광 버스트 스위칭 망에서 인공면역체계 기반의 이상 상태 탐지 및 제어 = Artificial Immune System based Anomaly Detection and Control in OBS Network

다국어 초록 (Multilingual Abstract)

Recently, as Internet users and Internet-related services has increased explosively, high bandwidth has been desired. Thus, Optical burst switching (OBS) was proposed as a new switching paradigm for optical network.
OBS network usually employs one-way reservation, which sends a burst control packet (BCP) with a specific offset time before transmitting each data burst frame (BDF). Due to such a property, multiple bursts contend with the same wavelength on the same output link simultaneously and lead to high burst losses. And the contention occurs more frequent as the offered load increases. So, for the better resource utilization of the network, it is required efficient control scheme to resolve contention.
Therefore, segmentation based congestion control scheme was introduced in OBS network. This scheme is able to regulate the congestion according to segmentation sizes. However, because OBS network employs one-way reservation, segmentation occurs in spite of a low offered load. Therefore, if we only use the information of segmentation sizes, the network state may be detected as congestion even though it is in normal case. So this paper proposes a congestion control scheme to improve the detection accuracy by analysing and studying the characteristics of segmentation information through the AIS(Artificial Immune System).
The proposed scheme is processed as the following.
Firstly, we define the data expression using extracted the segmentation feature through analyzing the segmentation information about normal/abnormal state. In order to reduce the complexity of congestion detection, we count the number of the 0s after executing AND operation among the continuously transmitted segmentation information. At the same time, to improve the detection accuracy, we count the number of the 0s among the continuously transmitted segmentation information.
Secondly, we define the matching rule which discriminates between normal and abnormal based on Euclidean distance.
Thirdly, we generate the detector set using Negative Selection algorithm considering the frequency of data pattern, especially. On the other hand, since the network status are dynamically changed, if congestion judgement that only depends on the detector set based on Negative Selection algorithm, false detection occurs frequently. In order to compensate these problems, we utilize fuzzy number theory which can infer the degree of threat. The degree of threat is calculated by monitoring the number of alarm signals occurrence time periodically. The performance of proposed scheme is evaluated through the OPNET simulation. Simulation results shows that our scheme efficiently detected congestion states.

목차 (Table of Contents)

• Ⅰ. 서론 = 1
• Ⅱ. 관련연구 = 4
• 2.1. 광 버스트 스위칭 망에서의 Staged Reservation 기법 = 4
• 2.1.1. 광 버스트 스위칭 기술 = 4
• 2.1.2. Staged Reservation 기법 = 6
• 1) 버스트 세그멘테이션 기법 = 6
• 2) Staged Reservation 기법 = 8
• 2.2. 인공 면역 시스템 = 10
• 2.2.1. 인간면역체계 = 11
• 1) 인간면역시스템의 구성 = 11
• 2) 항원 인식 및 면역 세포 활성화 메커니즘 = 13
• 2.2.2. 면역 원리 = 14
• 1) Self/Non-self 식별 = 14
• 2) 위험이론(Danger theory) = 15
• 2.2.3. 부정선택 기반 응용 모델 = 17
• Ⅲ. 광 버스트 스위칭 망에서 인공면역체계 기반의 이상 상태 탐지 및 제어 = 19
• 3.1. 인공면역체계 기반의 이상상태 탐지 = 19
• 3.1.1. Self/Non-self 정의 및 데이터 표현 방법 정의 = 19
• 1) Self/Non-self 정의 = 19
• 2) 유전자 결정 = 20
• 3) 데이터 표현 방법 = 22
• 3.1.2. 매칭 규칙 결정 및 검출기 집합 생성 = 26
• 1) 매칭 규칙 = 26
• 2) 부정 선택 원리를 응용한 검출기 집합 생성 = 27
• 3.1.3. 검출기를 이용한 망 상태 감시 = 31
• 3.1.4. Fuzzy 함수를 이용한 위험도 결정 = 32
• 1) 위험도 측정 파라미터 정의 = 32
• 2) 위험 매개 변수의 퍼지 변환 메커니즘 및 위험도 계산 = 33
• 3.1.5. 위험도를 이용한 차별적 혼잡제어 = 35
• Ⅳ. 성능 평가 및 분석 = 36
• 4.1. 시뮬레이션 환경 = 36
• 4.1.1. 네트워크 모델 = 36
• 4.1.2. 노드 모델 = 37
• 4.1.3. 프로세스 모델 = 39
• 1) 제어 유닛의 프로세스 구조 = 39
• 2) 스위치의 프로세스 구조 = 41
• 4.2. 혼잡제어 기법 성능 분석 = 42
• 4.2.1. 시뮬레이션 환경 설정 = 42
• 4.2.2. 시뮬레이션 결과 및 성능 분석 = 44
• 1) 검출기 성능 평가 = 45
• 2) 전송 성공률, 전송 실패율, 자원이용률 = 47
• Ⅴ. 결론 = 50
• 참고문헌 = 52