Because of the continuous promotion of cloud computing applications, the demand for data processing in cloud computing is increasing. Users have higher requirements for the service quality of cloud computing, the high efficiency of cloud computing task scheduling algorithm plays a key role in the cloud computing. How to scheduling the computing resources efficiently, all tasks can be completed in the least time and cost is an important issue in cloud computing research. In this paper, a method of initial optimization on the crossover mutation probability of adaptive genetic algorithm (AGA) using binary coded chromosomes is proposed. Through experiments, the improved adaptive genetic algorithm is compared with the adaptive genetic algorithm (AGA) and the standard genetic algorithm (SGA). The experimental results show that the improved algorithm is an effective cloud computing task scheduling algorithm.
Software And Hardware
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL