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PER-MARE: Adaptive Deployment of MapReduce over Pervasive Grids

机译:per-mare:普遍存在网格的MapReduce的自适应部署

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Map Reduce is a parallel programming paradigm successfully used to perform computations on massive amounts of data, being widely deployed on clusters, grid, and cloud infrastructures. Interestingly, while the emergence of cloud infrastructures has opened new perspectives, several enterprises hesitate to put sensible data on the cloud and prefer to rely on internal resources. In this paper we introduce the PER-MARE initiative, which aims at proposing scalable techniques to support existent Map Reduce data-intensive applications in the context of loosely coupled networks such as pervasive and desktop grids. By relying on the Map Reduce programming model, PER-MARE proposes to explore the potential advantages of using free unused resources available at enterprises as pervasive grids, alone or in a hybrid environment. This paper presents the main lines that orient the PER-MARE approach and some preliminary results.
机译:Map Refice是一个并行编程范例,成功用于在大量数据上执行计算,广泛部署在集群,网格和云基础架构上。有趣的是,虽然云基础设施的出现开辟了新的观点,但有几家企业犹豫地将明智的数据放在云上,更倾向于依靠内部资源。在本文中,我们介绍了每个MARE计划,该计划旨在提出支持存在的地图的可扩展技术,从诸如普遍耦合的网络等诸如普及和桌面网格中的上下文中降低数据密集型应用。通过依靠地图减少编程模型,Per-Mare建议探讨在企业中使用免费未使用的资源作为普遍存在网格,单独或混合环境中的潜在优势。本文介绍了定位各自母马方法的主线以及一些初步结果。

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