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

Transfer Cost of Virtual Machine Live Migration in Cloud Systems

Virtualised frameworks typically form the foundations of Cloud systems, where Virtual Machine (VM) instances provide execution environments for a diverse range of applications and services. Modern VMs support Live Migration (LM) – a feature wherein a VM instance is transferred to an alternative node without stopping its execution. The focus of this research is to analyse and evaluate the LM transfer cost which we define as the total size of data to be transferred to another node for a particular migrated VM instance. Several different virtualisation approaches are categorised with a shortlist of candidate VMs for evaluation. The selection of VirtualBox as the best representative VM for our experiments and analysis is then discussed and justified. The paper highlights the major areas of the LM transfer process – CPU registers, memory, permanent storage, and network switching – and analyses their impact on the volume of information to be migrated which includes the VM instance with the required libraries, the application code and any data associated with it. Then, using several representative applications, we report experimental results for the transfer cost of LM for respective VirtualBox instances. We also introduce a novel Live Migration Data Transfer (LMDT) formula, which has been experimentally validated and confirms the exponential nature of the LMDT process. Our estimation model supports efficient design and development decisions in the process of analysing and building Cloud systems. The presented methodology is also applicable to the closely-related area of virtual containers which is part of our current and future work

ON OPTIMIZATIONS OF VIRTUAL MACHINE LIVE STORAGE MIGRATION FOR THE CLOUD

Virtual Machine (VM) live storage migration is widely performed in the data cen- ters of the Cloud, for the purposes of load balance, reliability, availability, hardware maintenance and system upgrade. It entails moving all the state information of the VM being migrated, including memory state, network state and storage state, from one physical server to another within the same data center or across different data centers. To minimize its performance impact, this migration process is required to be transparent to applications running within the migrating VM, meaning that ap- plications will keep running inside the VM as if there were no migration operations at all. In this dissertation, a thorough literature review is conducted to provide a big picture of the VM live storage migration process, its problems and existing solutions. After an in-depth examination, we observe that a severe IO interference between the VM IO threads and migration IO threads exists and causes both types of the IO threads to suffer from performance degradation. This interference stems from the fact that both types of IO threads share the same critical IO path by reading from and writing to the same shared storage system. Owing to IO resource contention and requests interference between the two different types of IO requests, not only will the IO request queue lengthens in the storage system, but the time-consuming disk seek operations will also become more frequent. Based on this fundamental observation, this dissertation research presents three related but orthogonal solutions that tackle the IO interference problem in order to improve the VM live storage migration performance. First, we introduce the Workload-Aware IO Outsourcing scheme, called WAIO, to improve the VM live storage migration efficiency. Second, we address this problem by proposing a novel scheme, called SnapMig, to improve the VM live storage migration efficiency and eliminate its performance impact on user applications at the source server by effectively leveraging the existing VM snapshots in the backup servers. Third, we propose the IOFollow scheme to improve both the VM performance and migration performance simultaneously. Finally, we outline the direction for the future research work. Advisor: Hong Jian

Resource-Efficient Replication and Migration of Virtual Machines.

Continuous replication and live migration of Virtual Machines (VMs) are two vital tools in a virtualized environment, but they are resource-expensive. Continuously replicating a VM's checkpointed state to a backup host maintains high-availability (HA) of the VM despite host failures, but checkpoint replication can generate significant network traffic. Each replicated VM also incurs a 100% memory overhead, since the backup unproductively reserves the same amount of memory to hold the redundant VM state. Live migration, though being widely used for load-balancing, power-saving, etc., can also generate excessive network traffic, by transferring VM state iteratively. In addition, it can incur a long completion time and degrade application performance. This thesis explores ways to replicate VMs for HA using resources efficiently, and to migrate VMs fast, with minimal execution disruption and using resources efficiently. First, we investigate the tradeoffs in using different compression methods to reduce the network traffic of checkpoint replication in a HA system. We evaluate gzip, delta and similarity compressions based on metrics that are specifically important in a HA system, and then suggest guidelines for their selection. Next, we propose HydraVM, a storage-based HA approach that eliminates the unproductive memory reservation made in backup hosts. HydraVM maintains a recent image of a protected VM in a shared storage by taking and consolidating incremental VM checkpoints. When a failure occurs, HydraVM quickly resumes the execution of a failed VM by loading a small amount of essential VM state from the storage. As the VM executes, the VM state not yet loaded is supplied on-demand. Finally, we propose application-assisted live migration, which skips transfer of VM memory that need not be migrated to execute running applications at the destination. We develop a generic framework for the proposed approach, and then use the framework to build JAVMM, a system that migrates VMs running Java applications skipping transfer of garbage in Java memory. Our evaluation results show that compared to Xen live migration, which is agnostic of running applications, JAVMM can reduce the completion time, network traffic and application downtime caused by Java VM migration, all by up to over 90%.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111575/1/karenhou_1.pd

Reducing Response Time with Preheated Caches

CPU performance is increasingly limited by thermal dissipation, and soon aggressive power management will be beneficial for performance. Especially, temporarily idle parts of the chip (including the caches) should be power-gated in order to reduce leakage power. Current CPUs already lose their cache state whenever the CPU is idle for extended periods of time, which causes a performance loss when execution is resumed, due to the high number of cache misses when the working set is fetched from external memory. In a server system, the first network request during this period suffers from increased response time. We present a technique to reduce this overhead by preheating the caches in advance before the network request arrives at the server: Our design predicts the working set of the server application by analyzing the cache contents after similar requests have been processed. As soon as an estimate of the working set is available, a predictable network architecture starts to announce future incoming network packets to the server, which then loads the predicted working set into the cache. Our experiments show that, if this preheating step is complete when the network packet arrives, the response time overhead is reduced by an average of 80%