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Research on Optimized Deployment of Virtual Network Functions in Network Function Virtualization Environment
Author(s) -
Mingyue Liu,
Feng Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1748/3/032020
Subject(s) - computer science , software deployment , simulated annealing , greedy algorithm , mathematical optimization , bandwidth (computing) , distributed computing , virtual network , virtual machine , heuristic , algorithm , computer network , mathematics , operating system , artificial intelligence
This paper mainly studies the optimization of dynamically arrived SFCs deployment in the SDN scenarios, and optimizes the end-to-end delay and bandwidth consumption during the deployment. First we build the model of the optimization problem, it is expressed as a 0-1 planning problem. We use ILP to get the optimal solution, cause the problem is NP hard, its runtime increases as network scales increases, so we choose the heuristic algorithm instead to reduce algorithm runtime. During our heuristic algorithm, we design a new method to sort VNFs and network nodes, then use the greedy algorithm to select nodes literately to place VNFs, in order to avoid local optimality, further use the simulated annealing algorithm with the results of the greedy algorithm as its initial solution, and design two methods to generate new deployments, and still repeat iterating to find a better deployment until reaching iteration limits. This paper also considers the SFCs’ lifecycle and trade-off between the two parameters. The simulation proves that the algorithm proposed in this paper can significantly reduce the end-to-end delay and bandwidth consumption than the traditional method, and 80% of its results are very close to the optimal solution with less than 5% error, and the ratio of its runtime and optimal solution’s is at most 0.003, the algorithm also has certain applicability and can be used in other scenarios.

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