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1. Efficient Bare Metal Auto-Scaling for NFV Platform | |||
Pang Xudong,Wang Jing,Wang Jingyu,QiQi | |||
Computer Science and Technology 19 November 2017 | |||
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Abstract:There are always a cloud data center behind the Network Function Virtualisation(NFV) system.Typically, elasticity is an essential attribute of cloud data center, which is critical for operating resources in face of peaks and valleys of business. At present, the automatic scaling technique of virtual machines is widely studied, but barely for physical machines. Despite lack of flexibility, we all know that physical server can perform faster and more efficiently than virtualized instances, especially in NFV systems. Some virtual network functions (VNFs) actually require high performance computing, which is a hard task for virtual machines. Besides, good management of bare metal resources can be significant for the data center power cost and human maintenance cost. Accordingly, we think that auto-scaling of physical machine is worth studying. This paper proposes a bare metal automatic scaling scheme based on workload prediction, and finally make tests on an open source NFV platform.The new scheme obtains good result on computation intensive VNFs scenario, including complete the scale in half an hour, guarantee for the continuity of VNF processing business, and can cope with the load fluctuation better. | |||
TO cite this article:Pang Xudong,Wang Jing,Wang Jingyu, et al. Efficient Bare Metal Auto-Scaling for NFV Platform[OL].[19 November 2017] http://en.paper.edu.cn/en_releasepaper/content/4742224 |
2. Large-scale Data Analysis based on Hadoop | |||
Liu Yijun | |||
Computer Science and Technology 15 December 2009 | |||
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Abstract:With the development of complex network and the increase of data scale, the performance of data analysis becomes more and more important. In this paper, we present a new approach for network analysis about the flight data based on Hadoop which is an implementation of the MapReduce parallel framework: Firstly, we identity and group the information of passengers and flights. Secondly, we extract graph nodes, which represent the passengers, and graph edges, which represent the relationship between the passengers. Finally we visualize the passenger’s egocentric network to help network analysis. | |||
TO cite this article:Liu Yijun. Large-scale Data Analysis based on Hadoop[OL].[15 December 2009] http://en.paper.edu.cn/en_releasepaper/content/37600 |
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