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M.R.: A learning automatabased heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs
, 2010
"... During the last decades, a host of efficient algorithms have been developed for solving the minimum spanning tree problem in deterministic graphs, where the weight associated with the graph edges is assumed to be fixed. Though it is clear that the edge weight varies with time in realistic applicatio ..."
Abstract

Cited by 8 (4 self)
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During the last decades, a host of efficient algorithms have been developed for solving the minimum spanning tree problem in deterministic graphs, where the weight associated with the graph edges is assumed to be fixed. Though it is clear that the edge weight varies with time in realistic applications and such an assumption is wrong, finding the minimum spanning tree of a stochastic graph has not received the attention it merits. This is due to the fact that the minimum spanning tree problem becomes incredibly hard to solve when the edge weight is assumed to be a random variable. This becomes more difficult, if we assume that the probability distribution function of the edge weight is unknown. In this paper, we propose a learning automata‐based heuristic algorithm to solve the minimum spanning tree problem in stochastic graphs wherein the probability distribution function of the edge weight is unknown. The proposed algorithm taking advantage of learning automata determines the edges that must be sampled at each stage. As the presented algorithm proceeds, the sampling process is concentrated on the edges that constitute the spanning tree with the minimum expected weight. The proposed learning automata‐based sampling method decreases the number of samples that need to be taken from the graph by reducing the rate of unnecessary samples. Experimental results show the superiority of the proposed algorithm over the well‐known existing methods both in terms of the number of samples and the running time of algorithm.
Treestructured Data Regeneration in Distributed Storage Systems with Regenerating Codes
"... Abstract—Distributed storage systems provide largescale reliable data storage by storing a certain degree of redundancy in a decentralized fashion on a group of storage nodes. To recover from data losses due to the instability of these nodes, whenever a node leaves the system, additional redundancy ..."
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Cited by 4 (0 self)
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Abstract—Distributed storage systems provide largescale reliable data storage by storing a certain degree of redundancy in a decentralized fashion on a group of storage nodes. To recover from data losses due to the instability of these nodes, whenever a node leaves the system, additional redundancy should be regenerated to compensate such losses. In this context, the general objective is to minimize the volume of actual network traffic caused by such regenerations. A class of codes, called regenerating codes, has been proposed to achieve an optimal tradeoff curve between the amount of storage space required for storing redundancy and the network traffic during the regeneration. In this paper, we jointly consider the choices of regenerating codes and network topologies. We propose a new design, referred to as RCTREE, that combines the advantage of regenerating codes with a treestructured regeneration topology. Our focus is the efficient utilization of network links, in addition to the reduction of the regeneration traffic. With the extensive analysis and quantitative evaluations, we show that RCTREE is able to achieve a both fast and stable regeneration, even with departures of storage nodes during the regeneration.
Learning automatabased algorithms for solving stochastic minimum spanning tree problem
 Applied Soft Computing
, 2011
"... Due to the hardness of solving the minimum spanning tree (MST) problem in stochastic environments, the stochastic MST (SMST) problem has not received the attention it merits, specifically when the probability distribution function (PDF) of the edge weight is not a priori known. In this paper, we fir ..."
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Cited by 2 (2 self)
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Due to the hardness of solving the minimum spanning tree (MST) problem in stochastic environments, the stochastic MST (SMST) problem has not received the attention it merits, specifically when the probability distribution function (PDF) of the edge weight is not a priori known. In this paper, we first propose a learning automata‐based sampling algorithm (Algorithm 1) to solve the MST problem in stochastic graphs where the PDF of the edge weight is assumed to be unknown. At each stage of the proposed algorithm, a set of learning automata is randomly activated and determines the graph edges that must be sampled in that stage. As the proposed algorithm proceeds, the sampling process focuses on the spanning tree with the minimum expected weight. Therefore, the proposed sampling method is capable of decreasing the rate of unnecessary samplings and shortening the time required for finding the SMST. The convergence of this algorithm is theoretically proved and it is shown that by a proper choice of the learning rate the spanning tree with the minimum expected weight can be found with a probability close enough to unity. Numerical results show that Algorithm 1 outperforms the standard sampling method. Selecting a proper learning rate is the most challenging issue in learning automata theory by which a good trade off can be achieved between the cost and efficiency of algorithm. To improve the efficiency (i.e., the convergence speed and convergence rate) of Algorithm 1, we
unknown title
, 2010
"... A learning automatabased heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs ..."
Abstract
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A learning automatabased heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs
unknown title
"... A learning automatabased heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs ..."
Abstract
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A learning automatabased heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs