Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution—the part of the distribution representing large but rare events—and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov (KS) statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data, while in others the power law is ruled out.

/pdf/power-law-distributions-in-empirical-data-4dybihkltl.pdf

Power-Law Distributions in Empirical Data

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

https://www.cs.montana.edu/courses/csci566/Ref/WMSN-Survey.pdf

A survey on wireless multimedia sensor networks

Mininet is a system for rapidly prototyping large networks on the constrained resources of a single laptop The lightweight approach of using OS-level virtualization features, including processes and network namespaces, allows it to scale to hundreds of nodes Experiences with our initial implementation suggest that the ability to run, poke, and debug in real time represents a qualitative change in workflow We share supporting case studies culled from over 100 users, at 18 institutions, who have developed Software-Defined Networks (SDN) Ultimately, we think the greatest value of Mininet will be supporting collaborative network research, by enabling self-contained SDN prototypes which anyone with a PC can download, run, evaluate, explore, tweak, and build upon

/pdf/a-network-in-a-laptop-rapid-prototyping-for-software-defined-27edokofdp.pdf

A network in a laptop: rapid prototyping for software-defined networks

http://dtstc.ugr.es/~jedv/descargas/2009_CoSe09-Anomaly-based-network-intrusion-detection-Techniques,-systems-and-challenges.pdf

Anomaly-based network intrusion detection: Techniques, systems and challenges

As networks of computing devices grow larger and more complex, the need for highly accurate and scalable network simulation technologies becomes critical. Despite the emergence of large-scale testbeds for network research, simulation still plays a vital role in terms of scalability (both in size and in experimental speed), reproducibility, rapid prototyping, and education. With simulation based studies, the approach can be studied in detail at varying scales, with varying data applications, varying field conditions, and will result in reproducible and analyzable results.

The ns-3 Network Simulator

Research on performance, robustness, and evolution of the global Internet is fundamentally handicapped without accurate and thorough knowledge of the nature and structure of the contractual relationships between Autonomous Systems (ASs). In this work we introduce novel heuristics for inferring AS relationships. Our heuristics improve upon previous works in several technical aspects, which we outline in detail and demonstrate with several examples. Seeking to increase the value and reliability of our inference results, we then focus on validation of inferred AS relationships. We perform a survey with ASs' network administrators to collect information on the actual connectivity and policies of the surveyed ASs. Based on the survey results, we find that our new AS relationship inference techniques achieve high levels of accuracy: we correctly infer 96.5% customer to provider (c2p), 82.8% peer to peer (p2p), and 90.3% sibling to sibling (s2s) relationships. We then cross-compare the reported AS connectivity with the AS connectivity data contained in BGP tables. We find that BGP tables miss up to 86.2% of the true adjacencies of the surveyed ASs. The majority of the missing links are of the p2p type, which highlights the limitations of present measuring techniques to capture links of this type. Finally, to make our results easily accessible and practically useful for the community, we open an AS relationship repository where we archive, on a weekly basis, and make publicly available the complete Internet AS-level topology annotated with AS relationship information for every pair of AS neighbors.

/pdf/as-relationships-inference-and-validation-3un9mtc082.pdf

AS relationships: inference and validation

We introduce a new network simulation environment, developed by our research group, called the Georgia Tech Network Simulator (GTNetS). Our simulator is designed specifically to allow much larger-scale simulations than can easily be created by existing network simulation tools. The design of the simulator very closely matches the design of real network protocol stacks and hardware. Thus, anyone with a good understanding of networking in general can easily understand how the simulations are constructed. Further, our simulator is implemented completely in object-oriented C++, which leads to easy extension by users to experiment with new or modified behavior of existing simulation models. Our tool is designed from the beginning with scalability in mind, including the support for distributed simulations on a network of workstations as part of the basic design.We give an overview of the features of GTNetS, and present some preliminary scalability results we have obtained by running GTNetS on a computing cluster at the Pittsburgh Supercomputer Center.

/pdf/the-georgia-tech-network-simulator-3pjm1wi4yl.pdf

The Georgia Tech Network Simulator

This paper reports on the project plan to develop a new major version of the popular ns-2 networking simulator. The authors have organized an NSF-funded, four-year community infrastructure project to develop the next version of ns. The project will also be oriented towards community development and open source software practices to encourage participation from the broader research and educational community. The purpose of this paper is to expand on the goals and initial design concepts for this new software development effort.

ns-3 project goals

http://www.icir.org/floyd/papers/wns2.pdf

George F. Riley

Papers

The ns-3 Network Simulator

AS relationships: inference and validation

The Georgia Tech Network Simulator

ns-3 project goals

ns-3 project goals