What Is Mean Time to Repair (MTTR)?

Mean time to repair (MTTR) is a metric that measures the average time an IT team takes to respond to and repair a disrupted IT service, system, or software component. Any IT system can fail due to network outages, software errors, system overloading, natural disasters, and other unknown causes. MTTR is the time between failure occurrence and the system becoming fully functional again. It includes the time taken to detect the failure, diagnose the cause, and resolve the problem.

The MTTR metric originates from the mechanical field, measuring how long a piece of machinery or a component typically takes to fix. It is still used in this sense but now extends to software-based systems.

Defining software performance

In software, mean time to repair is associated with uptime, robustness, and error detection and recovery. It is combined with other metrics to measure two crucial quality-based performance indicators: reliability and availability. These indicators quantify service delivery quality between software providers and customers.

Service level indicators (SLIs) are quantitative metrics that measure desirable system values.

Service level objectives (SLOs) set a target value for measured SLIs.

Service level agreements (SLAs) are legal contracts that outline SLOs and the terms of compensation if they are not met.

MTTR is often a key SLI in SLAs. For instance, a given service may have an SLO of 48 hours MTTR, and the observable MTTR might be 24 hours, meaning the SLA has observably been met. If the SLO is not met, the customer is entitled to compensation.

Mean time to repair indicates a fix that makes systems operational. The commonly used formula to calculate MTTR is Time spent in repairs/number of repairs. Downtime is essential but not used in MTTR calculations, as failover systems may be in place while systems are undergoing restoration. The time interval is also important when calculating MTTR. If the given period is too short, tracking MTTR will not accurately reflect the actual situation.

MTTR is not a standardized measure; the provider decides how to define the ‘repair’ window. The MTTR clock time typically starts when preparation for repairs begins and stops when systems are fully restored. However, some organizations only track from when the repairs begin until testing is complete, regardless of the system’s go-live date.

Other calculations

In incident management, the abbreviation MTTR can also refer to mean time to recover, mean time to respond or mean time to resolve.

Mean time to respond starts when an incident is detected and lasts until the system is fully operational.

Mean time to resolve starts from when the failure occurs (before detection) until the issue is fully resolved and will never be a problem again.

These metrics may all be reported in incident tracking and reporting. Ensuring the right one is reported in the right place is critical to avoid confusion.

Building better quality software prioritizes prevention as a method of reducing MTTR. Refactoring code, updating legacy components, and choosing new architectures based on performance all increase software quality. High-quality software performs well and does not require repair.

However, even after implementing best practices, product or system failure may occur due to factors beyond the team's control. To optimize MTTR, implement the following measures.

Reduce system complexity

The complexity and interconnection between parts also grow when systems grow and become more extensive. Unfortunately, this leads to longer repair times when things go wrong.

Maintenance teams spend more repair time troubleshooting, researching root causes, and following a long and complex reverse engineering process. Refactoring systems and delivering microservice-style architectures can reduce the mean time to repair.

Repeatable recovery procedures

By following incident management playbooks for response and recovery and returning to previous operations, an organization saves time that would be spent manually determining a course of action. This approach requires pre-planning and incorporating new lessons learned into the playbook following each new incident when a failure occurs.

More resources

The faster a technician, operations member, developer, or maintenance team can fix a system component, the lower the mean time to repair will be. This can be achieved in many ways, including:

• Upskilling
• Providing more powerful tooling
• Hiring more team members.

By enabling the team with the right resourcing, they will be more efficient and, in turn, reduce the MTTR.

Correct reporting

Performance metrics are more useful when accurate, timely, used in the proper context, and used alongside the best-paired metrics. Identifying the distinct start and end of the mean time to repair period is essential. You should accurately report MTTR internally and to customers through live dashboards and regular reports.

Some common challenges that teams face when reducing MTTR are given below.

Process and tooling

An immature incident management process adversely impacts MTTR. Teams require a response plan and playbooks, automation to bring up failover systems, and tools to help determine the root cause of problems. Organizations can start repairing problems by maturing failure, repair processes, and tooling. Now, with machine learning abilities, organizations can more easily create documentation and response playbooks based on similar incidents in the past.

Budgeting

Fixing things when a product or system fails is one thing. However, building quality into systems and services from the outset requires talent, strategy, well-architected systems, an as-code approach, measurement, and clear management. Organizations must dedicate the necessary time and money to achieve positive outcomes. Even a skilled team needs resourcing investment to reduce the MTTR further and continuously improve.

Legacy code and systems

Older systems are often kept in service long after they should be, as the cost to build a new one feels too high. However, the costs and time sunk into repairing problems can outweigh the cost of building new systems. Organizations should carefully consider the cost-risk ratios when using legacy code and systems.

Unreliable service providers

Systems need dependable infrastructure to run correctly. Investing in unreliable third parties that offer poor SLAs can increase your MTTR in the long run. Choosing a reliable service provider like AWS cloud services for your IT infrastructure requirements is better for MTTR.

Monitoring and observability services on AWS allow you to understand what is happening across your technology stack at anytime. You can collect, correlate, aggregate, and analyze telemetry in your network, infrastructure, and applications in the cloud, hybrid, or on-premises environments.

Gain insights into your system to detect problems faster and reduce MTTR. Use artificial intelligence and machine learning to react, predict, and prevent problems proactively. For example, you can use:

• AWS X-Ray to perform distributed tracing across multiple applications.
• Amazon CloudWatch to monitor applications and respond to performance changes.
• Amazon Managed Grafana for scalable and secure data visualization.

Get started with MTTR tracking and optimization on AWS by creating a free account today.