An overview of virtual machine live migration techniques

In a cloud computing the live migration of virtual machines shows a process of moving a running virtual machine from source physical machine to the destination, considering the CPU, memory, network, and storage states. Various performance metrics are tackled such as, downtime, total migration time, performance degradation, and amount of migrated data, which are affected when a virtual machine is migrated. This paper presents an overview and understanding of virtual machine live migration techniques, of the different works in literature that consider this issue, which might impact the work of professionals and researchers to further explore the challenges and provide optimal solutions

Autonomous migration of vertual machines for maximizing resource utilization

Virtualization of computing resources enables multiple virtual machines to run on a physical machine. When many virtual machines are deployed on a cluster of PCs, some physical machines will inevitably experience overload while others are under-utilized over time due to varying computational demands. This computational imbalance across the cluster undermines the very purpose of maximizing resource utilization through virtualization. To solve this imbalance problem, virtual machine migration has been introduced, where a virtual machine on a heavily loaded physical machine is selected and moved to a lightly loaded physical machine. The selection of the source virtual machine and the destination physical machine is based on a single fixed threshold value. Key to such threshold-based VM migration is to determine when to move which VM to what physical machine, since wrong or inadequate decisions can cause unnecessary migrations that would adversely affect the overall performance. The fixed threshold may not necessarily work for different computing infrastructures. Finding the optimal threshold is critical. In this research, a virtual machine migration framework is presented that autonomously finds and adjusts variable thresholds at runtime for different computing requirements to improve and maximize the utilization of computing resources. Central to this approach is the previous history of migrations and their effects before and after each migration in terms of standard deviation of utilization. To broaden this research, a proactive learning methodology is introduced that not only accumulates the past history of computing patterns and resulting migration decisions but more importantly searches all possibilities for the most suitable decisions. This research demonstrates through experimental results that the learning approach autonomously finds thresholds close to the optimal ones for different computing scenarios and that such varying thresholds yield an optimal number of VM migrations for maximizing resource utilization. The proposed framework is set up on a cluster of 8 and 16 PCs, each of which has multiple User-Mode Linux (UML)-based virtual machines. An extensive set of benchmark programs is deployed to closely resemble a real-world computing environment. Experimental results indicate that the proposed framework indeed autonomously finds thresholds close to the optimal ones for different computing scenarios, balances the load across the cluster through autonomous VM migration, and improves the overall performance of the dynamically changing computing environment

A Virtual Machine Migration System Based on a CPU Emulator

Migration of virtual computing environments is a useful mechanism for advanced management of servers and utilization of a uniform computing environment on different machines. There have been a number of studies on migration of virtual computing environments based on virtual machine monitors (e.g., VMware) or language-level virtual machines (e.g., Java). However, migration systems based on a CPU emulator have not received much attention and their viability in a practical setting is not clear. In this paper, we describe Quasar, a virtual machine (VM) migration system implemented on top of the QEMU CPU emulator. Quasar can migrate a whole operating system between physical machines whose architectures are different (e.g., between an x86 machine and a PowerPC machine). Quasar provides a virtual networking facility, which allows migrating VMs to continue communication without disconnecting sockets for migration. Quasar also provides a staged migration function to reduce the downtime of migrating VMs. We have examined the viability of Quasar through experiments, in which Quasar was compared with Xen, SBUML, and UML. The experiments assessed the performance of virtual server hosting, the sizes of the files that represent VMs, and the amount of downtime for VM migration. 1