In today’s fast-paced digital world, your customers expect your services to be available 24 hours a day, seven days a week. If your services are unreliable, these customers will likely take their business elsewhere — and spread the word. To retain their business, you must understand and optimize your service and system health to ensure your services are reliable.

Gauging your service and system health requires much more than knowing whether they’re on or off. You need to know about MTBF, MTTR, and  MTTF. But, how do you make sense of these many metrics and acronyms that are supposed to help you?

We’re here to help demystify incident metrics! Join us as we explain how to calculate MTBF — mean time between failures — and learn how you can ensure your MTBF is the best it can be for your internal teams and customers.

What is MTBF?

MTBF stands for mean time between failures. MTBF is an incident management metric that measures the average time between repairable system or application failures. In DevOps and SRE practices, MTBF is typically used to measure the availability and reliability of IT environments. In short, it measures the quality of the workloads or platform services used.

More time between failures is better, right? It depends on what counts as a “failure”. Let’s say we’re looking at a fully workable system, such as a complex application architecture. If this system is down, then yes — the longer the MTBF, the more solid the architecture and application are.

But let’s say a bug in a system is causing failures due to a broken line of code. In this situation, the MTBF might be relatively short. However, this isn’t necessarily a bad sign because the MTTR is also likely to be short. The failures can be resolved quickly as the DevOps or SRE teams implement fast, repeatable fixes.

A failure can be a major incident, such as a complete network outage, or a smaller incident, such as a miscommunication or one-second blip. How you define a failure affects your MTBF metric: major outages should be relatively infrequent, with a long MTBF, while minor blips likely occur frequently, with a short MTBF.

Different businesses, and different industries, have different ideas of what constitutes a failure. A small retail store, for example, might be able to tolerate an hour-long app outage in the middle of the night. In contrast, even a seconds-long outage may be intolerable for a large telecommunications provider whose customers rely on their network to conduct business. Also, enterprise-level applications often have service-level agreements (SLAs) and must compensate their clients for the time the application is unavailable. After all, that downtime affects the client’s bottom line as their workers are unable to work. Long or frequent failures become expensive for the client and for the service provider.

You can calculate MTBF as follows:

Total Hours of Uptime
____________________________
Total Number of Failures

Imagine you attempt to run an application workload for 100 hours. It only runs for 90 hours, with 10 hours of downtime due to 3 failures. In this case, the MTBF is 90/3 = 30 hours.

This simple example helps identify possible misconceptions about this metric. Based on the calculated MTBF, one might assume that this application workload fails precisely every 30 hours. However, it’s unlikely that failures would happen on a regular schedule. If they did, you would be able to prepare for and avoid them entirely! Still, organizations can use the MTBF to predict approximately when subsequent failures will happen and determine the average time between failures.

Now that you have a good understanding of what MTBF means, let’s look at some of its typical causes and consequences.

Causes and Consequences

MTBF depends on the service’s condition, including everything from equipment to code quality. Buggy code may cause frequent failures and a short MTBF.

Let’s return to the earlier example of repeated failures caused by a broken line of code. Here, we have a short MTBF, as failures arise frequently. Since the failures are identical, developers can implement a fast, repeatable solution each time, keeping MTTR as low as possible. However, we can’t effectively improve the MTBF without taking the time to understand the root cause.

It’s important to understand the relationship between MTBF and mean time to resolution (MTTR). Improving one without the other can only take you so far. While it’s valuable to keep MTTR low, you don’t want to overlook a more solid, long-term solution. Doing an analysis to understand everything you can about a failure in a system can help you prevent that failure from happening again, or at least happen less often. Yes, this might mean increasing the MTTR, but this allows you to move past short-term fixes and towards a long-term solution.

Understanding the relationship between MTBF and MTTR allows us to make effective, informed decisions. For example, if you have a microservice that crashes 50 times a day but customers don’t notice because it can restart itself quickly, fixing it may not be your top priority. However, if your service frequently crashes and each failure takes a long time to resolve, it’s likely to lead to unhappy customers and violated SLAs.

Rather than focusing on improving MTBF or MTTR alone, we should aim to improve them in unison. Alone, long MTBF doesn’t mean your service is always reliable. But, paired with a short MTTR, this is a sign of reliability. A strong service reliability platform like xMatters helps improve both MTBF and MTTR by facilitating detailed postmortem.

How to Improve MTBF

DevOps and SRE teams are responsible for improving the MTBF for the infrastructure and applications they are building and supporting.

Real-time Monitoring

Before you can fix a problem, first you need to identify it — and the sooner, the better. Having 24/7 visibility into the performance of your systems ensures you have access to the necessary data to solve these issues. Start with metrics and logs, and integrate distributed tracing as part of your monitoring practice. This way, your team can immediately determine the root cause of the problem whenever equipment or applications fail.

Automated Incident Management

xMatters Flow Designer’s low-code workflows can help you proactively address the reliability issue. Workflow process automation simplifies incident response — from incident detection to response and resolution. Supported by automated workflows, DevOps and SRE teams can start fixing the incident right away.

Incident Postmortems

After incidents are resolved, understanding what failed and why is a crucial step towards preventing problems from recurring. Conducting postmortems ensures that details are documented and the team understands the root causes. This can increase MTBF by developing a long-term solution that minimizes future incidents and their impact.

Conclusion

A longer MTBF keeps your systems running and your customers happily using your services. That said, a short MTBF with a quick MTTR may not be a big deal as your systems get back up and running quickly without your customers noticing. It is important to understand the interactions between the two metrics to optimize your uptime within your available resources.

To determine your MTBF and other metrics, you first need access to information. Monitoring your systems and performing detailed postmortems when failures occur helps give you the information you need to improve MTBF. Automating resolutions also helps reduce the impact of these failures.

To learn more about the importance of monitoring and how workflow automation can help to extend your MTBF, sign up for an xMatters demo.

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