Controller placement in SDN using game theory and a discrete hybrid metaheuristic algorithm

Software-defined networking (SDN) is a network architecture where the control and data plane are separated. As the network size grows, relying on just one controller can lead to various problems. Thus, in highly scalable networks, multiple controllers are needed. This critical issue of determining t...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of supercomputing 2024-03, Vol.80 (5), p.6552-6600
Main Authors: Khojand, Mahnaz, Majidzadeh, Kambiz, Masdari, Mohammad, Farhang, Yousef
Format: Article
Language:English
Subjects:
Algorithms
Compilers
Computer Science
Controllers
Energy consumption
Game theory
Heuristic methods
Interpreters
Mapping
Optimization
Placement
Processor Architectures
Programming Languages
Simulated annealing
Software-defined networking
Switches
Topology
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Software-defined networking (SDN) is a network architecture where the control and data plane are separated. As the network size grows, relying on just one controller can lead to various problems. Thus, in highly scalable networks, multiple controllers are needed. This critical issue of determining the number and placement of controllers is known as the controller placement problem (CPP). In this paper, game theory is used to solve CPP by identifying the optimal number of controllers. Two algorithms, golden eagle optimization (GEO) and grey wolf optimization (GWO), are utilized to find the most efficient mapping between switches and controllers. Since CPP is a discrete problem, GEO and GWO have first been discretized and then hybridized to form a new algorithm called GEWO. This algorithm is used to discover the most efficient mapping between switches and controllers. Additionally, simulated annealing is employed for better local search. The effectiveness of this approach is evaluated using different numbers of controllers on four well-known software-defined networks from the Internet Topology Zoo. The results are compared against various existing algorithms in the field, and it is observed that GEWO outperforms the competition. The findings demonstrate that GEWO reduces load imbalance by 24.07%, decreases end-to-end delay by 20.95%, and lowers average energy consumption by 11.65%.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-023-05709-y