Enhancing energy efficiency in cloud computing through task scheduling with hybrid cuckoo search and transformer models

In cloud computing, task scheduling has become more and more important for maximizing resource allocation, cutting down on energy use, and enhancing system performance. However, traditional algorithms often fall short in dynamic environments, leading to performance inefficiencies. For cloud computing energy efficiency, this research suggests a hybrid task scheduling method that combines Transformer-based fine-tuning and Cuckoo Search (HCS) optimization. The integration of bio-inspired optimization (HCS) and Modified BERT-Based Transformer allows for dynamic task reallocation, enhancing both energy consumption and resource utilization while maintaining high Service Level Agreement (SLA) compliance. Fine-tuning with Transformer models enables adjustments to improve scheduling efficiency and minimize energy wastage. The results show that the proposed method reduced energy consumption to 0.430 J (from 0.440 J) while maintaining 100% SLA compliance and task completion rate. Resource utilization improved to 88.2%, with energy efficiency of 0.0043 J/task. This hybrid approach offers significant advantages over traditional methods, such as BENBO + Bi-LSTM (0.687 J), PSO-PGA (0.617 J), and GAECS (0.537 J), achieving a reduced energy consumption of 0.440 J for 15 PM with 20 VMs, making it an ideal solution for scalable cloud computing environments.