Emulation of the connectivity of backbone and management for the layer 3 service of INTERNET2: 2016 topology

Castillo-Velázquez, Jose-Ignacio; Serrano-Martinez, Daniel-Javier; Morales, Augusto

doi:10.1109/concapan.2017.8278476

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…All solving algorithms and models are implemented using Python at a laptop of which the development environment is 64‐bit Windows 10, the CPU is Intel i5‐7300HQ @2.50GHz, the memory is 16.00 GB. We selected 7 WAN topologies randomly from The Internet Topology Zoo and another topology called Internet 2 as simulation instances 42,43 . The distance between nodes is calculated according to the latitude and longitude of the node, the unit is m. The speed transmitting the data packet is set to two‐thirds of the speed of light,

2 \times 10^{8}

m/s 44 .…”

Section: Methodsmentioning

confidence: 99%

“…We selected 7 WAN topologies randomly from The Internet Topology Zoo and another topology called Internet 2 as simulation instances. 42,43 The distance between nodes is calculated according to the latitude and longitude of the node, the unit is m. The speed transmitting the data packet is set to two-thirds of the speed of light, 2 × 10 8 m/s. 44 The information of the eight topologies is shown in Table 1: Since the parameters of the algorithm have nothing to do with topology, we adopt Internet 2 topology as the topology instance for PSO to optimize the three parameters of FA.…”

Section: Methodsmentioning

confidence: 99%

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A multi‐controller placement method for software defined network based on improved firefly algorithm

Guan

Li²,

et al. 2022

Trans Emerging Tel Tech

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Placing multiple controllers in a large-scale software defined network (SDN) is an NP-hard problem. Currently, it is still a challenge to design a solution algorithm with high accuracy, short time consumption, and compliance with engineering standards. To overcome this challenge, we proposed the concept of synthetical delay, and took the controller capacity as a constraint condition, and established a controller placement model. The firefly algorithm (FA) was chosen as the solving algorithm for the controller placement problem. However, the FA has the drawbacks of insufficient search breadth in the early stage and low convergence efficiency in the later stage. To this end, we first introduced the dynamic parameter strategy for the light attractiveness, the light absorption coefficient, and the parameter to control the random walk of the FA. Then, we adopt the particle swarm optimization to improve the initial static parameters of the FA. Experimental results in the real large-scale SDN topologies show that, compared with the latest controller placement algorithms based on cuckoo optimization, chaotic salp swarm algorithm, Varna-based Optimization, and Teaching-Learning-based Optimization, the controller placement scheme obtained by the improved FA has a lower synthetical delay and more balanced controller load. Simultaneously, the time consumption of the improved FA is acceptable.

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