What is MTTR?
MTTR (mean time to repair) is the average time needed to restore a failed component, system or service to working order. It is calculated as the total repair or downtime across incidents divided by the number of incidents. The acronym has several expansions: mean time to repair, recover, restore, respond or resolve. Each one starts and stops the clock at different points, so teams should define which events open and close an incident before comparing figures across groups or vendors.
How to calculate MTTR: a worked example
A storage team records six incidents in a quarter, with outages of 40, 25, 180, 15, 60 and 220 minutes. Total downtime is 540 minutes, so MTTR is 540 ÷ 6 = 90 minutes. The median is 50 minutes, which shows that two long incidents dominate the average. Reporting both values, and splitting MTTR by failure type such as disk, network or software change, gives a clearer picture than a single number.
MTTR vs MTBF and related metrics
MTTR is one of several related reliability metrics. Together they describe how often things fail, how quickly failures are noticed and how long each one lasts. They are most useful as a set, because improving one can hide a problem in another, and each depends on clear definitions of when a failure starts and when it ends in day-to-day practice.
- MTBF (mean time between failures): average operating time between failures of a repairable system.
- MTTD (mean time to detect): average time from the start of a failure until it is noticed.
- MTTA (mean time to acknowledge): average time from an alert to a person taking ownership.
- MTTF (mean time to failure): used for non-repairable parts, such as a single disk drive.
Inherent availability can be estimated as MTBF ÷ (MTBF + MTTR); with an MTBF of 2,000 hours and an MTTR of 4 hours, availability is about 99.8 percent.
What makes up repair time
An incident timeline usually has several phases: detection, diagnosis, locating the faulty device or component, the repair itself, and verification. In hardware incidents the physical fix, such as swapping a disk or reseating a module, is often short. More time goes to answering basic questions: which alert matters, which server is affected, where it sits and what changed recently. Breaking MTTR into these phases shows which one to work on first.
Ways to reduce MTTR
Improvements usually target one phase of the incident at a time. Measure the phases first, for example by timestamping detection, acknowledgment, diagnosis and recovery in the ticket, then work on the slowest one first. The practices below are common starting points for infrastructure and data center operations teams that handle hardware faults.
- Faster detection: hardware sensor and log monitoring that flags component faults early.
- Less alert noise: correlating related alerts so responders see one incident instead of dozens.
- Faster location: accurate asset records and topology that link devices to services.
- Remote access: out-of-band console and power control that avoid waiting for a site visit.
- Runbooks: documented, tested steps for common failures.
- Spares and warranty data: knowing coverage and part availability before calling support.
How CloudSino approaches MTTR
CloudSino's three products address different MTTR phases. DCOS monitors hardware through server BMCs, independent of the operating system, and provides remote power control and KVM over IP console access, which reduces dependence on site visits. iDCOS links alerts to resources, configuration items and responsible parties, triggers events and assigns tasks by rule, and records the handling process. SmartBSM correlates and merges alerts on the same fault chain and uses business topology to assist root cause analysis across infrastructure, network, database and middleware.
Using MTTR in reviews and SLAs
MTTR is most useful as a trend metric. Track it monthly, by service and failure type, and examine outliers in post-incident reviews. Be careful with targets: a team measured only on MTTR may close tickets early or split incidents. Pair it with incident count, MTBF and customer-facing availability so a gain in one area is not offset elsewhere. Contracts often specify response and restoration times per incident, so check which measure an SLA actually uses.
Frequently asked questions
Is MTTR the same as an SLA resolution time?
No. MTTR is an average across many incidents. An SLA usually sets a maximum time for each incident, often by priority, such as restoring a priority 1 service within four hours. A team can meet its MTTR goal and still breach individual SLAs if a few incidents run long.
Should MTTR include time outside business hours?
For services that run around the clock, yes, because users experience the full outage. For support contracts with business-hours coverage, some teams also report MTTR in business hours. Whichever method you choose, state it and keep it consistent so trends remain meaningful.
Does a lower MTTR always mean better reliability?
Not on its own. A falling MTTR alongside a rising number of incidents can still mean more total downtime. Look at total downtime, incident count and MTBF together. Lower MTTR is most useful when failure frequency is stable or falling.
Which MTTR definition should we use?
Use the one that matches the question you need to answer. Mean time to restore service suits customer-facing reporting, since the clock stops when users can work again. Mean time to resolve, which ends when the underlying cause is fixed, suits problem management. Many teams track both and label them clearly on dashboards.