Grid resource discovery based on distributed learning automata

Hasanzadeh, Mohammad; Meybodi, Mohammad Reza

doi:10.1007/s00607-013-0337-x

Cited by 28 publications

(10 citation statements)

References 29 publications

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“…So, an edge detection algorithm like CED that has lower MSE and higher PSNR means is a reliable one. The inverse relation of MSE and PSNR in (9) and (10) proves this relationship.…”

Section: Discussion Of Mse and Psnrsupporting

confidence: 55%

Cellular edge detection: Combining cellular automata and cellular learning automata

Mofrad

Sadeghi

Rezvanian

et al. 2015

AEU - International Journal of Electronics and Communications

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“…So, an edge detection algorithm like CED that has lower MSE and higher PSNR means is a reliable one. The inverse relation of MSE and PSNR in (9) and (10) proves this relationship.…”

Section: Discussion Of Mse and Psnrsupporting

confidence: 55%

Cellular edge detection: Combining cellular automata and cellular learning automata

Mofrad

Sadeghi

Rezvanian

et al. 2015

AEU - International Journal of Electronics and Communications

Self Cite

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“…Let the requested resources are not found in P10, so the query is bounced back to P0. Similarly, for the left side of P0, in the pessimistic status, we through the following routes (1) q: new incoming query (2) ClusterResource: a resource in cluster (3) Cache: a query result in cache table (4) BestNeighbor: a neighbor super-peer selected by goodness function (5) Neighbor: next neighbor super-peer (6) for incoming q do (7) ClusterResource � MatchQueryClusterResource (q) (8) if (ClusterResource � � null) then (9) Cache � MatchQueryCache (q) (10) if (Cache � � null) then (11) BestNeighbor � HRI (q, Neighbor) (12) if (BestNeighbor � � null) then (13) Receiver � Sender (q) (14) else (15) Receiver � BestNeighbor (16) end if (17) ForwardQueryToReceiver (q, Receiver) (18) else (19) SendResponseToRequester (q) (20) end if Wireless Communications and Mobile Computing…”

Section: Stability Of New Approachmentioning

confidence: 99%

“…ese methods collect the grid resource information and use the central index server to sustain them. e grid environments su er from two main drawbacks: bottleneck and single point of failure [10][11][12]. To avoid the mentioned problems, the P2P model has been adopted [3,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Resource Discovery Technique in the P2P Grid Systems

Wang

Pourqasem

Edalatpanah

2020

Wireless Communications and Mobile Computing

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Distributed discovery service is a main concept in the scalable and dynamic grid environments. In this paper, based on the super-peer technique, we propose a new topology for the grid discovery service. The model is designed in such a way that each super-peer within the cluster has the routing indices (RIs) based on cobweb and uses the hop-count routing index (HRI) to select the best neighbor. Besides, each super-peer includes a cache table, which stores the query and the query results. Furthermore, from the point of view of the response time and the number of submitted messages, we compare the new model with an existing method. An illustrative simulation is also presented to show the efficiency and validation of the new technique.

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“…28 Distributed learning automata is a network of learning automata which collectively cooperate to solve a particular problem with many applications including: Vehicle Routing Problem, 29 wireless networks, [30][31][32] data mining, 33 information retrieval, 34 complex networks 2,35 and grid computing. 36 A DLA can be modeled by a directed graph in which the set of vertices of graph constitutes the set of actions for corresponding automaton. When a LA selects one of its actions, another LA on the other end of edge corresponding to the selected action will be activated.…”

Section: Distributed Learning Automatamentioning

confidence: 99%

Distributed learning automata-based algorithm for community detection in complex networks

Khomami

Rezvanian

Meybodi

2016

Int. J. Mod. Phys. B

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Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

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Grid resource discovery based on distributed learning automata

Cited by 28 publications

References 29 publications

Cellular edge detection: Combining cellular automata and cellular learning automata

Cellular edge detection: Combining cellular automata and cellular learning automata

Optimizing Resource Discovery Technique in the P2P Grid Systems

Distributed learning automata-based algorithm for community detection in complex networks

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