A method of characterizing network topology based on the breadth-first search tree

Using dominating set can aggregate the complex physical network topologies into simple virtual topologies and reduce the cost of the networks. In the Multi-Protocol Label Switching (MPLS) network, the dominating set constructed based on label router can effectively aggregate MPLS network topology and reduce the amount of Label Switching Path (LSP), so as to save the expenses of network maintain information. However, simply considering the size of dominating set can't guarantee the best performance of the networks after aggregating. Therefore, an improved algorithm based on breadth-first search spanning tree is proposed, considering the size of the dominating set, bandwidth performance of the nodes and path length between nodes, which can effectively extend the MPLS network, with excellent bandwidth performance and reduce the data transmission delay.

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An efficient distributed algorithm for constructing a breadth-first search tree

Systems and Computers in Japan ◽

10.1002/scj.4690201002 ◽

1989 ◽

Vol 20 (10) ◽

pp. 15-30 ◽

Cited By ~ 2

Author(s):

Jungho Park ◽

Nobuki Tokura ◽

Toshimitsu Masuzawa ◽

Ken'Ichi Hagihara

Keyword(s):

Distributed Algorithm ◽

Search Tree ◽

Breadth First Search

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Topology Algorithm of Power Network Based on Breadth-First-Search

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.4591 ◽

2014 ◽

Vol 556-562 ◽

pp. 4591-4595

Author(s):

Lin Wei Li ◽

Xing He Ma ◽

Lian Ming Zhang

Keyword(s):

Network Topology ◽

Traditional Method ◽

New Method ◽

Power Network ◽

Breadth First Search ◽

Slow Speed ◽

Adjacency List ◽

Network Connection ◽

Electric Equipment ◽

Low Efficiency

Aiming at the defects of repeating searching the path, low efficiency and the slow speed of the traditional topology algorithm, proposes a network topology algorithm based on Breadth-First-Search in this paper.Introduces the topology modeling method,and the electric equipment is abstracted into nodes and branches,making the topology process not constrained by the network connection and structure,enhancing universal property of the algorithm.Then introduces the traditional method, and analysises the disadvantages. At the same time,puts forward a new method. Uses node-branch adjacency list in the new method,which improves the topology speed.When the local switch information is changed, can modify the node-branch adjacency list to conduct the local re-topology,so it improves the topology flexibility. Finally, through the example, proves the superiority and practicability of this algorithm.

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Applying machine learning in network topology optimization

ITU Journal on Future and Evolving Technologies ◽

10.52953/fkid2877 ◽

2021 ◽

Vol 2 (4) ◽

pp. 91-99

Author(s):

Zhouwei Gang ◽

Qianyin Rao ◽

Lin Guo ◽

Lin Xi ◽

Zezhong Feng ◽

...

Keyword(s):

Machine Learning ◽

Topology Optimization ◽

Human Resources ◽

Network Topology ◽

Absorption Capacity ◽

Transmission Network ◽

Breadth First Search ◽

Communication Services ◽

Node Number ◽

Network Topology Optimization

Nowadays, telecommunications have become an indispensable part of our life, 5G technology brings better network speeds, helps the AR and VR industry, and connects everything. It will deeply change our society. Transmission is the vessel of telecommunications. While the vessel is not so healthy, some of them are overloaded, meanwhile, others still have lots of capacity. It not only affects the customer experience, but also affects the development of communication services because of a resources problem. A transmission network is composed of transmission nodes and links. So that the possible topology numbers equal to node number multiplied by number of links means it is impossible for humans to optimize. We use Al instead of humans for topology optimization. The AI optimization solution uses an ITU Machine Learning (ML) standard, Breadth-First Search (BFS) greedy algorithm and other mainstream algorithms to solve the problem. It saves a lot of money and human resources, and also hugely improves traffic absorption capacity. The author comes from the team named "No Boundaries". The team attend ITU AI/ML in 5G Challenge and won the Gold champions (1st place).

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A Breadth-first Search Tree Construction for Multiplicative Circulant Graphs

European Journal of Pure and Applied Mathematics ◽

10.29020/nybg.ejpam.v14i1.3884 ◽

2021 ◽

Vol 14 (1) ◽

pp. 248-264

Author(s):

John Rafael Macalisang Antalan ◽

Francis Joseph Campena

Keyword(s):

Spectral Radius ◽

Average Distance ◽

Wiener Index ◽

Search Tree ◽

Recursive Method ◽

Breadth First Search ◽

Circulant Graphs ◽

Tree Construction

In this paper, we give a recursive method in constructing a breadth-first search tree for multiplicative circulant graphs of order power of odd. We then use the proposed construction in reproving some results concerning multiplicative circulant graph's diameter, average distance and distance spectral radius. We also determine the graph's Wiener index, vertex-forwarding index, and a bound for its edge-forwarding index. Finally, we discuss some possible research works in which the proposed construction can be applied.

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Breadth-First Search Multi-Dimensional Binary Search Tree based Algorithms for Structural

10.31224/osf.io/3xkm8 ◽

2022 ◽

Author(s):

Zhifeng Xu

Keyword(s):

Reliability Index ◽

Structural Reliability ◽

Limit State ◽

Computational Cost ◽

Search Tree ◽

Binary Search ◽

Reliability Estimation ◽

Binary Search Tree ◽

Breadth First Search ◽

Fast Searching

This research proposes a set of novel algorithms for structural reliability estimation based on muti-dimensional binary search tree and breadth-first search, namely the reliability accuracy supervised searching algorithm, the limit-state surface resolution supervised searching algorithm and the reliability index precision supervised fast searching algorithm. The proposed algorithms have the following strengths: 1, all the proposed algorithms have satisfactory computational efficiency by reducing redundant samplings; 2, their computational costs are stable and computable; 3, performance functions of high non-linearity can be will handled; 4, the reliability accuracy supervised searching algorithm can adapt its computational cost according to a prescribed accuracy; 5, the limit-state surface resolution supervised searching algorithm is able to probe sharp changes on limit-state surfaces; 6, the reliability index precision supervised fast searching algorithm computes the reliability index with sufficient precision in a fast way.

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