云计算环境下的大数据处理

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引用次数: 0

摘要

本专题论文的主题主要集中在云计算、大数据处理在Web博客、搜索引擎、图像处理、工业等各个领域的最新研究、发展和应用。在所有提交的稿件中,有15篇论文被选中并收录在这篇专题论文中。创造性的思想和有趣的灵感提出,讨论和传播在本文中。首先,有三篇论文是关于大数据处理的云架构和优化。J. Peng等在考虑数据处理效率和节点能力的情况下,讨论了云上海量数据处理的负载均衡问题。为了提高数据处理的均衡性和处理效率,提出了一种基于一致性哈希方法的海量数据负载均衡方法。大规模传感器网络和物联网是大数据最重要的应用领域之一。Y. Sun等人提出了一种支持能源应用中PaaS的云计算系统——proxzone。PROXZONE提供云服务统一监控授权和云消息服务,在处理大数据时可以支持良好的性能。同时,S. Xu等人讨论了一种改进的多任务移动网络协作动态集群模型,提出了一种协作消息转发机制来提高网络性能。
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Big Data Processing over Cloud Computing Environment
The topics of this special paper are mainly devoted to the most recent research, development and applications in the field of cloud computing, big data processing in the various fields, e.g. Web blog, search engine, image processing, and industry. Among all of the submitted manuscripts, 15 papers were selected and included in this special paper. Creative thoughts and interesting inspirations are presented, discussed, and disseminated in this paper. First, there are three papers on the cloud architecture and optimization for big data processing. J. Peng, et al. discussed the problems of load balance for massive data processing on cloud considering the data processing efficiency and nodes’ capability. A load balance approach for massive data, based on the consistent hashing method, was presented to improve the data processing equalization and high processing efficiency. Large-scale sensor networks and internet of things are one of the most important application fields in big data. Y. Sun, et al. proposed a cloud computing system to support PaaS in energy power applications—PROXZONE. PROXZONE provides the cloud services uniform monitoring and authorization, and cloud message service, which can support good performance when dealing with big data. Meanwhile, S. Xu, et al. discussed an improved cooperative dynamic cluster model in multitasks mobile network, and proposed a cooperative message forwarding mechanism to improve the network performance.
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