An Efficient Management of Network Traffic Performance Using Framework-Based Performance Management Tool

  • Seong-Man Choi
  • Cheol-Jung Yoo
  • Ok-Bae Chang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3982)


As the network-related technology develops the number of both internet users and the usage are explosively increasing. The networking traffic is increasing in the campus as the networking system inside universities, following the trend, adds more nodes and various networking services. Nonetheless, the quality of services for users has been degraded. Accordingly, core problems, which can cause troubles for network management, design and expansion of the network, and the cost policy, have developed. To effectively cope with the problems an analysis of a great number of technicians, tools, and budget are needed. However, it is not possible for mid and small-sized colleges to spend such a high expenditure for professional consulting. To reduce the cost and investment of creating an optimized environment, analysis of the replacement of the tools, changing the network structure, and performance analysis about capacity planning of networking is necessary. For this reason, in this paper, framework-based performance management tools are used for all steps that are related to the subject of the analysis for the network management. As the major research method, the current data in detailed categories are collected, processed, and analyzed to provide a meaningful solution to the problems. As a result we will be able to manage the network, server, and application more systematically and react efficiently to errors and degrading of performance that affect the networking tasks. Also, with the scientific and organized analyses the overall efficiency is upgraded by optimizing the cost for managing the operation of entire system.


Performance Analysis Network Traffic Network Management Enterprise Environment Simple Network Management Protocol 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Seong-Man Choi
    • 1
  • Cheol-Jung Yoo
    • 1
  • Ok-Bae Chang
    • 1
  1. 1.Dept. of Computer Science & Statistical InformationChonbuk National UniversityJeonbukSouth Korea

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