Web cluster systems provide cost-effective solutions when scalable and reliable
web services are required. However, as the number of servers in web cluster systems
increase, web cluster systems incur long and unpredictable delays to manage servers.
This study presents the efficient management schemes for web cluster systems.
First of all, we propose an efficient request distribution scheme in web cluster
systems. Distributor-based systems forward user requests to a balanced set of waiting
servers in complete transparency to the users. The policy employed in forwarding
requests from the frontend distributor to the backend servers plays an important
role in the overall system performance. In this study, we present a proactive request
distribution (ProRD) to provide an intelligent distribution at the distributor.
Second, we propose the heuristic memory management schemes through a web
prefetching scheme. For this study, we design a Double Prediction-by-Partial-Match
Scheme (DPS) that can be adapted to the modern web frameworks. In addition, we
present an Adaptive Rate Controller (ARC) to determine the prefetch rate depending
on the memory status dynamically. For evaluating the prefetch gain in a server node,
we implement an Apache module.
Lastly, we design an adaptive web streaming system in wireless networks. The
rapid growth of new wireless and mobile devices accessing the internet has contributed
to a whole new level of heterogeneity in web streaming systems. Particularly, in-home
networks have also increased in heterogeneity by using various devices such as laptops, cell phone and PDAs. In our study, a set-top box(STB) is the access pointer between
the internet and a home network. We design an ActiveSTB which has a capability of
buffering and quality adaptation based on the estimation for the available bandwidth
in the wireless LAN.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-12-7608 |
| Date | 2009 December 1900 |
| Creators | Lee, Heung Ki |
| Contributors | Kim, Eun Jung |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | application/pdf |
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