상세정보
학술지
NetCube: A Comprehensive Network Traffic Analysis Model Based on Multidimensional OLAP Data Cube
- 저자
- 박대희, 유재학, 박준상, 김명섭
- 발행일
- 201303
- 출처
- International Journal of Network Management, v.23 no.2, pp.101-118
- ISSN
- 1055-7148
- 출판사
- ACM, John Wiley & Sons
- 협약과제
- 12MC2500, 차세대 USN기반의 스마트 사회안전 프레임워크 기술 개발, 이병복
- 초록
- Network traffic monitoring and analysis are essential for effective network operation and resource management. In particular, multidimensional analysis for long-term traffic data is necessary for comprehensive understanding of the traffic trend and effective quality-of-service provision considering the extremely dynamic behavior of the current Internet, where various types of traffic occur from high-speed network links and greatly increasing number of applications. However, only limited analysis results are provided, as the existing network traffic analysis tools and systems are developed and deployed focusing on their own specialized analysis purposes. Consequently, it is difficult to understand the network comprehensively and deeply, which increases the necessity for multilateral analysis of long-term traffic data. In this paper, we propose a novel traffic analysis model for large volumes of Internet traffic accumulated over a long period of time. The NetCube, the proposed network traffic analysis model using online analytical processing (OLAP) on a multidimensional data cube, provides an easy and fast way to construct a multidimensional traffic analysis system for comprehensive and detailed analysis of long-term traffic data by utilizing simple OLAP operations and powerful data-mining techniques on various abstraction levels of traffic data to complete the analysis purpose. We validate the feasibility and applicability of the proposed NetCube traffic analysis model by implementing a traffic analysis system and applying it to our campus network. Copyright © 2012 John Wiley & Sons, Ltd.
- KSP 제안 키워드
- Abstraction Levels, Analysis Model, And systems, Campus Network, Data mining(DM), Dynamic behaviors, High speed network, Long period, Multidimensional data cube, Network Traffic Monitoring and Analysis, Network traffic analysis