The Cloud Parameters Specification and Scheduling Optimization on Multidimensional Qos Constraints

Abstract

In order to distribute cloud resources and improve the efficiency of tasks, this paper proposes a resource scheduling strategy based on the improved ant colony algorithm. Based on cluster service and user quality of service preference, we construct an ant optimization model to design the parameterization definition and select the preference constraints; the fitness function of multi-dimensional quality of service is given and then the local or global update is performed accordingly. The search for Pareto optimal set of multi-objective problems is implemented. Finally, the optimum node distribution structure with the highest fitness value is obtained. It's shown that the approach gives sufficient consideration of multidimensional user quality of service requirements. The results from the test show a significant improvement in throughput rate, service time and request times compared with other similar algorithms.