DIS体系结构的分析设计:混合模型

B. Prakash, M. Hanumanthappa, H. Dattasmita, V. Kavitha
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引用次数: 0

摘要

在过去的几十年里,由于互联网设备的出现,数据的使用有了战略性的增加,这对存储和挖掘技术产生了很大的影响。我们还观察到,科学/研究领域产生的数据呈锯齿状结构,即结构化、半结构化和非结构化数据。相比之下,由于要求苛刻,这类数据的处理相对增加。通过有效的物理基础设施(在采矿方面)、智能网络解决方案和有用的软件方法,有可持续的技术来应对挑战并加速可扩展的服务。事实上,云计算的目标是数据密集型计算,通过促进大数据的可扩展处理。但是,对于庞大的数据来说,这个问题仍然没有得到解决,相反,数据正在以指数级的速度增长。在这个关键时刻,推荐的算法是众所周知的MapReduce模型来压缩海量的数据。对当前模型的任何问题的概念化是,较低的容错性和可靠性,这可能会被Hadoop架构所克服。相反,Hadoop是容错的,并且具有高吞吐量,这对于需要流式访问的大量数据集和文件系统的应用程序是值得推荐的。本文研究并揭示了需要进行哪些有效的架构/设计更改才能带来Everest模型、HBase算法和现有MR算法的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analytical Design of the DIS Architecture: The Hybrid Model
In the last decades, and due to emergence of Internet appliance, there is a strategical increase in the usage of data which had a high impact on the storage and mining technologies. It is also observed that the scientific/research field’s produces the zig-zag structure of data viz., structured, semi-structured, and unstructured data. Comparably, processing of such data is relatively increased due to rugged requirements. There are sustainable technologies to address the challenges and to expedite scalable services via effective physical infrastructure (in terms of mining), smart networking solutions, and useful software approaches. Indeed, the Cloud computing aims at data-intensive computing, by facilitating scalable processing of huge data. But still, the problem remains unaddressed with reference to huge data and conversely the data is growing exponentially faster. At this juncture, the recommendable algorithm is, the well-known model i.e., MapReduce, to compress the huge and voluminous data. Conceptualization of any problem with the current model is, less fault-tolerant and reliability, which may be surmounted by Hadoop architecture. On Contrary case, Hadoop is fault tolerant, and has the high throughput which is recommendable for applications having huge volume of data sets, file system requiring the streaming access. The paper examines and unravels, what efficient architectural/design changes are necessary to bring the benefits of the Everest model, HBase algorithm, and the existing MR algorithms.
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