This paper investigates the opportunities and limitations of adaptive virtual machine (VM) migration to reduce communication costs in a virtualized environment. We introduce a new formal model for the problem of online VM migration in two scenarios: (1) VMs can be migrated arbitrarily in the substrate network; e.g., a private cloud provider may have an incentive to reduce the overall communication cost in the network. (2) VMs can only be migrated within a given tenant; e.g., a user that was assigned a set of physical machines may exchange the functionality of the VMs on these machines. We propose a simple class of Destination-Swap algorithms which are based on an aggressive collocation strategy (inspired by splay datastructures) and which maintain a minimal and local amount of per-node (amortized cost) information to decide where to migrate a VM and how; thus, the algorithms react quickly to changes in the load. The algorithms come in two main flavors, an indirect and distributed variant which keeps existing VM placements local, and a direct variant which keeps the number of affected VMs small. We show that naturally, inter-tenant optimizations yield a larger potential for optimization, but generally also a tenant itself can improve its embedding. Moreover, there exists an interesting tradeoff between direct and indirect strategies: indirect variants are preferable under skewed and sparse communication patterns due to their locality properties.
Seamless VM migration can be implemented in different ways. One may @cite_17 the VM memory to the destination host before releasing the VM at the source, or defer the memory transfer until the processor state has been sent to the destination (the so-called @cite_9 approach). @cite_25 investigate automated black-box, gray-box, and hybrid strategies for VM migration in datacenters. Researchers also conduct measurements and derive models for VM migration costs, e.g., under Web 2.0 workloads and quality-of-service sensitive applications. @cite_5
@cite_5 @cite_9 @cite_25 @cite_17
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We present the design, implementation, and evaluation of post-copy based live migration for virtual machines (VMs) across a Gigabit LAN. Live migration is an indispensable feature in today's virtualization technologies. Post-copy migration defers the transfer of a VM's memory contents until after its processor state has been sent to the target host. This deferral is in contrast to the traditional pre-copy approach, which first copies the memory state over multiple iterations followed by a final transfer of the processor state. The post-copy strategy can provide a "win-win" by reducing total migration time closer to its equivalent time achieved by non-live VM migration. This is done while maintaining the liveness benefits of the pre-copy approach. We compare post-copy extensively against the traditional pre-copy approach on top of the Xen Hypervisor. Using a range of VM workloads we show improvements in several migration metrics including pages transferred, total migration time and network overhead. We facilitate the use of post-copy with adaptive pre-paging in order to eliminate all duplicate page transmissions. Our implementation is able to reduce the number of network-bound page faults to within 21 of the VM's working set for large workloads. Finally, we eliminate the transfer of free memory pages in both migration schemes through a dynamic self-ballooning (DSB) mechanism. DSB periodically releases free pages in a guest VM back to the hypervisor and significantly speeds up migration with negligible performance degradation. Virtual Machines are becoming increasingly valuable to resource consolidation and management, providing efficient and secure resource containers, along with desired application execution environments. This paper focuses on the VM-based resource reservation problem, that is, the reservations of CPU, memory and network resources for individual VM instances, as well as for VM clusters. In particular, it considers the scenario where one or several physical servers need to be vacated to start a cluster of VMs for dedicated execution of parallel jobs. VMs provide a primitive for transparently vacating workloads through migration; however, the process of migrating several VMs can be time-consuming and needs to be estimated. To achieve this goal, this paper seeks to provide a model that can characterize the VM migration process and predict its performance, based on a comprehensive experimental analysis. The results show that, given a certain VM's migration time, it is feasible to predict the time for a VM with other configurations, as well as the time for migrating a number of VMs. The paper also shows that migration of VMs in parallel results in shorter aggregate migration times, but with higher per-VM migration latencies. Experimental results also quantify the benefits of buffering the state of migrated VMs in main memory without committing to hard disks. Online services hosted in data centers show significant diurnal variation in load levels. Thus, there is significant potential for saving power by powering down excess servers during the troughs. However, while techniques like VM migration can consolidate computational load, storage state has always been the elephant in the room preventing this powering down. Migrating storage is not a practical way to consolidate I O load. This paper presents Sierra, a power-proportional distributed storage subsystem for data centers. Sierra allows powering down of a large fraction of servers during troughs without migrating data and without imposing extra capacity requirements. It addresses the challenges of maintaining read and write availability, no performance degradation, consistency, and fault tolerance for general I O workloads through a set of techniques including power-aware layout, a distributed virtual log, recovery and migration techniques, and predictive gear scheduling. Replaying live traces from a large, real service (Hotmail) on a cluster shows power savings of 23 . Savings of 40--50 are possible with more complex optimizations. The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) using live migration and switching idle nodes to the sleep mode allows Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy-performance trade-off, as aggressive consolidation may lead to performance degradation. Because of the variability of workloads experienced by modern applications, the VM placement should be optimized continuously in an online manner. To understand the implications of the online nature of the problem, we conduct a competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Furthermore, we propose novel adaptive heuristics for dynamic consolidation of VMs based on an analysis of historical data from the resource usage by VMs. The proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the service level agreement. We validate the high efficiency of the proposed algorithms by extensive simulations using real-world workload traces from more than a thousand PlanetLab VMs. Copyright © 2011 John Wiley & Sons, Ltd.