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Provisioning big data applications as services on containerised cloud: a microservices-based approach

机译:将大数据应用程序提供为集装箱云的服务:基于微服务的方法

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摘要

We present the fundamental challenges for dynamic provisioning of big data applications. The findings are based on our previous experience in this domain, as well as a comprehensive study on a selected set of state-of-the-art tools in the big data ecosystem. We then incorporate these findings in a framework aiming at dynamically provisioning big data applications as services on containerised cloud. The innovations behind the framework are to optimise the whole lifecycle of big data applications in a holistic manner by the adoption of microservices(mu Services) methodologies. The feasibility of our approach is verified through a case study of provisioning a large-scale user traffic data processing application in a private cloud environment backed by Kubernetes. We also show that while hosting big data applications in containerised cloud can significantly eliminate the presumed complexity of deployment and operation, it in the meantime also comes with a certain amount of cost in terms of learning curve and traceability. Our research helps technical decision makers to assess the adoption of microservices for big data applications more objectively.
机译:我们为大数据应用提供了动态配置的根本挑战。调查结果基于我们以前的这一领域的经验,以及关于大数据生态系统中所选一组最先进的工具的全面研究。然后,我们将这些发现在框架中纳入了旨在将大数据应用的框架作为容器化云的服务。框架背后的创新是通过采用微服务(MU服务)方法以整体方式优化大数据应用的整个生命周期。通过在Kubernetes支持的私有云环境中配置大规模用户流量数据处理应用程序的案例研究验证了我们方法的可行性。我们还表明,在托管集装箱云中的大数据应用时,可以显着消除部署和操作的假定复杂性,与此同时,它也在学习曲线和可追溯性方面具有一定的成本。我们的研究有助于技术决策者更客观地评估大数据应用的微服务。

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