Unstructured P2P Systems Research Articles

ZRR-P2P: Zone-Based Mechanism for Data Replication and Research Optimization in Unstructured P2P Systems

Peer-to-peer systems (P2P) have enjoyed great popularity in terms of sharing content in distributed environments. In mobile P2P networks, how to improve the efficiency of the resource lookup has been an important topic of research. Among the existing solutions, we find lookup mechanisms based on data replication that can increase data availability and reduce search latency. On the other side, these solutions have certain limits such as in the selection of resources to be replicated and in increasing the storage space of peers with additional data. Therefore, this work ZRR-P2P (Zone-based mechanism for data Replication and Research optimization in P2P) comes with the aim of partitioning the network zone on sub-zones and of replicating popular data shared in the network. Thus, in order to improve the research process without increasing storage space of peers. The simulation results show that our strategy manages to increase data availability and improve the search process in terms of hops count and search latency, while avoiding increasing the storage space of peers.

Resource discovery mechanisms in pure unstructured peer-to-peer systems: a comprehensive survey

The concept of unstructured Peer-to-Peer (P2P) systems—setting free from any structural constraints—has put forward an appropriate paradigm for sharing a wide assortment of resources in a distributed-sharing manner efficiently. More importantly, unstructured P2P systems’ architecture and concepts have permeated diverse spheres of today’s successful and world-famous computer science areas, including NoSQL databases for excellently sharing data. However, pinpointing any given appropriate resource in such massive systems, namely unstructured P2P systems, is a challenging task; the two of the most dispensable rationales behind this proclaim are large scales of such systems and unstructured nature of overlay networks. Finding a decent resource with low response time, low bandwidth consumption, and high success-rate has played a crucial role in both the overall system performance and the functionality of P2P systems. Briefly, an efficacious resource discovery mechanism is the lifeblood of any productive P2P system. Given these points, in this study, we present an exhaustive survey on state-of-the-art resource discovery mechanisms employed in file-sharing pure unstructured P2P systems; we offer a new resource discovery categorization accordingly. Furthermore, we deeply delve into a plethora of resource searching methods and their merits as well as demerits to furnish the paper with an in-depth evaluation.

A Novel Data Consistence Model Based on Virtual Peers in Peer-to-Peer Systems

In recent years, peer-to-peer (P2P) systems have become a promising paradigm to provide efficient storage service in distributed environments. Although its effectiveness has been proven in many areas, the data consistency problem in P2P systems are still an opening issue. This article proposes a novel data consistence model, virtual peers-based data consistency (VPDC), which introduces a set of virtual peers to provide guaranteed data consistency in decentralized and unstructured P2P systems. The VPDC model can be easily implemented in any P2P system without introducing any interference to data retrieval. Theoretical analysis on VPDC is presented to analyze its effectiveness and efficiency, and massive experiments are conducted to evaluate the performance of a VPDC model in a real-world P2P system. The results indicate that it can significantly improve the data consistence of P2P systems and outperform many similar approaches in various experimental settings.

Search Unstructured Data Performance in P2P Networks Based on User Interest Model

Distributed (P2P) systems build up approximately coupled application-level overlays on high of the web to encourage affordable sharing of assets. They'll be generally delegated either organized or unstructured systems. while not requesting requirements over the topology, unstructured P2P systems is made appallingly speedily and ar so contemplated fitting to the web air. Be that as it may, the arbitrary pursuit strategies received by these systems now and then perform ineffectively with a larger than usual system Size. during this paper, we tend to search for to help the pursuit execution in unstructured P2P arranges through misusing clients' basic intrigue designs caught among a likelihood theoretic structure named the client intrigue model (UIM). an exploration convention and a directing table change convention ar increasingly arranged in order to speed up the hunt technique through self sorting out the P2P organize into somewhat world. Each hypothetical and test examination are Conducted and incontestable the viability and strength of our methodology.

Reinforcement Learning Based Query Routing Approach for P2P Systems

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.

Efficient Search and Lookup in Unstructured P2P Overlay Networks Inspired by Swarm Intelligence

Finding an efficient way to locate incomplete data in complex distributed systems is a challenging task and, due to dynamic nature of the Internet, requires to be updated constantly. As the problem refers to selection of an efficient search algorithm, different types of algorithms are proposed up to now. A huge complexity and dynamics presented in such systems imply a necessity of usage of an intelligent, self-organized solution. However, such intelligent algorithm should not possess an additional complexity. In this paper, we propose a new, simple and effective swarm-based metaheuristic for search and lookup in an unstructured P2P system inspired by behavior of bark beetles in nature. Also, a Physarum Polycephalum mechanism is adapted for this purpose. Both algorithms are compared with Dictyostelium discoideum (Dd)-slime mold, Gnutella, AntNet and k-Walker search mechanisms and tested by using two different models, Actor and Peer. The benchmarks measured by different metrics cover a parameter sensitivity analysis, comparative analysis and scalability analysis. Both algorithms show very promising results in terms of performance and scalability.

DPAS: A dynamic popularity-aware search mechanism for unstructured P2P systems

One of the pivotal challenges of unstructured Peer-to-Peer (P2P) systems is resource discovery. Search mechanisms generally utilize blind, or informed search strategies wherein nodes locally store metadata to quicken resource discovery time compared to blind search mechanisms. Dynamic behavior of P2P systems profoundly affects the performance of any deployed resource-searching mechanism and that of the whole system in turn. Therefore, efficient search mechanisms should be adaptable to the dynamic nature of P2P systems whose nodes frequently join and leave the system. Nonetheless, existing informed search mechanisms have failed to accord with dynamicity of P2P systems properly, thereby becoming inefficient. To address this issue, we put forth a new resource-searching mechanism called Dynamic Popularity-Aware Search mechanism (DPAS). Our mechanism estimates the dynamic responsiveness states of candidate nodes to direct search selection process by exploiting temporal number of hits, temporal penalty, and node heterogeneity. Besides, it controls the search scope at each step by estimating both the dynamic temporal popularity of resources and recently obtained results. It thus considers at each step of the search decision-making process to conform itself with the dynamics of P2P systems. Extensive experiments have demonstrated that DPAS has enhanced performance in comparison to other pertinent search mechanisms by virtue of an upsurge in the success-rate and decrease in the response time and bandwidth consumption.

LAAPS: an efficient file-based search in unstructured peer-to-peer networks using reinforcement algorithm

An efficient searching algorithm in the absence of control over the object locations is a challenging issue in the design of unstructured peer-to-peer (P2P) networks. In this paper, we report files searching constraints in the unstructured P2P systems. Hence, we exploit learning automata adaptive probabilistic search (LAAPS) algorithm for those constraints which are distributed entirely with adequate bandwidth. LAAPS optimize the learning automata using file container nodes which are leveraged with the types of file. In the simulation, we compare the proposed algorithm with the random probabilistic walk, adaptive search algorithm, and improved adaptive probabilistic search algorithm. The numerical result demonstrates that LAAPS improves the number of hits per query, achieving high success rate with significant message reduction.

Swarm-Inspired Routing Algorithms for Unstructured P2P Networks

Due to extreme complexity in nowadays networks, routing becomes a challenging task. This problem is especially delicate in unstructured P2P networks, as there is neither a global view on the network nor a global address mapping. Although different conventional solutions are commercially available, swarm-intelligent approaches are promising in case of frequently changing conditions in P2P networks. In this article, an approach inspired by Dictyostelium discoideum slime molds and bees with distributive and autonomous properties is proposed. Both bio-mechanisms are “tailored” for routing in unstructured P2P systems, resulting in swarm-inspired routing algorithms, SMNet and BeeNet. They are compared with three swarm-based routing algorithms and two conventional approaches. The benchmarks include parameter sensitivity-, comparative-, statistical- and scalability-analysis. SMNet outperforms the other algorithms in the comparative analysis regarding the average data packet delay, especially for bigger network sizes and data packet traffic levels. Both algorithms show good scalability.

CCLBR: Congestion Control-Based Load Balanced Routing in Unstructured P2P Systems

Given the growing popularity of the peer-to-peer (P2P) network systems in the recent years, efficient query routing under highly dynamic environments is still lacking in several P2P network systems. In response to this challenge, this paper proposes a new churn-resilient system to find alternative routing paths for the purpose of balancing the query loads under higher network churns and heavy workloads, ultimately to improve the search efficiency. Two novel methods are devised to balance the network query loads among both inter- and intragroup level peers. First, a resource grouping and a rewiring method is proposed to spontaneously organize and cluster the peers having same resources together. This strategy facilitates the peers to evolve the network into a cluster-like topology and balances the query loads among the intergroup peers. Second, a collaborative Q -learning method is proposed to balance the query loads among the intragroup peers in order to intelligently avoid queries being forwarded to the congested peers in the network. Experiments conducted under dynamic network scenarios demonstrate that our proposed method achieves better search performances with a more balanced network load than the existing methods, and further exhibits higher robustness and adaptability under higher network churns and heavy network loads.

A group-based data-driven approach for data synchronization in unstructured mobile P2P systems

This paper addresses the problem of data consistency on mobile peer-to-peer (MP2P) systems (or MP2P networks). The considered MP2P system is based on a mobile ad-hoc network, where the energy and connectivity on the mobile devices are limited. Since different mobile nodes may store copies of shared information, data synchronization on an MP2P system becomes crucial and challenging due to frequent disconnection and change on network topology caused by node mobility. We propose an effective approach to improving the performance of data synchronization with a dynamic inverted data indexing structure and group-based data-driven consistency management. The proposed approach can effectively synchronize the data items without using a central cache node in each group (or cluster) and perform well in terms of the coverage of successfully synchronized nodes and the number of redundant messages. Last, the proposed approach is validated through extensive simulation experiments.

A distributed data clustering algorithm in P2P networks

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.

Bio-inspired search algorithms for unstructured P2P overlay networks

Abstract Efficient location and manipulation of complex and often incomplete data is a difficult, challenging task in nowadays extremely complex IT systems and on the Internet, overwhelmed with a huge amount of information. The problem itself is present in numerous different practical use-cases (e.g., in P2P streaming applications that rapidly gain more attention) and refers to the selection of the proper, efficient search algorithm. Research and commercial efforts resulted in a prolific offer of different algorithms that try to address this problem in the best possible way. Due to the huge complexity, intelligent algorithms are the most promising ones. However, everyday changing conditions impose finding even more advantageous approaches that will better cope with the problem, or at least address some “corner cases” better, than previously realized ones. In this paper, we propose a self-organizing approach inspired by bio-intelligence of slime molds that possesses distributive and autonomous properties with the goal to achieve a good query capability. A slime mold mechanism is adapted for search in an unstructured P2P system, and compared with Antnet and Gnutella search mechanisms. The benchmarks cover parameter sensitivity analysis, and comparative analysis. To validate the obtained results, a statistical analysis is performed. The obtained results show good scalability of slime mold algorithm and point to the selected “corner” cases where the slime mold algorithm has a total good performance (measured by different metrics).

Alleviating the topology mismatch problem in distributed overlay networks: A survey

Peer-to-peer (P2P) systems have enjoyed immense attention and have been widely deployed on the Internet for well over a decade. They are often implemented via an overlay network abstraction atop the Internet’s best-effort IP infrastructure. P2P systems support a plethora of desirable features to distributed applications including anonymity, high availability, robustness, load balancing, quality of service and scalability to name just a few. Unfortunately, inherent weaknesses of early deployments of P2P systems, prevented applications from leveraging the full potential of the paradigm. One major weakness, identified early on, is the topology mismatch problembetween the overlay network and the underlying IP topology. This mismatch can impose an extraordinary amount of unnecessary stress on network resources and can adversely affect both the scalability and efficiency of the operating applications. In this paper, we survey over a decade’s worth of research efforts aimed at alleviating the topology mismatch problem in both structured and unstructured P2P systems. We provide a fine-grained categorization of the suggested solutions by discussing their novelty, advantages and weaknesses. Finally, we offer an analysis as well as pictorial comparisons of the reviewed approaches since we aim to offer a comprehensive reference for developers, system architects and researchers in the field.

Impacts of Selfish Behaviors on the Scalability of Hybrid Client–Server and Peer-to-Peer Caching Systems

This paper considers a hybrid peer-to-peer (p2p) system, a dynamic distributed caching system with an authoritative server dispensing contents only if the contents fail to be found by searching an unstructured p2p system. We study the case when some peers may not be fully cooperative in the search process and examine the impact of various noncooperative behaviors in the aspect of scalability, more specifically average server load and average peer load as the peer population size increases. We categorize selfish peers into three classes: impatient peers that directly query the server without searching the p2p system, non-forwarders that refuse to forward query requests, and non-resolvers that refuse to share contents. It is shown that in the hybrid p2p system, impatient and/or non-forwarding behaviors prevent the system from scaling well because of the high server load, while the system scales well under the non-resolving selfish peers. Our study implies that the hybrid p2p system does not mandate an incentive mechanism for content sharing, which is in stark contrast to unstructured p2p systems, where incentivizing peers to share contents is known to be a key factor for the system's scalability.

Gossip‐Based Resource Location Strategy in Interest Community for P2P Networks

Resource location strategy has become very popular for mass data distribution in complex and dynamic network. Searching for the objects is a fundamental problem to unstructured Peer to peer (P2P) networks. In this paper, we propose a resource location strategy for user requirements, which employs the collaborative exchange of information between peers to construct interest communities. This strategy can gather similar peers and disseminate useful information among them. Furthermore, we design an algorithm in unstructured P2P systems named Appropriate degree gossip search algorithm (ADGSA). Using this search algorithm, the performance including search success ratio, recall rate and search response time has been improved dramatically. An efficient resource location system with a fast organization of users cluster based on their requirements can be provided, preventing the creation of unliked communities. The simulation results show that our strategy has better search efficiency, short response time and high recall ratio.

BSI: Bloom Filter-Based Semantic Indexing for Unstructured P2P Networksh

Resource management and search is very important yet challenging in large-scale distributed systems like P2Pnetworks. Most existing P2P systems rely on indexing to efficiently route queries over the network. However, searches based on such indices face two key issues. First, majority of existing search schemes often rely on simply keyword based indices that can only support exact string based matches without taking into account the meaning of words. Second it is difficult, if not impossible, to devise query based indexing schemes that can represent all possible concept combinations without resulting in exponential index sizes. To address these problems, we present BSI, a novel P2P indexing and query routing strategy to support semantic based content searches. The BSI indexing structure captures the semantic content of documents using a reference ontology. Our indexing scheme can efficiently handle multi-concept queries by maintaining summary level information for each individual concept and concept combinations using a novel space-efficient Two-level Semantic Bloom Filter(TSBF) data structure. By using TSBFs to represent a large document and query base, BSI significantly reduces the communication cost and storage cost of indices. Furthermore, We devise a low-overhead mechanism to allow peers to dynamically estimate the relevance strength of a peer for multi-concept queries with high accuracy solely based on TSBFs. We also propose a routing index compression mechanism to observe peers’ dynamic storage limitations with minimal loss of information by exploiting a reference ontology structure. Based on the proposed index structure, we design a novel query routing algorithm that exploits semantic based information to route queries to semantically relevant peers. Performance evaluation demonstrates that our proposed approach can improve the search recall of unstructured P2P systems up to 383.71% while keeping the communication cost at a low level compared to state-of-art search mechanism OSQR [7].

Unifying Models of Churn and Resilience for Unstructured P2P Graphs

Previous analytical results on the resilience of unstructured P2P systems have not explicitly modeled heterogeneity of user churn (i.e, difference in online behavior) or the impact of in-degree on system resilience. To overcome these limitations, we introduce a unifying model of heterogeneous user churn and derive the distribution of the various metrics observed in prior experimental studies. We also show that the arrival process of in-edges to each user converges to Poisson when system size tends to infinity, model transient behavior of in-degree, and apply these results to obtain the joint in/out-degree isolation probability.

An Improved Multipoint Relaying Scheme for Message Propagation in Distributed Peer-to-Peer System

Message propagation is a primary means to locate resources in current unstructured peer-to-peer (P2P) systems and how to reduce effectively the redundant messages of propagation while keeping its high network coverage is what many researchers go in for in this field. In this paper, we propose the algorithm of LMPR, a scheme of improved MPR for message propagation in distributed unstructured P2P system. Based on analyzing the process and message redundancy of MPR, this paper proposes to add to such scheme a mechanism of specified relay-list to reduce further the number of its retransmitting of message. By attaching to each relay-peer a relay-list with peers it will relay message to, the scheme can assure that in the view of each decision-peer a broadcast message can be dispatched and only be dispatched once to every neighbor peer within 2 hops of it. Simulation results show that the scheme of LMPR has better performance than that of MPR in message redundancy, network overhead, and fluctuation brought to the system.

An efficient and distributed file search in unstructured peer-to-peer networks

Peer-to-peer (P2P) networks are gaining increased attention from both the scientific community and the larger Internet user community. Data retrieval algorithms lie at the center of P2P networks, and this paper addresses the problem of efficiently searching for files in unstructured P2P systems. We propose an Improved Adaptive Probabilistic Search (IAPS) algorithm that is fully distributed and bandwidth efficient. IAPS uses ant-colony optimization and takes file types into consideration in order to search for file container nodes with a high probability of success. We have performed extensive simulations to study the performance of IAPS, and we compare it with the Random Walk and Adaptive Probabilistic Search algorithms. Our experimental results show that IAPS achieves high success rates, high response rates, and significant message reduction.