Deadline-aware network coding for video on demand service over P2P networks

We are interested in providing Video-on-Demand (VoD) streaming service to a large population of clients using peer-to-peer (P2P) approach. Given the asynchronous demands from multiple clients, continuously changing of the buffered contents, and the continuous video display requirement, how to collaborate with potential partners to get expected data for future content delivery are very important and challenging. In this paper, we develop a novel scheduling algorithm based on deadline- aware network coding (DNC) to fully exploit the network resource for efficient VoD service. DNC generalizes the existing net- work coding (NC) paradigm, an elegant solution for ubiquitous data distribution. Yet, with deadline awareness, DNC improves the network throughput and meanwhile avoid missing the play deadline in high probability, which is a major deficiency of the con- ventional NC. Extensive simulation results demonstrated that DNC achieves high streaming continuity even in tight network conditions.

Mathematical Model Validation of Search Protocols in MP2P Networks

Broadcasting is a basic technique in Mobile ad-hoc network(MANET),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive flooding technique,floods the network with query messages,while the random walk technique operates by contacting the subsets of every node’s neighbors at each step,thereby restricting the search space.One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource.Many earlier works have mainly focused on the simulation-based analysis of flooding,and its variants under a wired network.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of P2P systems running over MANET.In this paper,we describe how P2P resource discovery protocols perform badly over MANETs.To address the limitations,we propose a new protocol named ABRW(Address Broadcast Random Walk),which is a lightweight search approach,designed considering the underlay topology aimed to better suit the unstructured architecture.We provide the mathematical model,measuring the performance of our proposed search scheme with different widely popular benchmarked search techniques.Further,we also derive three relevant search performance metrics,i.e.,mean no.of steps needed to find a resource,the probability of finding a resource,and the mean no.of message overhead.We validated the analytical expressions through simulations.The simulation results closely matched with our analyticalmodel,justifying our findings.Our proposed search algorithm under such highly dynamic self-evolving networks performed better,as it reduced the search latency,decreased the overall message overhead,and still equally had a good success rate.

METrust: A Mutual Evaluation-based Trust Model for P2P Networks

It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer （P2P） network and enhance the security and reliability of P2P systems. The current trust models only focus on the consumers＇ evaluation to a transaction, which may be abused by malicious peers to exaggerate or slander the provider deliberately. In this paper, we propose a novel trust model based on mutual evaluation, called METrust, to suppress the peers＇ malicious behavior, such as dishonest evaluation and strategic attack. METrust considers the factors including mutual evaluation, similarity risk, time window, incentive, and punishment mechanism. The trust value is composed of the direct trust value and the recommendation trust value. In order to inhibit dishonest evaluation, both participants should give evaluation information based on peers＇ own experiences about the transaction while computing the direct trust value. In view of this, the mutual evaluation consistency factor and its time decay function are proposed. Besides, to reduce the risk of computing the recommendation trust based on the recommendations of friend peers, the similarity risk is introduced to measure the uncertainty of the similarity computing, while similarity is used to measure credibility. The experimental results show that METrust is effective, and it has advantages in the inhibition of the various malicious behaviors.

FPSblo:A Blockchain Network Transmission Model Utilizing Farthest Point Sampling

Peer-to-peer(P2P)overlay networks provide message transmission capabilities for blockchain systems.Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems.However,traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upper-layer traffic requirements and the underlying physical network topology.This mismatch results in redundant data transmission and inefficient routing,severely constraining the scalability of blockchain systems.To address these pressing issues,we propose FPSblo,an efficient transmission method for blockchain networks.Our inspiration for FPSblo stems from the Farthest Point Sampling(FPS)algorithm,a well-established technique widely utilized in point cloud image processing.In this work,we analogize blockchain nodes to points in a point cloud image and select a representative set of nodes to prioritize message forwarding so that messages reach the network edge quickly and are evenly distributed.Moreover,we compare our model with the Kadcast transmission model,which is a classic improvement model for blockchain P2P transmission networks,the experimental findings show that the FPSblo model reduces 34.8%of transmission redundancy and reduces the overload rate by 37.6%.By conducting experimental analysis,the FPS-BT model enhances the transmission capabilities of the P2P network in blockchain.

Greedysearch based service location in P2P networks

A model is built to analyze the performance of service location based on greedy search in P2P networks. Hops and relative QoS index of the node found in a service location process are used to evaluate the performance as well as the probability of locating the top 5% nodes with highest QoS level. Both model and simulation results show that, the performance of greedy search based service location improves significantly with the increase of the average degree of the network. It is found that, if changes of both overlay topology and QoS level of nodes can be ignored during a location process, greedy-search based service location has high probability of finding the nodes with relatively high QoS in small number of hops in a big overlay network. Model extension under arbitrary network degree distribution is also studied.

Cluster based hierarchical resource searching model in P2P network

For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer （P2P） network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.

Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks

One of the key challenges in ad-hoc networks is the resource discovery problem.How efciently&quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question?Broadcasting is a basic technique in the Mobile Ad-hoc Networks(MANETs),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive ooding technique oods the network with query messages,while the random walk scheme operates by contacting subsets of each node’s neighbors at every step,thereby restricting the search space.Many earlier works have mainly focused on the simulation-based analysis of ooding technique,and its variants,in a wired network scenario.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of mobile P2P networks.In this article,we mathematically model different widely used existing search techniques,and compare with the proposed improved random walk method,a simple lightweight approach suitable for the non-DHT architecture.We provide analytical expressions to measure the performance of the different ooding-based search techniques,and our proposed technique.We analytically derive 3 relevant key performance measures,i.e.,the avg.number of steps needed to nd a resource,the probability of locating a resource,and the avg.number of messages generated during the entire search process.

Deep Q-Learning Based Optimal Query Routing Approach for Unstructured P2P Network

Deep Reinforcement Learning(DRL)is a class of Machine Learning(ML)that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future efficiently.DRL has been used in many application fields,including games,robots,networks,etc.for creating autonomous systems that improve themselves with experience.It is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing especially.Therefore,a novel query routing approach called Deep Reinforcement Learning based Route Selection(DRLRS)is proposed for unstructured P2P networks based on a Deep Q-Learning algorithm.The main objective of this approach is to achieve better retrieval effectiveness with reduced searching cost by less number of connected peers,exchangedmessages,and reduced time.The simulation results shows a significantly improve searching a resource with compression to k-Random Walker and Directed BFS.Here,retrieval effectiveness,search cost in terms of connected peers,and average overhead are 1.28,106,149,respectively.

Machine Learning-based Stable P2P IPTV Overlay

Live video streaming is one of the newly emerged services over the Internet that has attracted immense interest of the service providers.Since Internet was not designed for such services during its inception,such a service poses some serious challenges including cost and scalability.Peer-to-Peer(P2P)Internet Protocol Television(IPTV)is an application-level distributed paradigm to offer live video contents.In terms of ease of deployment,it has emerged as a serious alternative to client server,Content Delivery Network(CDN)and IP multicast solutions.Nevertheless,P2P approach has struggled to provide the desired streaming quality due to a number of issues.Stability of peers in a network is one of themajor issues among these.Most of the existing approaches address this issue through older-stable principle.This paper first extensively investigates the older-stable principle to observe its validity in different scenarios.It is observed that the older-stable principle does not hold in several of them.Then,it utilizes machine learning approach to predict the stability of peers.This work evaluates the accuracy of severalmachine learning algorithms over the prediction of stability,where the Gradient Boosting Regressor(GBR)out-performs other algorithms.Finally,this work presents a proof-of-concept simulation to compare the effectiveness of older-stable rule and machine learning-based predictions for the stabilization of the overlay.The results indicate that machine learning-based stability estimation significantly improves the system.

A Scalable and Robust DHT Protocol for Structured P2P Network

Distributed Hash Tables (DHTs) were originated from the design of structured peer-to-peer (P2P) systems. A DHT provides a key-based lookup service similar to a hash table. In this paper, we present the detailed design of a new DHT protocol, Tambour. The novelty of the protocol is that it uses parallel lookup to reduce retrive latency and bounds communication overhead to a dynamically adjusted routing table. Tambour estimates the probabilities of routing entries' liveness based on statistics of node lifetime history and evicts dead entries after lookup failures. When the network is unstable, more routing entries will be evicted in a given period of time, and the routing tables will be getting smaller which minimize the number of timeouts for later lookup requests. An experimental prototype of Tambour has been simulated and compared against two popular DHT protocols. Results show that Tambour outperforms the compared systems in terms of bandwith cost, lookup latency and the overall efficiency.

一种支持多维资源描述的高效P2P路由算法

在分析现有P2P(peer to peer)路由算法的基础上,提出了一种基于二阶矩定位、支持多维资源数据描述的高效资源路由算法——FAN(flabellate addressable network)路由算法.FAN算法将节点映射到统一的多维笛卡尔空间,并以节点相对空间原点的二阶矩作为子空间管理和资源搜索的依据.FAN路由算法具有O(log(N/k))的高路由效率,在节点加入和退出FAN网络时,更新路由信息的代价为O(klog(N/k)).实验结果表明,FAN路由算法具有路由效率高、维护代价小的优点,是一种P2P环境中支持多维资源数据描述的高效结构化资源路由算法.而且,目前部分基于CAN(content-addressable network)网络的改进算法也可以在FAN网络中适用,并获得更好的路由效率和更低的维护代价.

一种基于Overlay Network的自优化网格架构

网格和OverlayNetwork已经引起研究领域和工业界的广泛关注,然而,工业、学术以及商业界的研究主要围绕网格的应用、服务和中间件,很少研究基础网络。论文提出了一种基于OverlayNetwork的自优化网格架构,并且展示了这一架构在数据管理领域的应用,如副本的生成、放置、访问优化、同步等等,这些都是利用部署在OverlayNetwork上独立的P2P组件协作实现的,并且所有组件是并发执行的,每一个组件根据自己的目标优化系统,通过很少交互且易处理的组件来完成优化,这个独立优化的方案与整体优化相比较,得到的结果相近,甚至更好,而且其复杂性更小。

P2P energy trading via public power networks:Practical challenges,emerging solutions,and the way forward

Peer-to-peer(P2P)energy trading is an emerging energy supply paradigm where customers with distributed energy resources(DERs)are allowed to directly trade and share electricity with each other.P2P energy trading can facilitate local power and energy balance,thus being a potential way to manage the rapidly increasing number of DERs in net zero transition.It is of great importance to explore P2P energy trading via public power networks,to which most DERs are connected.Despite the extensive research on P2P energy trading,there has been little large-scale commercial deployment in practice across the world.In this paper,the practical challenges of conducting P2P energy trading via public power networks are identified and presented,based on the analysis of a practical Local Virtual Private Networks(LVPNs)case in North Wales,UK.The ongoing efforts and emerging solutions to tackling the challenges are then summarized and critically reviewed.Finally,the way forward for facilitating P2P energy trading via public power networks is proposed.

Random walk search in unstructured P2P

Unstructured P2P has power-law link distribution, and the random walk in power-law networks is analyzed. The analysis results show that the probability that a random walker walks through the high degree nodes is high in the power-law network, and the information on the high degree nodes can be easily found through random walk. Random walk spread and random walk search method （RWSS） is proposed based on the analysis result. Simulation results show that RWSS achieves high success rates at low cost and is robust to high degree node failure.

BulkTree: an overlay network architecture for live media streaming

Peer-to-peer (P2P) systems are now very popular. Current P2P systems are broadly of two kinds, structured and unstructured. The tree structured P2P systems used technologies such as distributed hash tables (DHT) and hierarchical clustering can search the required target quickly, however, in a tree, the internal node has a higher load and its leave or crash often causes a large population of its offspring's problems, so that in the highly dynamic Internet environment the tree structure may still suffer frequent breaks. On the other hand, most widely used unstructured P2P networks rely on central directory servers or massive message flooding, clearly not scalable. So, we consider both of the above systems' advantages and disadvantages and realize that in the P2P systems one node may fail easily, but that when a number of nodes organized as a set, which we call "super node", the set is robust. Super nodes can be created and updated aware of topology-aware, and used with simple protocol such as flooding or "servers" to exchange information. Furthermore the entire robust super node can be organized into exquisite tree structure. By using this overlay network architecture, P2P systems are robust, efficient, scalable and secure. The simulation results demonstrated that our architecture greatly reduces the alteration time of the structure while decreasing the average delay time, compared to the common tree structure.

A P2P Platform for Collaborative Aggregated Multimedia Sharing

Peer-to-peer technologies have emerged as a powerful and scalable communication model for large scale content shar-ing. However, they are not yet provided with optimized heterogeneous aggregated content management functionality since they lack rich semantic specifications. To overcome these shortcomings, we elaborated a reference model of P2P architecture for a dynamic aggregation, sharing and retrieval of heterogeneous multimedia contents (simple or aggre-gated). This architecture was mainly developed under the CAM4Home European research project and is fully based on the CAM4Home semantic metadata model. This semantic model relies on RDF (Resource Description Framework) and is rich (but simple enough), extensible and dedicated for the description of any kind of multimedia content.In this paper, we detail and evaluate an original semantic-based community network architecture for heterogeneous multimedia con-tent sharing and retrieval. Within the presentedarchitecture, multimedia contents are managed according to their asso-ciated CAM4Home semantic metadata through a structured P2P topology. This topology relies on a semantically en-hanced DHT (Distributed Hash Table) and is also provided with an additional indexing system for offering semantic storage and search facilities and overcoming the problem of exact match keywords in DHTs.

A P2P Approach to Routing in Hierarchical MANETs

We present an effective routing solution for the backbone of hierarchical MANETs. Our solution leverages the storage and retrieval mechanisms of a Distributed Hash Table (DHT) common to many (structured) P2P overlays. The DHT provides routing information in a decentralized fashion, while supporting different forms of node and network mobility. We split a flat network into clusters, each having a gateway who participates in a DHT overlay. These gateways interconnect the clusters in a backbone network. Two routing approaches for the backbone are explored: flooding and a new solution exploiting the storage and retrieval capabilities of a P2P overlay based on a DHT. We implement both approaches in a network simulator and thoroughly evaluate the performance of the proposed scheme using a range of static and mobile scenarios. We also compare our solution against flooding. The simulation results show that our solution, even in the presence of mobility, achieved well above 90% success rates and maintained very low and constant round trip times, unlike the flooding approach. In fact, the performance of the proposed inter-cluster routing solution, in many cases, is comparable to the performance of the intra-cluster routing case. The advantage of our proposed approach compared to flooding increases as the number of clusters increases, demonstrating the superior scalability of our proposed approach.

A Review of P2P Financial Research

Since 2007,people's spending habits began to change:from cash to the bank card,from mobile phone to non-cash transactions,from traditional finance to network finance.And the rapid rise of emerging industries results in many of the P2P Financial Corporation established,it makes the P2P platform trading volume continue to rise at an alarming rate.In this context,the industry self-discipline environment is like the mining of good and evil.The atmosphere was foul,bad phenomenon,investors difficult activist phenomenon,the phenomenon of credit default as one falls,another rises to a series of problems,such as serious absence of P2P financial supervision to promote the healthy and sustainable development,is a major problem for all participants,which must to be solved.Firstly,P2P development of management mode in the current situation in our country is discussed,and then it analyzes the P2P network of financial management platform,finally it proposes the existing problems in the relative countermeasures and suggestions of effective P2P financing mode,hoping to promote its advantages,overcoming the disadvantages of P2P.

PPLS与稀疏鉴别流形正则化的双模型协同宽度神经网络

宽度神经网络(broad neural networks,BNN)被认为是继深度神经网络之后的一种主流机器学习算法,然而BNN没有考虑数据不确定性及局部几何结构信息。为此,提出概率偏最小二乘(probabilistic partial least square,PPLS)与稀疏鉴别流形正则化的双模型协同宽度神经网络建模方法。该方法首先使用PPLS对BNN输入特征以及增强特征构成的高维数据提取低维隐藏变量,消除数据不确定信息以及冗余特征;基于稀疏表示方法自适应构建样本局部与非局部近邻矩阵,并结合PPLS模型投影矩阵,提出一种新颖的融合模型信息迁移、鉴别流形正则化以及l_(2,p)-范数约束的BNN建模方法,有效增强BNN模型的鲁棒性、建模精度,同时消除数据的随机不确定性;最后给出迭代优化求解方法获取模型最优参数。在不同规模数据集、不同光照和角度图像数据集对所提算法进行仿真验证,结果表明该算法对不同规模数据集均能取得满意的效果;对图像数据集仿真结果表明其具有很强的鲁棒性和泛化性能。