Multi-Objective Scheduling Model for OpenStack-Based Cloud

Efficient and flexible resource management is a critical issue in cloud computing context. At first, we study the scheduling process of virtual machine in Open Stack cloud platform in detail. And then propose a new model considering multiple parameters such as memory, CPU utilization and current workload of the hosts. This work proposes for the use of machine learning classifier to classify whether host is overloaded or under loaded, focuses on PSO based VM scheduling strategy for VM placement in cloud infrastructure. Experimental results show that proposed algorithm outperforms default scheduler and it leads to efficient usage of underlying resources of hosts.