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

A Survey of Virtual Machine Migration Techniques in Cloud Computing

Cloud computing is an emerging computing technology that maintains computational resources on large data centers and accessed through internet, rather than on local computers. VM migration provides the capability to balance the load, system maintenance, etc. Virtualization technology gives power to cloud computing. The virtual machine migration techniques can be divided into two categories that is pre-copy and post-copy approach. The process to move running applications or VMs from one physical machine to another, is known as VM migration. In migration process the processor state, storage, memory and network connection are moved from one host to another.. Two important performance metrics are downtime and total migration time that the users care about most, because these metrics deals with service degradation and the time during which the service is unavailable. This paper focus on the analysis of live VM migration Techniques in cloud computing. Keywords: Cloud Computing, Virtualization, Virtual Machine, Live Virtual Machine Migration.

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

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

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres