SDN Controller Selection and Secure Resource Allocation

Software Defined Networking (SDN) has emerged as a promising paradigm for network management. However, in energy-effective task scheduling and security, the centralized control architecture of SDN brings challenges. This research proposes a new approach for blockchain-based secure resource allocation with controller selection in SDN, utilizing the Entropy Oppositional Based Learning-Interpolation Blue Monkey Optimization Algorithm (EOBL-IBMOA). By establishing a blockchain-centric secure resource allocation with controller selection, the proposed technique addresses challenges in SDN. Here, user registration, load balancing, attack detection, controller selection, and resource allocation phases are included. Utilizing XOR Left Shift (XORLS), the user details are secured by IP traceback and hash codes are generated using the Mid Square-based KECCAK 512 (MS-KECCAK 512) algorithm. For effective traffic balancing, the load balancer uses the Minshev-KMeans algorithm. Attack classification is attained through the Quantile Transformer Scaling based SoftmaxGELU Gated Recurrent Units (QTS-SGGRU) approach. EOBL-IBMOA is used by controller selection and resource allocation for optimal Virtual Machine (VM) selection. The proposed technique’s superiority is described by experimental comparisons. The proposed approach attains effective resource allocation with reduced response time and high throughput, outperforming the prevailing works.