Task scheduling optimization based on QoS cloud computing
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Abstract:
Cloud computing technology is in rapid development. In order to meet the increasingly diverse cloud computing user service quality (QoS) requirements and to improve the efficiency of cloud computing resource scheduling, a cloud computing resource scheduling optimization algorithm based on improved ant colony algorithm is proposed, including establishing cloud computing-resource model and user QoS requirements model. In order to obtain better results and solve the problem of the local optimal solution caused by the fast convergence of traditional ant colony algorithm, a random selection mechanism is added to the traditional ant colony algorithm. The time, cost and effective availability fitness factor of results are optimized and improved to obtain the global optimal solution. The traditional ant colony algorithm, Min-Min scheduling algorithm and improved ant colony optimization algorithm are compared by simulation experiments. Experimental results show that the improved ant colony optimization algorithm has advantages in scheduling efficiency, cost saving, time-saving and quality results in task execution.
