Path selection and multipath congestion control.

ABSTRACT In this paper we investigate the benefits that accrue from the use of multiple paths by a session coupled with rate control over those paths. In particular, we study data transfers under two classes of multipath control, coordinated control where the rates over the paths are determined as a function of all paths, and uncoordinated control where the rates are determined independently over each path. We show that coordinated control exhibits desirable load balancing properties; for a homogeneous static random paths scenario, we show that the worst-case throughput performance of uncoordinated control behaves as if each user has but a single path (scaling like log(log(N ))/ log(N ) where N is the system size, measured in number of resources). Whereas coordinated control yields a worst-case throughput allocation bounded away from zero. We then allow users to change their set of paths and introduce the notion of a Nash equilibrium. We show that both coordinated and uncoordinated control lead to Nash equilibria corresponding to desirable welfare maximizing states, provided in the latter case, the rate controllers over each path do not exhibit any RTT bias (as in TCP Reno). Finally, we show in the case of coordinated control that more paths are better, leading to greater welfare states and throughput capacity, and that simple path reselection polices that shift to paths with higher net benefit can achieve these states

Distributed Load Balancing in Peer-to-Peer Computing

In this paper, we address the load balancing problem in the context of peer-to-peer computing environments. The key challenge to employ peer-to-peer networks for distributed computing is to exploit the heterogeneous processing capability of the participating hosts as well as the diverse network conditions. The contribution of our work is twofold. First, we model the load balance problem as an optimization problem with the objective of minimizing the system response time. This modeling considers not only the current loading of hosts, but also the fluctuation of network delay, which completely captures the characteristics of the P2P systems. Second, we propose a gradient projection algorithm to solve the optimization problem, which is fully distributed and easy for implementation. Simulation results demonstrate that our scheme has satisfied performance in terms of convergence, response time and load distribution

Service-Driven Networking

This thesis presents our research on service-driven networking, which is a general design framework for service quality assurance and integrated network and service management in large scale multi-domain networks. The philosophy is to facilitate bi-party open participation among the users and the providers of network services in order to bring about better service customization and quality assurance, without sacrificing the autonomy and objectives of the individual entities. Three primary research topics are documented: service composition and adaptation, self-stabilization in uncoordinated environment, and service quality modeling. The work involves theoretical analysis, algorithm design, and simulations as evaluation methodology

Selfish grids: Game-theoretic modeling and NAS/PSA benchmark evaluation

Selfish behaviors of individual machines in a Grid can potentially damage the performance of the system as a whole. However, scrutinizing the Grid by taking into account the noncooperativeness of machines is a largely unexplored research problem. In this paper, we first present a new hierarchical game-theoretic model of the Grid that matches well with the physical administrative structure in real-life situations. We then focus on the impact of selfishness in intrasite job execution mechanisms. Based on our novel utility functions, we analytically derive the Nash equilibrium and optimal strategies for the general case. To study the effects of different strategies, we have also performed extensive simulations by using a well-known practical scheduling algorithm over the NAS (Numerical Aerodynamic Simulation) and the PSA (Parameter Sweep Application) workloads. We have studied the overall job execution performance of the Grid system under a wide range of parameters. Specifically, we find that the Optimal selfish strategy significantly outperforms the Nash selfish strategy. Our performance evaluation results can serve as a valuable reference for designing appropriate strategies in a practical Grid. © 2007 IEEE.published_or_final_versio

Faster Algorithms for Semi-Matching Problems

We consider the problem of finding \textit{semi-matching} in bipartite graphs which is also extensively studied under various names in the scheduling literature. We give faster algorithms for both weighted and unweighted case. For the weighted case, we give an $O(nm\log n)$-time algorithm, where $n$ is the number of vertices and $m$ is the number of edges, by exploiting the geometric structure of the problem. This improves the classical $O(n^3)$ algorithms by Horn [Operations Research 1973] and Bruno, Coffman and Sethi [Communications of the ACM 1974]. For the unweighted case, the bound could be improved even further. We give a simple divide-and-conquer algorithm which runs in $O(\sqrt{n}m\log n)$ time, improving two previous $O(nm)$-time algorithms by Abraham [MSc thesis, University of Glasgow 2003] and Harvey, Ladner, Lov\'asz and Tamir [WADS 2003 and Journal of Algorithms 2006]. We also extend this algorithm to solve the \textit{Balance Edge Cover} problem in $O(\sqrt{n}m\log n)$ time, improving the previous $O(nm)$-time algorithm by Harada, Ono, Sadakane and Yamashita [ISAAC 2008].Comment: ICALP 201

Optimizing on-demand resource deployment for peer-assisted content delivery

Increasingly, content delivery solutions leverage client resources in exchange for services in a pee-to-peer (P2P) fashion. Such peer-assisted service paradigm promises significant infrastructure cost reduction, but suffers from the unpredictability associated with client resources, which is often exhibited as an imbalance between the contribution and consumption of resources by clients. This imbalance hinders the ability to guarantee a minimum service fidelity of these services to clients especially for real-time applications where content can not be cached. In this thesis, we propose a novel architectural service model that enables the establishment of higher fidelity services through (1) coordinating the content delivery to efficiently utilize the available resources, and (2) leasing the least additional cloud resources, available through special nodes (angels) that join the service on-demand, and only if needed, to complement the scarce resources available through clients. While the proposed service model can be deployed in many settings, this thesis focuses on peer-assisted content delivery applications, in which the scarce resource is typically the upstream capacity of clients. We target three applications that require the delivery of real-time as opposed to stale content. The first application is bulk-synchronous transfer, in which the goal of the system is to minimize the maximum distribution time - the time it takes to deliver the content to all clients in a group. The second application is live video streaming, in which the goal of the system is to maintain a given streaming quality. The third application is Tor, the anonymous onion routing network, in which the goal of the system is to boost performance (increase throughput and reduce latency) throughout the network, and especially for clients running bandwidth-intensive applications. For each of the above applications, we develop analytical models that efficiently allocate the already available resources. They also efficiently allocate additional on-demand resource to achieve a certain level of service. Our analytical models and efficient constructions depend on some simplifying, yet impractical, assumptions. Thus, inspired by our models and constructions, we develop practical techniques that we incorporate into prototypical peer-assisted angel-enabled cloud services. We evaluate these techniques through simulation and/or implementation

Optimizing on-demand resource deployment for peer-assisted content delivery (PhD thesis)

Increasingly, content delivery solutions leverage client resources in exchange for service in a peer-to-peer (P2P) fashion. Such peer-assisted service paradigms promise significant infrastructure cost reduction, but suffer from the unpredictability associated with client resources, which is often exhibited as an imbalance between the contribution and consumption of resources by clients. This imbalance hinders the ability to guarantee a minimum service fidelity of these services to the clients. In this thesis, we propose a novel architectural service model that enables the establishment of higher fidelity services through (1) coordinating the content delivery to optimally utilize the available resources, and (2) leasing the least additional cloud resources, available through special nodes (angels) that join the service on-demand, and only if needed, to complement the scarce resources available through clients. While the proposed service model can be deployed in many settings, this thesis focuses on peer-assisted content delivery applications, in which the scarce resource is typically the uplink capacity of clients. We target three applications that require the delivery of fresh as opposed to stale content. The first application is bulk-synchronous transfer, in which the goal of the system is to minimize the maximum distribution time -- the time it takes to deliver the content to all clients in a group. The second application is live streaming, in which the goal of the system is to maintain a given streaming quality. The third application is Tor, the anonymous onion routing network, in which the goal of the system is to boost performance (increase throughput and reduce latency) throughout the network, and especially for bandwidth-intensive applications. For each of the above applications, we develop mathematical models that optimally allocate the already available resources. They also optimally allocate additional on-demand resource to achieve a certain level of service. Our analytical models and efficient constructions depend on some simplifying, yet impractical, assumptions. Thus, inspired by our models and constructions, we develop practical techniques that we incorporate into prototypical peer-assisted angel-enabled cloud services. We evaluate those techniques through simulation and/or implementation. (Major Advisor: Azer Bestavros