LDR | | 02067nam u200397 4500 |
001 | | 000000418017 |
005 | | 20190215162511 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
020 | |
▼a 9780438174061 |
035 | |
▼a (MiAaPQ)AAI10823246 |
035 | |
▼a (MiAaPQ)washington:18482 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
082 | 0 |
▼a 004 |
100 | 1 |
▼a Zhang, Qiao. |
245 | 10 |
▼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 |
502 | 1 |
▼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 |
710 | 20 |
▼a University of Washington.
▼b Computer Science and Engineering. |
773 | 0 |
▼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 |
856 | 40 |
▼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 |