Balancing module in evolutionary optimization and Deep Reinforcement Learning for multi-path selection in Software Defined Networks
- Title
- Balancing module in evolutionary optimization and Deep Reinforcement Learning for multi-path selection in Software Defined Networks
- Creator
- Praveena H.D.; Srilakshmi V.; Rajini S.; Kolluri R.; Manohar M.
- Description
- Software Defined Network (SDN) has been used in many organizations due to its efficiency in transmission. Machine learning techniques have been applied in SDN to improve its efficiency in resource scheduling. The existing models in SDN have limitations of overfitting, local optima trap and lower efficiency in path selection. This study applied Balancing Module (BM)-Spider Monkey Optimization (SMO)-Crow Search Algorithm (CSA) for multi path selection in SDN to improve its efficiency. The balancing module applies Gaussian distribution to balance between exploration and exploitation in the multi-path selection process. The Balancing module helps to escape local optima trap and increases the convergence rate. Deep Reinforcement learning is applied for resource scheduling in SDN. The Deep reinforcement learning technique uses the reward function to improve the learning performance, and the BM-SMO-CSA technique has 30 J energy consumption, where the existing models: DRL has 40 J energy consumption, and Graph-ACO has 62 J energy consumption. 2022
- Source
- Physical Communication, Vol-56
- Date
- 2023-01-01
- Publisher
- Elsevier B.V.
- Subject
- Balancing module; Crow Search Algorithm; Deep Reinforcement Learning; Software Defined Network; Spider Monkey Optimization
- Coverage
- Praveena H.D., Department of ECE, Sree Vidyanikethan Engineering College, Andhra Pradesh, Tirupati, 517102, India; Srilakshmi V., Department of Computer Science and Engineering (AI & ML), B V Raju Institute of Technology, Narsapur, India; Rajini S., Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India; Kolluri R., Department of ECE, Narasaraopeta Engineering College, Andhra Pradesh, Guntur, India; Manohar M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 18744907
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Praveena H.D.; Srilakshmi V.; Rajini S.; Kolluri R.; Manohar M., “Balancing module in evolutionary optimization and Deep Reinforcement Learning for multi-path selection in Software Defined Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/14422.