Dynamic P2P Environments Research Articles

Perspective of space and time based replica population organizing strategy in unstructured peer-to-peer networks

File replication and replica management are effective strategies to ensure file availability and improve the efficiency of user access in peer-to-peer (P2P) networks. In this paper, inspired by the replica population model in population ecology, we propose a replica population organizing strategy for unstructured P2P networks. We model the activities of a P2P node from a time-and-space perspective, and model a file׳s availability from the perspective of node accessibility. The strategy automatically adjusts the relationship between the replica population and the environment, as well as the relationship between the replica population and the demand of users. Replica reproduction is well controlled by the strategy to adapt to the dynamic nature of P2P networks. We employ a push–pull approach, which enables replicas to change adaptively, both in their quantity and their distribution. Experimental results show that our strategy is able to adapt to the dynamic P2P environments. It effectively improves file availability, boosts retrieval efficiency, and meanwhile reduces the cost in network bandwidth.

Propagation of Active Worms in P2P Networks: Modeling and Analysis

Active worms, a category of self-replicating malicious programs which could spread in an automated fashion and flood particular Peer-to-Peer(P2P) networks within very short time, have drawn significant attention. However, only limited number of studies focus on propagation model of active worms with fair consideration of P2P nodes' dynamic features consisting of P2P churn, random quarantine, regular immunization, dynamic fragmentation and etc. This paper proposes three propagation models of active worms under dynamic P2P environment, conducts a mathematical analysis on the propagation of active worms under presented models and provides extensive numerical studies to the impact of relevant parameters on active worms' propagation speed under dynamic P2P environment. Models presented in this paper are simple, effective and thus applicable for defending against active worms in real P2P networks.

State-based search strategy in unstructured P2P

As a major pattern for design of future heterogeneous Cloud and service-oriented networks, research on P2P networking is extremely important and could possibly significantly alter the way of implementation of large-scale of Internet applications–Cloud Computing. However, we will not reap all the benefits of utilising the distributed resources in future P2P Clouds, unless we have an efficient and scalable way to discover the desirable services regarding users’ needs. Through analysis of peer node’s multi-dimensional quality-of-service (QoS) attributes for resource discovery, it is found that peer node state’s information is essential for improving search performance in distributed systems. This paper presents a novel efficient search algorithm, named State-Based Search (SBS), according to peer node’s state information. This algorithm has been simulated and evaluated in dynamic P2P environments. By comparing other related interest-based algorithms, the experimental results show that the SBS algorithm is able to effectively improve performance by reducing search response time and achieving better load balance.

Query-Trail-Mediated Cooperative Behaviors of Peers in Unstructured P2P File Sharing Networks

We propose two types of autonomic and distributed cooperative behaviors of peers for peer-to-peer (P2P) file-sharing networks. Cooperative behaviors of peers are mediated by query trails, and allows the exploration of better trade-off points between file search and storage load balancing performance. Query trails represent previous successful search paths and indicate which peers contributed to previous file searches and were at the same time exposed to the storage load. The first type of cooperative behavior is to determine the locations of replicas of files through the medium of query trails. Placement of replicas of files on strong query trails contributes to improvement of search performance, but a heavy load is generated due to writing files in storage to peers on the strong query trails. Therefore, we attempt to achieve storage load balancing between peers, while avoiding significant degradation of the search performance by creating replicas of files in peers adjacent to peers on strong query trails. The second type of cooperative behavior is to determine whether peers provide requested files through the medium of query trails. Provision of files by peers holding requested files on strong query trails contributes to better search performance, but such provision of files generates a heavy load for reading files from storage to peers on the strong query trails. Therefore, we attempt to achieve storage load balancing while making only small sacrifices in search performance by having peers on strong query trails refuse to provide files. Simulation results show that the first type of cooperative behavior provides equal or improved ability to explore trade-off points between storage load balancing and search performance in a static and nearly homogeneous P2P environment, without the need for fine tuning parameter values, compared to replication methods that require fine tuning of their parameters values. In addition, the combination of the second type and the first type of cooperative behavior yields better storage load balancing performance with little degradation of search performance. Moreover, even in a dynamic and heterogeneous P2P environment, the two types of cooperative behaviors yield good ability to explore trade-off points between storage load balancing and search performance.

Evolutionary P2P Networking That Fuses Evolutionary Computation and P2P Networking Together

In the present paper, we propose an evolutionary P2P networking technique that dynamically and adaptively optimizes several P2P network topologies, in which all of the nodes are included at the same time, in an evolutionary manner according to given evaluation criteria. In addition, through simulations, we examine whether the proposed evolutionary P2P networking technique can provide reliable search capability in dynamic P2P environments. In simulations, we assume dynamic P2P environments in which each node leaves and joins the network with its own probability and in which search objects vary with time. The simulation results show that topology reconstruction by the evolutionary P2P networking technique is better than random topology reconstruction when only a few types of search objects are present in the network at any moment and these search objects are not replicated. Moreover, for the scenario in which the evolutionary P2P networking technique is more effective, we show through simulations that when each node makes several links with other nodes in a single network topology, the evolutionary P2P networking technique improves the reliable search capability. Finally, the number of links that yields more reliable search capability appears to depend on how often nodes leave and join the network.

Agent-based distributed decision-making in dynamic operational environments

The paper presents an approach and the supporting software tools for integration of intelligent multi-agent systems and the peer-to-peer (P2P) technologies to support the development of heterogeneous distributed decision making applications operating in dynamic ubiquitous environment. Particularly, the proposed technology integrates two software tools, MASDK 4.0 software tool and a P2P agent platform. MASDK 4.0 is provided with a graphical application specification language that supports the thorough and consistent conceptual analysis, detailed design, code generation and deployment of multi-agent systems applications including those operating in dynamic environments. P2P agent platform is a FIPA compliant middleware enabling transparent P2P interaction of agents operating in heterogeneous and ubiquitous environment. The platform is fully compatible with the multi-agent applications developed by MASDK 4.0. The combined application of MASDK 4.0 and P2P agent platform is demonstrated by a case study of the air traffic control and emergency management. In this case study a model of autonomous air traffic control is implemented by a set of distributed intelligent agents. This application is a typical example of a modem agent-based distributed decision making system operating in a dynamic P2P environment.

Sampling cluster endurance for peer-to-peer based content distribution networks

Several types of Content Distribution Networks are being deployed over the Internet today, based on different architectures to meet their requirements (e.g., scalability, efficiency and resiliency). Peer-to-peer (P2P) based Content Distribution Networks are promising approaches that have several advantages. Structured P2P networks, for instance, take a proactive approach and provide efficient routing mechanisms. Nevertheless, their maintenance can increase considerably in highly dynamic P2P environments. In order to address this issue, a two-tier architecture called Omicron that combines a structured overlay network with a clustering mechanism is suggested in a hybrid scheme. In this paper, we examine several sampling algorithms utilized in the aforementioned hybrid network that collect local information in order to apply a selective join procedure. Additionally, we apply the sampling algorithms on Chord in order to evaluate sampling as a general information gathering mechanism. The algorithms are based mostly on random walks inside the overlay networks. The aim of the selective join procedure is to provide a well balanced and stable overlay infrastructure that can easily overcome the unreliable behavior of the autonomous peers that constitute the network. The sampling algorithms are evaluated using simulation experiments as well as probabilistic analysis where several properties related to the graph structure are revealed.