Live migration of user environments across wide area networks

A complex challenge in mobile computing is to allow the user to migrate her highly customised environment while moving to a different location and to continue work without interruption. I motivate why this is a highly desirable capability and conduct a survey of the current approaches towards this goal and explain their limitations. I then propose a new architecture to support user mobility by live migration of a user’s operating system instance over the network. Previous work includes the Collective and Internet Suspend/Resume projects that have addressed migration of a user’s environment by suspending the running state and resuming it at a later time. In contrast to previous work, this work addresses live migration of a user’s operating system instance across wide area links. Live migration is done by performing most of the migration while the operating system is still running, achieving very little downtime and preserving all network connectivity. I developed an initial proof of concept of this solution. It relies on migrating whole operating systems using the Xen virtual machine and provides a way to perform live migration of persistent storage as well as the network connections across subnets. These challenges have not been addressed previously in this scenario. In a virtual machine environment, persistent storage is provided by virtual block devices. The architecture supports decentralized virtual block device replication across wide area network links, as well as migrating network connection across subnetworks using the Host Identity Protocol. The proposed architecture is compared against existing solutions and an initial performance evaluation of the prototype implementation is presented, showing that such a solution is a promising step towards true seamless mobility of fully fledged computing environments

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

Adaptive live VM migration over a WAN: modeling and implementation

Recent advances in virtualization technology enable high mobility of virtual machines and resource provisioning at the data-center level. To streamline the migration process, various migration strategies have been proposed for VM live migration over a local-area network (LAN). The most common solution uses memory pre-copying and assumes the storage is shared on the LAN. While applied to a wide-area network (WAN), the VM live migration algorithms need a new design philosophy to address the challenges of long latency, limited bandwidth, unstable network conditions and the movement of storage. This paper proposes a three-phase fractional hybrid pre-copy and post-copy solution for both memory and storage to achieve highly adaptive migration over a WAN. In this hybrid solution, we selectively migrate an important fraction of memory and storage in the pre-copy and freeze-and-copy phase, while the rest (non-critical data set) is migrated during post-copying. We propose a new metric called performance restoration agility, which considers both the downtime and the VM speed degradation during the post-copy phase, to evaluate the migration process. We also develop a profiling framework and a novel probabilistic prediction model to adaptively find a predictably optimal combination of the memory and storage fractions to migrate. This model-based hybrid solution is implemented on Xen and evaluated in an emulated WAN environment. Experimental results show that our solution wins over all others in adaptiveness for various applications over a WAN, while retaining the responsiveness of post-copy algorithms.published_or_final_versio

Optimizing Virtual Machine I/O Performance in Cloud Environments

Maintaining closeness between data sources and data consumers is crucial for workload I/O performance. In cloud environments, this kind of closeness can be violated by system administrative events and storage architecture barriers. VM migration events are frequent in cloud environments. VM migration changes VM runtime inter-connection or cache contexts, significantly degrading VM I/O performance. Virtualization is the backbone of cloud platforms. I/O virtualization adds additional hops to workload data access path, prolonging I/O latencies. I/O virtualization overheads cap the throughput of high-speed storage devices and imposes high CPU utilizations and energy consumptions to cloud infrastructures. To maintain the closeness between data sources and workloads during VM migration, we propose Clique, an affinity-aware migration scheduling policy, to minimize the aggregate wide area communication traffic during storage migration in virtual cluster contexts. In host-side caching contexts, we propose Successor to recognize warm pages and prefetch them into caches of destination hosts before migration completion. To bypass the I/O virtualization barriers, we propose VIP, an adaptive I/O prefetching framework, which utilizes a virtual I/O front-end buffer for prefetching so as to avoid the on-demand involvement of I/O virtualization stacks and accelerate the I/O response. Analysis on the traffic trace of a virtual cluster containing 68 VMs demonstrates that Clique can reduce inter-cloud traffic by up to 40%. Tests of MPI Reduce_scatter benchmark show that Clique can keep VM performance during migration up to 75% of the non-migration scenario, which is more than 3 times of the Random VM choosing policy. In host-side caching environments, Successor performs better than existing cache warm-up solutions and achieves zero VM-perceived cache warm-up time with low resource costs. At system level, we conducted comprehensive quantitative analysis on I/O virtualization overheads. Our trace replay based simulation demonstrates the effectiveness of VIP for data prefetching with ignorable additional cache resource costs

A SECURE ENERGY EFFICIENT VM PREDICTION AND MIGRATION FRAMEWORK FOR OVERCOMMITED CLOUDS

Propose an included, energy efficient, resource allocation framework for overcommitted clouds. The concord makes massive energy investments by 1) minimizing Physical Machine overload occurrences via virtual machine resource usage monitoring and prophecy, and 2) reducing the number of active PMs via efficient VM relocation and residency. Using real Google data consisting of a 29 day traces collected from a crowd together contain more than 12K PMs, we show that our proposed framework outperforms existing overload avoidance techniques and prior VM migration strategies by plummeting the number of unexpected overloads, minimizing migration overhead, increasing resource utilization, and reducing cloud energy consumption.&nbsp

A checkpointing mechanism for virtual clusters using memory-bound time-multiplexed data transfers

Transparent hypervisor-level checkpoint-restart mechanisms for virtual clusters (VCs) or clusters of virtual machines (VMs) offer an attractive fault tolerance capability for cloud data centers. However, existing mechanisms have suffered from high checkpoint downtimes and overheads. This paper introduces Mekha, a novel hypervisor-level, in-memory coordinated checkpoint-restart mechanism for VCs that leverages precopy live migration. During a VC checkpoint event, Mekha creates a shadow VM for each VM and employs a novel memory-bound timed-multiplex data (MTD) transfer mechanism to replicate the state of each VM to its corresponding shadow VM. We also propose a global ending condition that enables the checkpoint coordinator to control the termination of the MTD algorithm for every VM in a VC, thereby reducing overall checkpoint latency. Furthermore, the checkpoint protocols of Mekha are designed based on barrier synchronizations and virtual time, ensuring the global consistency of checkpoints and utilizing existing data retransmission capabilities to handle message loss. We conducted several experiments to evaluate Mekha using a message passing interface (MPI) application from the NASA advanced supercomputing (NAS) parallel benchmark. The results demonstrate that Mekha significantly reduces checkpoint downtime compared to traditional checkpoint mechanisms. Consequently, Mekha effectively decreases checkpoint overheads while offering efficiency and practicality, making it a viable solution for cloud computing environments