Review of GRNET s-NOC virtualization Network

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1 Scaling up your virtualization infrastructure Apollon Oikonomopoulos and Faidon Liambotis Greek Research and Technology Network 56 Mesogeion Ave., , Athens, Greece Abstract During the past two years, Greek Research and Technology Network (GRNET) has gradually moved all of its services to an in-house developed virtualized infrastructure, thus allowing for better hardware utilization, reduced operation costs and higher service availability for all of GRNET s core services. Currently, GRNET also provides Virtual Private Server (VPS) hosting for all connected customers on the same infrastructure and is working towards an in-house developed cloud computing platform for large-scale Virtual Machine hosting. This work aims to outline the experience gained from the successive iterations of GRNET s virtualization service and provide best practices for working with consolidated systems. We cover the different architectural choices, the problems encountered and the solutions provided during the three iterations of the service. Keywords virtualization, infrastructure, large-scale, cloud Paper Type case study I. INTRODUCTION Virtualization is a very powerful tool in the hands of system administrators. During the last couple of years, improved hypervisors and tools have made virtualization a viable solution for the consolidation and segregation of production services. The benefits of virtualization are well-known; independence from the underlying hardware, increased hardware efficiency and easier service isolation are only a few to name. However, along with these benefits, virtualization brings in new challenges for the system and network administrators; the now-common one-machine-per-service practice has led to rapid increase of managed operating systems instances, the increasing virtual machine density of modern servers incurs potential downtime of multiple services due to a single physical machine failure and virtual networks of increasingly complex topologies change the perspective from that of a typical network administrator s point-of-view. This paper outlines GRNET Network Operation Centre s experience in the transition of GRNET s services from bare hardware to a fully virtualized environment. The authors hope the experience gained sofar will be useful for any NRENs seeking to deploy virtualization solutions. The rest of this paper is structured as follows: Section II provides a short description of the transition history of GRNET s core services; Section III outlines the automated management mechanisms GRNET uses in its infrastructure; Section IV continues with the identification and analysis of issues in the network layer; Section V provides an insight into GRNET s public cloud service currently under development and conclusions are presented in Section VI. II. SERVICE HISTORY AND MIGRATION PROCESS Until 2009, GRNET operated its network and services using a Virtual Network Operations Centre (VNOC), i.e. a number of physically disjoint groups from different universities and research institutes operating a subset of the services each. Most of the core network services (DNS, primary and backup MX, directory server) were running on the same two physical machines in a traditional UNIX fashion. Other services (Web, Video-on-demand, VoIP services, etc) were implemented using a one-boxper-service approach, with the exception of a Xen[1] and a VMWare R GSX host, running a limited number of secondary virtual machines. Beginning with 2009, an in-house Network Operation Centre was created to replace the VNOC model. At the same time, the primary operations focus point was shifted from the network itself to both network and end-user services. New-generation hardware was to be used for the deployment of new end-user services, so virtualization came up as a way to move services away from aged and out-of-support hardware. Initially, the Xen[1] hypervisor was used to consolidate services on individual servers running Debian GNU/Linux[2]. In less than 6 months, all of GRNET s core services (DNS, , WHOIS, news server, etc.) had been migrated to virtual machines running on top of shared storage provided by a FibreChannel-connected SAN appliance. Service availability improved by being able to move Virtual Machines between physical hosts. However it was soon evident that as the number of VMs increased, the administrative overhead of both the number of operating system instances to maintain and the virtualization infrastructure itself became a bottleneck. Manual placement of virtual machines on hardware nodes made it difficult to end-up with a balanced set of servers. Furthermore, manual management of virtual machines involved many ambiguities; questions like should this VM be running have no clear answer in this context and simply making sure that a virtual machine is not running elsewhere in a shared-storage scenario takes extra effort. Furthermore, a number of instabilities encountered in Debian s Xen-enabled kernels, as well as the uncertainty about the Xen project s future at the time, urged us to search for alternative hypervisor implementations.

2 Fig. 1. The architecture of a Ganeti cluster. cluster.example.com is the roaming address associated with the cluster master node. Ganeti can optionally use separate networks for control and storage-related (DRBD) traffic. III. INFRASTRUCTURE AUTOMATION To reduce the administrative overhead of the virtualization infrastructure, GRNET s NOC deployed automation solutions covering two basic areas: A. Automated operating system management The initial operating system configuration as well as massive configuration changes are performed using Puppet[3]. Puppet s flexible configuration language and advanced features, such as exported resources, allow detailed configuration of every aspect of a Linux system. GRNET s puppet configuration sets up all common parameters, like user accounts, SSH public keys, firewall rules with role-specific exceptions, automatic Nagios check- and munin host definitions. Furthermore, Puppet s facts system provides an excellent way of collecting information from the operating system and the various servers and making it available for centralized processing. Using this facility, GRNET s NOC has implemented a central server dashboard, providing automatically updated machine health status, software security update availability and hardware inventory information for all systems from a single location. B. Virtualization cluster management As outlined in section I, using a number of hosts as independent virtual machine containers results in an increased administrative overhead. Furthermore, as the number of physical machines increases, it becomes increasingly difficult to maintain a balanced workload and ensure N+1 redundancy, i.e. continued service availability when one hardware node fails. Moving along those lines, we opted to build the next generation of our service around Google s Ganeti[4] cluster virtualization management software, using the Linux KVM[5] hypervisor and extend the platform to suit our needs. A number of features made Ganeti an attractive solution: Cluster management A number of nodes are grouped into a cluster, with one node assuming the role of the cluster master. All configuration and control commands are issued to the cluster master, which delegates commands to the rest of the nodes. There is also a number of master candidates in each cluster, i.e. nodes that possess the whole cluster configuration and can assume the master role in case of failure. The overall architecture of Ganeti can be seen in Fig. 1. REST API The cluster master can be externally controlled using a simple REST API, exposing most of the functionality to cater for both virtual machine provisioning and control. Operating system providers Ganeti has the notion of an operating system provider, i.e. an external programme that, given a virtual machine s disk, will take care of installing and configuring an operating system on it.

3 Instance allocators Ganeti provides a well-documented API for external programmes called instance allocators (or iallocators in short), that are invoked during virtual machine creation to decide which hardware nodes the virtual machine will be placed on. Apart from providing a ready implementation for automatic instance allocation, Ganeti also provides a set of tools to dynamically rebalance a running cluster using live migrations. Free software Ganeti is Free and Open-Source software distributed under the GNU General Public License. This allowed us to perform modifications and be able to contribute them to the rest of the users. 300 Virtual machine creation history VM count / / / / / / / / / / / / / /2011 Date Fig. 2. Virtual machine count history on GRNET s Ganeti clusters A number of modifications and extensions were made and contributed to the Ganeti project, in order to cater for GRNET s needs: By default, Ganeti uses DRBD[6] as a way to provide high availability to virtual machines. GRNET on the other hand had already deployed its virtual machines on a shared-storage system backed by a SAN appliance. Thus, modifications were made to Ganeti to allow virtual machines to live on external shared storage devices. A way to provide out-of-band access to virtual machines was sought. Using Ganeti s REST API, a proof-of-concept web application was written that allowed virtual machine owners to connect using VNC. Gradually, the number of virtual machines rose from 50 to more than 250 (see Fig. 2), including third-party servers hosted as part of GRNET s Virtual Private Server (VPS) service. Making use of live migrations, we have been able to achieve carrier-grade uptime while constantly upgrading the underlying hardware (both servers and storage). IV. VIRTUAL MACHINE NETWORKING Having eliminated a large part of the management overhead of both systems and virtualization infrastructure, we were able to identify the next source of scalability limits, the network. Originally, during the early days of GRNET s VPS service, a decision was made to allocate a 802.1q VLAN and a /30 IPv4 subnet to each third-party virtual machine, in order to achieve Layer-2 host isolation. This soon proved to be inefficient however, due to the following: 802.1q VLANs had to be manually provisioned, as network equipment is part of a different administrative domain. /30 IPv4 subnets lead to significant IPv4 address space underutilization, as only 25% of the IP addresses in a network block end up being usable by virtual machines.

4 We sought for a solution fulfilling the following requirements: 1) Layer-2 isolation between untrusted virtual machines was to be maintained, 2) both IPv6 and large IPv4 subnets would be used for multiple Virtual Machine hosting, 3) no dynamic routing protocols were to run on the hardware nodes, 4) there should be support for centralized IP pool management for IPv4 spaces and support for automatic guest network configuration through DHCP and IPv6 stateless autoconfiguration These requirements could be addressed in two possible ways: A. Layer 2 filtering Allow Layer-2 communication between VMs but perform Layer-2 filtering on traffic sent. This solution required filtering of a number of different protocols (Ethernet, ARP, IP, ICMPv6) which resulted in a complicated and long ruleset. B. Routing The other solution is to set up the hardware nodes to perform transparent routing on the Virtual Machine interfaces. For IPv4 this can be achieved using proxy-arp, which is built into the Linux kernel and used in various scenaria. Proxy- ARP forces the host to answer all ARP requests made by the virtual machines for IPs in connected LANs with its own MAC address, thus acting as a transparent IPv4 router. Using the same MAC address for the host-side of all tap interfaces, results in the virtual machines being able to preserve constant network connectivity across migrations from one hardware node to the other, without waiting for the guest OS ARP cache to expire. In the case of IPv6 the solution is not straightforward. Getting the virtual machines to use the host as a router is a simple matter of sending out ICMPv6 Router Advertisements to the tap interfaces connecting the virtual machines to the host. In order to be able to preserve the same router address irrespective of the host the virtual machine is running on, the host-side of all tap interfaces is configured with the same MAC address which results in the same IPv6 link-local address used for routing. Because of the link-local nature of these addresses, there is no problem for a host to have multiple tap interfaces with the same MAC and IPv6 link-local address. However, a serious problem arises with the intra-vm communication, because the Layer-3 IPv6 subnet spans multiple Layer- 2 broadcast domains; in IPv6 the broadcast ARP has been replaced by multicast ICMPv6 Neighbor Discovery, complicating things as no longer all nodes receive all address resolution queries. Neighbour solicitations and neighbour advertisements are sent to special multicast groups ( solicited-node addresses ), which are formed by appending the last 24 bits of the IPv6 address in question to the ff02::1:ff00:0/104 prefix. The Linux kernel can perform Proxy ICMPv6 Neighbor Discovery as an equivalent to proxy-arp, however because of the multicast nature of ICMPv6 ND, the kernel can only proxy individual addresses and not whole subnets, as it has to actively subscribe to a link-local multicast group per address. Since the interfaces connecting the virtual machines to the host are point-to-point Layer-2 interfaces, all multicast traffic originating from the virtual machine is actually visible by the host, although it never ends up in the host s IPv6 stack. Thus, special software was written using the kernel s netfilter subsystem hooks (NFQUEUE) to relay ICMPv6 ND messages to userspace and respond to ICMPv6 Neighbor Solicitations for non-link-local ICMPv6 addresses. The same software generates ICMPv6 Router Advertisements. A prototype implementation written in Python can be found at [7]. V. FROM VPS TO PUBLIC CLOUD During the past couple of years, GRNET went from two nodes of virtual machines, to hosting its whole infrastructure using virtualization and then to provide infrastructure-as-a-service (IaaS) to its customers. The next logical step, both from the perspective of moving this service forward and following the decade s trend, would be to scale this infrastructure up and provide IaaS services to the order of hundreds or thousands. One of the options would be to expand the VPS userbase and target university computer labs, research experiments or the Grid infrastructure users; this direction has already been followed by other NRENs. Rather than doing that, a strategic decision was made to launch a public cloud service, codenamed Okeanos. The project has the goal to provide virtual servers and desktops to all members of GRNET s customer organizations, including faculty, post-graduate and undergraduate students, leveraging the existing SAML2 federation. The project is under development and expected to launch a private beta in Q and a public beta in Q The current plans include physical hosts, hosting virtual machines during the first year. Apart from the increase of scale, it was decided to also provide a number of attractive new features; these include a public API compatible with industry standards, a simple, federated, user front-end and integration with a storage-as-a-service solution, preferably GRNET s existing S3-like storage service[8], additionally with block-storage, unified access control and accounting. Scaling up the infrastructure to hundreds of thousands, as well as planning the biggest service GRNET has ever provided, presents its own set of challenges in all possible aspects. Moreover, it was decided to avoid closed turn-key cloud solutions and

5 Fig. 3. Wizard for creating a new virtual machine on the synnefo web user interface instead leverage commodity hardware and Free/Open Source software to cut down costs, albeit without sacrificing reliability or raising operation expenditure. A decision was made early-on to use a community-based Linux distribution and reuse existing components such as KVM, and more importantly Ganeti, for the low-level management of the virtual machines. Other solutions meeting the same standards, such as Eucalyptus[9] and the then-new OpenStack[10], were evaluated. However, Ganeti, the existing solution, had already gained a number of new features with the goal of increasing cluster sizes by an order of magnitude; thus it was selected, both because of its proven reliability based on prior experience and GRNET s involvement in the project s development. Having a solid base, we started moving up the stack: we opted to use the OpenStack API for our public service; OpenStack, together with Amazon s EC2, are the two most popular cloud APIs in widespread use. OpenStack API however, unlike EC2, is RESTful and modular and provides pluggable authentication modules in a cleanly defined architecture, making it suitable for GRNET s cloud implementation. The use of a popular API at the same time meant compatibility with third-party clients written for other services using the same API. Hence, a new middleware is being written, bridging OpenStack API with Ganeti and adding features that Ganeti lacked, such as user management, on a higher level. The middleware is complemented by a user-friendly web frontend implemented as a client to the public API, but with a target base of end-users rather than developers and system administrators. The frontend project is called synnefo (Fig. 3) or +nefo (Greek for cloud ) and the technologies used are Python and Django on the server side and Javascript and jquery on the browser side. The project s software will be released under a free software license along with the first beta, available for everyone to reuse and modify. Work is currently on-going for the first version; however, there is already a roadmap for features to be added until at least mid The agenda includes a credits/billing system to ration the infrastructure for all users; an integration with cloud storage technologies such as RADOS/Ceph[11] to provide users with thin provisioning/snapshotting; using VDI technologies for true virtual desktops; integrating with the Grid world (e.g. Project StratusLab[12]). VI. CONCLUSIONS Virtualization is a very powerful and versatile tool, allowing service consolidation, increased hardware utilization and rapid machine provisioning. Cloud computing and Public Cloud services open the door to a new era in computing, abstracting away hardware and presenting unlimited resources to the end-users. However, at the same time, virtualization presents a new set of challenges as the numbers of virtual machines increase. These challenges are especially apparent on the operational level, as large-scale, centralized infrastructures are built to host large numbers of virtual machines of ever-increasing importance. In this paper a small fragment of these challenges was presented, using GRNET s experience with virtualization as an example.

6 [1] The Xen project, [2] The Debian project, [3] Puppet Labs, [4] Ganeti, [5] Linux KVM hypervisor, [6] DRBD, [7] NFDHCPd, [8] GRNET Simple Storage, [9] Eucalyptus, [10] OpenStack, [11] Ceph distributed filesystem, [12] Project StratusLab, REFERENCES AUTHOR BIOGRAPHIES Apollon Oikonomopoulos is a systems engineer in the servers and services team of GRNET s Network Operations Centre. He holds a Diploma on Mechanical Engineering from the National Technical University of Athens where he is currently pursuing his PhD in robotics. He has worked as a freelance systems administrator and IT consultant for a number of Greek web hosting companies. He has been with GRNET since late Faidon Liambotis is a systems engineer working in the servers and services team of GRNET s Network Operations Centre. He has been part of GRNET s Virtual Network Operations Centre since late 2004.

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