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首页> 外文期刊>Concurrency, practice and experience >Interoperable and network-aware service workflows for big data executions at internet scale
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Interoperable and network-aware service workflows for big data executions at internet scale

机译:Internet刻度的互操作和网络感知服务工作流程为大数据执行

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摘要

Sharing of computing resources and workload across different big data frameworks is challenging due to their lack of interoperable interfaces. In contrast, web services natively support an interoperable execution. Therefore, an increasing number of big data workflows are composed of data services and web service implementations that access and process big data. On the other hand, big data execution in the wide area networks needs to minimize latency and communication overheads to be able to scale seamlessly. Lack of network-awareness of classic web service execution beyond data centers significantly challenges the scope of data services.Software-Defined Networking (SDN) offers better control and management to the network, by unifying the control plane centrally, away from the distributed data plane devices. In this paper, we propose Software-Defined Data Services (SDDS), an SDN-based distributed service composition and workflow placement approach for data services in wide area networks. We first present the design of an SDDS framework that models the big data executions as composable data service workflows in multi-domain network environments. We then evaluate the performance of a prototype SDDS framework through microbenchmarks. The benchmarks highlight the efficiency of SDDS in data service execution inside and beyond data centers.
机译:由于缺乏可互操作的接口,共享不同大数据框架的计算资源和工作负载是具有挑战性的。相比之下,Web服务本身支持可互操作的执行。因此,越来越多的大数据工作流由访问和处理大数据的数据服务和Web服务实现组成。另一方面,广域网中的大数据执行需要最小化延迟和通信开销,以便能够无缝缩放。超出数据中心的经典Web服务执行的网络意识大大挑战了数据服务的范围。使用软件定义的网络(SDN)通过统治控制平面,远离分布式数据平面,为网络提供更好的控制和管理设备。在本文中,我们提出了软件定义的数据服务(SDD),基于SDN的分布式服务组合和用于广域网的数据服务的工作流程放置方法。我们首先介绍了SDDS框架的设计,将大数据执行模型为多域网络环境中的可组合数据服务工作流程。然后,我们通过微磁盘评估原型SDDS框架的性能。基准测试突出了数据服务执行中的SDDS的效率。

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