SKIP: an efficient search mechanism in unstructured P2P networks

Shen, Wenwu; Su, Sen; Shuang, Kai; Yang, Fangchun

doi:10.1016/s1005-8885(09)60509-4

Cited by 9 publications

(8 citation statements)

References 5 publications

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“…The former gather meta-data about neighbors' collections, which are then exploited by a specific scoring function to rank neighbors with respect to their shared documents and the query content. Hence the query will be routed to the first k neighbors with the highest scores [20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Based Query Routing Approach for P2P Systems

Alanazi

Yeferny

2019

Future Internet

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Peer-to-peer (P2P) systems have offered users an efficient way to share various resources and access diverse services over the Internet. In unstructured P2P systems, resource storage and indexation are fully distributed among participating peers. Therefore, locating peers sharing pertinent resources for a specific user query is a challenging issue. In fact, effective query routing requires smart decisions to select a certain number of peers with respect to their relevance for the query instead of choosing them at random. In this respect, we introduce here a new query-oriented approach, called the reinforcement learning-based query routing approach (RLQR). The main goal of RLQR is to reach high retrieval effectiveness as well as a lower search cost by reducing the number of exchanged messages and contacted peers. To achieve this, the RLQR relies on information gathered from previously sent queries to identify relevant peers for forthcoming queries. Indeed, we formulate the query routing issue as the reinforcement learning problem and introduce a fully distributed approach for addressing it. In addition, RLQR addresses the well-known cold-start issue during the training stage, which allows it to improve its retrieval effectiveness and search cost continuously, and, therefore, goes quickly through the cold-start phase. Performed simulations demonstrate that RLQR outperforms pioneering query routing approaches in terms of retrieval effectiveness and communications cost.

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Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Based Query Routing Approach for P2P Systems

Alanazi

Yeferny

2019

Future Internet

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“…To tackle these drawbacks, we propose a new search mechanism avoiding the random selection based upon semantic overlay network construction. Unlike the existing methods of peers clustering that gather peers sharing similar documents [10] into the same cluster, our method organizes the P2P overlay network into semantic clusters of peers sharing similar knowledge bases. Indeed, in our proposal each peer p i in the network makes new connections to link friend peers.…”

Section: Rem In Dinmentioning

confidence: 99%

“…learning routing methods), we propose to organize the P2P network into clusters of peers sharing similar knowledge bases (semantic overlay). Unlike the existing methods of peers clustering that gather peers sharing similar documents [10] into the same cluster, our method organizes the P2P overlay network into semantic clusters of peers sharing similar knowledge bases. The knowledge bases contains information related to the past sent queries, query hits, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Our proposed method is based upon peer's query history instead of shared documents which allows to construct a network topology that improves the retrieval effectiveness and routing efficiency of methods based on query history. To the best of our knowledge, the existing methods [10] for constructing P2P semantic overlays networks are based on the common preferences of users. The preferences of the user are deduced from its shared documents, thus peers that share similar documents are grouped together.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Peer-to-Peer Semantic Overlay Network for Learning Query Routing

Yeferny¹,

Arour²,

Bouzeghoub³

2013

2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA)

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In unstructured P2P systems, peers organize themselves into a random overlay. A challenging problem in these systems is to efficiently locate appropriate peers to answer a specific query. Recently, research works have focused on methods based on query history, which use the historical information of past queries and query hits to build a local knowledge base per peer. When a peer forwards a given query, it runs a learning algorithm that evaluates the query against the local knowledge base in order to select a set of relevant peers to whom the query will be routed. If the current peer fails to select a sufficient number of relevant peers it floods the query through the random overlay network, which badly affects the routing efficiency and effectiveness. To address the unsuccessful relevant peers search problem, we propose to organize the P2P network into semantic clusters of peers sharing similar knowledge bases. We implemented the proposed approach, and tested (i) its retrieval effectiveness in term of recall and precision, (ii) its routing efficiency in term of messages traffic. Experimental results show that our approach improves the recall and precision metrics while it dramatically reduce network traffic.

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“…Random walk and Flooding are the most used searching techniques in unstructured networks . Unstructured networks are less scalable, produce high network overhead, and have lower search precision during searching data by directing queries to irrelevant peers . Though techniques such as use ant‐colony optimization techniques to improve the efficiency of Unet comparatively.…”

Section: Introductionmentioning

confidence: 99%

A community‐based social P2P network for sharing human life digital memories

Rahman

Merabti

Llewellyn‐Jones³

et al. 2020

Trans Emerging Tel Tech

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Social peer-to-peer (P2P) networks are usually designed by reflecting a user's interest/behavior for structuring the underlying network. Human interest is affected by various factors such as age, locality, and so on which changes after some time. The behavior when reflected in a network, results in peers moving within the network in order to connect the peer with peers of the same behavior/interest. Especially in community-based schemes when a peer leaves a community the data that a peer was sharing will not be accessible in the same community anymore. It has an effect on the performance of the network due to the inaccessibility of data and the unavailability of connections, which affect network robustness. We address this issue by considering entities in data in the form of digital memories of a user and structuring network according to entity-based communities. The simulation results for the proposed entity-based community are demonstrated, which shows the effect on network performance during varying network size and traffic.Abbreviations: EBCs, entity-based communities; IRF, interferon regulatory factor; P2P, peer-to-peer.Trans Emerging Tel Tech. 2020;31:e3866.wileyonlinelibrary.com/journal/ett of the individual, will result in peers moving within the network by disconnecting from some peers and connecting to others. The behavior of peers, that is, moving within a network, which we have identified is a distinct behavior in social P2P networks, which to the best of our knowledge has not been mentioned before. However, the two similar behaviors to the mentioned are: peers churn and peers' mobility. This behavior is different than peers churn where peers go completely off the network. 10,11 It is also different than the mobility of peers where peers move from one location to another. In the problem we identified, peers keep their credentials and location and are not disconnected from the network, which makes it different and distinct than the two issues. Community-based schemes, 12-14 for example, interest-based communities (IBCs) 15,16 are more affected by this behavior because any change in an individual's social network disconnects its peer from the whole community which makes the data inaccessible to all peers of that community. As described earlier that human interest is affected by many factors, which any change in these factors results in a change of a human interest. So, change in the interest of a person, when reflected in a social P2P network results in leaving the community by the peer and joining another community of the new interest. People capture their data using various devices such as camera, mobile phones, etc. which form their digital memories. A memory for life (M4L) system 17 is one of the grand challenges in UK which aims to capture, store, annotate, organize, analyze, and share an individual's digital memories. By analyzing digital memories, an M4L system will identify various entities such as places, people, events, etc. in digital memories and which will further be used to generate meanin...

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SKIP: an efficient search mechanism in unstructured P2P networks

Cited by 9 publications

References 5 publications

Reinforcement Learning Based Query Routing Approach for P2P Systems

Reinforcement Learning Based Query Routing Approach for P2P Systems

An Efficient Peer-to-Peer Semantic Overlay Network for Learning Query Routing

A community‐based social P2P network for sharing human life digital memories

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