A novel self-adaptive search algorithm for unstructured peer-to-peer networks utilizing learning automata

Ghorbani, Mahdi; Meybodi, Mohammad Reza; Saghiri, Ali Mohammad

doi:10.1109/rios.2013.6595306

Cited by 10 publications

(2 citation statements)

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“…Learning automata have found applications in many areas, such as sensor networks [27][28][29][30], wireless data broadcasting systems [31][32][33], cognitive networks [34][35][36], mesh networks [37], peer-to-peer networks [38][39][40][41][42], channel assignment [43], image processing [44], neural networks engineering [45,46] and evolutionary computing [47,48], to mention a few.…”

Section: Learning Automatamentioning

confidence: 99%

A distributed adaptive landmark clustering algorithm based on mOverlay and learning automata for topology mismatch problem in unstructured peer‐to‐peer networks

Saghiri

Meybodi

2015

Int J Communication

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Peer-to-peer networks are overlay networks that are built on top of communication networks that are called underlay networks. In these networks, peers are unaware of the underlying networks, so the peers choose their neighbors without considering the underlay positions, and therefore, the resultant overlay network may have mismatches with its underlying network, causing redundant end-to-end delay. Landmark clustering algorithms, such as mOverlay, are used to solve topology mismatch problem. In the mOverlay algorithm, the overlay network is formed by clusters in which each cluster has a landmark peer. One of the drawbacks of mOverlay is that the selected landmark peer for each cluster is fixed during the operation of the network. Because of the dynamic nature of peer-to-peer networks, using a non-adaptive landmark selection algorithm may not be appropriate. In this paper, an adaptive landmark clustering algorithm obtained from the combination of mOverlay and learning automata is proposed. Learning automata are used to adaptively select appropriate landmark peers for the clusters in such a way that the total communication delay will be minimized. Simulation results have shown that the proposed algorithm outperforms the existing algorithms with respect to communication delay and average round-trip time between peers within clusters. peers in the overlay network. This graph is called as overlay graph. The topology of the underlay network can be represented by a graph G′ = (V′, E′) in which V′ = {position 1 , position 2 , …, position n } is a set of positions in the underlay network and E′ ⊆ V′ × V′ is a set of links connecting the positions in the underlay network. In peer-to-peer networks, each peer in V is mapped to a position in V′ according to a one-to-one function H : V → V′. In landmark clustering algorithms, the overlay graph is formed by clusters in which each cluster has a landmark peer. The peers that are not selected as landmark peers are called ordinary peers. The communication between ordinary peers should be handled by landmark peers. Let C i (where i ∈ {1, 2, 3, 4, …, p}) represent the set of p clusters in the network and L i ∈ V denote the landmark peer of cluster C i . The topology of the landmark peers network can be represented by a graph G l = (V l , E l ) in which V l ⊆ V is a set of landmark peers and E l ⊆ V × V is a set of links connecting the landmark peers in the overlay network. This graph is called landmark graph. In the peer-to-peer network that is constructed based on the landmark clustering algorithms, each landmark peer in V l is mapped to several ordinary peers in V according to a one-to-many function H′ : V l → V. Each landmark peer in V l is mapped to several positions in V′ using H′ and H. Figure 1 shows an example of a peer-to-peer network that uses two clusters to manage the overlay topology. In this example, peer m and peer j are two landmark peers.A primary goal of landmark clustering algorithms is to improve the performance of the overlay network in terms of communication dela...

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Section: Learning Automatamentioning

confidence: 99%

A distributed adaptive landmark clustering algorithm based on mOverlay and learning automata for topology mismatch problem in unstructured peer‐to‐peer networks

Saghiri

Meybodi

2015

Int J Communication

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“…In the IS method, rst for each peer, a pro le is built and then this pro le is used for the sending query. Ghorbani et al [31] presented a self-adaptive resource discovery in the unstructured P2P environment based on the feedback of the network nodes. ey also used the learning automata (LA) algorithm [32] to educate peers in the discovery process for nding the best neighbor peer.…”

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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