Faculty of Electrical and Computer Engineering Communications Laboratory Deutsche Telekom Chair of Communication Networks Bachelor Thesis Improving Simultaneous Migration Time of Virtual Machines using SDN Anna Triginer Perera Born on: 28.03.1996 in Barcelona Matriculation number: 4812628 Matriculation year: 2018 Referee Prof. Dr.-Ing. Dr. h.c. Frank Fitzek Supervisor Dipl.-Ing. Robert-Steve Schmoll Submitted on: 08.03.2019 Statement of authorship I hereby certify that I have authored this Bachelor Thesis entitled Improving Simultane- ous Migration Time of Virtual Machines using SDN independently and without undue assistance from third parties. No other than the resources and references indicated in this thesis have been used. I have marked both literal and accordingly adopted quotations as such. There were no additional persons involved in the intellectual preparation of the present thesis. I am aware that violations of this declaration may lead to subsequent withdrawal of the degree. Dresden, 08.03.2019 Anna Triginer Perera Abstract Cloud computing is a key technology in providing services in networks and will play an even bigger role in restructuring the Internet for enabling the 5G mobile communication network. For cloud providers, economy of scale is important as is consolidation of re- sources. In order to always have optimal use of available servers, live migration is used intensively for load balancing, fault tolerance, power management, etc. This also applies to a cross-data-center case, where network resources are limited compared to a local mi- gration between hosts, which often support multiple dedicated networks for different tasks. The goal of this thesis is to use a programmable network mechanism in order to decrease the time needed for migrating several Virtual Machines concurrently. For this purpose, a measure environment has been created with Kernel-based Virtual Machine (KVM) as virtualization technology and a Ryu Software-defined Network (SDN) controller has been set through Openflow to optimize the migration performance in terms of bandwidth allo- cation. Contents List of Figures4 List of Tables5 1 Introduction6 2 Theoretical Background8 2.1 Virtual Machines . .8 2.1.1 Live Migration . .9 2.1.1.1 Memory Migration . 10 2.2 Software Defined Networking . 14 2.2.1 OpenFlow . 15 2.3 Related work . 17 3 Environment 20 3.1 Hardware Specifications . 20 3.2 Network Infrastructure . 21 3.3 Software Setup . 23 3.3.1 Virtualization Setup . 23 3.3.2 Open vSwitch and SDN controller . 24 3.3.2.1 Ryu Rest API . 26 3.4 Migration Procedure and Measurement Tools . 30 4 Experiments and Results 33 5 Conclusions and Future Work 41 References 43 3 List of Figures 2.1 Hypervisor and Virtual Machines . .9 2.2 Pre-copy Flowchard. Image source: [1] . 11 2.3 SDN Architecture . 15 2.4 Open vSwitch Components. Image source: [2] . 16 3.1 Current Network Topology . 21 3.2 Distributed Virtual Switch . 22 4.1 Single VM Live Migration Throughput . 34 4.2 Live Migration Comparison: 2 VMs . 35 4.3 Live Migration Comparison: 8 VMs . 35 4.4 Single VM Live Migration 50% stressed .................... 36 4.5 Relation between Bandwidth Allocation and Time . 37 4.6 Live Migration Comparison: 3 VMs . 38 4.7 Burstable Bandwidth Migration of 3VMs . 39 4.8 Comparison between four Live Migration Techniques . 40 4 List of Tables 2.1 Pre-copy Algorithm Migration. VM size = 16GB . 13 3.1 Ubuntu0-2 Specifications . 20 3.2 Servers Operating System . 21 3.3 Virtual Machine Characteristics . 23 3.4 Queue Setting Example . 27 3.5 QoS Rules Example . 28 4.1 Live Migration Metrics: 8 VMs . 35 4.2 Bandwith Allocation Queue Settings . 37 4.3 Bandwidth Allocation QoS Rules . 38 4.4 Burstable Bandwidth Allocation Queue Settings . 39 4.5 Live Migration Metrics Comparison . 40 5 1 Introduction Nowadays data centers are indispensable to manage the amount of data we generate contin- uously. Data centers are centralized locations where computing and networking equipment is concentrated for the purpose of collecting, storing, processing, distributing or allowing access to large amounts of data [3]. Due to the need to access to this information without a physical interface, these data centers have become Cloud Data Centers. The cloud is a virtual infrastructure which is accessible by a local network or Internet from a remote location. Currently, economy of scale and energy consumption in cloud infrastructures are important for Cloud providers. The 5G mobile communication network is going to be close related to Cloud computing [4]. This combination will provide substantial richness in capacity, flexibility and functionality to mobile network operators. Virtualization technology has been used by these data centers to become more efficient and reduce the cost associated with purchase, set up, cooling and maintenance. Many operat- ing system instances can be currently run on a single physical machine providing better use of physical resources [5, 6]. The migration of virtual machines (VM) located in data cen- ters provides more flexibility in managing their infrastructure. The migration procedure is also necessary for load balancing the traffic over the network and prevent congestion and bottlenecks on the infrastructure. Furthermore, migration is used for maintenance purposes considering that the equipment and software need upgrades and replacements regularly. 6 This thesis proposes to improve live migration performance between one physical host to another in terms of time. The focus of this improvement is placed on the network connec- tion. To achieve this, the VMs are migrated simultaneously through the same link under a programmable network. With a Software-defined Network (SDN) controller, bandwidth allocation rules are created to manage the migration process. Parallel live migration topic has not yet been deeply investigated but theoretical studies have demonstrated improvement on the migration with this method. Therefore, this thesis wants to focus on this research gap at the same time as using SDN technology which is the near future way to design, build and operate networks. The thesis content has been separated in two chapters, one regarding the theoretical background explanation necessary to carry out simultaneous migrations and the other one regarding the environment developed to test the parallel live migration. On the theoretical part, virtualization mechanism are explained focusing on live migration of virtualized instances. On the other hand, it is presented Software Defined Networks (SDN) technology emphasizing the SDN controllers used for the experimental development. As to the experimental part, the system design, hardware and software used is described. Finally, experiments and and testing results illustrate the performance of parallel live migration under a programmable network. 1 Introduction 7 2 Theoretical Background 2.1 Virtual Machines Virtualization technology allows to create multiple simulated environments or dedicated resources from a single physical hardware system [6]. Virtualization provides the virtual environments with an easier backup and recovery, better scalability and decreases the energy consumption because there are less physical machines. The hypervisor is the soft- ware that connects directly to the hardware and allows to split one system into separate, distinct and secure environments known as virtual machines (VMs). Therefore, a virtual machine (VM) is an emulation of an operating system (OS) created and executed by a hypervisor. The hypervisor can emulate multiple virtual hardware platforms isolated from each other (except KVM hypervisor that can not by itself). Thus, on the same physical host it is possible to run virtual machines with different operating system as we can see in the Figure 2.1. Kernel-based Virtual Machine (KVM) is a virtualization module in the Linux kernel that allows the kernel to function as a hypervisor. The KVM kernel module cannot, by itself, create a VM. To do so, it must use QEMU, a user-space process. QEMU is inherently a hardware emulator. It is provided as operations support systems (OSS) for emulating [7]. The QEMU emulator interacts with the KVM kernel module to execute guest-system processing while the KVM kernel module handles the VM exit from guest systems and executes VM entry instructions. The tool used for managing the virtualization platform is libvirt [8], an Open-source API, daemon and management tool. The libvirt project has developed a virtualization abstraction layer which is able to manage a set of virtual ma- 8 chines across different hypervisors. The goal of libvirt is to provide a library that offers all necessary operations for hypervisor management without implementing functionalities [9]. In addition, it is possible to manage the VMs through a graphical user interface (GUI), Virt-manager [10] which calls libvirt functions. Figure 2.1: Hypervisor and Virtual Machines 2.1.1 Live Migration Virtual machine live migration process consists on moving a virtual machine from one physical host to another without perceiving the interruption of its services. The machine remains on, the network connections remain active and applications continue to run while the VM is relocated. Live migration imposes certain restrictions on the source and desti- nation physical hosts requiring compatible virtualization software, comparable CPU types and the hosts' membership to the same subnetwork. During a migration process there is a data transfer taking place between the source and the destination. The data transfer involves three aspects [11]: • CPU state. These states can be: Idle (nothing running), Running a user-space program or Running kernel (servicing interrupts or resources management). • Memory content. Includes the memory state of the VM operating system and all the processes running on it. It is the most significant procedure during a migration and it is data-intensive. 2 Theoretical Background 9 • Storage content. Consists on the migration of the disk virtual machine image1. It is the most data-intensive transfer. For this reason, it is generally an optional phase of the migration because the disk image is often accessible from source and destination hosts.