Master of Information and Communication Technology - Research
School of Information Systems and Technology
Piao, Jing Tai, Network aware virtual machine allocation and decision tree based MapReduce run time prediction in the cloud, Master of Information and Communication Technology - Research thesis, School of Information Systems and Technology, University of Wollongong, 2013. https://ro.uow.edu.au/theses/4279
The emergence of cloud computing brings an entire new computing paradigm which allows the user to request virtualized physical computing resources from data center. These physical computing resources are represented in the vision of virtual machines (VMs). Within a cloud datacenter, all of the computing resources are virtualized as a pool. These VMs share the entire computing resources in this pool, including CPU cores, memory and disks. Theoretically, the user could acquire infinite computing capability. In addition, the computing resource virtualization facilitates the migration of the VM from a physical node to another so that the downtime could be eliminated. However, the concurrent VM allocation and migration policies aim at maximizing the utilization rate of physical computing resources in the datacenter. The network communication cost is largely ignored. As the growth of the scale of data center, the logical distance between the VMs and its data could be further so that the communication cost increases.
This research aims at proposing a VM allocation and migration policy with network I/O performance consideration so that the communication cost can be optimized. The allocation policy will decide the first physical place of the VM in the datacenter, whereas the migration policy will migrate the next physical place when the network status deteriorates. Also, the implementation of the proposed VM allocation and migration policy would cause the decrease of the physical resource utilization rate. We propose an approach to comprise the network status optimization and decrease of resource utilization rate. In this approach, we predict the execution time of the VM so that the available CPU time of a physical node can be known before actual deploy the VM. If the VM is allocated on the physical node with maximum available CPU time, the utilization rate could be optimized. Among the nodes with better network status and the nodes with more CPU available time, we assign index to them so that we can make a balance between communication cost and physical resource utilization rate.