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020 ▼a 9780438174061
035 ▼a (MiAaPQ)AAI10823246
035 ▼a (MiAaPQ)washington:18482
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 004
1001 ▼a Zhang, Qiao.
24510 ▼a Failure Diagnosis for Datacenter Applications.
260 ▼a [S.l.]: ▼b University of Washington., ▼c 2018.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2018.
300 ▼a 99 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Advisers: Thomas E. Anderson
5021 ▼a Thesis (Ph.D.)--University of Washington, 2018.
520 ▼a 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
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0250.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a University of Washington. ▼b Computer Science and Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0250
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998549 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033