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020 ▼a 9781088317464
035 ▼a (MiAaPQ)AAI13903364
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 004
1001 ▼a Warrior, Marc Anthony.
24510 ▼a Understanding and Improving Content Distribution Through Expansive Network Measurements.
260 ▼a [S.l.]: ▼b Northwestern University., ▼c 2019.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2019.
300 ▼a 139 p.
500 ▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
500 ▼a Advisor: Kuzmanovic, Aleksandar.
5021 ▼a Thesis (Ph.D.)--Northwestern University, 2019.
506 ▼a This item must not be sold to any third party vendors.
520 ▼a In response to exponentially increasing demand for digital media, today's Internet landscape has evolved into a multitude of diverse and interdependent distribution systems designed to move content as efficiently as possible. While many of these systems have individually been explored in depth by both academic and industrial communities, a cross-sectional investigation of the relationships between competing or coexisting content distribution systems and resources is generally absent from the current narrative. Further, when such expansivestudies are given consideration, they are avoided due to the daunting challenges they present. Scope and vantage point concerns become non-trivial when designing experiments that span multiple network resources, and third-party systems may lack transparency for the curious researcher.In this thesis, I assert that expansive network measurements such as these are not only feasible, but essential to our efforts to understand and improve modern content distribution systems. I demonstrate that anchoring cross-sectional measurements in client-side machines provides the real-world perspectives necessary for optimizing actual client experience. Rather than examine the performance of a single resource-client pair, I instead obtain, for each client considered, measurements across the set of systems and resources visible to the client or its peers. Each additional considered Internet resource or system provides relative context that highlights otherwise unobservable outlier properties.With this approach, I achieve the following: First, I discover and resolve sub-optimal resource-client mappings using only a lightweight, client-side implementation. Next, I quantify the extent to which clients are exposed to the same network resources as each other, and I further leverage these results to systematically identify opportunities to improve client performance. Finally, Ienable scalable assessment of a crowdsourced ecosystem's content aggregation and distribution patterns.
590 ▼a School code: 0163.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a Northwestern University. ▼b Computer Science.
7730 ▼t Dissertations Abstracts International ▼g 81-04B.
773 ▼t Dissertation Abstract International
790 ▼a 0163
791 ▼a Ph.D.
792 ▼a 2019
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15492455 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 202002 ▼f 2020
990 ▼a ***1008102
991 ▼a E-BOOK