Mean Time Metrics: the Differences Between MTTR, TTRS, MTTF...

In today's fast-paced environment, detecting failures early and recovering the system as soon as possible is essential. Software teams must identify outages in the system's functioning and recover it smoothly. Engineering teams are trained to be more efficient and agile in system recovery by introducing various practices. 

In this article, we decode the different MTTR metrics and their variations: TTRS, MTBF, MTTA, and MTTF. 

What Is MTTR?

MTTR was initially introduced by DORA as Mean Time to Recovery (or Mean Time to Restore), one of four metrics that distinguish high-performance development teams from average ones. This abbreviation has several synonyms, and we’ll elaborate on them below. 

The main goal of these MTTRs is to denote the team’s and product’s capability to recover quickly after an outage. DORA metrics give the team leaders truthful insights as they analyze the metrics to estimate their performance. By looking at MTTRs, leaders can ensure their system is stable and improve their internal processes.

What Are MTTR’s Multiple Meanings?

MTTR is an acronym for several metrics (KPIs) referring to the IT team’s ability to resolve incidents. It can denote the mean time to repair, mean time to restore, and mean time to recover. Although the terms can be conflated, they all have some specifics.

• Mean Time to Repair – refers to the average time it takes to repair a failed component or system and return it to operational status (from issue diagnostics until successful operation).
• Mean Time to Restore or Mean Time to Recovery – refers to the time taken to restore a system or application to its previous working condition after a failure (resilience and reliability of a system). In some contexts, it reflects the average time a system (or network) takes to recover from a failure that does not necessarily require repair, such as a temporary outage or service interruption.  
• Mean Time to Respond – refers to the average time it takes to recover from a product or system failure from when you are first alerted to that failure.

What Is Mean Time to Repair (MTTR)?

As stated above, the Mean Time to Repair refers to the average time it takes to repair a failed component or system and return it to operational status. This metric includes the time taken to diagnose the issue, prepare for repair (such as gathering documentation), perform the actual repair, and then confirm that the system is operational again.

How to Calculate Mean Time to Repair?

Mean Time to Repair tracks the average time spent on issue repair.

For instance, if you have spent 60 hours on unplanned maintenance of an asset that has broken down 10 times over a year, the mean time to repair would be 6 hours. The repair time starts when the incident is discovered.

How to Use Mean Time to Repair?

MTTR is commonly used in maintenance and is a critical metric in industries where system uptime is essential. It provides insight into how quickly an organization can respond to and resolve failures, which is crucial for minimizing downtime.

What Are the Benefits of Measuring Mean Time to Repair?

MTTR affects many points of operational management, not just the repair of defects. 

• Faster repairs reduce downtime, which improves overall efficiency and productivity. 
• Reduced downtime improves reliability, service delivery, and customer satisfaction. 
• They are getting valuable information for making informed decisions about maintenance and technical debt.

What Is Mean Time to Restore or Mean Time to Recovery?

Mean Time to Restore can be used interchangeably with Mean Time to Recovery. It reflects the time taken to restore a system or application to its previous working condition after a failure that does not necessarily require repair, such as a temporary outage or service interruption. It measures the time until normal operations resume after a disruption, which may include failover to backup systems. It reflects a system's resilience and reliability.

How to calculate Mean Time to Restore/Recovery?

Mean Time to Restore or Recovery is calculated by measuring total downtime over a specific time and dividing it by the total number of incidents within that time.

Take, for example, a system that goes down three times within a month. The first incident took 3 hours to restore, the second 2 hours, and the third 1 hour, for a total of 6 hours. The MTTR for that month would be 6 hours of total downtime / 3 incidents = 2 hours.

How to Use Mean Time to Restore/Recovery?

MTTR is about returning the system to its fully functional state, which could mean different things depending on the design of the system's resilience and contingency plans. It could also incorporate the time to restore data from backups and repair the original issue.

What Are the Benefits of measuring Mean Time to Restore/Recovery?

Considering the numbers and qualitative aspects of incident response is good. It helps you uncover the team’s capacity to recover the system and make it more reliable. Mean Time to Restore or Recovery can help you in:

• Understanding system complexity, personnel’s skill level, and availability of resources.
• Focusing on automated CI/CD, automated testing, and rollbacks. 
• Continuous improvement and adoption of changes to improve the incident response process.

What Is Mean Time to Respond?

Mean Time to Respond typically refers to the average time it takes to respond to an incident or service request after it has been reported. It measures the speed at which a team acknowledges the existence of a problem and begins to take action to resolve it.

The formula to calculate Mean Time to Respond is:

So, if your system was down for 1 hour in two separate incidents in 24 hours – 60 minutes divided by two is 30, your MTTR is 30 minutes.

How to Use Mean Time to Respond?

Mean Time to Respond is a valuable metric used in incident management to evaluate the efficiency and effectiveness of response efforts. Overall, Mean Time to Respond is a critical metric for evaluating and improving incident management processes, ultimately contributing to the reliability and resilience of your systems and services.

What Are the Benefits of Measuring Mean Time to Respond?

Overall, measuring Mean Time to Respond enables organizations to optimize their incident management processes.

• You can identify areas where response times can be optimized. This may involve streamlining communication channels, improving automation, or enhancing team training to reduce response times.
• Faster response times lead to quicker resolution of issues, which in turn improves customer satisfaction.
• A focus on reducing Mean Time to Respond promotes operational efficiency by minimizing the time and resources spent on incident resolution. This allows your team to handle a greater volume of incidents with the same resources, leading to cost savings and improved productivity.

What Is Time to Restore Service?

TTRS is a DORA metric that denotes the time an organization takes to recover from a production failure.

How to Calculate Time to Restore Service?

The formula to calculate the Time to Restore Service is as follows:

So, if you have 2 incidents, one needing an hour for the system to restore and the other 30 minutes – the total TTRS time would be 45 minutes.

How to Use Time to Restore Service?

The "Time to Restore Service" concept is similar to Mean Time to Recovery, and some organizations might use these terms interchangeably. It is about the time it takes to recover when a service incident occurs, with a focus on measuring the efficiency of incident response and resolution.

What Are the Benefits of Measuring Time to Restore Service?

Like MTTR, TTRS indicates a system’s ability to restore its state from failures. Efficient restoration of normal functioning improves overall operational stability, ensures smoother processes, and reduces downtime, positively affecting the system’s reliability.

What Are the Challenges when Measuring TTRS Metrics?

MTTR metrics defined above provide helpful insights for companies, but there can be issues with their calculation and interpretation. Here we mention some challenges that you can face:

• Multiple failures: Multiple concurrent failures in a system can make establishing a clear start and end time for each repair difficult. This can hinder the calculation of MTTRs.
• Different definitions: Different IT teams can have different definitions of MTTR-s. When an incident is first reported, some may start the clock, while others begin after the professional addresses the issue. This difference can make measuring and comparisons difficult.
• Irregular data gathering: Reliable MTTR metrics need accurate data collection methodologies. Data collected randomly or not recorded incidents can harm the MTTRs values.

What Are the Causes of Bad TTRS?

Even if your code is robust and well-tested, and you have a reasonable change failure rate, you may have high TTRS. If your application breaks and your team doesn’t have an excellent process to detect, fix, and deploy the solution, your TTRS will be poor. The causes for poor TTRS can be multiple:

• There needs to be proper tools for problem detection. The time to measure the TTRS starts when your system becomes unavailable, not when you realize that. Slow problem detection results in slow recovery. To avoid end-user dissatisfaction and to complete their tasks, the DevOps team needs an uptime monitor, helpdesk tools, testing/alerting tools, etc.
• Inert and clumsy deployment processes. A manual deployment process negatively impacts both deployment frequency and TTRS. E.g. you have a single deployment engineer (who performs deployments manually), and he is on leave. So, you must have a smooth and automated deployment process.
• There is no incident management plan. When an incident occurs, the DevOps team perceives the stress of a system outage, frustrated end-users, and disappointed stakeholders. Your team should have a plan for handling an incident. You should assign a responsible person and a procedure to resolve the situation. 

How Can TTRS Metrics Be Improved?

To maintain a low TTRS, consider the following best practices:

• Set up effective monitoring and alerting for your software systems. This will help you detect issues quickly and proactively before they impact users.
• Make smaller changes. More minor changes make it easier to "detect" the incident since the last change.
• Adopt an effective incident-handling procedure with clear roles, responsibilities, and escalation steps. Ensure that all team members are trained on the process and that it is regularly reviewed and updated.
• Automated testing lets you spot the incident quicker, so you know faster where the problem is when diagnosing the bug.
• To identify areas for improvement - regularly review and analyze your incident data. Implement changes to optimize the incident response process and reduce TTRS overtime.
• Perform deep root cause analysis for all incidents to identify the underlying causes, address them, and prevent future incidents.
• Apply automation to streamline incident response processes, such as automated alerts, diagnostics, and fixes. It will reduce the time required to resolve incidents.
• Conduct regular tests to identify and resolve issues before they impact users. It will prevent incidents and reduce TTRS.
• Enable effective communication among your team members during incident response. This will ensure that issues are resolved quickly and prevent delays due to miscommunication. 

What Are the Other Incident/Failure Metrics?

Along with the aforementioned MTTR metrics, the software industry has other failure KPIs. These are sparsely used but can be significant in some cases.

• MTBF – Mean Time between Failures.
• MTTA or MTTD - Mean Time to Acknowledge or Mean Time to Detect.
• MTTF – Mean Time to Failure. 

What Is Mean Time Between Failures?

MTBF denotes the time between a system's previous and subsequent failures. This metric helps you predict how long the service will run before the subsequent failure occurs. MTBF is important because it says that failure within applications will happen at some point, regardless of your internal processes.

What Is Mean Time to Acknowledge or Mean Time to Detect?

Mean Time to Detect computes the length it takes for the actual response to begin. It shows how quickly a team starts responding to an incident. Tracking Mean Time to Detect is crucial for improving effective incident analysis and resolution. Teams that know these data can minimize the time required to analyze alerts and determine priority levels for resolving failures.

What Is Mean Time to Failure?

MTTF is similar to MTBF but denotes failures a team can’t repair, such as faulty database servers, tape drives, or hard drives. The metrics represent the expected length of time until a failure occurs. The value is measured by estimating the failures of a particular system over time and calculating the average time before failure.

How Can Axify Be Helpful for Failure Metrics?

While manual calculations can provide valuable insights, an automated tool like Axify.io can efficiently predict system behaviour and track MTTR metrics. Axify is a platform that enables you to monitor all the essential performance indicators and helps you enhance your development and delivery processes. It contains superior dashboards that provide constant tracking of DORA metrics in real-time, thus simplifying the whole process. Also, it empowers teams to concentrate on making improvements.

Axify implements all four DORA metrics:

• Time to Restore Service (now known as failed deployment recovery time) - measures the time needed for the system to recover from an incident in production.
• Deployment Frequency - measures how often an organization successfully deploys to production.
• Lead Time for Changes – the time from the first commit to successfully executing code in production.
• Change Failure Rate - measures the percentage of deployments causing a failure in production.

Keeping your eye on MTTR can reveal insights about the overall system's health. Lowering these metrics will improve system stability, promote streamlined operation, and satisfy the team.