As computers become pervasive and better connected, the popularity of peer-to-peer computing has grown immensely. The sharing of unused resources at peers is desirable and practically important because they can collectively comprise a powerful system. The potential benefit, however, can be undermined by uncooperative behavior of some peers because they are managed individually and hence may not follow the expected protocols. To build robust systems, we must incorporate proper trust and incentive mechanisms so that peers would rather cooperate. In this dissertation, we demonstrate that building robust peer-to-peer information dissemination systems is important and viable, using four concrete cases. First, we investigate the incentive mechanism of BitTorrent, an exchange-based file distribution protocol. Our framework based on iterated prisoner's dilemma provides an insight into users' tension between eagerness to download and unwillingness to upload. By using both analytical and experimental approaches, we show that the current incentive mechanism of BitTorrent is susceptible to free riding. We propose an improved mechanism that punishes free riders effectively. Second, we present a trust-aware overlay multicast system that performs well in the presence of uncooperative nodes, which may block, delay, fabricate, or forge the messages they forward. We develop (1) a set of protocols that detect uncooperative behavior, (2) a scheme of trust value assignment according to the behavior of nodes, and (3) an algorithm that adapts the multicast tree based on trust values, all of which allows the system to remain stable and responsive over time. Third, we propose an alternative news feed dissemination system, called FeedEx, in which feed subscribers mesh into a network and exchange news feeds with neighbors. The collaborative exchange in FeedEx, with the help of the incentive-compatible design using the pair-wise fairness principle, reduces the server load and hence increases the scalability. Fourth, we introduce a new concept of peer-to-peer computing, that is, continual service using ephemeral servers. To this end, we develop a system model for the concept and implement a discrete-time simulator to find the conditions and the system support for eliciting cooperation. All four cases are substantiated by experimental results.

Thesis (Ph. D.)--University of California, Riverside, 2008. / Includes abstract. Title from first page of PDF file (viewed February 3, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references. Also issued in print.

Thesis (Ph. D.)--College of Computing, Georgia Institute of Technology, 2004. Directed by Mostafa H. Ammar. / Vita. Includes bibliographical references (leaves 114-118).

Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains xvii, 119 p.; also includes graphics. Includes abstract and vita. Advisor: Gerald Baumgartner, Dept. of Computer and Information Science. Includes bibliographical references (p. 111-119).

Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 52-55). Also available in print.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 127-134).

Includes bibliographical references (p. 34-37).

Includes bibliographical references (p. 31-34).

This thesis targets at modeling and resolving practical problems using mathematical tools in two representative networking systems nowadays, i.e., peer-to-peer (P2P) video streaming system and cloud computing system. In the first part, we study how to mitigate the following tussle between content service providers and ISPs in P2P video streaming systems: network-agnostic P2P protocol designs bring lots of inter-ISP traffic and increase traffic relay cost of ISPs; in turn, ISPs start to throttle P2P packets, which significantly deteriorates P2P streaming performance. First, we investigate the problem in a mesh-based P2P live streaming system. We use end-to-end streaming delays as performance, and quantify the amount of inter-ISP traffic with the number of copies of the live streams imported into each ISP. Considering multiple ISPs at different bandwidth levels, we model the generic relationship between the volume of inter-ISP traffic and streaming performance, which provides useful insights on the design of effective locality-aware peer selection protocols and server deployment strategies across multiple ISPs. Next, we study a similar problem in a hybrid P2P-cloud CDN system for VoD streaming. We characterize the relationship between the costly bandwidth consumption from the cloud CDN and the inter-ISP traffic. We apply a loss network model to derive the bandwidth consumption under any given chunk distribution pattern among peer caches and any streaming request dispatching strategy among ISPs, and derive the optimal peer caching and request dispatching strategies which minimize the bandwidth demand from the cloud CDN. Based on the fundamental insights from our analytical results, we design a locality-aware, hybrid P2P-cloud CDN streaming protocol. In the second part, we study the profit maximization and cost minimization problems in Infrastructure-as- a- Service (IaaS) cloud systems. The first problem is how a geo-distributed cloud system should price its datacenter resources at different locations, such that its overall profit is maximized over long-term operation. We design an efficient online algorithm for dynamic pricing of VM resources across datacenters, together with job scheduling and server provisioning in each datacenter, to maximize the cloud's profit over the long run. Theoretical analysis shows that our algorithm can schedule jobs within their respective deadlines, while achieving a time-averaged overall profit closely approaching the offline maximum, which is computed by assuming perfect information on future job arrivals is freely available. The second problem is how federated clouds should trade their computing resources among each other to reduce the cost, by exploiting diversities of different clouds' workloads and operational costs. We formulate a global cost minimization problem among multiple clouds under the cooperative scenario where each individual cloud's workload and cost information is publicly available. Taking into considerations jobs with disparate length, a non-preemptive approximation algorithm for leftover job migration and new job scheduling is designed. Given to the selfishness of individual clouds, we further design a randomized double auction mechanism to elicit clouds' truthful bidding for buying or selling virtual machines. The auction mechanism is proven to be truthful, and to guarantee the same approximation ratio to what the cooperative approximation algorithm achieves. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

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