QoS-aware and multi-objective virtual machine dynamic scheduling for big data centers in clouds

Abstract Efficient resource scheduling is one of the most critical issues for big data centers in clouds to provide continuous services users. Many existing schemes based on tasks virtual machine (VM), pursued either load balancing or migration cost under certain response time energy efficiency, which cannot meet true balance supply and demand between users cloud providers. The paper focuses following multi-objective optimization problem: how pay little as much possible keep system meeting quality service (QoS) via dynamic VM limited physical nodes a heterogeneous cluster. To make these conflicting objectives coexist, joint function designed an overall evaluation basis estimation method, method QoS method. optimize consolidation score, array mapping tree crossover model are introduced, improved genetic algorithm (GA) them proposed. Finally, empirical results Eucalyptus platform demonstrate proposed scheme outperforms exiting models.