4,679 research outputs found

    Practical algorithms for distributed network control

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    Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 135-138).Optimal routing and scheduling algorithms have been studied for decades, however several practical issues prevent the adoption of these network control policies on the Internet. This thesis considers two distinct topics in distributed network control: (i) maximizing throughput in wireless networks using network coding, and (ii) deploying controllable nodes in legacy networks. Network coding is a relatively new technique that allows for an increase in throughput under certain topological and routing conditions. The first part of this thesis considers jointly optimal routing, scheduling, and network coding strategies to maximize throughput in wireless networks. We introduce a simple network coding strategy and fully characterize the region of arrival rates supported. We propose a centralized dynamic control policy for routing, scheduling, and our network coding strategy, and prove this policy to be throughput optimal subject to our coding constraint. We further propose a distributed control policy based on random access that optimizes for routing, scheduling, and pairwise coding, where pairwise coding captures most of the coding opportunities on random topologies. We prove this second policy to also be throughput optimal subject to the coding constraint. Finally, we reduce the gap between theory and practice by identifying and solving several problems that may occur in system implementations of these policies. Throughput optimal policies typically require every device in the network to make dynamic routing decisions. In the second part of this thesis, we propose an overlay routing architecture such that only a subset of devices (overlay nodes) need to make dynamic routing decisions, and yet maximum throughput can still be achieved. We begin by formulating an optimization problem that searches for the minimum overlay node placement that achieves maximum throughput. We devise an efficient placement algorithm which solves this problem optimally for networks not subject to interference constraints. Then we propose a heuristic control policy for use at overlay nodes, and show by simulation that this policy performs optimally in all studied scenarios.by Nathaniel Matthew Jones.Ph. D

    Fine Grained Component Engineering of Adaptive Overlays: Experiences and Perspectives

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    Recent years have seen significant research being carried out into peer-to-peer (P2P) systems. This work has focused on the styles and applications of P2P computing, from grid computation to content distribution; however, little investigation has been performed into how these systems are built. Component based engineering is an approach that has seen successful deployment in the field of middleware development; functionality is encapsulated in ‘building blocks’ that can be dynamically plugged together to form complete systems. This allows efficient, flexible and adaptable systems to be built with lower overhead and development complexity. This paper presents an investigation into the potential of using component based engineering in the design and construction of peer-to-peer overlays. It is highlighted that the quality of these properties is dictated by the component architecture used to implement the system. Three reusable decomposition architectures are designed and evaluated using Chord and Pastry case studies. These demonstrate that significant improvements can be made over traditional design approaches resulting in much more reusable, (re)configurable and extensible systems

    Optimal Network Control in Partially-Controllable Networks

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    The effectiveness of many optimal network control algorithms (e.g., BackPressure) relies on the premise that all of the nodes are fully controllable. However, these algorithms may yield poor performance in a partially-controllable network where a subset of nodes are uncontrollable and use some unknown policy. Such a partially-controllable model is of increasing importance in real-world networked systems such as overlay-underlay networks. In this paper, we design optimal network control algorithms that can stabilize a partially-controllable network. We first study the scenario where uncontrollable nodes use a queue-agnostic policy, and propose a low-complexity throughput-optimal algorithm, called Tracking-MaxWeight (TMW), which enhances the original MaxWeight algorithm with an explicit learning of the policy used by uncontrollable nodes. Next, we investigate the scenario where uncontrollable nodes use a queue-dependent policy and the problem is formulated as an MDP with unknown queueing dynamics. We propose a new reinforcement learning algorithm, called Truncated Upper Confidence Reinforcement Learning (TUCRL), and prove that TUCRL achieves tunable three-way tradeoffs between throughput, delay and convergence rate
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