지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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- 자료유형
- 학위논문
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- 지도교수
- 柳爀
- 발행연도
- 2016
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이 논문의 연구 히스토리 (4)
2016
- 이 논문의 후속연구가 궁금하신가요?
- 연관 학술논문 또는 학술발표를 통해 보다 발전된 연구결과를 확인하실 수 있습니다.
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초록· 키워드
오류제보하기
가상화 기술은 한정된 물리자원을 추상화하여 다수의 가상 자원 형태로 사용자에게 제공하는 기술로써 자원 활용률을 높이고 유동적으로 서비스를 제공할수 있다는 장점이 있다. 하지만 한정된 물리자원을 다수의 가상 머신이 공유하는 가상화 기술의 특성상, 자원 공유로 인한 성능 간섭 현상이 발생하는 문제가 있다. 이는 호스트 운영체제의 CPU 스케줄러가 가상 머신에서 실행중인 프로세스의 특성을 고려하지 않고 스케줄링하기 때문이다. 이러한 문제를 해결하기 위해 다양한 연구들이 진행되었지만 실제 근본적인 성능 간섭의 원인 분석에 대해서는 다루고 있지 않다.
본 논문에서는 KVM 기반 가상화 환경에서 가상 머신의 성능 간섭의 원인을 분석하기 위해 다양한 시나리오에서의 프로파일링을 수행하고, 그 결과를 분석하여 CPU 스케줄링 관점에서 성능 간섭 현상의 원인과 그 해결 방안을 제시한다.
본 논문에서는 KVM 기반 가상화 환경에서 가상 머신의 성능 간섭의 원인을 분석하기 위해 다양한 시나리오에서의 프로파일링을 수행하고, 그 결과를 분석하여 CPU 스케줄링 관점에서 성능 간섭 현상의 원인과 그 해결 방안을 제시한다.
목차
1. 서론 ··················································································································12. 배경지식 ··········································································································42.1 KVM ·············································································································42.2 CFS ···············································································································43. Network I/O 성능간섭 현상 발생원인 분석 ·······································54. 성능 간섭 실험 및 결과 분석 ···································································64.1 실험 방법 및 실험 환경 ·························································································64.2 VCPU 스케줄링 지연시간 측정 ···········································································84.3 실험 결과 분석 ·········································································································95. I/O 성능 보장을 위한 CPU-Network 통합 스케줄링 구조 ········115.1 CPU-Network 통합 스케줄링 구조의 설계 ····················································115.2 PI-제어기 설계 ······································································································155.2.1 first-order 모델 수립 ······················································································155.2.2 PI-제어기 계수 계산 ·······················································································205.3 성능 평가 ·················································································································226. 관련 연구 ···················································································································257. 결론 및 향후 진행 방향 ························································································28
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