Collaborative Scheduling of Multi-cloud Distributed Multi-cloud Tasks Based on Evolutionary Multi-tasking Algorithm

With the rapid development of information technology making the scale of the Internet increasing day by day, collaborative optimization of multiple scheduling tasks in a multi-cloud environment provides users with faster scheduling options. Meanwhile, there is a certain similarity between cloud scheduling tasks, and in order not to waste the similarity between tasks, similar tasks are linked together to find an optimal scheduling solution for multiple tasks, making it possible to handle multiple scheduling tasks simultaneously. Firstly, we construct a multi-objective optimization model considering time, cost and VM resource load balance; secondly, since there are not only independent optimization problems in real scenarios, we adapt the constructed multiple similar optimization models and propose a multi-task multi-objective optimization model; finally, to be able to solve the constructed model better, we use a proposed objective function-based Finally, we propose an evolutionary multitasking algorithm based on weighted summation of the objective functions, which allows the algorithm to find the optimal solution among multiple multi-objective models. Simulation experiments show that the proposed algorithm has better performance.