Generating Representative ISP Technologies From First-Principles

Understanding and modeling the factors that underlie the growth and evolution of network topologies are basic questions that impact capacity planning, forecasting, and protocol research. Early topology generation work focused on generating network-wide connectivity maps, either at the AS-level or the router-level, typically with an eye towards reproducing abstract properties of observed topologies. But recently, advocates of an alternative "first-principles" approach question the feasibility of realizing representative topologies with simple generative models that do not explicitly incorporate real-world constraints, such as the relative costs of router configurations, into the model. Our work synthesizes these two lines by designing a topology generation mechanism that incorporates first-principles constraints. Our goal is more modest than that of constructing an Internet-wide topology: we aim to generate representative topologies for single ISPs. However, our methods also go well beyond previous work, as we annotate these topologies with representative capacity and latency information. Taking only demand for network services over a given region as input, we propose a natural cost model for building and interconnecting PoPs and formulate the resulting optimization problem faced by an ISP. We devise hill-climbing heuristics for this problem and demonstrate that the solutions we obtain are quantitatively similar to those in measured router-level ISP topologies, with respect to both topological properties and fault-tolerance

Study of Evolution Model of China Education and Research Network

By searching the hyperlinks with domain name “.edu.cn” which constitutes the China Education and Research Network, we build a complex directed network containing 366,422 web pages containing 540,755 URLs. These URLs constitute a complex directed network through self-organization. By analyzing the topology of China Education and Research Network, we found that it is different from the common Internet in several aspects. Most of the vertices have incoming links, a few vertices have outgoing links, and very few vertices have both incoming and outgoing links. The vertex distribution has a power-law tail. A large proportion of newly added edges always connect with those pages selected from one subnetwork that they belong to, instead of connecting with the pages selected from the whole network. According to these features, we presented the evolution model of this complex directed network. The results indicate that this model reflects some main characteristics of China Education and Research Network

Differentiating complex network models: An engineering perspective

AbstractNetwork models that can capture the underlying network’s topologies and functionalities are crucial for the development of complex network algorithms and protocols. In the engineering community, the performances of network algorithms and protocols are usually evaluated by running them on a network model. In most if not all reported work, the criteria used to determine such a network model rely on how close it matches the network data in terms of some basic topological characteristics. However, the intrinsic relations between a network topology and its functionalities are still unclear. A question arises naturally: For a network model which can reproduce some topological characteristics of the underlying network, is it reasonable and valid to use this model to be a test-bed for evaluating the network’s performances? To answer this question, we take a close look at several typical complex network models of the AS-level Internet as examples of study. We find that although a model can represent the Internet in terms of topological metrics, it cannot be used to evaluate the Internet performances. Our findings reveal that the approaches using topological metrics to discriminate network models, which have been widely used in the engineering community, may lead to confusing or even incorrect conclusions

Towards Modeling of Traffic Demand of Node in Large Scale Network

Abstract—Understanding actual network and traffic proper-ties of the Internet is essential to determine network parame-ters in large-scale network simulations. However, there is little knowledge about the distribution of macroscopic traffic demand for each node, though the topological properties of the network have been focused on. This paper investigates the distribution of traffic volume to and from a node at an organization level. As traffic volume data, we used byte counter data of all interfaces in all backbone routers in a nation-wide research and education (R&E) network in Japan. First, we show that traffic volumes to and from a node in the network are characterized by a lognormal distribution, which has a slower decay than a normal distribution, but a faster decay than a power-law distribution. Thus, an assumption in which the traffic demand is uniformly random or Gaussian distributed is not appropriated to model the traffic demand in large-scale network simulation. This finding implies that one has more possibility to observe an increase of delay or packet drop in simulation, comparing to the result that uses uniformly-random or Gaussian traffic demand, because of the locality of traffic. Moreover, we observed that in 87 % of nodes, a traffic volume from the backbone to the node is 1-10 times larger than that for the opposite direction. This is a similar usage pattern appeared in residential light-user broadband traffic. Finally, we introduce a simple model to explain the distribution of traffic demand, based on a multiplicative growth of traffic volume. We confirm that the multiplicative model can reproduce a lognormal distribution of traffic volume by simple numerical simulation. I

Provider and peer selection in the evolving internet ecosystem

The Internet consists of thousands of autonomous networks connected together to provide end-to-end reachability. Networks of different sizes, and with different functions and business objectives, interact and co-exist in the evolving "Internet Ecosystem". The Internet ecosystem is highly dynamic, experiencing growth (birth of new networks), rewiring (changes in the connectivity of existing networks), as well as deaths (of existing networks). The dynamics of the Internet ecosystem are determined both by external "environmental" factors (such as the state of the global economy or the popularity of new Internet applications) and the complex incentives and objectives of each network. These dynamics have major implications on how the future Internet will look like. How does the Internet evolve? What is the Internet heading towards, in terms of topological, performance, and economic organization? How do given optimization strategies affect the profitability of different networks? How do these strategies affect the Internet in terms of topology, economics, and performance? In this thesis, we take some steps towards answering the above questions using a combination of measurement and modeling approaches. We first study the evolution of the Autonomous System (AS) topology over the last decade. In particular, we classify ASes and inter-AS links according to their business function, and study separately their evolution over the last 10 years. Next, we focus on enterprise customers and content providers at the edge of the Internet, and propose algorithms for a stub network to choose its upstream providers to maximize its utility (either monetary cost, reliability or performance). Third, we develop a model for interdomain network formation, incorporating the effects of economics, geography, and the provider/peer selections strategies of different types of networks. We use this model to examine the "outcome" of these strategies, in terms of the topology, economics and performance of the resulting internetwork. We also investigate the effect of external factors, such as the nature of the interdomain traffic matrix, customer preferences in provider selection, and pricing/cost structures. Finally, we focus on a recent trend due to the increasing amount of traffic flowing from content providers (who generate content), to access providers (who serve end users). This has led to a tussle between content providers and access providers, who have threatened to prioritize certain types of traffic, or charge content providers directly -- strategies that are viewed as violations of "network neutrality". In our work, we evaluate various pricing and connection strategies that access providers can use to remain profitable without violating network neutrality.Ph.D.Committee Chair: Dovrolis, Constantine; Committee Member: Ammar, Mostafa; Committee Member: Feamster, Nick; Committee Member: Willinger, Walter; Committee Member: Zegura, Elle

Large-Scale Networks: Algorithms, Complexity and Real Applications

Networks have broad applicability to real-world systems, due to their ability to model and represent complex relationships. The discovery and forecasting of insightful patterns from networks are at the core of analytical intelligence in government, industry, and science. Discoveries and forecasts, especially from large-scale networks commonly available in the big-data era, strongly rely on fast and efficient network algorithms. Algorithms for dealing with large-scale networks are the first topic of research we focus on in this thesis. We design, theoretically analyze and implement efficient algorithms and parallel algorithms, rigorously proving their worst-case time and space complexities. Our main contributions in this area are novel, parallel algorithms to detect k-clique communities, special network groups which are widely used to understand complex phenomena. The proposed algorithms have a space complexity which is the square root of that of the current state-of-the-art. Time complexity achieved is optimal, since it is inversely proportional to the number of processing units available. Extensive experiments were conducted to confirm the efficiency of the proposed algorithms, even in comparison to the state-of-the-art. We experimentally measured a linear speedup, substantiating the optimal performances attained. The second focus of this thesis is the application of networks to discover insights from real-world systems. We introduce novel methodologies to capture cross correlations in evolving networks. We instantiate these methodologies to study the Internet, one of the most, if not the most, pervasive modern technological system. We investigate the dynamics of connectivity among Internet companies, those which interconnect to ensure global Internet access. We then combine connectivity dynamics with historical worldwide stock markets data, and produce graphical representations to visually identify high correlations. We find that geographically close Internet companies offering similar services are driven by common economic factors. We also provide evidence on the existence and nature of hidden factors governing the dynamics of Internet connectivity. Finally, we propose network models to effectively study the Internet Domain Name System (DNS) traffic, and leverage these models to obtain rankings of Internet domains as well as to identify malicious activities

Wealth-based evolution model for the Internet AS-level topology

Abstract — In this paper, we seek to understand the intrinsic reasons for the well-known phenomenon of heavy-tailed degree in the Internet AS graph and argue that in contrast to traditional models based on preferential attachment and centralized optimization, the Pareto degree of the Internet can be explained by the evolution of wealth associated with each ISP. The proposed topology model utilizes a simple multiplicative stochastic process that determines each ISP’s wealth at different points in time and several “maintenance ” rules that keep the degree of each node proportional to its wealth. Actual link formation is determined in a decentralized fashion based on random walks, where each ISP individually decides when and how to increase its degree. Simulations show that the proposed model, which we call Wealth-based Internet Topology (WIT), produces scale-free random graphs with tunable exponent α and high clustering coefficients (between 0.35 and 0.5) that stay invariant as the size of the graph increases. This evolution closely mimics that of the Internet observed since 1997. I