Enhancing Quality-of-Service in Software-Defined Networks Through the Integration of Firefly-Fruit Fly Optimization and Deep Reinforcement Learning

The Software Defined Networking (SDN) paradigm has emerged as a critical tool for meeting the dynamic demands of network management with respect to efficiency and flexibility. Quality of Service (QoS) optimization, which encompasses essential features including bandwidth allocation, latency, and packet loss, is a major problem in SDN systems due to its direct influence on network application performance and user experience. To deal with these important issues, this paper tackles the critical problem of Software-Defined Networks (SDNs) Quality-of-Service (QoS) optimization, which is a critical factor affecting network application performance and user experience. Within the Firefly-Fruit Fly Optimised Deep Reinforcement Learning (DQ-FFO-DRL) framework, a novel combination of optimization techniques derived from Fruit Fly and Firefly behaviors with Deep Q-Learning is presented in this suggested approach, which is called Deep Q-Learning. The framework effectively investigates ideal network configurations by utilizing the distinct advantages of the Fruit Fly and Firefly optimization components, while the Deep Q-Learning component dynamically adjusts to changing network circumstances by drawing conclusions from prior experiences. Extensive testing and modeling reveal that the DQ-FFO-DRL approach performs very well in SDNs compared to conventional QoS management solutions. When it comes to negotiating the always changing world of resource allocation, network usage, and overall network performance, this algorithm demonstrates exceptional adaptability. The suggested system, which is implemented in Python, offers an advanced and flexible method for enhancing QoS in SDN systems.

@article{Aboughaly2024,
  title     = {Enhancing Quality-of-Service in Software-Defined Networks Through the Integration of Firefly-Fruit Fly Optimization and Deep Reinforcement Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mahmoud Aboughaly and Shaikh Abdul Hannan},
  doi       = {10.14569/IJACSA.2024.0150138},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150138}
}