Study on cloud resource scheduling in power multi service scenarios based on large language model technology framework

Due to the complexity of various business scenarios in the power industry, it is difficult to achieve load balancing, resulting in long cloud resource scheduling and system execution times. Propose a cloud resource scheduling algorithm for power multi service scenarios based on the big language model technology framework. Using triangular fuzzy number analysis to determine the uncertainty of execution time, and using logarithmic method to unify the data scale, the optimisation objective of cloud resource scheduling is determined. Using the linear variation of sine functions in big language modelling techniques to determine scheduling order. By utilising multi head self attention and feedforward neural networks for internal transmission, a pre trained model is constructed, and combined with fine-tuning and implementation stages, cloud resource scheduling is achieved. Experiments have shown that this algorithm reduces the execution time and cost of cloud resource scheduling in multi service scenarios of electricity.