BRITE: an approach to universal topology generation

By Alberto Medina, Anukool Lakhina, Ibrahim Matta, John W. Byers. MASCOTS 2001, Proceedings Ninth International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, 2001

1,382 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/ca83b3bee35ce2e1086a41c92ee14b90999a047b

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Citations in the corpus

Power laws and the AS-level internet topology (2003, background): “In addition, significant work has been devoted in understanding the origin [36], and generating power-law topologies [26], [35], [36], [42], [54], [58].”
Measuring ISP topologies with rocketfuel (2002, methodology): “There is enough uncertainty in the properties of real ISP topologies (such as whether router outdegree distribution follows a power law as suggested by Faloutsos [8]) that it is unclear whether synthetic topologies generated by tools such as GT-ITM [28] or Brite [ 14 ] are representative [27].”
Versatile, scalable, and accurate simulation of distributed applications and platforms (2014, neutral): “The third platform comprises 200 nodes and is generated with the Waxman model [84] and the BRITE generator [57].”
CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms (2011, background): “By using an external network description file (stored in BRITE format), we allow reuse of same topology in different experiments.”
Experience-driven Networking: A Deep Reinforcement Learning based Approach (2018, methodology): “Besides, we randomly generated a network topology with 20 nodes and 80 links, using the widely-used network topology generator, BRITE [19].”
Collecting the internet AS-level topology (2005, methodology): “In this work, weassemblethe most completeAS-leveltopologyby extending theconventional method along twodimensions.”
A routing scheme for content-based networking (2004, methodology): “The topologies created for our experiments are ﬂat, random, router-level topologies generated by BRITE [10] using the Waxman edge selection algorithm [14].”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, result): “BRITE [147], reveal that the proposed DQL scheme significantly reduces the end-to-end delay and improves the network utility, compared with the baseline schemes including DDPG”
PRIME: Peer-to-Peer Receiver-drIven MEsh-Based Streaming (2007, methodology): “Simulation Setting: We use the following default settings in our simulations: the physical topology is generated with Brite [20] using 15 ASs with 10 routers per AS in top-down mode, the overlay is directed, the bandwidth-degree condition is satis.ed, and the delay on each access link is randoml”
Measuring ISP topologies with Rocketfuel (2004, methodology): “To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior speci.c permission and/or a fee. topologies generated by tools such as GT-ITM [26] or Brite [12] are representative [25].”
Mesh or Multiple-Tree: A Comparative Study of Live P2P Streaming Approaches (2007, methodology): “In our simulations, the physical topology is generated using Brite [6] with 15 AS, 10 routers per AS in top-down mode, and RED queue management at all routers.”
Systematic topology analysis and generation using degree correlations (2006, background): “…but then quickly decreases, and it is surely 1 in the limit of d = n, cf. the example in Section 3. with more sophisticated forms of annotations, in which case the dK-series would describe correlations among different types of nodes connected by different types of links within d-sized geometries.”
Towards capturing representative AS-level Internet topologies (2002, background): “For example, these activities include (1) analyzing and modeling measurements to infer the Internet’s AS connectivity graph to describe its properties [2], (2) explaining the origins and causes of some of the observed surprising features [3], [4], (3) building top ology generators that produce graph”