Cell Attention Networks Integrated with Spiking Neural Networks and Binary Light Spectrum Optimization for Efficient Task Scheduling in Cloud Computing: Blockchain-Enhanced

Task scheduling in cloud computing remains a persistent and complex challenge due to inherently dynamic resource requirements and fluctuating workload patterns. Traditional methodologies—whether heuristic or AI-driven—often fall short in delivering real-time adaptability, robust scalability, and verifiable security. To address these limitations, this study introduces a composite framework integrating Cell Attention Networks (CANs), Spiking Neural Networks (SNNs), and Binary Light Spectrum Optimization (BLSO), underpinned by a Blockchain infrastructure for secure and trustworthy task management. CANs facilitate dynamic task prioritization by attending in real-time to resource-sensitive attributes. SNNs, inspired by the spike-based signaling of biological neurons, enable efficient temporal encoding and representation of task data. BLSO, as a discrete binary metaheuristics technique, ensures lightweight yet effective optimization in the task-to-resource allocation process. Security and trust are reinforced through blockchain deployment, guaranteeing immutability, auditability, and decentralized validation via smart contracts. The proposed architecture was empirically validated using simulated cloud workload environments and standard benchmark datasets within a Python–TensorFlow–BindsNET framework, integrated with Ethereum Testnet for decentralized verification. Experimental outcomes demonstrated significant enhancements in execution latency, energy efficiency, SLA compliance, and trustworthiness when compared to conventional scheduling algorithms.