Design of cloud computing task offloading algorithm based on dynamic multi-objective evolution

摘要

The traditional cloud computing task offloading algorithm consumes abundant energy in task scheduling, which results in a longer average task waiting time. For this reason, a cloud computing task offloading algorithm based on dynamic multi-objective evolution is proposed in this research. In order to ensure the parallel completion of multiple tasks, the dynamic multi-objective evolution method is used to construct the cloud computing task scheduling model and complete the cloud computing task scheduling. Then, based on the calculated effectiveness and validity of energy consumption to complete the initial operation distribution and offloading priority, the time and cost of task offloading are calculated according to the raking results of task offloading priority. The cloud computing tasks are distributed with minimum time and minimum cost as the goal. At the same time, the trade-off coefficients of all utility parameters are effectively combined and dynamically adjusted according to the battery capacity of mobile terminal, in order to achieve the offloading of cloud computing tasks. The average task carrying time, average task waiting time and average task completion time are selected as the parameters to evaluate the algorithm performance. The experimental results show that compared with the existing algorithms, the proposed algorithm shows the best performance, which fully proves the feasibility of the proposed algorithm.