Leveraging deployment models on low-resource devices for cloud services in community networks

Community networks are crowd-sourced IP networks that evolved into regional-scale computing platforms. This has led to adapting the cloud computing model for services that can operate and use computing resources inside a community network. The network and computing infrastructure is contributed by individuals, companies, organizations and maintained by its members. Community cloud devices are often low-capacity computing devices, such as home gateways or cabinet servers, with limited capabilities. These devices are used to install and operate specific personal or community services, but can be turned into multi-purpose execution environments applying machine or operating system (container) virtualization. However that requires addressing the problems of resource sharing in low-capacity devices, related to predictable performance and isolation. Our comparative analysis with the current infrastructure in community networks gives evidence about how devices can concurrently run multiple services, the trade offs between the number and resource requirements of services and the degradation of quality that services may suffer.     

An optimal inherently stabilizing routing algorithm for star P2P overlay networks

Star graphs possess many desirable properties such as scalable node degrees and diameters, which are essential to facilitate reduced routing table sizes and low maximum path length for routing in large P2P networks. In addition, because a large number of disjoint paths are available and each data/replica in an n‐star can be placed in an (n − 1)‐star, load balancing and alleviation of network bottlenecks can be implemented in star P2P overlay networks. Therefore, star networks have been proposed as viable alternatives to existing overlay topologies for large P2P networks. In this paper, we propose an optimal stabilizing and inherently stabilizing algorithm for routing messages over all disjoint paths between two peers in a star P2P overlay network. The algorithm is optimal in terms of its time complexity in rounds and the length of the longest path traversed by the messages, and fault tolerant due to being stabilizing and inherently stabilizing, allowing the system to withstand transient faults. The algorithm can be used to increase network reliability and survivability in P2P networks. In addition, the usage of all disjoint paths to route messages between two peers leads to increased network bandwidth while distributing the communication overhead across the network and eliminating network bottlenecks in P2P networks. Copyright © 2016 John Wiley & Sons, Ltd.     

P2P网络中的社区结构发现方法

针对P2P网络的分布式特征,提出了一种可扩展的分布式社区发现方法PDC,采用节点Power值度量社区结构,并选择中心节点,其中包括新的社区度量方法和新的中心节点选择算法。仿真实验结果证明,与FDC和CDC方法相比,和其他算法相比PDC的社区发现效果率比FDC和CDC至少提高了510%,但是产生的消息数量却很少比FDC少一个数量级,具有良好的可扩展性。     

Chord-PKI: A distributed trust infrastructure based on P2P networks

Many P2P applications require security services such as privacy, anonymity, authentication, and non-repudiation. Such services could be provided through a hierarchical Public Key Infrastructure. However, P2P networks are usually Internet-scale distributed systems comprised of nodes with an undetermined trust level, thus making hierarchical solutions unrealistic. In this paper, we propose Chord-PKI, a distributed PKI architecture which is build upon the Chord overlay network, in order to provide security services for P2P applications. Our solution distributes the functionality of a PKI across the peers by using threshold cryptography and proactive updating. We analyze the security of the proposed infrastructure and through simulations we evaluate its performance for various scenarios of untrusted node distributions.     

结构化P2P网络上可靠的基于内容路由协议

在结构化P2P网络上构建基于内容的发布/订阅系统,可以很好地支持大规模、高度动态的分布式应用.然而,现有的基于内容的路由协议在P2P网络上只能提供弱的可靠性保证.根据结构化P2P网络的路由协议的特点,设计了一种新型的基于内容的路由协议--基于编码区间的路由(identifier range based routing,简称IRBR)协议.IRBR协议具有良好的容错性,只要事件的发布者与订阅者之间在P2P网络中是可达的,则订阅者一定能够收到它所订阅的事件,且只收到一次.同时,该协议也比现有的协议具有更高的事件路由效率.在Pastry上开发了一个原型系统,模拟实验表明了该协议的效率和容错性.     

一种基于交互迭代式的多尺度社团发现算法研究

社团发现是复杂网络研究领域的重要研究内容之一。为了提高社团发现的性能,本文提出了一种交互迭代式的多尺度社团发现算法。将网络中的社团定量描述为邻居节点、外来节点和重叠节点多个尺度的线性组合,并针对每个尺度给出了相应的矩阵计算描述。在应用上述定量描述指标对网络进行社团发现时,提出了一种包含两个阶段的迭代式社团发现算法。在这两个阶段中,分别固定社团集合和主社团集合,并且分别调整主社团集合和社团集合来最大化上述社团量化指标。实验表明,本文提出的算法与其它社团发现算法相比不仅准去性和效率高,而且具有很好的灵活性     

Optimized P2P streaming for wireless distributed networks

The future Internet will support pervasive applications and communications models that require end-nodes cooperation, such as fog computing and machine-to-machine communications. Among the many applications, also video streaming can be provided with a cooperative and peer-to-peer approach. Cooperative distribution requires building a distribution overlay on top of the physical topology (the underlay). This work proposes an optimized, cross-layer approach to build this overlay minimizing the impact on the underlay. We design an optimal strategy, which is proven to be NP-complete, and thus not solvable with a distributed, lightweight protocol. The optimal strategy is relaxed exploiting the knowledge on the betweenness centrality of the nodes in the underlay topology, obtaining two easily implementable solutions applicable to any link-state protocol for distributed wireless mesh networks. The additional introduction of heuristic improvements further optimizes the performance in real network scenarios. Extensive simulation results support the theoretical findings using three different network topologies. They show that the relaxed implementations are reasonably close to the optimal solution, and provide vast gains compared to the traditional overlay topologies that peer-to-peer applications build.     

一种基于兴趣协助的P2P网络共享文件搜索方法

分析了传统的结构化P2P网络和非结构化P2P网络搜索方法的优点和缺点,提出了一种基于兴趣协助的搜索方法,通过在结构化P2P网络节点上发布兴趣来加速非结构化P2P网络上的查询。实验结果表明,此方法在查询成功率和搜索延迟等方面都有着较好的性能。     

异质网络中基于关键词属性的Truss社区搜索

社区搜索旨在信息网络中寻找与用户指定的查询节点高度相关的稠密连通子图,是社会网络分析的重要研究内容。现有的社区搜索方法大多是针对同质网络,但现实中的信息网络通常是包含多种节点类型和多种关系类型的属性异质网络。提出了异质网络中基于元路径P和元结构S的P-距离和S-距离及(k,d,P)-truss和(k,d,S)-truss社区模型以度量子图的结构内聚性,同时提出了关键词属性得分函数用于度量不同子图的关键词属性相关性,最后提出了搜索具有最高关键词属性得分的(k,d,P)-truss和(k,d,S)-truss的社区搜索算法。搜索算法能够找到同时具有结构内聚性和关键词属性相关性的个性化社区,并且支持限制查询节点与社区内任意节点的最大距离d来控制社区搜索的范围。在真实数据集上与相关的社区搜索算法进行了实验对比,结果证明了所提算法的有效性和可行性。     

语义对等覆盖网中社区结构的发现和评价

最近,通过建立语义覆盖网络来提高大规模分布式网络环境中信息检索服务的性能成为对等计算领域的研究热点.目前,研究者们在语义覆盖协议和搜索算法方面已经做了大量研究,证明了语义覆盖在基于对等网络模型的内容定位应用方面极为有效.然而,分析和评价语义覆盖网络特征的研究工作确非常有限.文中通过建立数学模型和设计启发式回溯-贪婪混合算法、确认了语义覆盖网络的一种主要内在特性——社区结构特性.利用评价模型比较了SemreX语义覆盖网络和Gnutella网络的性能,实验结果显示SemreX覆盖网具有显著的社区结构特征,而Gnutella网络却没有这样的特征.另外,通过分别在两种覆盖网中仿真洪泛协议发现具有显著社区结构特征的覆盖网在内容定位方面效率更高.     

Community Detection Using Jaacard Similarity with SIM-Edge Detection Techniques

The structure and dynamic nature of real-world networks can be revealed by communities that help in promotion of recommendation systems. Social Media platforms were initially developed for effective communication, but now it is being used widely for extending and to obtain profit among business community. The numerous data generated through these platforms are utilized by many companies that make a huge profit out of it. A giant network of people in social media is grouped together based on their similar properties to form a community. Community detection is recent topic among the research community due to the increase usage of online social network. Community is one of a significant property of a network that may have many communities which have similarity among them. Community detection technique play a vital role to discover similarities among the nodes and keep them strongly connected. Similar nodes in a network are grouped together in a single community. Communities can be merged together to avoid lot of groups if there exist more edges between them. Machine Learning algorithms use community detection to identify groups with common properties and thus for recommendation systems, health care assistance systems and many more. Considering the above, this paper presents alternative method SimEdge-CD (Similarity and Edge between's based Community Detection) for community detection. The two stages of SimEdge-CD initially find the similarity among nodes and group them into one community. During the second stage, it identifies the exact affiliations of boundary nodes using edge betweenness to create well defined communities. Evaluation of proposed method on synthetic and real datasets proved to achieve a better accuracy-efficiency trade-of compared to other existing methods. Our proposed SimEdge-CD achieves ideal value of 1 which is higher than existing sim closure like LPA, Attractor, Leiden and walktrap techniques.     

基于蚁群算法的非结构化P2P网络资源搜索策略

针对非结构化P2P网络资源搜索算法中冗余消息数过多、搜索效率低等问题,提出一种基于蚁群算法的非结构化P2P网络资源搜索策略,该策略利用蚂蚁信息素的正反馈原理,同时综合考虑邻居节点度和邻居-邻居节点信息,选择下一条邻居节点路径转发查询消息,有效地指导资源搜索路径的生成。实验结果表明,该算法在一定程度上减少了大量的冗余查询消息,提高了资源搜索的成功率,是一种有效的非结构化P2P网络资源搜索策略。     

一种P2P环境下点割集的被动分布式发现算法

P2P系统的可靠性主要取决于覆盖网节点问的连通性，而割点和小规模点割集对网络连通性的危害很大，它们的失效或离开能使覆盖网变得四分五裂。本文提出一种P2P环境下点割集的被动分布式发现算法，在无法获得网络全局信息的情况下，节点仅依靠对收到消息的统计和分析就能够自主判断自己是否为割点或属于2点割，并采取相应措施消除其为系统带来的不稳定因素。该算法准确性高、开销低，割集消除对提高覆盖网可靠性的效果显著。     

社区云的关键技术、架构及挑战——以科技云为例

社区云是云计算四种部署模式中应用较少的一种,但对于面向科研和学术界的云服务却有其特点和优势,非常值得探索。本文结合中国科学院科技云的实践,讨论了身份管理与授权、服务集成、测量与运行管理三项关键技术,提出了一种适合社区云的联盟和微服务架构,并指出社区云在模式和机制、可靠性、安全、组织管理等方面所面临的挑战。     

移动社会网络中基于社区的消息机会传输策略

移动社会网络是一种由大量具有社会特征的节点组成的机会网络.已有的基于社区的路由算法大多选用社会性最优的节点参与转发,而没有考虑到社区分布对节点移动的影响,将这些算法直接用于移动社会网络中会导致网络资源消耗高、传输成功率低等问题.针对这些问题,提出一种基于社区的消息机会传输算法,在社区间根据节点到目标社区的传输概率选择社区间的最优传输路径,在社区内选择与目标节点相遇概率较高的节点完成社区内传输.仿真实验结果表明,在移动社会网络中,该算法与 Prophet,Spray and Wait 等经典算法相比,提高了消息传输成功率,降低了网络开销.     

Community detection for emerging social networks

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.