Survive, Adapt, Overcome: MTBF, MTTR, MTTA, and MTTF

You see this?! 

This is a digital computer and the Display & Keyboard (DSKY) instrument panel,  built in the late 1960s for the Apollo 11 mission. The Apollo Guidance Computer (AGC) provided guidance, navigation, and control functions to the spacecraft. 

The reason we are discussing this, is pretty interesting…

The engineers tested the resilience of this system by willingly injecting faults into it. Though the idea of chaos engineering wasn’t popular there, it laid the foundation for it. Here are the top fault-tolerant principles used to ensure mission success.

• Redundancy 

• Fail-Safe Design

• Graceful Degradation

• Extensive Testing & Simulation

• Modular Design

• Real-Time Error Detection & Correction

Fast-forward to 2008, when Netflix faced hours of downtime due to massive database corruption, they decided to move to AWS cloud infrastructure (but by ensuring system resilience). 

To achieve this, they built Chaos Monkey. It is a resilience testing tool that works by randomly shutting down live production instances to put engineers in a position where they can easily identify weak points and improve system robustness. 

After a wonderful internal success, Netflix decided to open-source it in 2012, and this move is believed to be the kick-off moment of the chaos engineering movement worldwide.

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Netflix did not stop there. They launched an entire ecosystem named Simian Army with a single vision of system resilience.

• Latency Monkey – Simulates slow networks

• Conformity Monkey – Finds configuration issues

• Chaos Gorilla – Simulates AWS zone outages

• Doctor Monkey – Monitors system health

Even beyond that, in 2017, engineers from Netflix and Gremlin formalized the chaos engineering principles (which are adopted by all big tech giants like Amazon, Google, Facebook, and Microsoft): 

• Hypothesize about system behavior

• Introduce failures in controlled ways

• Minimize blast radius to protect users

• Automate experiments to improve reliability

These two examples of Apollo 11 and Netflix achieving high system reliability and availability clearly depict that Hope is not a strategy. It’s all about building systems that expect failure and thrive through it.

High system reliability and availability are defined by many metrics, and one being most prominent is Incident Metrics, which include,

So, let’s discuss each of these in detail with examples.

What are Incident Metrics? (MTBF, MTTR, MTTA, and MTTF)

Originating from the fields of reliability engineering and systems maintenance during different time periods (between the 1950s to 2000s) from aerospace, telecommunications, and military industries, tech companies and high-velocity teams adopted these metrics to maintain system uptime, optimize response, and improve customer experience. 

The following is a detailed overview of each metric.

1. What is MTBF?

The definition: 

MTBF (Mean Time Between Failures) measures the average time a system runs or operates without failures. Applicable to repairable systems, it indicates how reliable a system is. 

How to calculate MTBF? 

MTBF = Total Uptime / Number of Failures 

For example, if your system runs for 1000 hours and during this time, it fails 5 times, then the MTBF = 1000/5 = 200 hours. 

Interpretation: Your system runs an average of 200 hours before encountering a failure. 

Why is MTBF Important?

• Predict System Reliability: It helps you estimate when the next failure might occur. 

• Plan Maintenance: With you already knowing the estimate of the next failure, you can schedule preventive repairs before failures happen. 

• Improve Uptime: Since you take measures before failures, it directly leads to high system uptime.

Limitations of MTBF:

• Simplistic Assumptions: MTBF assumes a constant failure rate. However, most systems follow a bathtub curve with 3 failure phases: Infant Mortality with a high failure rate due to early life, Random Failures due to well-maintained systems, and Aging Failures due to wear and age. This clearly shows that any system does not have a constant failure rate throughout its operational cycle.

• Limited Context: MTBF only cares about the time between failures but does not consider the severity or impact of failures. Thus, it treats both minor glitches and catastrophic outages equally. 

• Not Ideal for Complex Architectures: MTBF assumes that failures are independent, which is true in the case of simple, isolated systems. But when applying the same metric to complex distributed architectures like the cloud, failures are most likely interdependent, and it cascades across several components.

However, when we combine MTBF with other incident metrics, it starts making a lot of sense.

2. What is MTTR? 

The definition: 

MTTR (Mean Time to Repair) is another important incident metric, which measures the average time you take to repair or fully restore a system, service, or component after a failure. 

How to calculate MTTR? 

MTTR = Total Downtime / Number of Repairs 

For example, if your system encountered 5 failures in the last 30 days, and the total time invested in fixing those issues was 10 hours, MTTR = 10 hours / 5 failures = 2 hours. 

Interpretation: Your team takes an average of 2 hours to fix an issue during the timeframe of those 30 days. 

Types of MTTR: 

• Mean Time to Repair: Time your team takes to fix and restore the system. It focuses on the repair process itself.  

• Mean Time to Resolve: This also includes root cause analysis and efforts taken to ensure no future issues.  

• Mean Time to Respond: How quickly the team acknowledges the problem. 

• Mean Time to Recovery: Time to fully restore service after a disruption.

What is the ideal MTTR? 

Well, the ideal MTTR depends on many factors, including the system’s criticality and business impact, but in general, the faster, the better. 

The following are the MTTR Benchmarks by Industry we have come across during the deep-dive discovery phase of building Hivel - Software Intelligence Platform.

1. Critical Systems (e.g., Finance, Healthcare, Cloud Providers) 

Ideal MTTR: Less than 5 minutes for automated recovery. 

2. High-Traffic Digital Services (e.g., Streaming, E-commerce)

Ideal MTTR: 5-15 minutes 

3. Enterprise Applications (e.g., Internal Business Systems) 

Ideal MTTR: 1-4 hours 

4. Non-critical Systems (e.g., Batch Processing) 

Ideal MTTR: 24 hours

What are the factors that influence ideal MTTR?

• System Complexity: Easy and early system recovery can be achieved in a simple system, but it takes time to fix problems in distributed or microservices architectures. 

• Automation Level: Self-healing systems with automated IT infrastructures that detect, diagnose, and resolve problems without human intervention, called AIOps, shrink MTTR to the furthest point.

• Monitoring & Alerting: Faster detection using observability (logs, traces, and metrics) brings down MTTR to a significant level.

• Incident Response Processes: High-velocity teams rely on well-defined playbooks and SRE practices to accelerate repair times.

Just for your context: Google SREs maintain 99.999% uptime (only 5 minutes and 15 seconds of downtime per year)!

3. What is MTTA?

The definition: 

MTTA (Mean Time to Acknowledge) measures the responsiveness of an organization’s monitoring and alerting systems.  It measures how quickly your team identifies and acknowledges a failure or incident after it occurs. 

How to measure MTTA?

MTTA = Total Time From Alert to Acknowledgment for All Incidents / Number of Incidents 

For example, if your system has experienced 5 incidents, and your team respectively took 7, 2, 4, 3, and 7 minutes to acknowledge them, then MTTA = Total Time to Acknowledge (23 minutes) / Number of Incidents (5) = 4.6 Minutes. 

‍Interpretation: Your team takes an average of 4.6 minutes to identify an incident.

Why is MTTA important? ​

• Enhanced operational resilience: The faster your team acknowledges an issue and responds to it, the quicker you solve it, leading to reduced downtime. This is the first step toward achieving operational resilience. 

• Strengthened SLA compliance: In industries with strong Service Level Agreements (SLAs), a faster MTTA reduces the time customers wait for acknowledgment. This helps you foster partnership on trust, which talks about how proactively you address issues. 

• Improves incident lifecycle management: A good score of MTTA represents an effective incident lifecycle management practice with rapid issue prioritization, prompt resource allocation, and quick incident resolution action.

Ideal MTTA benchmark: 

There is no universal "perfect" MTTA, due to obvious reasons - as the ideal benchmark depends on the industry, system complexity, and incident severity. However, top-performing DevOps teams aim for MTTA under 5 minutes.

What does MTTA not include? 

• Root Cause Analysis – Diagnosing why the failure occurred.

• Fixing the Issue – Implementing patches or system recovery.

• Verifying the Solution – Ensuring the system operates as expected post-fix.

MTTA Lifecycle:

4. What is MTTF? 

The definition: 

Majorly applied to non-repairable components or systems, MTTF (Mean Time to Failure) measures the average time until a component or system fails permanently. 

MTTF is crucial for hardware reliability, cloud infrastructure, and distributed systems as in such an environment, permanent component failures are inevitable, and it can disrupt service delivery on a larger scale. 

How to calculate MTTF? 

MTTF = Total Operating Hours / Number of Failures 

For example, if 100 virtual machines collectively operate for 1,000,000 hours and with 50 failures, MTTF = 1,000,000 / 50 = 20,000 hours. 

Interpretation: Each virtual machine runs for 20,000 hours before it fails, suggesting a high level of system reliability. 

Why is MTTF important?

• Reliability Planning: With the MTTF benchmark, you can predict failure rates to build robust, fault-tolerant systems. 

• Service Level Objectives (SLOs): You can align with SRE practices for uptime guarantees and system reliability.

Can MTTF apply to repairable systems? 

MTTF (Mean Time to Failure) is not the primary metric for repairable systems. For more accurate results, it is advised to track MTBF (Mean Time Between Failures) in the case of repairable systems. The following is a quick breakdown between MTTF vs MTBF.

Then, why does DevOps adopt MTTF?

Ever since the introduction of automation in DevOps, the line between repairable and non-repairable has become blurred. 

• Kubernetes Pods: MTTF applies. Because, it gets automatically replaced upon failures. 

• Ephemeral Instances: MTTF applies. Because, these cloud instances follow the self-destruct characteristics. 

• AIOps Systems: MTTF applies. Because, automated remediation and self-healing systems track MTTF to prevent failures. 

How to Track Incident Metrics MTBF, MTTR, MTTA, MTTF (in the Easiest Way Humanly Possible)?

Due to fragmented data across multiple tools and the complexity of modern engineering systems, tracking incident metrics like MTBF, MTTR, MTTA, and MTTF can be difficult. 

What makes it more difficult to gain real-time visibility into system health and performance is inconsistent tracking and manual reporting. 

And the real culprit? 

Well, siloed data from project management, ticketing, and DevOps tools. 

Hivel eliminates these challenges by providing a unified, data-driven platform that integrates easily with popular DevTools like GitHub, GitLab, Bitbucket, Jira, and more.

With this, you gain a real-time, bird’s-eye view of your entire engineering operation, including critical metrics, without shuffling between tools and doing manual calculations. 

The end result? 

You will never have to know the difference between Chaos and Catastrophe! 

Happy Engineering!

Also, read: What and How to catch Hidden Ghost Developers Efforts?