Abstract
In recent years, cloud technologies have developed well. As a result, we can increase the utilization of hardware virtualization technology. However, how efficiently one can allocate and manage virtual machines (VMs), has become a very important issue.Under the cloud computing technology, there are a variety of data center in order to reduce cost and to increase the efficiency of the system virtualization technology.But, as the hardware resources are limited and with the increase of data, the system will need the correct architecture, i.e. especially, in the long-term operation ofthe system, there is need of system migration. Thus in this work, we discuss the experimentation of the way to maintain the normal operation of the system by migration, and the complete system architecture for migration.We use the hypervisor to control and manage VMs of the system, to allow the migration of systems and to maintain the operation we use Live Migration technology. However, after the migration is complete, the two VMs still need to switch so it interrupts the communication. With different VMs migration order,we still want to maintain the quality of communication. Thus, with the help of few calculations we changed the original scheduling, as a result, the whole systemcommunication could become more effective in its operation.Our proposed system will migrate in two different physical machine simulate.Thus we want to make sure before the migration that the destination has minimal resources, so that the system can operate normally. The average utilization time,of monitoring system of the VMs migrate to maximum, certainly as we want the system in the shortest possible time to recover to the ready state. That is the stage of system switching, including network settings, service status and to continue reading until this task gets completed, make communication interruption. So even though the system has completed the migration, but still the VMs data communication remains idle.We proposed the concept of queuing migration, for different network topologies to provide the best scheduling approach. In our proposed algorithm, we tested four different network topologies, and in later chapters we explain them in detail. Finally, we present the results that we obtained from our improved system design.