THE DISSERTATION DEFENSE FOR THE DEGREE OF Ph.D. IN COMPUTER SCIENCE FOR
Sukhyun Song
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 for the dissertation.
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 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.
Examining Committee:
Committee Chair: Dr. Alan Sussman
Co-Chair: Dr. Peter J. Keleher
Dean's Representative: Dr. Derek C. Richardson
Committee Members: Dr. Bobby Bhattacharjee
Dr. Jeffrey K. Hollingsworth