Conference Paper

An Analysis of Content Sharing Hops for Dual-Structural Network Based on General Random Graph

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Dual-Structural Network (DSN), pioneered by our group for content sharing, is a networking paradigm with the Internet as primary structure and the broadcast-storage network (BSN) as secondary structure. In order to quantitatively evaluate its content sharing capability, in this paper, we generally adopt a deductive methodology, namely that DSN is formalized as a complex network and then its sharing capability is derived according to graph theory. In specific, according to bipartite graph theory, we first construct a bipartite dual-structural network model to obtain an abstract content sharing graph through top-down projection, and then content sharing hops (CSH) in the graph is capitalized as a metric to evaluate the sharing capability between any two content nodes. Furthermore, we leverage the general random graph theory to generate the sharing graph for deriving quantitative upper bounds on average content sharing hops (ACSH) and maximum content sharing hops (MCSH) of DSN. Lastly, the theoretical derivations are validated by numerical simulation. Moreover, compared with content delivery network (CDN), content centric network (CCN) and information-centric mobile ad hoc networks (ICMANET), DSN is demonstrated to be superior in terms of the sharing capability.

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Vehicular Content-centric Network (VCCN) has been emerged as a future network technology for vehicular networks, where the focus of communication is shifted from the host to information centric. However, VCCN faces several challenges, including interest/data packet(s) flooding, provider/consumer mobility and so on. In this letter, we propose a scheme named as RobUst Forwarder Selection (RUFS) to mitigate the interest broadcast storm. In RUFS, each vehicle shares its satisfied interest(s) statistics with the neighbors. All neighbors store this information in their Neighbors Satisfied List (NSL) that helps to select the potential interest forwarder. Simulation results show that RUFS outperforms the recently proposed NAIF, DR Based and basic interest forwarding in VCCN.
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By introducing broadcast distribution into TCP/IP, Broadcast-Storage network has clear advantages in reducing the redundant traffic in the Internet and remitting information overload problem. Uniform content label (UCL) is used to express the needs of users and help users obtain the information resources in Broadcast-Storage network. In the process of UCL recommendation, one key problem that needs to be solved is that how to improve the diversity of recommendation based on the features of Broadcast-Storage network, e.g., rich semantic information and high novelty. To solve this problem, this paper proposes a diversification method UDSCT for UCL recommendation based on semantic cover tree. UDSCT consists of two components. The first one is constructing the semantic cover tree for UCLs, which obeys some proposed invariants and considers the semantic information of UCL and the ratings from users. Besides that, new UCLs are given priority to improve the novelty of the whole UCL list. The second component is the query of diversified UCL list, which uses simple tree query and heuristic list supplement operation to obtain the diversified UCL list fast and returns specified UCL sets rapidly according to users' need. Theoretical analysis and a series of experiments results show that, UDSCT outperforms some benchmark algorithms and is suitable for Broadcast-Storage network.
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The influence of social interactions among mobile devices and network components in wireless networks has attracted substantial attention due to its potential impact on resource allocation of spectrum and power in particular. We present an organized social graphical view on resource allocation and then extend to multi-objective resource allocation of wireless networks. We subsequently consider taking advantage of multi-dimensional resources, including radio resource, user behavior, and content characteristics, such that we can successfully integrate caching capability, interest similarity, and content popularity and distribution into wireless network design. As an illustration, device-to-device communications is utilized to form pairs and clusters of mobile devices regarding optimal resource matching via a bipartite graph. This socially enabled methodology highlights new potential to design wireless networks and 5G mobile communications.
As Internet traffic continues to grow in size and complexity, it has become an increasingly challenging task to understand behavior patterns of end-hosts and network applications. This paper presents a novel approach based on behavioral graph analysis to study the behavior similarity of Internet end-hosts. Specifically, we use bipartite graphs to model host communications from network traffic and build one-mode projections of bipartite graphs for discovering social-behavior similarity of end-hosts. By applying simple and efficient clustering algorithms on the similarity matrices and clustering coefficient of one-mode projection graphs, we perform network-aware clustering of end-hosts in the same network prefixes into different end-host behavior clusters and discover inherent clustered groups of Internet applications. Our experiment results based on real datasets show that end-host and application behavior clusters exhibit distinct traffic characteristics that provide improved interpretations on Internet traffic. Finally, we demonstrate the practical benefits of exploring behavior similarity in profiling network behaviors, discovering emerging network applications, and detecting anomalous traffic patterns.
The current Internet architecture was founded upon a host-centric communication model, which was appropriate for coping with the needs of the early Internet users. Internet usage has evolved however, with most users mainly interested in accessing (vast amounts of) information, irrespective of its physical location. This paradigm shift in the usage model of the Internet, along with the pressing needs for, among others, better security and mobility support, has led researchers into considering a radical change to the Internet architecture. In this direction, we have witnessed many research efforts investigating Information-Centric Networking (ICN) as a foundation upon which the Future Internet can be built. Our main aims in this survey are: (a) to identify the core functionalities of ICN architectures, (b) to describe the key ICN proposals in a tutorial manner, highlighting the similarities and differences among them with respect to those core functionalities, and (c) to identify the key weaknesses of ICN proposals and to outline the main unresolved research challenges in this area of networking research.
We consider a family of random graphs with a given expected degree sequence. Each edge is chosen independently with probability proportional to the product of the expected degrees of its endpoints. We examine the distribution of the sizes/volumes of the connected components which turns out depending primarily on the average degree d and the second-order average degree d~. Here d~ denotes the weighted average of squares of the expected degrees. For example, we prove that the giant component exists if the expected average degree d is at least 1, and there is no giant component if the expected second-order average degree d~ is at most 1. Examples are given to illustrate that both bounds are best possible.
It appeared recently that the classical random graph model used to represent real-world complex networks does not capture their main properties. Since then, various attempts have been made to provide accurate models. We study here a model which achieves the following challenges: it produces graphs which have the three main wanted properties (clustering, degree distribution, average distance), it is based on some real-world observations, and it is sufficiently simple to make it possible to prove its main properties. This model consists in sampling a random bipartite graph with prescribed degree distribution. Indeed, we show that any complex network may be viewed as a bipartite graph with some specific characteristics, and that its main properties may be viewed as consequences of this underlying structure. We also propose a growing model based on this observation.
Random graph theory is used to examine the "small-world phenomenon"; any two strangers are connected through a short chain of mutual acquaintances. We will show that for certain families of random graphs with given expected degrees the average distance is almost surely of order log nlog d, where d is the weighted average of the sum of squares of the expected degrees. Of particular interest are power law random graphs in which the number of vertices of degree k is proportional to 1kbeta for some fixed exponent beta. For the case of beta > 3, we prove that the average distance of the power law graphs is almost surely of order log nlog d. However, many Internet, social, and citation networks are power law graphs with exponents in the range 2 < beta < 3 for which the power law random graphs have average distance almost surely of order log log n, but have diameter of order log n (provided having some mild constraints for the average distance and maximum degree). In particular, these graphs contain a dense subgraph, which we call the core, having n(clog log n) vertices. Almost all vertices are within distance log log n of the core although there are vertices at distance log n from the core.
Many massive graphs (such as the WWW graph and Call graphs) share certain universal characteristics which can be described by so-called the "power law". Here we determine the diameter of random power law graphs up to a constant factor for almost all ranges of parameters. These results show a strong evidence that the diameters of most massive graphs are about logarithm of their sizes up to a constant factor.
End-user mapping: Next generation request routing for content delivery
  • F Chen
  • R K Sitaraman
  • M Torres
Complex graphs and networks
  • F Chung
  • L Lu