An Efficient Clustering Algorithm for Peer-to- Peer Networks
Co. SEP
Sign up for access to the world's latest research
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Abstract
Peer-to-peer systems and applications have attracted much attention as they are more scalable than traditional client-server ones. To provide efficient communications among nodes in the network, node clustering can be utilized to avoid flooding messages. In this paper, a distributed node clustering algorithm was proposed which adopts a new way to choose originators; then the ns-2 simulator was applied to evaluate the proposed clustering algorithm. Experimental results showed that the proposed algorithm can achieve better clustering accuracy than existing algorithms for different types of network topologies. More importantly, the number of messages required for clustering is less than the compared algorithms.
Related papers
A Distributed Approach to Node Clustering in Decentralized Peer-to-Peer Networks
Connectivity-based node clustering has wide-ranging applications in decentralized peer-to-peer (P2P) networks such as P2P file sharing systems, mobile ad-hoc networks, P2P sensor networks, and so forth. This paper describes a Connectivity-based Distributed Node Clustering scheme (CDC). This scheme presents a scalable and efficient solution for discovering connectivity-based clusters in peer networks. In contrast to centralized graph clustering algorithms, the CDC scheme is completely decentralized and it only assumes the knowledge of neighbor nodes instead of requiring a global knowledge of the network (graph) to be available. An important feature of the CDC scheme is its ability to cluster the entire network automatically or to discover clusters around a given set of nodes. To cope with the typical dynamics of P2P networks, we provide mechanisms to allow new nodes to be incorporated into appropriate existing clusters and to gracefully handle the departure of nodes in the clusters. These mechanisms enable the CDC scheme to be extensible and adaptable in the sense that the clustering structure of the network adjusts automatically as nodes join or leave the system. We provide detailed experimental evaluations of the CDC scheme, addressing its effectiveness in discovering good quality clusters and handling the node dynamics. We further study the types of topologies that can benefit best from the connectivitybased distributed clustering algorithms like CDC. Our experiments show that utilizing message-based connectivity structure can considerably reduce the messaging cost and provide better utilization of resources, which in turn improves the quality of service of the applications executing over decentralized peer-to-peer networks.
A distributed data clustering algorithm in P2P networks
Applied Soft Computing, 2017
Clustering is one of the important data mining issues, especially for large and distributed data analysis. Distributed computing environments such as Peer-to-Peer (P2P) networks involve separated/scattered data sources, distributed among the peers. According to unpredictable growth and dynamic nature of P2P networks, data of peers are constantly changing. Due to the high volume of computing and communications and privacy concerns, processing of these types of data should be applied in a distributed way and without central management. Today, most applications of P2P systems focus on unstructured P2P systems. In unstructured P2P networks, spreading gossip is a simple and efficient method of communication, which can adapt to dynamic conditions in these networks. Recently, some algorithms with different pros and cons have been proposed for data clustering in P2P networks. In this paper, by combining a novel method for extracting the representative data, a gossip-based protocol and a new centralized clustering method, a Gossip Based Distributed Clustering algorithm for P2P networks called GBDC-P2P is proposed. The GBDC-P2P algorithm is suitable for data clustering in unstructured P2P networks and it adapts to the dynamic conditions of these networks. In the GBDC-P2P algorithm, peers perform data clustering operation with a distributed approach only through communications with their neighbours. The GBDC-P2P does not need to rely on a central server and it performs asynchronously. Evaluation results demonstrate the superior performance of the GBDC-P2P algorithm. Also, a comparative analysis with other well-established methods illustrates the efficiency of the proposed method.
A Cooperative Peer Clustering Scheme for Unstructured Peer-to-Peer Systems
International Journal of Peer to Peer Networks, 2013
This paper proposes a peer clustering scheme for unstructured Peer-to-Peer (P2P) systems. The proposed scheme consists of an identification of critical links, local reconfiguration of incident links, and a retaliation rule. The simulation result indicates that the proposed scheme improves the performance of previous schemes and that a peer taking a cooperative action will receive a higher profit than selfish peers.
Investigation of Peer Grouping Methods in Peer-to-Peer Computing Networks
International Journal of Computer Applications, 2013
The Peer-to-Peer (P2P) technology provides support to build virtual computing system over the Internet which is dedicated for large scale computation problems. In such systems to achieve higher scalability and decentralization participating peers are classified into different groups. In P2P computing systems each peer group is responsible to carry out certain functionality of the system. Selection of different peers in these peer groups i.e. grouping criterion is one of the issues which is to be used to improve performance of the P2P computing systems. In this paper we investigate different grouping strategies possible in P2P computing networks. To compare them parameters like reliability, scalability, execution time etc. are taken into account. This study shows that if participating peers in a peer group are spread over different geographic locations then it make system more reliable.
Distributed data clustering in multi-dimensional peer-to-peer networks
Proceedings of the Twenty-First …, 2010
Several algorithms have been recently developed for distributed data clustering, which are applied when data cannot be concentrated on a single machine, for instance because of privacy reasons or due to net-work bandwidth limitations, or because of the huge amount of distributed ...
Cluster-based Search Technique for P2P Systems
2008
Abstract:,We consider network,clustering as the way to improve,the perfor- mance,of locating data in unstructured,P2P systems. Connectivity-based Dis- tributed node Clustering (CDC), and SCM-based Distributed Clustering (SDC) are two major,protocols that allow partitioning a network,topology,into clus- ters, based on node connectivity. These protocols focus on the accuracy of the clustering scheme, i.e. using the Scale Coverage Measure (SCM), and its maintenance
IP-based Clustering for Peer-to-Peer Overlays
Journal of Software, 2007
The efficiency of overlay networks built on top of the IP network is often threatened by the mismatch between the topologies of the overlay and the underlying IP network, resulting in unnecessary traffic and increased latencies. Substantial improvement can be achieved by optimizing the logical links between overlay nodes to better match the IP network topology.
Affinity P2P: A self-organizing content-based locality-aware collaborative peer-to-peer network
Computer Networks, 2010
The last years have brought a dramatic increase in the popularity of collaborative Web 2.0 sites. According to recent evaluations, this phenomenon accounts for a large share of Internet traffic and significantly augments the load on the end-servers of Web 2.0 sites. In this paper, we show how collaborative classifications extracted from Web 2.0-like sites can be leveraged in the design of a self-organizing peer-to-peer network in order to distribute data in a scalable manner while preserving a high-content locality. We propose Affinity P2P (AP2P), a novel cluster-based locality-aware self-organizing peer-to-peer network. AP2P self-organizes in order to improve content locality using a novel affinity-based metric for estimating the distance between clusters of nodes sharing similar content. Searches in AP2P are directed to the cluster of interests, where a logarithmic-time parallel flooding algorithm provides high recall, low latency, and low communication overhead. The order of clusters is periodically changed using a greedy cluster placement algorithm, which reorganizes clusters based on affinity in order to increase the locality of related content. The experimental and analytical results demonstrate that the locality-aware cluster-based organization of content offers substantial benefits, achieving an average latency improvement of 45%, and up to 12% increase in search recall.
Node Clustering in Mobile Peer-to-Peer Multihop Networks
Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW'06)
In mobile peer-to-peer (MP2P) networks, nodes tend to gather together rather than scattered uniformly across the network area. This paper considers the clustering of peer nodes and its performance impact in MP2P networks. The model for node clustering based on a heavy-tail distribution is first introduced and then a topology generation method that produces a clustered network is presented. Experiments based on ns-2 simulation with AODV routing protocol and IEEE 802.11 MAC reveal that the clustered layout significantly degrades the network performance and the main trouble comes from the MAC layer mechanisms. Node clustering results in as much as 77.6% lower packet delivery ratio compared to random node distribution. Moreover, it results in larger variation in packet delivery service, and thus has a serious impact on QoS, which is important in MP2P networks.
Simple, efficient peer-to-peer overlay clustering in mobile, ad-hoc networks
2004
DHT-based peer-to-peer (P2P) overlays significantly reduce the ove rlay traffic that is needed to locate a random object on the overlay network. However, DHT-based overlays are often largely oblivious to the underlying physical network and only assign second-rate effort to the exploitation of physical proximity. Hence, a single overlay hop often amounts to an unnecessarily large number of physical hops. While this might at best be considered inefficient in stationary networks, it could prove disastrous in mobile (and wireless) networks, thus, effectively limiting the deployability of P2P overlays on top of mobile and wireless networks. We present an approach that forms clusters in DHT-based P2P overlays based on physical proximity. By grouping physically close nodes into common overlay clusters, we can decrease the number of physical hops per overlay hop. Thus, the amount of physical traffic generated by overlays deployed on top of mobile and wireless ad-hoc networks can be reduced significantly.
Related topics
Related papers
Clustering distributed data streams in peer-to-peer environments
Information Sciences, 2006
This paper describes a technique for clustering homogeneously distributed data in a peer-to-peer environment like sensor networks. The proposed technique is based on the principles of the K-Means algorithm. It works in a localized asynchronous manner by communicating with the neighboring nodes. The paper offers extensive theoretical analysis of the algorithm that bounds the error in the distributed clustering process compared to the centralized approach that requires downloading all the observed data to a single site. Experimental results show that, in contrast to the case when all the data is transmitted to a central location for application of the conventional clustering algorithm, the communication cost (an important consideration in sensor networks which are typically equipped with limited battery power) of the proposed approach is 0020-0255/$ -see front matter Ó Sciences 176 (2006Sciences 176 ( ) 1952Sciences 176 ( -1985 www.elsevier.com/locate/ins significantly smaller. At the same time, the accuracy of the obtained centroids is high and the number of samples which are incorrectly labeled is also small.
PCSM: an efficient multihop proximity aware clustering scheme for mobile peer-to-peer systems
Journal of Ambient Intelligence and Humanized Computing, 2018
A mobile peer-to-peer (MP2P) system results from overlaying a peer-to-peer (P2P) system over a mobile ad hoc network (MANET). A cluster/superpeer based architecture can be used as an efficient solution to reduce communication redundancy and network traffic induced by flooding. In this paper, we propose an efficient multihop Proximity aware Clustering Scheme for Mobile peer-to-peer systems (PCSM). PCSM is based on the physical proximity of peers and reduces the mismatch between the P2P overlay and the network layer. PCSM integrates three factors to allow the new peer to efficiently select the cluster to join, namely the number of physical hops, the cluster size and the availability of the clusterhead. Additionally, a maintenance process manages the mobility of peer. The simulation results show that our overlay topology fits the MANET underlay and that PCSM enhances the results of the search process in terms of the average file-discovery delay and falsenegative ratio. Additionally, PCSM performs better than the existing cluster-based P2P overlay regarding of load balancing and routing overhead.
HP2PC: Scalable Hierarchically-Distributed Peer-to-Peer Clustering
Proceedings of the 2007 SIAM International Conference on Data Mining, 2007
In distributed data mining models, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose a hierarchically-distributed peer-to-peer architecture and algorithm for data clustering (HP2PC). The architecture is based on a multi-layer overlay network of peer neighborhoods. Supernodes, which act as representatives of neighborhoods, are recursively grouped to form higher level neighborhoods. Peers at a certain level of the hierarchy cooperate within their respective neighborhoods to perform clustering. Using this model, we can partition the clustering problem in a modular way, solve each part individually, then successively combine clusterings up the hierarchy where increasingly global solutions are computed. The algorithm was applied to a distributed document clustering problem and achieved decent speedup with comparable clustering quality to the centralized approach.
Efficient data clustering over peer-to-peer networks
2011 11th International Conference on Intelligent Systems Design and Applications, 2011
Due to the dramatic increase of data volumes in different applications, it is becoming infeasible to keep these data in one centralized machine. It is becoming more and more natural to deal with distributed databases and networks. That is why distributed data mining techniques have been introduced. One of the most important data mining problems is data clustering. While many clustering algorithms exist for centralized databases, there is a lack of efficient algorithms for distributed databases. In this paper, an efficient algorithm is proposed for clustering distributed databases. The proposed methodology employs an iterative optimization technique to achieve better clustering objective. The experimental results reported in this paper show the superiority of the proposed technique over a recently proposed algorithm based on a distributed version of the well known K-Means algorithm (Datta et al. 2009) [1].
Finding the highest performance for one-to-many content distribution in peer-to-peer networks based on the clustering and loading bandwidth of peer Nodes
Considering the problem of IP multicast implementation in routers, in recent years, a lot of alternative methods have been introduced of the application layer multicast (ALM), for one-to-many content distribution. The present study aims to provide a new Algorithm in the field of ALM, to reduce the delay in peer to peer content distribution network (P2P), based on cooperation of M-ary and cluster nodes. All nodes which are close to one another gather in a cluster by means of a fixed number of landmarks that are known nodes. After the close nodes come to each other in a cluster, a tree structure is used to connect them. The algorithm is based on a cooperation between the source node and the content requesting nodes. In this algorithm, the source divides the content into blocks and the blocks are distributed in each cluster through m-ary trees that are all rooted in that source. Based on the mechanism used in this algorithm, all the participating nodes are used as a distributor of content, at least for one time. This algorithm exploits maximum upload capacity of the participating nodes and maximizes the final throughput. Due to the proximity of the nodes in each cluster, the delay in sub-trees of each cluster is less than the delay in similar techniques. On the other hand, the proximity of nodes causes the sub-trees not to be under much stress. Therefore, the final tree will have a very low delay and stress.
P2P Network Node Aggregation Algorithm Research Based on Community
The network nodes in a Peer‐to‐peer (P2P) system are aggregated at the application layer of the TCP/IP suite. because the application layer do not match the physical network, it reduced the performance of the application layer. To address the problem, this paper presents a community‐ based node aggregation algorithm in P2P networks. This algorithm takes into account the existing infrastructure of the community, the node to the existing network delay coding technology roadmap was used, Network nodes are divided into different communities so that the application‐ layer topology can be mapped to the physical‐layer structure. This algorithm is able to optimize the network utilization and provide insights for the theoretical research in P2P networks.
Decentralized Clustering In Pure P2P Overlay Networks Using Schelling's Model
2007 Ieee International Conference on Communications, 2007
Clustering involves arranging a P2P overlay network's topology so that peers having certain characteristics are grouped together as neighbors. Clustering can be used to organize a P2P overlay network so that requests are routed more efficiently. The peers lack of a global awareness of the overlay network's topology in a P2P network makes it difficult to develop algorithms for clustering peers. This paper presents two decentralized algorithms for clustering peers. The algorithms are concrete realizations of of an algorithm called the abstract Schelling's algorithm (based on a model from sociology by Thomas Schelling) that can be used to create a family of self-* topology adaptation algorithms for P2P overlay networks. The proposed clustering algorithms are easy to implement, are not designed for clustering on a specific criteria and do not require separate algorithms to handle the flux of peers on the overlay network. The paper presents simulation results for applying the algorithm on random small-world topologies.
Peer clustering
Proceedings of 5th ACM SIGCOMM workshop on Network and system support for games - NetGames '06, 2006
This paper proposes a hybrid architecture for distributed virtual environments, utilizing servers alongside peer-to-peer components. Current research into peer-based systems seeks to alleviate resource constraints, but it largely ignores a number of difficult problems, from bootstrapping and persistence to user authentication and system security (i.e., cheat resistance). This work proposes a hybrid architecture that turns the massive scale of the system from a problem into an asset, while still providing the features essential to a distributed virtual environment. Peers work together to distribute the workload, allowing redundant peer clusters to overcome failures and detect unacceptable behavior. The goal is to reduce cost and significantly increase the size of the concurrent user base while providing equivalent levels of robustness, persistence, and security. Simulations show that the hybrid architecture can handle massive populations.
A Hierarchical P2P Clustering Framework for Video Streaming Systems
Computer Standards & Interfaces, 2017
In this study, a novel overlay architecture for constructing hierarchical and scalable clustering of Peer-to-Peer (P2P) networks is proposed. The proposed architecture attempts to enhance the clustering of peers by incorporating join, split, merge and cluster leader election mechanisms in a fully distributed manner. It takes delay proximity of peers into account as distance measure. By constructing hierarchical clustering of peers, the control message overhead and maintenance such as host departure/host join overhead are decreased. Theoretical comparisons on overheads of the proposed system with that of other systems from literature are studied. The control mechanism for dynamic peer behaviour of the architecture is tested over PlanetLab. The performance metrics used are end-to-end delay, diameter, cluster head distance, occupancy rate, peer join latency, accuracy and correctness. The test results are compared with Hierarchical Ring Tree (HRT) and mOverlay architecture. In addition, a P2P video streaming application is run over the proposed network overlay. Streaming tests show that video streaming applications perform well in terms of received video quality if hierarchical clusters considering delay proximity are used as underlying network architecture.
CRAM: Clustering-based Resource Availability Measurement in Mobile Peer-to-Peer Systems
International Journal of Internet Protocol Technology, 2019
The peer-to-peer (P2P) systems are an alternative to solve the scalability issue raised by the client/server systems. P2P systems are composed of a set of entities that communicate directly without any central server and constitute scalable and robust distributed systems. In these systems, an entity only has a partial knowledge about the overlay state. The challenge is to provide a global knowledge in the system regarding a feature as the resources availability. In this paper, we propose a clustering-based resource availability measurement called CRAM for mobile P2P networks which relies on knowledge from all entities. CRAM can be used in resource search and in replication strategies to improve the network performances. Simulation results show that our estimation of availability is close to the real one such as the deviation between them can equal 0.008. They also show that our algorithm reacts efficiently to the appearance or depletion of a resource replica in the system.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.