Peer Selection Algorithm Research Articles

PeerNomination: A novel peer selection algorithm to handle strategic and noisy assessments

In peer selection a group of agents must choose a subset of themselves, as winners for, e.g., peer-reviewed grants or prizes. We take a Condorcet view of this aggregation problem, assuming that there is an objective ground-truth ordering over the agents. We study agents that have a noisy perception of this ground truth and give assessments that, even when truthful, can be inaccurate. Our goal is to select the best set of agents according to the underlying ground truth by looking at the potentially unreliable assessments of the peers. Besides being potentially unreliable, we also allow agents to be self-interested, attempting to influence the outcome of the decision in their favour. Hence, we are focused on tackling the problem of impartial (or strategyproof) peer selection – how do we prevent agents from manipulating their reviews while still selecting the most deserving individuals, all in the presence of noisy evaluations? We propose a novel impartial peer selection algorithm, PeerNomination, that aims to fulfil the above desiderata. We provide a comprehensive theoretical analysis of the recall of PeerNomination and prove various properties, including impartiality and monotonicity. We also provide empirical results based on computer simulations to show its effectiveness compared to the state-of-the-art impartial peer selection algorithms. We then investigate the robustness of PeerNomination to various levels of noise in the reviews. In order to maintain good performance under such conditions, we extend PeerNomination by using weights for reviewers which, informally, capture some notion of reliability of the reviewer. We show, theoretically, that the new algorithm preserves strategyproofness and, empirically, that the weights help identify the noisy reviewers and hence to increase selection performance.1

Toward Quantifying Decentralization of Blockchain Networks With Relay Nodes

In this paper, we present a methodology for quantifying the decentralization degree of a blockchain network. To accomplish this, we use two well-known graph models of Erdös-Rény and Barabási–Albert in order to study the blockchain network topology. We then quantify the decentralization degree using the clustering coefficient of our network models. We validate our approach through extensive simulations and analyze the decentralization degree with respect to network parameters such as the number of connections per node and the peer selection algorithm. Our results expose the trade-off between the average shortest path and the decentralization degree. Furthermore, we observe the impact of the average shortest path on the network speed and traffic overhead. Finally, we demonstrate that the presence of hub-like nodes such as relay gateways negatively impacts the decentralization degree of blockchain networks.

A Supply Chain Traceability Scheme Based on Blockchain

The traditional supply chain management system generally uses the centralized server to store the data, which has the problems of opaque transaction information, high maintenance cost of the central server and low database security factor. In order to improve the transparency and security of data in the supply chain system, this paper uses blockchain technology for supply chain management. In addition, the cost of maintaining the anti-counterfeiting traceability platform in the traditional supply chain management system is very high, and the anti-counterfeiting code is easy to be stolen. In order to reduce the hardware cost of the anti-counterfeiting traceability platform, this paper designs a set of anti-counterfeiting traceability mechanism of supply chain products based on asymmetric encryption and digital signature technology, which improves the function of the system application layer. In order to reduce the hardware cost of the anti-counterfeiting traceability platform, this paper designs a set of anti-counterfeiting traceability mechanism of supply chain products based on asymmetric encryption and digital signature technology, which improves the function of the system application layer. In order to further improve the data processing efficiency and stability of blockchain network, this paper improves the master peer selection algorithm in fabric network.

EPS-TRA: Energy Efficient Peer Selection and Time Switching Ratio Allocation for SWIPT-Enabled D2D Communication

This paper considers device-to-device (D2D) network with Simultaneous Wireless Information and Power Transfer (SWIPT) enabled devices to ensure self-sustained communication in situations like disasters. Such direct link networks can ensure connectivity with devices having drained back-up, when trapped in collapsed infrastructure, through mutual sharing of energy on RF link. To guarantee successful execution of SWIPT session for an isolated device in wake of disasters, it is pertinent to select a reliable peer with ultimate aim to maximize link Energy Efficiency (EE). In practice, Energy Harvesting (EH) is not achievable after Information Decoding (ID); however, it has been made possible through splitting the signal in the time domain. Selection of D2D peer for self-sustained communication with an objective to maximize EE through optimum time based splitting of signal has not been extensively studied. In this paper to manifest the aforesaid goal, we worked out a joint problem of peer association and time switching ratio allocation with an objective to maximize the EE for a device contained under collapsed infrastructure. We propose an Energy efficient Peer Selection and Time switching Ratio Allocation (EPS-TRA) algorithm to solve the proposed mixed integer problem. Numerical results validate our proposed approach in acquiring better EE when compared with Uniform Allocation Scheme of time slots for EH & ID. Furthermore, results explain how EE of the link varies with the choice of constrained variables i.e., data rate and harvested energy.

Decreasing Big Data Application Latency in Satellite Link by Caching and Peer Selection

Although satellite communications have attracted wide attention in 6 G integrated space-air-ground networks, the link delay between the satellite and the satellite base station is a major issue with the advent of the era of big data. Considering this problem, this paper proposes cooperative strategies between peers in order to decrease the transmission latency for both downlink and uplink, respectively. In the downlink, we establish social relationship to find peer nodes who can cache contents and design a caching placement algorithm based on greedy methods to configure the cache. The approximation ratio of the solution obtained by the cache placement algorithm is no less than $1-1/e$ of the optimal solution. In the uplink, when channel conditions are severe, some users can be selected as relays to ensure the quality of communication. Such that another social relationship is established to prevent the disclosure of privacy in the process of data forwarding. We establish a dynamic optimization model, and propose a peer selection algorithm. Simulation results show that the performance of cache placement algorithm is more efficient other algorithms in the aspect of average latency and hit ratio in the downlink, and the peer selection algorithm outperforms other algorithms in terms of average channel rate and handover ratio in the uplink.

A hamming distance and fuzzy logic-based algorithm for P2P content distribution in enterprise networks

The rise in cloud storage and services in enterprise networks requires agile methods of content distribution, such as general-purpose files, software patches, image files, and other information. The work in this paper proposes a peer selection algorithm in BitTorrent to distribute content in enterprise networks. Peers execute this algorithm with a new special role called Internal Swarm Coordinator, ISC for short. Additions to the protocol allow peers from an Internal Swarm to cooperate in a coordinated manner, with an ISC acting as an orchestra director for a given broadcast domain. The proposed scheme uses two approaches: a Hamming distance to select the best peers enforcing productive connections between hosts, and fuzzy logic to handle peer load. This work includes a UDP-based protocol to select ISCs, and a procedure to define the behavior of this unique host. Also, to ensure backward compatibility, a description of how other peers can interact with this special role is given.

An Optimal Peer Selection Algorithm for Mesh-based Peer-to-Peer Networks

In order to achieve faster content distribution speed and stronger fault tolerance, a P2P peer can connect to multiple peers in parallel and receive chunks of the data simultaneously. A critical issue in this environment is selecting a set of nodes participating in swarming sessions. Previous related researches only focus on performance metrics, such as downloading time or the round-trip time, but in this paper, we consider a new performance metric which is closely related to the network and propose a peer selection algorithm that produces the set of peers generating optimal worst link stress. We prove that the optimal algorithm is practicable and has the advantages with the experiments on PlanetLab. The algorithm optimizes the congestion level of the bottleneck link. It means the algorithm can maximize the affordable throughput. Second, the network load is well balanced. A balanced network improves the utilization of resources and leads to the fast content distribution. We also notice that if every client follows our algorithm in selecting peers, the probability is high that all sessions could benefit. We expect that the algorithm in this paper can be used complementary to existing methods to derive new and valuable insights in peer-to-peer networking.

The Streaming Capacity of Sparsely Connected P2P Systems With Distributed Control

Peer-to-Peer (P2P) streaming technologies can take advantage of the upload capacity of clients, and hence can scale to large content distribution networks with lower cost. A fundamental question for P2P streaming systems is the maximum streaming rate that all users can sustain. Prior works have studied the optimal streaming rate for a complete network, where every peer is assumed to communicate with all other peers. This is however an impractical assumption in real systems. In this paper, we are interested in the achievable streaming rate when each peer can only connect to a small number of neighbors. We show that even with a random peer selection algorithm and uniform rate allocation, as long as each peer maintains Ω(logN) downstream neighbors, where N is the total number of peers in the system, the system can asymptotically achieve a streaming rate that is close to the optimal streaming rate of a complete network.We then extend our analysis to multi-channel P2P networks, and we study the scenario where “helpers” from channels with excessive upload capacity can help peers in channels with insufficient upload capacity. We show that by letting each peer select Ω(logN) neighbors randomly from either the peers in the same channel or from the helpers, we can achieve a close-to-optimal streaming capacity region. Simulation results are provided to verify our analysis.

Performance Improvement of an As-Friendly Peer Selection Algorithm for P2P Live Streaming

Minimum Physical Hop (MPH) has been proposed as a peer selection algorithm for decreasing inter-AS (Autonomous System) traffic volume in P2P live streaming. In MPH, a newly joining peer selects a peer whose physical hop count (i.e., the number of ASes traversed on the content delivery path) from it is the minimum as its providing peer. However, MPH shows high inter-AS traffic volume when the number of joining peers is large. In this paper, we propose IMPH that tries to further decrease the inter-AS traffic volume by distributing peers with one logical hop count (i.e., the number of peers or origin streaming servers (OSSes) traversed on the content delivery path from an OSS to the peer) to many ASes and encouraging the following peers to find their providing peers within the same AS. Numerical examples show that IMPH achieves at the maximum of 64% lower inter-AS traffic volume than MPH.

A Proposed Peer Selection Algorithm for Transmission Scheduling in P2P-VOD Systems

Video transmission in peer-to-peer video-on-demand faces some challenges. These challenges include long transmission delay and poor quality of service. The peer selection plays an important role in enhancing transmission efficiency. For this reason, a proposed algorithm for peer selection is introduced to overcome these challenges. The proposed algorithm consists of four steps. First, the peers exchange their own buffer maps with other peers. Second, the requested segments are ordered according to their priorities. Third, neighbors of the receiver are evaluated by the efficiency estimation. Finally, the efficient sender list is applied to solve the overloading and bottleneck on the highest efficient sender. A simulation is introduced to evaluate the performance of the proposed algorithm compared to a peer selection algorithm with context-aware adaptive (CAA) data scheduling algorithm. The results show that, the proposed algorithm reduces initial buffering delay and achieves high throughput rather than CAA algorithm.

Utilising social recommendation for decision-making in distributed multi-agent systems

Open multi-agent systems are typically formed from heterogeneous peers operating in a decentralised manner. Hence, their constituent agents must evaluate possible actions and opportunities based on local, subjective knowledge. When agents have insufficient personal experience, they may inevitably rely on their social connections to act as a source of relevant information or recommendations. We describe an agent-mediated electronic market for investigating social interaction within the context of evolving heterogeneous distributed networks. In our scenario, consumers look for appropriate services and this service choice is informed via peer recommendations. We define two alternative algorithms for selecting peers based on perceived similarity and we evaluate them on their ability to organise an overlay network such that it acts as a passive filter, tailoring the information that agents use to select services in the market. We use this scenario to explore the link between the peer selection algorithms and the emergent network topologies, as well as the impact of the peer selection algorithm on the agents’ performance in choosing services based on peer recommendations. Our simulation results demonstrate a qualitative difference in the behaviour of the algorithms, with optimal algorithm selection relying on information regarding the preferences of the wider population of agents.

An Effective Scheme for Performance Improvement of P2P Live Streaming Systems

To solve the problems of long start-up latency, low playback continuity and data distribution rate in P2P live streaming system, this paper proposes two optimization strategies. One is an adaptive peer selection algorithm based on peers' real-time service ability. In the algorithm, a simple but effective method is proposed to calculate the peers' ability. And source peer adjusts the amount of requested data and the priority of destination peers according to the destination peers' abilities and the limit of out-degree. Thus a load balance and efficiently system can be constructed. The other one is a push-pull combination data distribution mechanism. This mechanism adaptively chooses push or pull model to distribute data fast and efficiently. The results show that the strategies have a significant effect on reducing start-up latency and control message overhead and improving the playback continuity and data distribution rate.

Performance Investigation on Request Collisions in Peer-To-Peer Live Streaming Systems: Problems and Algorithms

A fundamental design issue in a peer-to-peer (P2P) live streaming system is how peers select neighbors for downloading media pieces. Inappropriate selection of neighbor peers for piece downloading can exacerbate the collisions of requests among peers and therefore degrade the system performance. In this paper, we first show how severe the request collision problem could be and also to which degree it could affect the performance of a P2P live streaming system by extensive measurements and simulations. We then propose a novel neighbor peer selection algorithm for piece downloading. In the algorithm, each peer needs to infer the severity of request collisions at each of its neighbors based on the most recent request-with-no-response history. Based on this information, each peer always sends requests to those neighbors with low collision probability at high priority. This algorithm can effectively avoid the difficulty in obtaining accurate network conditions and/or peer availability and also greatly reduce the signaling overhead for exchanging residual-capacity-related information among peers. In addition, the algorithm is easy to implement. Simulation results show that the proposed algorithm can effectively alleviate the request collision problem and improve the system performance.

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.

PROBING HIGH-CAPACITY PEERS TO REDUCE DOWNLOAD TIMES IN P2P FILE SHARING SYSTEMS WITH STOCHASTIC SERVICE CAPACITIES

The main problem for an individual user peer in a peer-to-peer network with heterogeneous source peers is the peer selection problem, namely, switching among source peers and finally settling on one, while keeping the total time of probing and downloading to a minimum. There has been little investigation on selecting source peers with stochastic service capacities. The main contribution of this paper is to address the problem of reducing download times in peer-to-peer file sharing systems with stochastic service capacities. A precise analysis of the expected download time is given when the service capacity of a source peer is a random variable. A chunk-based switching and peer selection algorithm using the method of probing high-capacity peers is proposed and the expected download time of the algorithm is analyzed. Two subproblems of the optimal choice of the threshold of high-capacity source peers and the optimal order of probing are also solved. The performance of the algorithm is compared with the random chunk-based switching method. It is shown that noticeable performance improvement can be obtained.

On Reducing Delay in Mesh-Based P2P Streaming: A Mesh-Push Approach

The peer-assisted streaming paradigm has been widely employed to distribute live video data on the internet recently. In general, the mesh-based pull approach is more robust and efficient than the tree-based push approach. However, pull protocol brings about longer streaming delay, which is caused by the handshaking process of advertising buffer map message, sending request message and scheduling of the data block. In this paper, we propose a new approach, mesh-push, to address this issue. Different from the traditional pull approach, mesh-push implements block scheduling algorithm at sender side, where the block transmission is initiated by the sender rather than by the receiver. We first formulate the optimal upload bandwidth utilization problem, then present the mesh-push approach, in which a token protocol is designed to avoid block redundancy; a min-cost flow model is employed to derive the optimal scheduling for the push peer; and a push peer selection algorithm is introduced to reduce control overhead. Finally, we evaluate mesh-push through simulation, the results of which show mesh-push outperforms the pull scheduling in streaming delay, and achieves comparable delivery ratio at the same time.

A peer-to-peer collaboration framework for multi-sensor data fusion

A peer-to-peer collaboration framework for multi-sensor data fusion in resource-rich radar networks is presented. In this high data volume real-time application, data from multiple radars are combined to improve the accuracy of radar scans (e.g., correct for attenuation) and to provide a composite view of the area covered by the radars. Data fusion process is subject to two constraints: (1) the accuracy requirement of the final fused results, which may be different at different end nodes, and (2) the real-time requirements of the application. The accuracy requirement is achieved by dynamically selecting the appropriate set of data to exchange among the multiple radar nodes. A mechanism for selecting a dataset based on current application-specific needs is presented. We also present a dynamic peer-selection algorithm, Best Peer Selection (BPS), that chooses a set of peers based on their computation and communication capabilities to minimize the data processing time per integration algorithm. Simulation-based results show that BPS can deliver a significant performance improvement, even when the peers have high variability in available network and computation resources.

Measurement-based peer selection for P2P-IPTV services in campus networks

P2P-based IPTV has been a gradual hot topic both for the academic institutions and service providers, which offers residential and commercial users digital television services through IP protocol and media sharing among peers at low cost. A rational peer selection algorithm plays an essential role on offering reliable media streaming transmission and excellent user experience. Meanwhile, convenient and effective quality of experience (QoE) assessment mechanism is not only an important evaluation index for IPTV service providers, but also can supply high-efficient performance feedback for them, especially under a limited network resource environment. Our contributions in this paper are three-fold: 1) we confirm the key parameters which affect QoE among P2P live application according to our 6-month trace, and establish a rational peer selection algorithm by virtue of the parameters above; 2) we set up a novel and passive model for QoE assessment representation; 3) during the popularization in campus networks, we approve the effectiveness of our new QoE evaluation method and the improvement of users' video quality caused by peer selection strategy. Whether or not, we hope our work can supply reliable references for IPTV service providers, moreover, actual improvement for future IPTV deployment.

Adaptive Search Radius – Using hop count to reduce P2P traffic

Peer-to-Peer (P2P) file sharing accounts for a very significant part of the Internet’s traffic, affecting the performance of other applications and translating into significant peering costs for ISPs. It has been noticed that, just like WWW traffic, P2P file sharing traffic shows locality properties, which are not exploited by current P2P file sharing protocols. We propose a peer selection algorithm, Adaptive Search Radius (ASR), where peers exploit locality by only downloading from those other peers which are nearest (in network hops). ASR ensures swarm robustness by dynamically adapting the distance according to file part availability. ASR aims at reducing the Internet’s P2P file sharing traffic, while decreasing the download times perceived by users, providing them with an incentive to adopt this algorithm. We believe ASR to be the first locality-aware P2P file sharing system that does not require assistance from ISPs or third parties nor modification to the server infrastructure. We support our proposal with extensive simulation studies, using the eDonkey/eMule protocol on SSFNet. These show a 19 to 29% decrease in download time and a 27 to 70% reduction in the traffic carried by tier-1 ISPs. ASR is also compared (favourably) with Biased Neighbour Selection (BNS), and traffic shaping. We conclude that ASR and BNS are complementary solutions which provide the highest performance when combined. We evaluated the impact of P2P file sharing traffic on HTTP traffic, showing the benefits on HTTP performance of reducing P2P traffic. A plan for introducing ASR into eMule clients is also discussed. This will allow a progressive migration to ASR enabled versions of eMule client software. ASR was also successfully used to download from live Internet swarms, providing significant traffic savings while finishing downloads faster.

A Robust P2P Media Streaming Scheme in Dynamic Environments

In this paper, we consider the problems of peer-to-peer media streaming in highly dynamic environments, including the peer selection and the aggregation of available bandwidth. To select reliable peers, we propose an adaptive peer selection algorithm based on the consideration of uplink bandwidth, life time, playback fault times, and the number of candidates for live broadcasting. In addition, the uplink bandwidth from one single sender cannot supply a streaming request alone so a receiver should download segments from cooperative senders. To aggregate the available bandwidth of multiple senders for streaming, we design a media segments assignment algorithm to resolve this problem. In the proposed media streaming scheme, the time delay of playback and the probability that playback faults happen are analyzed to evaluate the system performance. The simulation results show that our solution is reliable and robust for media streaming. Especially, if the peers join/leave the system frequently during media streaming, the benefits of the proposed schemes are obvious to receivers.