Dynamic VM migration (original) (raw)

A quantitative study of virtual machine live migration

Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, 2013

Virtual machine (VM) live migration is a critical feature for managing virtualized environments, enabling dynamic load balancing, consolidation for power management, preparation for planned maintenance, and other management features. However, not all virtual machine live migration is created equal. Variants include memory migration, which relies on shared backend storage between the source and destination of the migration, and storage migration, which migrates storage state as well as memory state. We have developed an automated testing framework that measures important performance characteristics of live migration, including total migration time, the time a VM is unresponsive during migration, and the amount of data transferred over the network during migration. We apply this testing framework and present the results of studying live migration, both memory migration and storage migration, in various virtualization systems including KVM, XenServer, VMware, and Hyper-V. The results provide important data to guide the migration decisions of both system administrators and autonomic cloud management systems.

Black-box and Gray-box Strategies for Virtual Machine Migration

Virtualization can provide significant benefits in data centers by enabling virtual machine migration to eliminate hotspots. We present Sandpiper, a system that automates the task of monitoring and detecting hotspots, determining a new mapping of physical to virtual resources and initiating the necessary migrations. Sandpiper implements a black-box approach that is fully OS-and application-agnostic and a gray-box approach that exploits OS-and application-level statistics. We implement our techniques in Xen and conduct a detailed evaluation using a mix of CPU, network and memory-intensive applications. Our results show that Sandpiper is able to resolve single server hotspots within 20 seconds and scales well to larger, data center environments. We also show that the gray-box approach can help Sandpiper make more informed decisions, particularly in response to memory pressure.

Incorporating Migration Control in VM Selection Strategies to Enhance Performance

Int. J. Web Appl., 2014

In this work we have designed three algorithms, by doing amalgamation of heuristic method and migration control. Virtual Machine (VM) selection is the immediate step after the host overload is detected. Now decision needs to be made to choice the VM(s) to migrate. VM selection is an important task and the energy consumption depends on the selection. Three VM selection strategies have been proposed in literature and been incorporated in CloudSim, a simulation framework to simulate Cloud architecture, resource provisioning etc. Migration control could influence the VM selection mechanism as steady and resource consuming VMs not to be selected. Reason behind is that, a steady VM which is continuously consuming recourses has high probability of consuming resource in the same manner in future. So if we migrate such VM it will also overload the next datacenter in which we are migrating it to. In this proposed model, CPU usage has been considered to identify a VM to be steady or not. The V...

Analysis of requirements for virtual machine migration in dynamic clouds

Highly dynamic environments like clouds by nature cause a high degree of unpredictability of resource utilization and performance. Failures, latencies and heterogeneity should always be the main concern for affecting the scheduling decisions in distributed infrastructures. As a result, the scheduling efficiency of jobs before their submission is very difficult to be achieved or either forecasted. Even in the cases of the most complex schedulers a comprehensive dynamic view cannot always be predicted. Thus, the rescheduling concept takes advantage of the current scheduling status and performs a dynamic scheduling decision. In this paper we present a discussion of the virtual machine migration strategies that are currently available in distributed systems based on the need of migrating virtualized resources in order to achieve better resource utilization and performance such as improve load balancing, makespan and higher throughput of jobs. We conclude our study with a critical discussion of vital requirements for virtual machine migration.

Costs of Virtual Machine Live Migration: A Survey

—Live migration allows moving a continuously running VM from one physical host to another. It provides special benefit for data centers in a variety of scenarios including load balancing, maintenance and power management. However virtual machine live migration leads to performance loss and energy overhead that cannot be ignored in modern data centers, especially if critical business goals are to be met. In this paper we summarize, classify and evaluate current approaches with respect to determine costs of virtual machine live migration.

IJERT-The Challenges in Virtual Machine Live Migration and Resource Management

International Journal of Engineering Research and Technology (IJERT), 2020

https://www.ijert.org/the-challenges-in-virtual-machine-live-migration-and-resource-management https://www.ijert.org/research/the-challenges-in-virtual-machine-live-migration-and-resource-management-IJERTCONV8IS11064.pdf Cloud computing is becoming increasingly popular and is hosted in huge data centres. Such huge data centres have a huge number of physical servers that are, in effect, virtualized into a large number of Virtual Machines (VMs) managed by cloud infrastructure such as open stack, cloud stack, etc. Virtual Machine migration has become an important criterion for saving resources, growing resource utilization, and maintaining the quality of service (QoS) in cloud data centres with the increasing growth of cloud environments. Different techniques for migration of VMs are established for the efficient use of resources. This paper addresses the live migration of the virtual machine, with its components, drawbacks and resource management.

An Experimental Study on Virtual Machine Live Migration Impact on Services Performance

Computing and Informatics, 2019

One important benefit of servers' virtualization is the reduction of the maintenance complexity of infrastructures. A key feature is servers' live migration which allows virtual servers to be exchanged between physical machines without stopping their services. However, virtualization also has some drawbacks caused by the overhead generated. Our research evaluated live migration process overhead, on real and virtual environments, noticed from the client's side regarding two different services: web and database. YCSB and ab Benchmark were adopted as workloads. Almost all tests on real environment overcame those on virtual, with both benchmarks. The impact of the live migration in the services was evident, proving to be more effective on real machines than on virtual machines. We found the DB service accommodated better to the virtual environment and to migration than Web service. We also considered an environment with multiple migrations which presented a higher degradation than when only one migration is performed.

Performance of Virtual Machine Live Migration with Various Workloads

2016

Virtual machine (VM) consolidation is one of the strategies implemented to accomplish energy e ciency in data centres. Data centres take advantage of VM live migration to reduce the energy consumption without application downtime. However, the cost of VM live migration is not considered in some of the VM consolidation approaches. The key focus of this paper is to show how di↵erent workloads can impact the time of VM live migration. We demonstrate through live experiment the link between various workload characteristics and the time of VM live migration. We used the Kernel-based Virtual Machine (KVM) as a hypervisor and SPECjvm2008 benchmark to generate various workloads. Our results show a link between VM migration time and memory size of the VM as well as the speed of the network. We also provide a testing framework to facilitate automated experimentation and benchmarking of VM live migration by other researchers.

Improving Virtual Machine live migration via application-level workload analysis

10th International Conference on Network and Service Management (CNSM) and Workshop, 2014

Virtual Machine (VM) live migration is key for implementing resource management policies to optimize metrics such as server utilization, energy consumption, and quality-ofservice. A fundamental challenge for VM live migration is its impact on both user and resource provider sides, including service downtime and high network utilization. Several VM live migration studies have been published in the literature. However, they mostly consider only system level metrics such as CPU, memory, and network usage to trigger VM migrations. This paper introduces ALMA, an Application-aware Live Migration Architecture that explores application level information, in addition to the traditional system level metrics, to determine the best time to perform a migration. Based on experiments with three real applications, by considering application characteristics to trigger the VM live migration, we observed a substantial reduction in data transferred over the network of up to 42% and the total live migration time decrease of up to 63%.

Performance and energy modeling for live migration of virtual machines

Proceedings of the 20th international symposium on High performance distributed computing, 2011

Live migration of virtual machine (VM) provides a significant benefit for virtual server mobility without disrupting service. It is widely used for system management in virtualized data centers. However, migration costs may vary significantly for different workloads due to the variety of VM configurations and workload characteristics. To take into account the migration overhead in migration decisionmaking, we investigate design methodologies to quantitatively predict the migration performance and energy cost. We thoroughly analyze the key parameters that affect the migration cost from theory to practice. We construct two application-oblivious models for the cost prediction by using learned knowledge about the workloads at the hypervisor (also called VMM) level. This should be the first kind of work to estimate VM live migration cost in terms of both performance and energy in a quantitative approach. We evaluate the models using five representative workloads on a Xen virtualized environment. Experimental results show that the refined model yields higher than 90% prediction accuracy in comparison with measured cost. Model-guided decisions can significantly reduce the migration cost by more than 72.9% at an energy saving of 73.6%.