Ahead of the Pack: the Pacemaker High-Availability Stack
A high-availability stack serves one purpose: through a redundant setup of two or more nodes, ensure service availability and recover services automatically in case of a problem. Florian Haas explores Pacemaker, the state-of-the-art high-availability stack on Linux.
Hardware and software are error-prone. Eventually, a hardware issue or software bug will affect any application. And yet, we're increasingly expecting services—the applications that run on top of our infrastructure—to be up 24/7 by default. And if we're not expecting that, our bosses and our customers are. What makes this possible is a high-availability stack: it automatically recovers applications and services in the face of software and hardware issues, and it ensures service availability and uptime. The definitive open-source high-availability stack for the Linux platform builds upon the Pacemaker cluster resource manager. And to ensure maximum service availability, that stack consists of four layers: storage, cluster communications, resource management and applications.
The storage layer is where we keep our data. Individual cluster nodes access this data in a joint and coordinated fashion. There are two fundamental types of cluster storage:
Single-instance storage is perhaps the more conventional form of cluster storage. The cluster stores all its data in one centralized instance, typically a volume on a SAN. Access to this data is either from one node at a time (active/passive) or from multiple nodes simultaneously (active/active). The latter option normally requires the use of a shared-cluster filesystem, such as GFS2 or OCFS2. To prevent uncoordinated access to data—a sure-fire way of shredding it—all single-instance storage cluster architectures require the use of fencing. Single-instance storage is very easy to set up, specifically if you already have a SAN at your disposal, but it has a very significant downside: if, for any reason, data becomes inaccessible or is even destroyed, all server redundancy in your high-availability architecture comes to naught. With no data to serve, a server becomes just a piece of iron with little use.
Replicated storage solves this problem. In this architecture, there are two or more replicas of the cluster data set, with each cluster node having access to its own copy of the data. An underlying replication facility then guarantees that the copies are exactly identical at the block layer. This effectively makes replicated storage a drop-in replacement for single-instance storage, albeit with added redundancy at the data level. Now you can lose entire nodes—with their data—and still have more nodes to fail over to. Several proprietary (hardware-based) solutions exist for this purpose, but the canonical way of achieving replicated block storage on Linux is the Distributed Replicated Block Device (DRBD). Storage replication also may happen at the filesystem level, with GlusterFS and Ceph being the most prominent implementations at this time.
The cluster communications layer serves three primary purposes: it provides reliable message passing between cluster nodes, establishes the cluster membership and determines quorum. The default cluster communications layer in the Linux HA stack is Corosync, which evolved out of the earlier, now all but defunct, OpenAIS Project.
Corosync implements the Totem single-ring ordering and membership protocol, a well-studied message-passing algorithm with almost 20 years of research among its credentials. It provides a secure, reliable means of message passing that guarantees in-order delivery of messages to cluster nodes. Corosync normally transmits cluster messages over Ethernet links by UDP multicast, but it also can use unicast or broadcast messaging, and even direct RDMA over InfiniBand links. It also supports redundant rings, meaning clusters can use two physically independent paths to communicate and transparently fail over from one ring to another.
Corosync also establishes the cluster membership by mutually authenticating nodes, optionally using a simple pre-shared key authentication and encryption scheme. Finally, Corosync establishes quorum—it detects whether sufficiently many nodes have joined the cluster to be operational.
Cluster Resource Management
In high availability, a resource can be something as simple as an IP address that "floats" between cluster nodes, or something as complex as a database instance with a very intricate configuration. Put simply, a resource is anything that the cluster starts, stops, monitors, recovers or moves around. Cluster resource management is what performs these tasks for us—in an automated, transparent, highly configurable way. The canonical cluster resource manager in high-availability Linux is Pacemaker.
Pacemaker is a spin-off of Heartbeat, the high-availability stack formerly driven primarily by Novell (which then owned SUSE) and IBM. It re-invented itself as an independent and much more community-driven project in 2008, with developers from Red Hat, SUSE and NTT now being the most active contributors.
Pacemaker provides a distributed Cluster Information Base (CIB) in which it records the configuration and status of all cluster resources. The CIB replicates automatically to all cluster nodes from the Designated Coordinator (DC)—one node that Pacemaker automatically elects from all available cluster nodes.
The CIB uses an XML-based configuration format, which in releases prior to Pacemaker 1.0 was the only way to configure the cluster—something that rightfully made potential users run away screaming. Since these humble beginnings, however, Pacemaker has grown into a tremendously useful, hierarchical, self-documenting text-based shell, somewhat akin to the "virsh" subshell that many readers will be familiar with from libvirt. This shell—unimaginatively called "crm" by its developers—hides all that nasty XML from users and makes the cluster much simpler and easier to configure.
In Pacemaker, the shell allows us to configure cluster resources—no surprise there—and operations (things the cluster does with resources). Besides, we can set per-node and cluster-wide attributes, send nodes into a standby mode where they are temporarily ineligible for running resources, manipulate resource placement in the cluster, and do a plethora of other things to manage our cluster.
Finally, Pacemaker's Policy Engine (PE) recurrently checks the cluster configuration against the cluster status and initiates actions as required. The PE would, for example, kick off a recurring monitor operation on a resource (such as, "check whether this database is still alive"); evaluate its status ("hey, it's not!"); take into account other items in the cluster configuration ("don't attempt to recover this specific resource in place if it fails more than three times in 24 hours"); and initiate a follow-up action ("move this database to a different node"). All these steps are entirely automatic and require no human intervention, ensuring quick resource recovery and maximum uptime.
At the cluster resource management level, Pacemaker uses an abstract model where resources all support predefined, generic operations (such as start, stop or check the status) and produce standardized return codes. To translate these abstract operations into something that is actually meaningful to an application, we need resource agents.
Resource agents are small pieces of code that allow Pacemaker to interact with an application and manage it as a cluster resource. Resource agents can be written in any language, with the vast majority being simple shell scripts. At the time of this writing, more than 70 individual resource agents ship with the high-availability stack proper. Users can, however, easily drop in custom resource agents—a key design principle in the Pacemaker stack is to make resource management easily accessible to third parties.
Resource agents translate Pacemaker's generic actions into operations meaningful for a specific resource type. For something as simple as a virtual "floating" IP address, starting up the resource amounts to assigning that address to a network interface. More complex resource types, such as those managing database instances, come with much more intricate startup operations. The same applies to varying implementations of resource shutdown, monitoring and migration: all these operations can range from simple to complex, depending on resource type.
Highly Available KVM: a Simple Pacemaker Cluster
This reference configuration consists of a three-node cluster with
single-instance iSCSI storage. Such a configuration is easily capable
of supporting more than 20 highly available virtual machine instances,
although for the sake of simplicity, the configuration shown here
includes only three. You can complete this configuration on any recent
Linux distribution—the Corosync/Pacemaker stack is universally
available on CentOS 6, Fedora, OpenSUSE and SLES, Debian, Ubuntu
and other platforms. It is also available in RHEL 6, albeit as a
currently unsupported Technology Preview. Installing the
open-iscsi packages should be
enough on all target platforms—your preferred package manager will
happily pull in all package dependencies.
This example assumes that all three cluster nodes—alice, bob and
charlie—have iSCSI access to a portal at 192.168.122.100:3260, and
are allowed to connect to the iSCSI target whose IQN is
iqn.2011-09.com.hastexo:virtcluster. Further, three libvirt/KVM virtual
machines—xray, yankee and zulu—have been pre-installed, and each
uses one of the volumes (LUNs) on the iSCSI target as its virtual
block device. Identical copies of the domain configuration files exist in the
default configuration directory, /etc/libvirt/qemu, on all three
- Chemistry on the Desktop
- HOSTING Monitoring Insights
- FinTech and SAP HANA
- Preseeding Full Disk Encryption
- Five HPC Cost Considerations to Maximize ROI
- William Rothwell and Nick Garner's Certified Ethical Hacker Complete Video Course (Pearson IT Certification)
- Two Factors Are Better Than One
- GRUB Boot from ISO
- Hodge Podge
- Two Ways GDPR Will Change Your Data Storage Solution
Pick up any e-commerce web or mobile app today, and you’ll be holding a mashup of interconnected applications and services from a variety of different providers. For instance, when you connect to Amazon’s e-commerce app, cookies, tags and pixels that are monitored by solutions like Exact Target, BazaarVoice, Bing, Shopzilla, Liveramp and Google Tag Manager track every action you take. You’re presented with special offers and coupons based on your viewing and buying patterns. If you find something you want for your birthday, a third party manages your wish list, which you can share through multiple social- media outlets or email to a friend. When you select something to buy, you find yourself presented with similar items as kind suggestions. And when you finally check out, you’re offered the ability to pay with promo codes, gifts cards, PayPal or a variety of credit cards.Get the Guide