چكيده به لاتين
Due to the instantaneous increase in the number of users and the very high volume of data uploaded in the cloud due to the existence of extensive benefits for users, including cost reduction, energy consumption reduction, time management, and also the very high volume of financial turnover of this industry, which is expected to to reach more than 518 billion dollars in 2030, has caused the speed of user access to decrease significantly. But the biggest obstacle for users to quickly access the cloud space is the failure of virtual machines and how to deal with this. Therefore, in this study, the method of removing this obstacle will be investigated in such a way that the damaged virtual machine is taken out of the cycle as soon as possible and replaced by another machine. The proposed method in this study is based on assigning K backup machines from m main virtual machines. In such a way, according to the tree structure and in order to replace quickly and cheaply, the backup machines should have the smallest distance with the main machines. In this regard, all virtual machines are placed in a subset including m+k of the host server. Also, in this method, by using k error tolerance, while timely replacement of virtual machines when an error occurs, it is possible to predict the occurrence of an error before it happens. And in order to obtain optimal parameters for parameters affecting the speed, such as the distance between the backup and main machines or the number of host servers, the Ant Colony Algorithm Optimizer 1 is used. Finally, by performing various experiments and simulating the proposed algorithm by the ant colony optimizer algorithm in MATLAB, it is possible to compare this method with other valid methods such as DCM and FDs, the results show that the proposed method improves by 13.1 and 26.4 percent, respectively. It has recorded 42 and 46 percent improvement in the amount of energy consumption and average execution time compared to the other two methods. Also, in order to compare the optimizer and prove the claim that ACO is preferable to the two optimizers PSO and GA, these two experiments were performed again with the same input values, and the results show that the algorithm optimized by the ant colony optimizer has improved 6% compared to particle optimization algorithm and 18% improvement compared to genetic algorithm in energy consumption.
