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Please use this identifier to cite or link to this item: http://hdl.handle.net/1903/13257

Title: DECENTRALIZED NETWORK BANDWIDTH PREDICTION AND NODE SEARCH
Authors: Song, Sukhyun
Advisors: Sussman, Alan
Department/Program: Computer Science
Type: Dissertation
Sponsors: Digital Repository at the University of Maryland
University of Maryland (College Park, Md.)
Subjects: Computer science
Keywords: Network Bandwidth Prediction
Node Search
Tree Metric Space
Issue Date: 2012
Abstract: As modern computing becomes increasingly data-intensive and distributed, it is becoming crucial to effectively manage and exploit end-to-end network bandwidth information from hosts on wide-area networks. Inspired by the finding that Internet bandwidth can be represented approximately in a tree metric space, we focus on three specific research problems. First, we have designed a decentralized algorithm for network bandwidth prediction. The algorithm embeds the bandwidth information as distance in an edge-weighted tree, without performing full n-to-n measurements. No central and fixed infrastructure is required. Each joining node performs a limited number of sampling measurements. Second, we designed a decentralized algorithm to search for a centroid node that has high-bandwidth connections with a given set of nodes. The algorithm can find a centroid accurately and efficiently using the bandwidth data produced by the prediction algorithm. Last, we have designed another type of decentralized search algorithm to find a cluster of nodes that have high-bandwidth interconnections. While the clustering problem is NP-complete in a general graph, our algorithm runs in polynomial time with the bandwidth data predicted in a tree metric space. We provide proofs that our algorithms for bandwidth prediction and node search have perfect accuracy and high scalability when a network is modeled as a tree metric space. Also, experimental results with real-world data sets validate the high accuracy and scalability of our approaches.
URI: http://hdl.handle.net/1903/13257
Appears in Collections:UMD Theses and Dissertations
Computer Science Theses and Dissertations

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