Super-peer Selection Research Articles

Super peer selection strategy in peer‐to‐peer networks based on learning automata

SummaryA super peer is a peer that has the highest capacity in comparison with other peers in the network. It is trying to reduce the load of the rest of the peers and improve network performance. Selecting a super peer in a peer‐to‐peer–based network is a very crucial challenge. As the ability of peers are very different, by considering capacity of each peer and selecting a proper role, we can use network components much more efficiently. Because of the dynamicity of these networks, comparative methods of selecting super peers is of special importance. Comparative selection is continuously trying to select proper super peer. In recent studies, learning automata was introduced as a powerful learning model to solve this issue. In most of the studies, learning automata with an S model is employed. In this article, another selection method of learning automata with a P model environment is presented and its capability for super peer selection is shown. Moreover, simulation results show that removing some of the super peers would result in better performance in terms of inversion time in the high level of super‐peer capacity, required time for selecting proper super peer, and super peer tolerance.

An adaptive super-peer selection algorithm considering peers capacity utilizing asynchronous dynamic cellular learning automata

Super-peer networks refer to a class of peer-to-peer networks in which some peers called super-peers are in charge of managing the network. A group of super-peer selection algorithms use the capacity of the peers for the purpose of super-peer selection where the capacity of a peer is defined as a general concept that can be calculated by some properties, such as bandwidth and computational capabilities of that peer. One of the drawbacks of these algorithms is that they do not take into consideration the dynamic nature of peer-to-peer networks in the process of selecting super-peers. In this paper, an adaptive super-peer selection algorithm considering peers capacity based on an asynchronous dynamic cellular learning automaton has been proposed. The proposed cellular learning automaton uses the model of fungal growth as it happens in nature to adjust the attributes of the cells of the cellular learning automaton in order to take into consideration the dynamicity that exists in peer-to-peer networks in the process of super-peers selection. Several computer simulations have been conducted to compare the performance of the proposed super-peer selection algorithm with the performance of existing algorithms with respect to the number of super-peers, and capacity utilization. Simulation results have shown the superiority of the proposed super-peer selection algorithm over the existing algorithms.

A Delay Aware Super-Peer Selection Algorithm for Gradient Topology Utilizing Learning Automata

Peer-to-peer network is organized on top of another network as an overlay network. Super peer network is one of the peer-to-peer networks. A super peer, in a super peer based network, is a peer that has more responsibility than other peers have and is responsible for some of the tasks of network management. Since different peers vary in terms of capabilities, selecting a super peer is a challenge in super peer based networks. Gradient topology is of the networks based on super peers. Existing adaptive algorithms, which have been proposed to select super peer in gradient topology, are not aware of delays among the peers. In this paper, the proposed algorithm being aware of the delay among super peers, using learning automata, which is a reinforcement model of learning, selects the new super peers in an adaptive manner. According to the simulation results, the proposed algorithm with respect to the average end-to-end delay in community of super peers, and error in the super peer selection, has better performance than existing algorithms.

An adaptive algorithm for super-peer selection considering peer’s capacity in mobile peer-to-peer networks based on learning automata

Mobile peer-to-peer (MP2P) networks refer to the peer-to-peer overlay networks superimposing above the mobile ad-hoc networks. Heterogeneity of capacity and mobility of the peers as well as inherent limitation of resources along with the wireless networks characteristics are challenges on MP2P networks. In some MP2P networks, in order to improve network performances, special peers, are called super-peers, undertake to perform network managerial tasks. Selection of super-peers, due to their influential position, requires a protocol which considers the capacity of peers. Lack of general information about the capacity of other peers, as well as peers mobility along with dynamic nature of MP2P networks are the major challenges that impose uncertainty in decision making of the super-peer management algorithms. This paper proposes an adaptive super-peer selection algorithm considering peers capacity based on learning automata in MP2P networks, called SSBLA. In the proposed algorithm, each peer is equipped with a learning automaton which is used locally in the operation of super-peer selection by that peer. It has been shown that the suggested algorithm is superior to the existing algorithms. The results of the simulation show that the proposed algorithm can maximize capacity utilization by minimum number of super-peer and improve robustness against failures of super-peers while minimizing selection communication overhead.

A Dynamic SGP Selection Algorithm based on Evolutionary Game for Hybrid P2P Streaming System

Due to high efficiency and good scalability, hierarchical hybrid P2P architecture has drawn more and more attentions in P2P video streaming applications recently. The problem about Super Group Peers (SGPs) selection, which is the key problem in hierarchical hybrid P2P architecture, is becoming highly challenging because super peers must be selected from a huge and dynamically changing network. In this paper, we propose a SGP selection game model based on evolutionary game framework and analyze its evolutionarily stable strategies in theory. Moreover, we propose a distributed Q-Learning algorithm, which has the ability to make the peers converge to the ESSs based on its own payoff history. Compared to the randomly super peer selection scheme in traditional P2P streaming systems, experiment results show that the proposed algorithm achieves better performance in terms of social welfare, average upload rates of SGPs, and keeps the upload capacity of the P2P streaming system increasing steadily with the number of peers increasing.

A Distributed Dynamic Super Peer Selection Method Based on Evolutionary Game for Heterogeneous P2P Streaming Systems

Due to high efficiency and good scalability, hierarchical hybrid P2P architecture has drawn more and more attention in P2P streaming research and application fields recently. The problem about super peer selection, which is the key problem in hybrid heterogeneous P2P architecture, is becoming highly challenging because super peers must be selected from a huge and dynamically changing network. A distributed super peer selection (SPS) algorithm for hybrid heterogeneous P2P streaming system based on evolutionary game is proposed in this paper. The super peer selection procedure is modeled based on evolutionary game framework firstly, and its evolutionarily stable strategies are analyzed. Then a distributedQ-learning algorithm (ESS-SPS) according to the mixed strategies by analysis is proposed for the peers to converge to the ESSs based on its own payoff history. Compared to the traditional randomly super peer selection scheme, experiments results show that the proposed ESS-SPS algorithm achieves better performance in terms of social welfare and average upload rate of super peers and keeps the upload capacity of the P2P streaming system increasing steadily with the number of peers increasing.

RobP2P: A Robust Architecture for Resource Sharing in Mobile Peer-to-Peer Networks

Peer-to-peer (P2P) systems are constructed to provide resource sharing among interested participants (peers) in a dis- tributed and self-organized fashion. The way P2P networks are formed is critical to the overall system performance due to communications and network maintenance overhead. Mobile environments pose additional challenges on P2P networks due to heterogeneity of nodes, inherent limited resources, dynamic context and wireless network characteris- tics. This paper presents RobP2P, a robust architecture to construct mobile P2P networks and efficiently maintain the network state. RobP2P introduces a novel super-peer selection protocol based on an aggregate utility function that takes into account peers’ capability and context. It also presents an agile scheme through which super-peers can delegate their responsibilities to more powerful and stable joining or existing peers. Our simulation results show that the RobP2P is efficient, less prone to failure, and generates lower overhead traffic, while reliably maintaining the consistency of network state.

An efficient selection algorithm for building a super-peer overlay

Super-peer overlay provides an efficient way to run applications by exploring the heterogeneity of nodes in a Peer-to-Peer overlay network. Identifying nodes with high capacity as super-peers plays an important role in improving the performance of P2P applications, such as live streaming. In this paper, we present a super-peer selection algorithm (SPS) to select super-peers for quickly building a super-peer overlay. In the SPS, each peer periodically builds its set of super-peer candidates through gossip communication with its neighbors, in order to select super-peers and client peers. Simulation results demonstrate that the SPS is efficient in selecting super-peers, and in quickly building a super-peer overlay. The proposed SPS also possesses good scalability and robustness to failure of super-peers.

Proximity-Aware Superpeer Overlay Topologies

The concept of superpeer has been introduced to improve the performance of popular P2P applications. A superpeer is a "powerful" node that acts as a server for a set of clients, and as an equal with respect to other superpeers. By exploiting heterogeneity, the superpeer paradigm can lead to improved efficiency, without compromising the decentralized nature of P2P networks. The main issues in constructing superpeer-based overlays are the selection of superpeers and the association between superpeers and clients. Generally, superpeers are either run voluntarily (without an explicit selection process), or chosen among the "best" nodes in the network, for example those with the most abundant resources, such as bandwidth or storage. In several contexts, however, shared resources are not the only factor; latency between clients and superpeers may play an important role, for example in online games and IP-Telephony applications. This paper presents SG-2, a novel protocol for building and maintaining proximity-aware superpeer topologies. SG-2 uses a gossip-based protocol to spread messages to nearby nodes and a biology-inspired task allocation mechanism to promote the "best" nodes to superpeer status. The paper includes extensive simulation experiments to prove the efficiency, scalability and robustness of SG-2.

CBT: A proximity-aware peer clustering system in large-scale BitTorrent-like peer-to-peer networks

In a large-scale BitTorrent-like peer-to-peer file sharing system, the track server could be overloaded to update the state information of constantly arriving and leaving peers. Upon the connection request from a peer, the track server responses with a random list of peers and such randomly selected peers among the whole peer-to-peer network could create a long delay of file sharing between two peers. To improve the file sharing performance, we propose a hierarchical architecture to group peers into clusters according to their proximity in the underlying overlay network in such a way that clusters are evenly distributed and that the peers within each cluster are relatively close to each other. We achieve this by constructing the CBT (Clustered BitTorrent) system with two novel algorithms: a peer joining algorithm and a super-peer selection algorithm. We develop a fluid model to compare the performance of the proposed CBT system with a original BitTorrent system. With this model, we find that the CBT system quite effectively improves the performance of the system. Finally, simulation results are given, which demonstrate that the CBT system achieves better results than a randomly organized BitTorrent network, improving the system scalability and efficiency while retaining the robustness and incentives of the original BitTorrent paradigm.