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

Machine Learning Models for Live Migration Metrics Prediction

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. Egger, Bernhard.오늘날 데이터 센터에서 가상머신의 라이브 마이그레이션 기술은 매우 중요하게 사용된다. 현존하는 데이터 센터 관리 프레임워크에서는 복잡한 알고리즘을 이용하여 언제, 어디서, 어디로 가상머신의 마이그레션을 실행할지를 결정한다. 하지만 어떤 마이그레이션 방법을 사용하는지에 따라서 성능이 크게 차이가 날 수 있음에도 불구하고 이에 대한 논의는 주요하게 다뤄지지 않았다. 이러한 성능의 차이는 라이브 마이그레이션 알고리즘의 차이나 가상머신에 할당된 워크로드의 양의 차이 그리고 마이그레이션을 하는 곳과 목적 host의 상태 차이에 의하여 일어난다. 빠르고 정확하게 올바른 마이그레이션 방법을 정하는 것은 필수적인 과제이다. 이러한 과제를 performance model을 이용하여 해결할 것이다. 본 논문에서는, 가상머신의 라이브 마이그레이션 성능을 예측하는 여러 머신 러닝 모델을 제시한다. 여기서 12개의 서로 다른 마이그레이션 알고리즘에 대해 7가지의 다른 metric들을 예측한다. 이 모델은 기존 연구에 비해 훨씬 정확한 예측을 성공하였다. 각각의 target metric과 여러 알고리즘들에 대하여 input feature evaluation을 수행하였고 각각의 특성에 맞는 모델을 만들어 84개의 서로다른 머신 러닝 모델들을 훈련시켰다. 이러한 모델들은 실제 라이브 마이그레이션 프레임워크에 쉽게 적용 가능하다. 각각의 마이그레이션 알고리즘에 대하여 target metric 예측을 사용함으로써 올바른 마이그레이션 알고리즘을 쉽게 결정할 수 있고 이는 결과적으로 다운타임과 마이그레이션에 소요되는 총 시간의 감소 효과를 볼 수 있다.Live migration of Virtual Machines (VMs) is an important technique in today's data centers. In existing data center management frameworks, complex algorithms are used to determine when, where, and to which host a migration of a VM is to be performed. However, very little attention is paid to the selection of the right migration technique depending on which the migration performance can vary greatly. This performance fluctuation is caused by the different live migration algorithms, the different workloads that each VM is executing, and the state of the destination and the source host. Choosing the right migration technique is a crucial task that has to be made quickly and precisely. Therefore, a performance model is the best and the right candidate for such a task. In this thesis, we propose various machine learning models for predicting live migration metrics of virtual machines. We predict seven different metrics for twelve distinct migration algorithms. Our models achieve a much higher accuracy compared to existing work. For each target metric and algorithm, an input feature evaluation is conducted and a strictly specific model is generated, leading to 84 different trained machine learning models. These models can easily be integrated into a live migration framework. Using the target metric predictions for each migration algorithm, a framework can easily choose the right migration algorithm, which can lead to downtime and total migration time reduction and less service-level agreement violations.Abstract Contents List of Figures List of Tables Chapter 1 Introduction and Motivation Chapter 2 Background 2.1 Virtualization 2.2 Live Migration 2.3 SLA and SLO 2.4 Live Migration Techniques 2.4.1 Pre-copy (PRE) 2.4.2 Post-copy (POST) 2.4.3 Hybrid Migration Techniques 2.5 Live Migration Performance Metrics 2.6 Artificial Neural Networks 2.6.1 Feedforward Neural Network (FNN) 2.6.2 Deep Neural Network (DNN) 2.6.3 Convolution Neural Network (CNN) Chapter 3 Related Work Chapter 4 Overview and Design Chapter 5 Implementation 5.1 Deep Neural Network design 5.2 Convolutional Neural Network design Chapter 6 Evaluation metrics 6.1 Geometric Mean Absolute Error (GMAE) 6.2 Geometric Mean Relative Error (GMRE) 6.3 Mean Absolute Error (MAE) 6.4 Weighted Absolute Percentage Error (WAPE) Chapter 7 Results 7.1 Deep Neural Network 7.2 SVR with bagging 7.3 DNN vs. SVR comparison 7.4 Overhead Chapter 8 Conclusion and Future Work 8.1 Conclusion 8.2 Future Work AppendicesMaste

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

Distributed Shared Memory based Live VM Migration

Cloud computing is the new trend in computing services and IT industry, this computing paradigm has numerous benefits to utilize IT infrastructure resources and reduce services cost. The key feature of cloud computing depends on mobility and scalability of the computing resources, by managing virtual machines. The virtualization decouples the software from the hardware and manages the software and hardware resources in an easy way without interruption of services. Live virtual machine migration is an essential tool for dynamic resource management in current data centers. Live virtual machine is defined as the process of moving a running virtual machine or application between different physical machines without disconnecting the client or application. Many techniques have been developed to achieve this goal based on several metrics (total migration time, downtime, size of data sent and application performance) that are used to measure the performance of live migration. These metrics measure the quality of the VM services that clients care about, because the main goal of clients is keeping the applications performance with minimum service interruption. The pre-copy live VM migration is done in four phases: preparation, iterative migration, stop and copy, and resume and commitment. During the preparation phase, the source and destination physical servers are selected, the resources in destination physical server are reserved, and the critical VM is selected to be migrated. The cloud manager responsibility is to make all of these decisions. VM state migration takes place and memory state is transferred to the target node during iterative migration phase. Meanwhile, the migrated VM continues to execute and dirties its memory. In the stop and copy phase, VM virtual CPU is stopped and then the processor and network states are transferred to the destination host. Service downtime results from stopping VM execution and moving the VM CPU and network states. Finally in the resume and commitment phase, the migrated VM is resumed running in the destination physical host, the remaining memory pages are pulled by destination machine from the source machine. The source machine resources are released and eliminated. In this thesis, pre-copy live VM migration using Distributed Shared Memory (DSM) computing model is proposed. The setup is built using two identical computation nodes to construct all the proposed environment services architecture namely the virtualization infrastructure (Xenserver6.2 hypervisor), the shared storage server (the network file system), and the DSM and High Performance Computing (HPC) cluster. The custom DSM framework is based on a low latency memory update named Grappa. Moreover, HPC cluster is used to parallelize the work load by using CPUs computation nodes. HPC cluster employs OPENMPI and MPI libraries to support parallelization and auto-parallelization. The DSM allows the cluster CPUs to access the same memory space pages resulting in less memory data updates, which reduces the amount of data transferred through the network. The thesis proposed model achieves a good enhancement of the live VM migration metrics. Downtime is reduced by 50 % in the idle workload of Windows VM and 66.6% in case of Ubuntu Linux idle workload. In general, the proposed model not only reduces the downtime and the total amount of data sent, but also does not degrade other metrics like the total migration time and the applications performance

Upgrade of lower layers in a High Availability environment

Various industries such as telecommunication, banking etc. require un-interrupted services throughout the year. The requirement takes into account the system maintenance and the upgrade operations. The Service Availability Forum (SA Forum) solution enables high availability of services even during the maintenance and the upgrade operation. This solution enables portability of application across various platforms. The SA Forum defined a service - Software Management Framework (SMF) that orchestrates the upgrade of SA Forum managed system. To perform an upgrade SMF requires an upgrade campaign. The solutions proposed in SMF are applicable only for the application layer but not for the lower layers such as Operating Systems and the virtualization facilities which include Virtual Machines and Virtual Machine Managers. On the other hand, the work done previously within the MAGIC project for the automatic generation of an upgrade campaign is limited to application entities only. The objective of this thesis is to propose solutions in the context of SMF for the upgrade of lower layers as well without impacting the availability of services. To accomplish this objective we proposed three new upgrade steps that properly handle the dependencies between the layers of a machine during the upgrade. We also devised an approach for the automatic generation of an upgrade campaign for lower layers. The extended SMF is capable of executing the generated upgrade campaign for upgrading the virtualization facilities which include VMs capable of live migration as well. The upgrade campaign generation approach has been implemented in a prototype tool as an eclipse plug-in and tested with a case study

On Improving The Performance And Resource Utilization of Consolidated Virtual Machines: Measurement, Modeling, Analysis, and Prediction

This dissertation addresses the performance related issues of consolidated \emph{Virtual Machines} (VMs). \emph{Virtualization} is an important technology for the \emph{Cloud} and data centers. Essential features of a data center like the fault tolerance, high-availability, and \emph{pay-as-you-go} model of services are implemented with the help of VMs. Cloud had become one of the significant innovations over the past decade. Research has been going on the deployment of newer and diverse set of applications like the \emph{High-Performance Computing} (HPC), and parallel applications on the Cloud. The primary method to increase the server resource utilization is VM consolidation, running as many VMs as possible on a server is the key to improving the resource utilization. On the other hand, consolidating too many VMs on a server can degrade the performance of all VMs. Therefore, it is necessary to measure, analyze and find ways to predict the performance variation of consolidated VMs. This dissertation investigates the causes of performance variation of consolidated VMs; the relationship between the resource contention and consolidation performance, and ways to predict the performance variation. Experiments have been conducted with real virtualized servers without using any simulation. All the results presented here are real system data. In this dissertation, a methodology is introduced to do the experiments with a large number of tasks and VMs; it is called the \emph{Incremental Consolidation Benchmarking Method} (ICBM). The experiments have been done with different types of resource-intensive tasks, parallel workflow, and VMs. Furthermore, to experiment with a large number of VMs and collect the data; a scheduling framework is also designed and implemented. Experimental results are presented to demonstrate the efficiency of the ICBM and framework