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Big-Data Traffic Analysis for the Campus Network Resource Efficiency
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 Title & Authors
Big-Data Traffic Analysis for the Campus Network Resource Efficiency
An, Hyun-Min; Lee, Su-Kang; Sim, Kyu-Seok; Kim, Ik-Han; Jin, Seo-Hoon; Kim, Myung-Sup;
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 Abstract
The importance of efficient enterprise network management has been emphasized continuously because of the rapid utilization of Internet in a limited resource environment. For the efficient network management, the management policy that reflects the characteristics of a specific network extracted from long-term traffic analysis is essential. However, the long-term traffic data could not be handled in the past and there was only simple analysis with the shot-term traffic data. However, as the big data analytics platforms are developed, the long-term traffic data can be analyzed easily. Recently, enterprise network resource efficiency through the long-term traffic analysis is required. In this paper, we propose the methods of collecting, storing and managing the long-term enterprise traffic data. We define several classification categories, and propose a novel network resource efficiency through the multidirectional statistical analysis of classified long-term traffic. The proposed method adopted to the campus network for the evaluation. The analysis results shows that, for the efficient enterprise network management, the QoS policy must be adopted in different rules that is tuned by time, space, and the purpose.
 Keywords
Enterprise network;Big data traffic;Statistical analysis;Long-term traffic;Network policy;
 Language
Korean
 Cited by
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