What is MTBF (Mean Time Between Failure)?

Quick Definition: Mean Time Between Failure (MTBF) is the average amount of time a system or component operates before experiencing a failure.
Things go wrong from time to time. In life, it may come in the form of being late to work, forgetting to bring lunch to school, or forgetting to pay a bill on time. When something goes wrong in the tech world, it takes the form of users being unable to access websites and services, lost revenue, and even security and health risks (depending on the system). This is called downtime.
The goal is always to go a long period between things going wrong—this concept is called Mean Time Between Failure, or MTBF. Mean Time Between Failure quantifies the period between one issue being resolved and another issue beginning. Similarly, High Availability (HA) helps increase the overall Mean Time Between Failure.
This article will further elaborate on Mean Time Between Failure, High Availability, and redundancy.
What is Redundancy and High Availability?
Redundancy is the duplication of critical components to ensure normal system operations when they might otherwise fail. Carrying a spare tire is a form of redundancy meant to ensure drivers can continue operating a vehicle in the event of a flat tire.
Similarly, maintaining cloud backups of your phone is a form of redundancy to ensure you don’t lose all your data and memories in the event your phone becomes lost or damaged. Redundancy in enterprise environments can take on the following forms:
Hardware Redundancy
Maintaining multiple physical devices and components, such as servers, switches, storage, and power supplies, helps mitigate single points of failure. Some common examples are RAID storage (Redundant Array of Independent Disks) and dual power supplies in server racks.
Software Redundancy
Sometimes critical application software fails, but thanks to software redundancy, operations can be quickly recovered and continue normally. Software redundancy includes backup software, failover clusters, and virtual environments that can be maintained and reverted to the state in which they were last known to be functional.
Network Redundancy
Network redundancy allows data to be transmitted even if one or more network components fail. This is achieved through methods such as redundant switches, failover Internet connections, and multiple network paths.
High availability leverages these redundancies to maintain normal operations when there might otherwise be downtime. There are a few high-availability architectures meant to help maintain smooth operations:
Active-Passive
Referring back to the previous example of a spare tire, active-passive architecture involves one system being the primary and handling all operations, while a backup system is ready to pick up the slack should the primary system fail. In a car, that means one is on your wheel and the other is not in use—but is ready to be deployed when needed.
Active-Active
In this architecture, multiple systems are operational simultaneously, spreading the workload evenly and picking up slack when one or more systems fail. In Active-Active architecture, these systems work together like load balancers to reduce the overall strain on individual systems by spreading the workload evenly across the entire set. Imagine two employees working together to lift a couch and move it into a truck—both carry an equal load.
N+1
The N+1 model simply means an organization maintains only one spare component. This may work for some organizations, and it may leave others woefully unprepared for unexpected outages.
N+M
The N+M model expands on the N+1 model by maintaining multiple spares. Carrying a single spare tire is probably okay for most drivers, but only keeping one spare battery in the house might leave most people unprepared for something like a TV remote being inoperable. Similarly, larger organizations might fare better with multiple spare components and devices.
Understanding all these concepts is the first step to understanding what MTBF is and how it impacts networks.
What is Mean Time Between Failure?
Mean Time Between Failure is a quantifiable metric that estimates the average time between system failures. This metric is used to predict when a system or one of its components will encounter another failure, allowing organizations to be better prepared for that occurrence.
Redundancy and high availability work together to design and refine the methods organizations use to mitigate future failures and reduce their impacts.
Calculating MTBF
Mean Time Between Failure is calculated using this formula:
MTBF = Total Operating Time / Number of Failures
So, if a device has a total operating time of 100 hours and experiences two failures during that period, its MTBF value is 50 hours. This isn’t great, of course, but this is just using simplified math for the sake of the example.
Several factors influence MTBF, such as the quality of the components and devices, environmental conditions, and maintenance. Using, maintaining, and operating higher-quality components in their ideal conditions will lead to a higher MTBF than using lower-quality components or operating the system outside its ideal conditions.
Some sample Mean Time Between Failure values for devices include higher-end servers from companies like Dell and HP lasting 100,000 hours or more under ideal conditions. Enterprise-grade Cisco networking equipment typically has an MTBF value of 200,000 hours under ideal conditions. Of course, redundancy and high availability practices should still be observed as a precaution.
What is the Relationship Between MTBF and Redundancy/High Availability?
Redundancy does not increase MTBF for any one component outright; rather, it improves reliability overall. Architectures focused on high availability reduce the risks and impacts of components and devices with lower MTBF values. Referring back to our previous example of load balancers, in an active-active architecture, the workload is evenly distributed among multiple systems, and a single system failing will have less impact on the remaining systems as they carry the extra workload.
Of course, there are costs associated with redundancy and high availability. Redundant systems require additional hardware, additional licenses, and additional maintenance, regardless of whether the systems are going to be actively or passively redundant.
Careful consideration should go into planning for redundancy and high availability to achieve as close to optimal as possible. Too many redundancies risk unnecessary spending and maintenance hours, whereas too few redundancies risk leaving the organization unprepared for system failures.
What are the Best Practices for MTBF in Redundancy and HA?
To extend MTBF and ensure true high availability, organizations need more than just redundant systems—they need a proactive strategy. These best practices help keep backup systems reliable, reduce failure rates, and ensure quick recovery when issues arise.
Regular Maintenance and Monitoring
Redundant devices and systems are only useful if they’ve been properly maintained. Whether a spare device, physical component, or software application is involved, it should be treated as if it were the primary—because one day it might be.
Clean, upgrade, and store backups the same way you would a primary device, application, or component. Ensure any spares maintain the same configuration as their primary counterparts. Several network monitoring tools are also available to detect and troubleshoot issues the moment they occur.
Proper Load Balancing
Evenly distributing the workload across all devices and components as a standard practice helps reduce everyday wear and tear on all devices. This is crucial in active-active high-availability architectures.
Disaster Recovery Planning
Mitigation alone is not enough, and as such, disaster recovery planning is highly recommended, and in many cases even required for compliance purposes. Organizations should create and maintain disaster recovery plans and test them periodically to ensure safeguards are working as expected.
Conclusion
The Mean Time Between Failure is a quantifiable metric used to estimate the length of time a device or component can go without failing. Redundancy and high availability help increase MTBF by providing spare devices and components to ensure minimal disruptions to activities when a device fails. MTBF is calculated by dividing a system’s total operating time by the number of failures it experiences within that same period. To help increase MTBF, be sure to employ devices optimally.
Want to learn more? Explore CBT Nuggets training for networking professionals. Sign up for a free 7-day trial.
delivered to your inbox.
By submitting this form you agree to receive marketing emails from CBT Nuggets and that you have read, understood and are able to consent to our privacy policy.