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008181129s2018 ||| | | | eng d
020 ▼a 9780438328631
035 ▼a (MiAaPQ)AAI10830296
035 ▼a (MiAaPQ)purdue:22904
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 004
1001 ▼a Cao, Lianjie.
24510 ▼a Data-driven Resource Allocation in Virtualized Environments.
260 ▼a [S.l.] : ▼b Purdue University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 118 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Adviser: Sonia Fahmy.
5021 ▼a Thesis (Ph.D.)--Purdue University, 2018.
520 ▼a Modern advances in virtualization technologies have revolutionized the way we build and manage computer systems. Virtualization technologies, however, adversely impact the predictability of system performance. This introduces several challenges
520 ▼a In this dissertation, we explore and address performance challenges introduced by virtualization in two application scenarios: network functions virtualization and distributed network emulation.
520 ▼a First, we investigate the performance of virtualized network functions (VNFs) and propose a framework, NFV-VITAL, that characterizes performance impacts of hardware and software options and determines the optimal configuration for initial deploy
520 ▼a Second, we study the experiment fidelity problem in a distributed network emulation cluster comprising heterogeneous physical machines. We quantify the traffic processing capability of the physical machines and design an algorithm, Waterfall, th
590 ▼a School code: 0183.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a Purdue University. ▼b Computer Sciences.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0183
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999416 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자