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Optimization Strategy of SDN Control Deployment Based on Simulated Annealing-Genetic Hybrid Algorithm
- Source :
- 2018 IEEE 4th International Conference on Computer and Communications (ICCC).
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- With the in-depth deployment of software-defined networks in various business scenarios, controller deployment problem has become a hot issue for software-defined network. At the same time, with the development of the private network of the smart grid, the demand for security, real-time and reliability of the grid transmission service has been continuously improved. In order to solve the problem of wide distribution of power grid system, large amount of data and high delay requirements, we propose an SDN controller deployment scheme based on simulated annealing-genetic hybrid algorithm. This algorithm combines the simulated annealing algorithm and the genetic algorithm. After the simulated annealing algorithm is used to quickly search for a better solution, the positive feedback information is used to narrow down the maximum number of searches and find the optimal path faster. Finally, simulation experiments show that this algorithm can achieve a lower cost controller deployment solution while ensuring the delay between the switch and the controller, and the time required for deployment is shorter.
- Subjects :
- Computer science
Reliability (computer networking)
05 social sciences
050801 communication & media studies
020206 networking & telecommunications
02 engineering and technology
Grid
Hybrid algorithm
0508 media and communications
Smart grid
Control theory
Software deployment
Simulated annealing
Genetic algorithm
0202 electrical engineering, electronic engineering, information engineering
Algorithm
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 IEEE 4th International Conference on Computer and Communications (ICCC)
- Accession number :
- edsair.doi...........6c82803445ce1f4510fadf6226e48e6d
- Full Text :
- https://doi.org/10.1109/compcomm.2018.8780728