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Downtime sucks.

I write this whilst sitting in an airport lounge, having been disembarked from my plane due to a technical fault. I don’t really begrudge the airline in question; it was a plumbing issue! This is a physical failure of the aircraft in question and just one of those things (unless I find out later they didn’t do the appropriate preventative maintenance of course)! Sometimes failures just happen and I would far rather it was just a plumbing issue, not an engine issue!

What is not excusable, however, is if the downtime is easily preventable; for example, if you are designing a solution which has no resilience!

This is obviously more common with small and medium sized businesses, but even large organisations can be guilty of it! I have had many conversations in the past with companies who have architected their solutions with significant single points of failure. More often than not, this is due to the cost of providing an HA stack. I fully appreciate that most IT departments are not swimming in cash, but there are many ways around a budgetary constraint and still provide more highly available, or at least “Disaster Resistant” solutions, especially in the cloud!

Now obviously there is High Availability (typically within a single region or Data Centre), and Disaster Recovery (across DCs or regions). An ideal solution would achieve both, but for many organisations it can be a choice between one and the other!

Budgets are tight, what can we do?

Typically HA can be provided at either the application level (preferred), or if not, then at the infrastructure level. Many solutions to improvise availability are relatively simple and inexpensive. For example:

• Building on a public cloud platform (and assuming that the application supports load balancing), why not test running twice as many instances with half the specification each? In most cases, unless there are significant storage quantities in each instance, the cost of scaling out this way is minimal.
  If there is a single instance, split it out into two instances, immediately doubling your availability. If there are two instances, what about splitting into 4? The impact of a node loss is then only 25% of the overall throughput capacity for the application, and can even bring down the cost of HA for applications where the +1 in N+1 is expensive!
• Again in cloud, if there are more than two availability zones in a region (e.g. on AWS), then take advantage of them! If an application can handle 2 AZs, then the latency of adding a third shouldn’t make much, if any difference, and costs will only increase slightly with a small amount of extra inter-AZ bandwidth or per-AZ services (e.g NAT gateways).
  Again, in this scenario the loss of an AZ will only take out 33% of the application servers, not 50%, so it is possible to reduce the number of servers which are effectively there for failover only.
• If you can’t afford to run an application as multi-AZ or multi-node, consider putting it in an auto-scaling group or scale-set with a minimum and maximum of 1 server. That way if an outage occurs or int he case of AWS, an entire AZ goes down, an instance will automatically be regenerated in an alternative AZ.

What if my app doesn’t like load balancers?

If you have an application which cannot be load balanced, you probably shouldn’t be thinking about running it in the cloud (not if you have any serious availability requirements anyway!). It amazes me how many business critical applications and services are still running in single servers all over the world!

• If your organisation is dead set on using cloud for a SPoF app, then making it as ephemeral as possible can help. Start by splitting the DBs from the apps, as these can almost always be made HA by some means (e.g. master/slave replication, mirroring, log shipping, etc). Failover nodes also often don’t attract a license fee from many vendors (e.g. MS SQL), so always check your license documentation to see what you can achieve on the cheap.
• Automate! If you can deploy application server(s) from a script, even if the worst happens, the application can be redeployed very quickly, in a consistent fashion.
  The trend at the moment is moving towards a more agile deployment process and automated CI/CD pipelines. This enables companies to recover from an outage by rebuilding their environments and redeploying code rapidly (as long as they have a replica of the data or a highly available datastore!).
• If it’s not possible to script or image the code deployment, then taking regular backups (and snapshots where possible) of application servers, and testing them often is an option! If you don’t want to go through the inflexible, unreliable and painful nightmare of doing system state restores, then take image-based backups (supported by the vast majority of backup vendors nowadays). Perhaps even syncing of application data to a warm standby server which can be brought online reasonably swiftly, or even use an inexpensive DR service such as Azure Site Recovery, to provide an avenue of last resort!
• If maybe cloud isn’t the best place to locate your application, then provide HA at the infrastructure layer by utilising the HA features of your favourite hypervisor!
  For example, VMware vSphere will have an instance back up and running within a minute or two of the failure of a host using the vSphere HA feature (which comes with every edition except Essentials!). On the assumption/risk that the power cycle does not corrupt OS, applications or data, you minimise exposure to hardware outages.
• If the budget is not enough to buy shared storage and all VMs are running on local storage in the hypervisor hosts (I have seen this more than you might imagine!), then consider using something like vSphere Replication or Hyper-V Replicas to copy at least one of each critical VM role to another host, and if there are multiple instances, then spread them around the hosts.

Finally, make sure whatever happens there is some form of DR, even if it is no more than a holding page or application notification and a replica or off-site backup of critical data! Customers and users would rather see something telling them that you’re working to resolve the problem, than getting a spinning wheel and a timeout! If you can provide something which is of limited functionality or performance, then it’s better than nothing!

TLDR; High Availability on a Budget

There are a million and one ways to provide more highly available applications; these are just a few. The point is that providing highly available applications is not as expensive as you might initially think.

With a bit of elbow grease, a bit of scripting and regular testing, even on the smallest budgets you can cobble together more highly available solutions for even the crummiest applications! 🙂

Now go forth and HA!