Internet Data Analysis Research Articles

AS-Level Internet Macroscopic Topology Centralization Evolvement Analysis

The As-level topology is a hotspot of the recent reseaches. We can understand the centralization of the network clearly by researching the evolvement trend of the Internet macroscopic topology. The massive data we use in this paper is from CAIDA (The Cooperative Association for Internet Data Analysis) Skitter project. And the time span of the data is from July, 2001 to January, 2008. This paper introduces the background of the AS-level topology at first, then carries out the evolvement of degree, core and layer. It is believed that the influence of the top-degree nodes on the other nodes decreases and the centralization of network is going to fall off with the decrease of the core. And the nucleus status of network declines.

Alias Filtering Technique in Alias Resolution

PDF HTML阅读 XML下载 导出引用 引用提醒 别名解析中的别名过滤技术 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Alias Filtering Technique in Alias Resolution Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为了提高大规模网络中别名解析的效率,在用traceroute测量得到的IP级网络拓扑的基础上,提出别名过滤的概念.首先从理论上研究别名关系具有的性质,由此提出处理traceroute数据的3个属性;然后提出并设计了别名过滤算法AF(alias filtering)和别名验证算法VAR(validation of alias relationship).最后,利用CAIDA(Cooperative Association for Internet Data Analysis)的Skitter项目得到的中国、日本、韩国这3个国家因特网的traceroute数据集对上述算法进行了验证分析.结果表明,别名过滤的概念非常重要并且文中提出的算法效率比较高. Abstract:To improve the efficiency of alias resolution in large scale network, a concept of alias filtering is proposed based on IP level topology measured by traceroute. The characteristics of alias relationship are explored theoretically, and three attributes are proposed to deal with traceroute data based on the characteristics. Then, an alias filtering algorithm called AF and a verification of alias filtering algorithm called VAR are put forward. Finally, both the algorithms are verified via the traceroute data from Internet covering China, Japan, and Korea, which are measured by Skitter of CAIDA (Cooperative Association for Internet Data Analysis). The results prove that the concept of alias filtering is very important and the algorithms proposed in this paper are valid and efficient. 参考文献 相似文献 引证文献

The subjective experience of taking antipsychotic medication: a content analysis of Internet data

We explored the subjective effects associated with olanzapine, risperidone and older antipsychotics. We conducted a content analysis of an Internet database of comments about prescribed medications. We analysed 223 comments on risperidone, 170 on olanzapine and 46 relating to three older antipsychotics. The predominant subjective effects produced by all drugs consisted of sedation, cognitive impairment and emotional flattening or indifference. Connections appeared between these effects and Parkinsonian-like symptoms with the older drugs, sexual impairment with risperidone and metabolic effects with olanzapine. The experience of akathisia was frequently linked to suicidal thoughts. Some respondents described how the drugs' subjective effects helped to reduce symptoms of mania, psychosis and anxiety. The generalisability of Internet data is uncertain. However, the data suggest that adverse subjective effects play a central role in the experience of taking antipsychotic drugs and may be related to the drugs' desired benefits.

The Study on the Hierarchy of Internet Router-Level Topology

Being a huge system, Internet topology structure is very complex. It can’t be treated as a plane simply, and its hierarchy must be analyzed. We used the k-core decomposition to disentangle the hierarchical structure of Internet Router-level topology. By analyzing the router-lever Internet topology measuring data from CAIDA (The Cooperative Association for Internet Data Analysis) ,we studied the characteristics of the nodes in the inner hierarchy and outer hierarchy respectively. The frequency-degree power law of the nodes which core-ness is lower and the regionally distribution of the nodes which coreness is higher were concluded. At last, the topology of every hierarchy was described by giving their figures. These descriptions can provide a valuable reference for modeling on the Internet topology.

Policy relationship annotations of predefined AS-level topologies

The contractual relationships between autonomous systems (AS) cannot be ignored in the research of large-scale communication protocols and architectures. It has been widely recognized that disregarding policy relationships leads to unrealistic routing paths in simulated communication networks and thus to inaccurate conclusions about the investigated problem. Current AS-level topology generators either completely overlook the relationships or make the annotation process inherent in topology generation. We propose a novel algorithm for annotating random graphs. Our approach differs from previous studies in focusing on the annotation process rather than on the topology generation, which enables reuse of the state-of-the-art topology generators. We identify five properties of viable annotations and formulate the problem as a type-of-relationship problem in random graphs (TRR) by analogy with the related problem of inferring AS relationships from measured routing data. We propose an annotation algorithm for solving the TRR problem by taking advantage of the stochastic properties found in the inferred annotations provided by the cooperative association for internet data analysis (CAIDA). The evaluation provides the evidence of high resemblance of our annotations to the measured ones.

Internet Data Analysis for the Undergraduate Statistics Curriculum

Statistics textbooks for undergraduates have not caught up with the enormous amount of analysis of Internet data that is taking place these days. Case studies that use Web server log data or Internet network traffic data are rare in undergraduate Statistics education. And yet these data provide numerous examples of skewed and bimodal distributions, of distributions with thick tails that do not follow the usual models studied in class, and many other interesting statistical curiosities. This paper summarizes the results of research in two areas of Internet data analysis: users' web browsing behavior and network performance. We present some of the main questions analyzed in the literature, some unsolved problems, and some typical data analysis methods used. We illustrate the questions and the methods with large data sets. The data sets were obtained from the publicly available pool of data and had to be processed and transformed to make them available for classroom exercises. Students in Introductory Statistics classes as well as Probability and Mathematical Statistics courses have responded to the stories behind these data sets and their analysis very well. The message in the stories can be conveyed at a descriptive or a more advanced level.

Internet Measurement

Network measurement was neglected in the Internet’s early stages, taking lower priority to increasing the network’s speed, capacity, and coverage. Recently, however, interest in network measurement has expanded, paving the way toward a growing understanding of the Internet’s structure and behavior. Unfortunately, as the number of Internet users has grown, antisocial and even malicious behavior has also increased. Countering these and other scaling challenges will require substantially more investment in Internet measurement and data analysis. The four articles that follow provide an introduction to this vital research area.

Large-scale topological and dynamical properties of the Internet.

We study the large-scale topological and dynamical properties of real Internet maps at the autonomous system level, collected in a 3-yr time interval. We find that the connectivity structure of the Internet presents statistical distributions settled in a well-defined stationary state. The large-scale properties are characterized by a scale-free topology consistent with previous observations. Correlation functions and clustering coefficients exhibit a remarkable structure due to the underlying hierarchical organization of the Internet. The study of the Internet time evolution shows a growth dynamics with aging features typical of recently proposed growing network models. We compare the properties of growing network models with the present real Internet data analysis.

What's next for Internet data analysis? Status and challenges facing the community

Most large providers currently collect basic statistics on the performance of their own infrastructure, typically including measurements of utilization and availability and possibly rudimentary assessments of delay and throughput. In today's commercial Internet, the only baseline against which organizations can calibrate their networks is past performance data. No data or even standard formats are available against which to compare performance with other networks or against an industry norm, nor are there reliable data with which customers can assess the performance of providers. Data characterization and traffic flow analysis also are virtually nonexistent at this time, yet they remain essential for understanding the internal dynamics of the Internet infrastructure. Path performance measurement tools enable users and operators to better evaluate and compare providers and to monitor service quality. Traffic flow characterization tools focus on the behavior and inner workings of these wide-area networks. This paper has two goals. We first provide background on the current Internet architecture and describe how measurements are a key element in the development of a robust and financially successful commercial Internet. We then discuss the current state of Internet metrics analysis and activities within various forums, particularly the Cooperative Association for Internet Data Analysis and the National Laboratory for Applied Network Research, to encourage the development and deployment of Internet performance monitoring and workload characterization tools.