Construction of Cloud Computing Task Scheduling Model Based on Simulated Annealing Hybrid Algorithm

With the development of cloud computing technology, effective task scheduling can help people improve work efficiency. Therefore, this study presented a hybrid algorithm on the grounds of simulated annealing and taboo search to optimize task scheduling in cloud computing. This study presented a hybrid algorithm for optimizing the cloud computing task scheduling model. The model used simulated annealing algorithm and taboo search algorithm to convert the objective function into an energy function, allowing atoms to quickly arrange in terms of a certain rule for obtaining the optimal solution. The study analyzed the model through simulation experiments, and the experiment showed that the optimal value of the hybrid algorithm in high-dimensional unimodal testing was 7.15E-247, far superior to the whale optimization algorithm's 3.99E-28 and the grey wolf optimization algorithm's 1.10E-28. The completion time of the hybrid algorithm decreased with the growth of virtual machines, and the shortest time was 8.6 seconds. However, the load balancing degree of the hybrid algorithm increased with the growth of virtual machines. The final results indicated that the proposed hybrid algorithm exhibits high efficiency and superior performance in cloud computing task scheduling, especially when dealing with large-scale and complex optimization problems.