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Failure Diagnosis for Datacenter Applications
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Failure Diagnosis for Datacenter Applications. |
| 개인저자 | Zhang, Qiao. |
| 단체저자명 | University of Washington. Computer Science and Engineering. |
| 발행사항 | [S.l.]: University of Washington., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 99 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438174061 |
| 학위논문주기 | Thesis (Ph.D.)--University of Washington, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Advisers: Thomas E. Anderson |
| 요약 | Fast and accurate failure diagnosis remains a major challenge for datacenter operators. Current datacenter applications are increasingly architected around loosely-coupled modular components: each component can scale and evolve independently. Ho |
| 요약 | My thesis is that fast and accurate failure diagnosis for datacenter applications is possible using three key ideas: (1) a global view of component interactions and dependencies, (2) a penalized-regression-based failure localization algorithm th |
| 요약 | I present two complementary systems to demonstrate this. The first, Deepview, is a system that can localize virtual hard disk (VHD) failures in Infrastructure-as-a-Service clouds. I show that Deepview localizes VHD failures accurately and quickl |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
