Resolving the Topology Mismatch Problem in Unstructured Peer-to-Peer Networks

Hsiao, Hung‐Chang; Liao, Hao; Huang, Cheng-Chyun

doi:10.1109/tpds.2009.24

Cited by 20 publications

(2 citation statements)

References 52 publications

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“…SMF can also be utilized in different applications, e.g., to adjust the connections for clustering influential peers, such as the clustering approach proposed in [18], [24], [39], [58], and [60], or to reconstruct a topology, like the minimal spanning tree technique used in [30], [31], and [61]. By utilizing the statistical matrix form, we can obtain more precise information and further improve the search performance in P2P networks.…”

Section: Discussionmentioning

confidence: 99%

“…The second type of search mechanism attempts to construct an optimized overlay topology. In unstructured P2P systems, peers join and leave the network in a random fashion, which induces the so-called topology mismatch problem [23], [31], [45] between the physical and logical networks. To reduce the traffic overhead in an inefficient topology, several algorithms have been developed to transform the original topology into a minimum spanning tree (MST) [33], [43], [67].…”

Section: Related Workmentioning

confidence: 99%

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Improving the Search Mechanism for Unstructured Peer-to-Peer Networks Using the Statistical Matrix Form

2015

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In a traditional file search mechanism, such as flooding, a peer broadcasts a query to its neighbors through an unstructured peer-to-peer (P2P) network until the time-to-live decreases to zero. A major disadvantage of flooding is that, in a large-scale network, this blind-choice strategy usually incurs an enormous traffic overhead. In this paper, we propose a method, called the statistical matrix form (SMF), which improves the flooding mechanism by selecting neighbors according to their capabilities. The SMF measures the following peer characteristics: 1) the number of shared files; 2) the content quality; 3) the query service; and 4) the transmission distance between neighbors. Based on these measurements, appropriate peers can be selected, thereby reducing the traffic overhead significantly. Our experimental results demonstrate that the SMF is effective and efficient. For example, compared with the flooding search mechanism in dynamic unstructured P2P networks, the SMF reduces the traffic overhead by more than 80%. Moreover, it achieves a good success rate and shorter response times.INDEX TERMS Unstructured peer-to-peer networks, flooding search mechanism, traffic overhead, statistical matrix form.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Improving the Search Mechanism for Unstructured Peer-to-Peer Networks Using the Statistical Matrix Form

2015

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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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QuickFlood: An Efficient Search Algorithm for Unstructured Peer-to-Peer Networks

Barjini

Othman

Ibrahim

2011

Networked Digital Technologies

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Resolving the Topology Mismatch Problem in Unstructured Peer-to-Peer Networks

Cited by 20 publications

References 52 publications

Improving the Search Mechanism for Unstructured Peer-to-Peer Networks Using the Statistical Matrix Form

Improving the Search Mechanism for Unstructured Peer-to-Peer Networks Using the Statistical Matrix Form

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

QuickFlood: An Efficient Search Algorithm for Unstructured Peer-to-Peer Networks

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