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Deterministic Learning Automata Solutions to the Equipartitioning Problem
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Learning automata: an introduction
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Improvements to an Algorithm for Equipartitioning
IEEE Transactions on Computers
A fast distributed mapping algorithm
CONPAR 90 Proceedings of the joint international conference on Vector and parallel processing
Static scheduling algorithms for allocating directed task graphs to multiprocessors
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Learning Automata and Stochastic Optimization
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Clustering and reassignment-based mapping strategy for message-passing architectures
Journal of Systems Architecture: the EUROMICRO Journal
A Fixed-Structure Learning Automaton Solution to the Stochastic Static Mapping Problem
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 18 - Volume 19
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
IEEE Transactions on Computers
Guest editorial learning automata: theory, paradigms, and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized pursuit learning schemes: new families of continuous and discretized learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A vision for a stochastic reasoner for autonomic cloud deployment
Proceedings of the Second Nordic Symposium on Cloud Computing & Internet Technologies
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This paper considers the NP-hard problem of object assignment with respect to multiple constraints: assigning a set of elements (or objects) into mutually exclusive classes (or groups), where the elements which are "similar" to each other are hopefully located in the same class. The literature reports solutions in which the similarity constraint consists of a single index that is inappropriate for the type of multiconstraint problems considered here and where the constraints could simultaneously be contradictory. Such a scenario is illustrated with the static mapping problem, which consists of distributing the processes of a parallel application onto a set of computing nodes. This is a classical and yet very important problem within the areas of parallel computing, grid computing, and cloud computing. We have developed four learning-automata (LA)-based algorithms to solve this problem: First, a fixed-structure stochastic automata algorithm is presented, where the processes try to form pairs to go onto the same node. This algorithm solves the problem, although it requires some centralized coordination. As it is desirable to avoid centralized control, we subsequently present three different variable-structure stochastic automata (VSSA) algorithms, which have superior partitioning properties in certain settings, although they forfeit some of the scalability features of the fixed-structure algorithm. All three VSSA algorithms model the processes as automata having first the hosting nodes as possible actions; second, the processes as possible actions; and, third, attempting to estimate the process communication digraph prior to probabilistically mapping the processes. This paper, which, we believe, comprehensively reports the pioneering LA solutions to this problem, unequivocally demonstrates that LA can play an important role in solving complex combinatorial and integer optimization problems.