。设计决策包括互操作性作为一项要求,使现有的 jModelTest 应用程序能够从 Helastic 中受益,而无需对代码进行重大更改。我们通过原型评估我们的提案,该原型在弹性和非弹性场景中进行了测试。本文提供了有关执行时间和资源使用情况的数据。结果证明了我们的解决方案的可行性以及使用双弹性方法而不是单一资源重排技术的好处。

更新日期:2021-06-20
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