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Scalable hybrid data dissemination f...

Zhang, Wenhui.

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Scalable hybrid data dissemination for Internet hot spots.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Scalable hybrid data dissemination for Internet hot spots./
作者:	Zhang, Wenhui.
面頁冊數:	121 p.
附註:	Adviser: Vincenzo Liberatore.
Contained By:	Dissertation Abstracts International67-12B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3244787

Scalable hybrid data dissemination for Internet hot spots.
Zhang, Wenhui.

Scalable hybrid data dissemination for Internet hot spots. - 121 p.

Adviser: Vincenzo Liberatore.

Thesis (Ph.D.)--Case Western Reserve University, 2007.

Server scalability can be greatly enhanced with hybrid data dissemination. Hybrid data dissemination uses a combination of multicast and unicast techniques. Multicast is ideal for popular data that are of interest to most clients. Multicast aggregates multiple unicast data transfers into a single transmission and is thus more scalable than traditional unicast. On the other hand, multicast is inappropriate for unpopular data since it would force unwanted contents on most clients. Therefore, unpopular data is transferred with traditional unicast. As a result, hybrid data dissemination would ideally achieve scalable utilization of server and network resources while avoiding the reception of unneeded contents.Subjects--Topical Terms:

626642
Computer Science.

Scalable hybrid data dissemination for Internet hot spots.
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245 1 0 $a Scalable hybrid data dissemination for Internet hot spots.
300 $a 121 p.
500 $a Adviser: Vincenzo Liberatore.
500 $a Source: Dissertation Abstracts International, Volume: 67-12, Section: B, page: 7184.
502 $a Thesis (Ph.D.)--Case Western Reserve University, 2007.
520 $a Server scalability can be greatly enhanced with hybrid data dissemination. Hybrid data dissemination uses a combination of multicast and unicast techniques. Multicast is ideal for popular data that are of interest to most clients. Multicast aggregates multiple unicast data transfers into a single transmission and is thus more scalable than traditional unicast. On the other hand, multicast is inappropriate for unpopular data since it would force unwanted contents on most clients. Therefore, unpopular data is transferred with traditional unicast. As a result, hybrid data dissemination would ideally achieve scalable utilization of server and network resources while avoiding the reception of unneeded contents.
520 $a In this dissertation, we first present a deployment framework for hybrid data delivery to support web data dissemination. The framework significantly improves the scalability of web servers. Second, we study a fundamental problem in hybrid data delivery: document selection, which determines the data transfer method that is most appropriate for each data item. We individuate special challenges, such as scalable and robust popularity estimation, appropriate classification of documents, and unpopular large documents. We propose solutions to these problems and evaluate them with collected traces and in the real Internet. Third, we examine the multicast push performance with application-perceived performance criteria. We give both theoretical and experimental analysis on the problem. Finally, we devise an efficient and effective application level protocol for transferring application data on a multicast channel.
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