Network Emulation as a Service (NEaaS): Towards a Cloud-Based Network Emulation Platform

Lai, Junyu; Tian, Jiaqi; Jiang, Dingde; Sun, Jiaming; Zhang, Ke

doi:10.1007/978-3-030-32216-8_49

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Similarly, dynamic-link networks can be modeled as such graphs, however, p i maps an edge to a nonempty set of integer values, i.e., p i : E → 2 N \ ∅, where 2 N denotes the power-set of N. An example dynamic network is depicted in Fig. 1, and its model N = (V, E, p 1 (e), p 2 (e)), where: (2,1), (1,3), (3,1), (1,4), (4, 1), (2, 4), (4, 2), (3,4), (4, 3)}…”

Section: B Dynamic-link Networkmentioning

confidence: 99%

“…A well-known strategy is to evaluate new components on an emulated environment. In contrast to using operational networks for testing, emulations allow to replicate the behavior of real networks without extensive hardware components [3]. Moreover, emulation facilitates testing novel implementations under different scenarios, topologies, environment, and overall input values.…”

Section: Introductionmentioning

confidence: 99%

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On using Cellular Automata for Modeling the Evolution of Dynamic-Link Network Parameters

Petersen

López

Kushik

et al. 2022

2022 IEEE 21st International Symposium on Network Computing and Applications (NCA)

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We present a novel formalism for describing the evolution of dynamic-link network parameters; it is based on the Cellular Automaton (CA) model. Such formalism is of wideuse for modeling natural (e.g., physical, chemical, etc.) processes. We propose a particular model and survey the related work, with respect to the use of CA to simulate various communication networks. We showcase the flexibility of the proposed approach to model different evolution patterns. These patterns can be used to emulate / simulate different network scenarios (states of the network parameters), and test novel implementations under distinct conditions. Additionally, we propose an algorithm for guaranteeing that the described patterns hold properties of interest, within a bounded time.

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