We consider iterative algorithms of the form x := f(x) , executed by a distributed network of processors. We first discuss a few possibilities for synchronous execution. We then consider asynchronous implementations w...
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We consider iterative algorithms of the form x := f(x) , executed by a distributed network of processors. We first discuss a few possibilities for synchronous execution. We then consider asynchronous implementations whereby each processor iterates on a different component of x , at its own pace, using the most recently received (but possibly outdated) information on the remaining components of x . While certain algorithms may fail to converge when implemented asynchronously, a large number of positive convergence results is available. We classify asynchronous algorithms into three main categories, depending on the amount of asynchronism they can tolerate, survey the corresponding convergence results, and discuss some applications
LYDIAN is an environment to support the teaching and learning of distributed algorithms. It provides a collection of distributed algorithms as well as continuous animations. Users can combine algorithms and animations...
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LYDIAN is an environment to support the teaching and learning of distributed algorithms. It provides a collection of distributed algorithms as well as continuous animations. Users can combine algorithms and animations with arbitrary network structures defining the interconnection and behavior of the distributed algorithm. Further, it facilitates the creation of algorithm descriptions as well as the creation of network structures. This makes LYDIAN a flexible tool to be used with students with different skills and backgrounds. This article gives an overview about various ideas and concepts behind LYDIAN by describing in detail the framework for an educational visualization and simulation environment for learning/teaching distributed algorithms as well as discussing possible extensions, which may improve possibilities for user interaction. Moreover, in our effort to understand better what visualization and simulation environments, such as LYDIAN, need to provide, we show results taken from a case study integrating LYDIAN in an undergraduate distributed-systems course.
DAJ (distributed algorithms in Java) is a framework for writing Java programs to implement distributed algorithms. The programs display the data structures at each node and enable the user to interactively construct s...
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DAJ (distributed algorithms in Java) is a framework for writing Java programs to implement distributed algorithms. The programs display the data structures at each node and enable the user to interactively construct scenarios. In a learning situation, active interactive execution is preferable to passively watching an animation. Programs have been implemented for commonly taught algorithms, including the Byzantine generals, mutual exclusion, termination, and snapshots. Adding a program for another algorithm requires only general Java programming experience, as the GUI aspects are encapsulated.
Given a set of demands between pairs of nodes, we examine the traffic engineering problem of maximal flow routing and fair bandwidth allocation where flows can be split to multiple paths (e.g., MPLS tunnels). In the p...
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Given a set of demands between pairs of nodes, we examine the traffic engineering problem of maximal flow routing and fair bandwidth allocation where flows can be split to multiple paths (e.g., MPLS tunnels). In the past we presented a polynomial solution for this problem but its complexity makes it hard to implement for large problem sizes. Thus, this paper presents a fully polynomial epsilon-approximation (FPTAS) algorithm for the max-min fair allocation problem which is based on a primal-dual alternation technique. In addition we present a fast and novel distributed algorithm where each source router can find the routing and the fair rate allocation for its commodities. We implemented the centralized algorithm to demonstrate its correctness, efficiency, and accuracy.
We consider large-scale networks with n nodes, out of which k are in possession, (e.g., have sensed or collected in some other way) k information packets. In the scenarios in which network nodes are vulnerable because...
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ISBN:
(纸本)9780769531571
We consider large-scale networks with n nodes, out of which k are in possession, (e.g., have sensed or collected in some other way) k information packets. In the scenarios in which network nodes are vulnerable because of, for example, limited energy or a hostile environment, it is desirable to disseminate the acquired information throughout the network so that each of the n nodes stores one (possibly coded) packet and the original k source packets can be recovered later in a computationally simple way from any (1 + isin)k nodes for some small isin > 0. We developed two distributed algorithms for solving this problem based on simple random walks and Fountain codes. Unlike all previously developed schemes, our solution is truly distributed, that is, nodes do not know n, k or connectivity in the network, except in their own neighborhoods, and they do not maintain any routing tables. In the first algorithm, all the sensors have the knowledge of n and k. In the second algorithm, each sensor estimates these parameters through the random walk dissemination. We present analysis of the communication/transmission and encoding/decoding complexity of these two algorithms, and provide extensive simulation results as well.
We propose a methodology to deploy large scale IEEE 802.11b telecommunications infrastructures, which are being currently used to provide broadband access in Spanish rural areas, as an expansion of shared asymmetric D...
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We propose a methodology to deploy large scale IEEE 802.11b telecommunications infrastructures, which are being currently used to provide broadband access in Spanish rural areas, as an expansion of shared asymmetric DVB-S or DVB-T gateways. Wireless broadband access networks have become possible with the advent of technologies like IEEE 802.11. Our approach is based on a set of basic rules to generate mesh topologies with survivability capabilities. These rules help to mitigate cochannel interference and inter-channel interference. Two infrastructure deployment algorithms are given: a distributed version, executed by infrastructure nodes themselves, and a centralized version derived from a mixed integer linear optimization model. Numerical results show that the optimization model is more efficient but the distributed algorithm is competitive, since it can be executed by the wireless network itself and generates good quality results.
We consider the distributed optimization problem, where a group of agents work together to optimize a common objective by communicating with neighboring agents and performing local computations. For a given algorithm,...
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ISBN:
(数字)9781728174471
ISBN:
(纸本)9781728174488
We consider the distributed optimization problem, where a group of agents work together to optimize a common objective by communicating with neighboring agents and performing local computations. For a given algorithm, we use tools from robust control to systematically analyze the performance in the case where the communication network is time-varying. In particular, we assume only that the network is jointly connected over a finite time horizon (commonly referred to as B-connectivity), which does not require connectivity at each time instant. When applied to the distributed algorithm DIGing, our bounds are orders of magnitude tighter than those available in the literature.
Three directory schemes are investigated: the local cache (LC) scheme in which there is a cache at the network nodes to store the results of a previous query for a remote resource so that a subsequent query for that r...
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Three directory schemes are investigated: the local cache (LC) scheme in which there is a cache at the network nodes to store the results of a previous query for a remote resource so that a subsequent query for that resource can be resolved locally, and two enhancements of this scheme, namely the regional cache server (RCS) scheme and the cooperating cache servers (CCS) scheme, in which the queries not resolved locally are funneled through designated nodes or servers to reduce multiple network searchers for a given resource. The performance of these schemes in terms of the network search rate is dependent on the cache sizes at the nodes and servers, the probability distribution for queuing resources, and the cache replacement policy used to displace resources from the cache to accommodate newly discovered ones. Two policies are investigated: A/sub 0/, which is the optimal policy for the LC scheme, and the least recently used policy, which is of practical importance.< >
We introduce distributed algorithms to find rate-optimal routes based on local knowledge of the pairwise error probability (reliability) matrix. The distributed algorithms are built by (re)-formulating optimization pr...
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We introduce distributed algorithms to find rate-optimal routes based on local knowledge of the pairwise error probability (reliability) matrix. The distributed algorithms are built by (re)-formulating optimization problems amenable to application of dual decomposition techniques. Convergence of our algorithms to the optimal routing matrix is guaranteed under mild conditions. Many rate-optimality criteria of practical interest can be casted in our framework including maximization of: i)worst user's rate; ii) weighted sum of rates; iii) product of rates; and iv) relay network rate. We test robustness of our algorithms to node mobility.
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