A SECURE AND OPTIMIZED FRAMEWORK FOR CONTROLLER SELECTION AND RESOURCE MANAGEMENT IN SDN

Abstract

One interesting approach to network management is Software Defined Networking, or SDN. However, the centralized control architecture of SDN poses challenges for energy-efficient task scheduling and security. In this paper, a novel approach to blockchain-based safe resource allocation with controller selection in SDN is proposed using the Entropy Oppositional Based Learning-Interpolation Blue Monkey Optimization Algorithm (EOBL-IBMOA). By creating controller selection as a blockchain-centric secure resource allocation, the suggested approach tackles SDN challenges. This one includes phases like resource allocation, load balancing, attack detection, controller selection, and user registration. The Mid Square-based KECCAK 512 (MS-KECCAK 512) technique is used to generate hash codes, and IP traceback protects user information using XOR Left Shift (XORLS). For effective traffic balancing, the load balancer uses the Minshev-KMeans algorithm. The Quantile Transformer Scaling based SoftmaxGELU Gated Recurrent Units (QTS-SGGRU) technique is used to classify attacks. For the best Virtual Machine (VM) selection, controller selection and resource allocation use EOBL-IBMOA. The superiority of the suggested approach is described through experimental comparisons. The suggested method outperforms the existing works by achieving efficient resource allocation with low response time and high throughput.