Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network
Source abstract
Abstract
In light of the escalating demand and intricacy of services in contemporary terrestrial, maritime, and aerial combat operations, there is a compelling need for enhanced service quality and efficiency in airborne cluster communication networks. Software-Defined Networking (SDN) proffers a viable solution for the multifaceted task of cooperative communication transmission and management across different operational domains within complex combat contexts, due to its intrinsic ability to flexibly allocate and centrally administer network resources. This study pivots around the optimization of SDN controller deployment within airborne data link clusters. A collaborative multi-controller architecture predicated on airborne data link clusters is thus proposed. Within this architectural framework, the controller deployment issue is reframed as a two-fold problem: subdomain partitioning and central interaction node selection. We advocate a subdomain segmentation approach grounded in node value ranking (NDVR) and a central interaction node selection methodology predicated on an enhanced Artificial Fish Swarm Algorithm (AFSA). The advanced NDVR-AFSA (Node value ranking-Improved artificial fish swarm algorithm) algorithm makes use of a chaos algorithm for population initialization, boosting population diversity and circumventing premature algorithm convergence. By the integration of adaptive strategies and incorporation of the genetic algorithm’s crossover and mutation operations, the algorithm’s search range adaptability is enhanced, thereby increasing the possibility of obtaining globally optimal solutions, while concurrently augmenting cluster reliability. The simulation results verify the advantages of the NDVR-IAFSA algorithm, achieve a better load balancing effect, improve the reliability of aviation data link cluster, and significantly reduce the average propagation delay and disconnection rate, respectively, by 12.8% and 11.7%. This shows that the optimization scheme has important significance in practical application, and can meet the high requirements of modern sea, land, and air operations to aviation airborne communication networks.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Software-Defined Networks and 5G · Advanced MIMO Systems Optimization · Cooperative Communication and Network Coding
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (44)
Show source-provided references
1. Symmetry-aware SFC framework for 5G networks10.1109/MNET.011.2000629 ↗
2. Software-defined networking: A comprehensive survey10.1109/JPROC.2014.2371999 ↗
3. State-of-the-art survey on software-defined networking (SDN)
4. Port scanning based model to detect Malicious TCP traffic and mitigate its impact in SDN
5. Advances in software-defined networking: A survey
6. Network slicing in software-defined networking: A comprehensive survey
7. Ethane: Taking control of the enterprise10.1145/1282427.1282382 ↗
8. Kandoo: A framework for efficient and scalable offloading of control applications
9. Research on port sharing technology in software-defined network
10. Design and implementation of an intelligent test system based on SDN port-sharing technology
11. A port-sharing approach to network function virtualization for Internet of Things
12. Dynamic service function chaining with port sharing in SDN
13. Partition-based control plane separation scheme in large-scale SDN networks
14. Design and implementation of zone-based partitioning technology in SDN
15. Multi-task routing and resource allocation in partitioned SDN networks
16. Distributed dynamic load balancing in SDN for traffic engineering
17. An enhanced dynamic load balancing algorithm for an SDN network using fuzzy-based decision-making approach
18. Multi-controller load balancing algorithm for test network based on IACO10.3390/sym13101901 ↗
19. A load balancing strategy based on control plane sharing in SDN controller pool
20. SDN-based resource pooling to provide transparent multi-path communications10.1109/MCOM.2017.1601214 ↗
21. A controller synchronization architecture based on controller pool for software-defined networks
22. A survey of controller placement in SDN networks
23. The controller placement problem10.1145/2377677.2377767 ↗
24. A dynamic controller partitioning method with minimal control overhead for large-scale SDN networks
25. Improved cooperative game algorithm for distributed controller coordination in SDN
26. Learning controller selection for end-to-end routing in software-defined networks
27. Intelligent SDN traffic classification using deep learning: Deep-SDN
28. An adaptive deep Q-learning strategy for routing schemes in SDN-based data centre networks
29. A modified genetic algorithm for controller placement problem in SDN distributed network
30. Simulated annealing-based multilink selection algorithm in SDN-enabled avionic networks10.1109/ACCESS.2021.3121622 ↗
31. SYNCOP: An evolutionary multi-objective placement of SDN controllers for optimizing cost and network performance in WSNs10.1016/j.comnet.2020.107727 ↗
32. A fuzzy controller placement algorithm for SDN centralized controller clusters
33. Event-triggered adaptive fuzzy control for stochastic nonlinear systems with unmeasured states and unknown backlash-like hysteresis10.1109/TFUZZ.2020.2973950 ↗
34. Sanner · SDN Day · 2016
35. Multiobjective genetic algorithm-based controller placement optimization in software-defined networks
36. An improved particle swarm optimization algorithm for SDN controller placement
37. Controller placement for software-defined networking via a parameter-controlled particle swarm optimization
38. Meta-reinforcement learning based resource management in software defined networks using bayesian network
39. Application of improved ant colony algorithm in load balancing of software-defined networks10.1007/s11227-022-04957-8 ↗
40. The terahertz image enhancement model based on adaptive teaching-learning based optimization algorithm with chaotic mapping and differential evolution
41. X. Li, “A new intelligent optimization method-artificial fish swarm algorithm,” Ph.D. dissertation, Zhejiang University, China, 2003.
42. Heuristics for SDN controller deployment using community detection algorithm
43. A K-means-based network partition algorithm for controller placement in software defined network
44. Heuristic approaches to the controller placement problem in large scale SDN networks10.1109/TNSM.2015.2402432 ↗