Incident metrics like MTTA, MTTR, and MTBF help teams measure uptime, recovery speed, and system reliability. Mean Time to Reduction (MTTR) specifically tracks how quickly a team reduces an incident to a target state. In cloud environments, MTTR focuses on resolving issues tied to resource usage, latency, or costs. Azul Intelligence Cloud helps lower MTTR by enabling root-cause analysis, verifying fixes, and detecting vulnerabilities in live production code. With features like real-time monitoring, accurate code analysis, and access to timely security patches, Azul IC streamlines incident response and reduces the time and effort needed to recover from disruptions.
There are several incident metrics that help companies quantify and measure their application or service’s uptime, downtime, and how quickly they can resolve these problems.
Most incident metrics measure the mean of a specific state in recovering your system. To calculate an arithmetic mean, you take the data from a particular period of time (such as six months), divide that period’s total operational time by the number of failures. The mean is the most common type of average.
Here are some of the top incident management metrics used in Java development:
- MTTA – Mean time to Acknowledge: Means the average time it takes from when an alert is made to when the team formally acknowledges that they have seen the alert and are aware of the issue.
- MTTR: There are many different interpretations of MTTR, and each has a separate definition:
- Mean time to respond: Measures the average time to respond from a system failure from the moment the alert goes out. This doesn’t include the time to fix the system failure. The difference between this metric and the MTTA is that the team has started to work on the solution, and so the mean time to respond is typically a longer time.
- Mean time to recovery: Also called mean time to restore, this DevOps metric measures the average time it takes to recover from a system or application failure. This adds onto the mean time to respond to include the time it takes to fix the issue and recover the system. This doesn’t include closing out the incident or resolving the underlying issue that caused the system failure.
- Mean time to repair: Measures the average time it takes to repair a failed system. The time starts when the repair begins, whereas the mean time to recovery starts when the failure is discovered. This also doesn’t include resolving the underlying issues to ensure this failure doesn’t happen again.
- Mean time to resolve: Also called the mean time to resolution or (specifically for vulnerabilities) mean time to remediation, it measures the average time it takes to fully resolve or close out the incident, which includes detection, diagnosis, repair, and mitigation against future occurrences.
- Mean time to reduction: Measures the average time it takes to reduce or decrease an issue until your system has reached a desired state, which is your target metric.
- MDT – Mean down time: Measures the average time that a component is non-operational. This is due to repairs, preventive maintenance, and administrative delays.
- MTBF – Mean time between failures: Measures the average time elapsed between the different failures of a repairable system. It’s a way to predict when the next failure might occur.
- MTTF – Mean time to failure: Measures the average time elapsed between the failures of non-repairable systems. For example, a lightbulb is a non-repairable system. You replace it when it burns out.
All these metrics are valuable at different times, and they can be used together to improve your recovery time and to reduce incidents.
What Is Mean Time to Reduction (MTTR)?
The mean time to reduction (MTTR) is similar to the other MTTR metrics above. This is the average amount of time that your team takes to reduce the issue in the system or application failure until it reaches a resolved state, which is your targeted metric.
The MTTA measures the time it takes to acknowledge the issue exists. The mean time to respond adds onto that time to include up to when the team begins resolving the issue. The mean time to recovery adds onto that time, measuring to when the system is back online and recovered. The mean time to repair covers just the part where the team is fixing the issue and recovering the system (not the time before it). The mean time to resolve adds onto the mean time to recovery to include resolving (or closing out) the incident, which means fixing the underlying issue so that it won’t happen again.
And then the mean time to reduction (MTTR) is when you identify a specific metric that you want to reduce that’s related to the incident. You continue reducing the issue until you achieve the targeted outcome. For example, your MTTR for analysis warnings in a module is two days, which means it takes your development team two days to address new warnings and bring the count of open warnings back down, below a target threshold.
What Is MTTR in the Cloud?
The key difference for your MTTR in the cloud is that you’re going to track different types of metrics for cloud Java development. Most likely, you’re going to be concerned with the
cloud resources that your solution is consuming. Here are some cloud metrics that you would measure with MTTR:
- The CPU or memory used by the Java application as it runs on virtual machines (VMs), containers (including Docker or Kubernetes, using services like Amazon’s EKS, Microsoft’s AKS, and Google’s GKE). Higher usage means that you might have to resolve code inefficiencies or reduce scaling issues in your solution.
- Resources from serverless functions (such as AWS Lambda, Azure Functions, or Google Cloud Run functions).
- Latency issues from API calls that are handled by your Java service.
- Queue lengths in cloud messaging services (using Amazon SQS, Azure Service Bus and Queue Storage, Google Cloud Tasks, and Kafka on Confluent Cloud).
- Error rates that are reported in cloud logs (such as AWS CloudWatch Logs, Azure Monitor Logs, and Google Cloud Logging).
- Interaction errors that you might encounter when your Java application interacts with cloud databases, storage, or other managed services.
- Cost optimization from unnecessary database resources, excessive API calls, or inefficient data processing.
How to Calculate MTTR
A mean is a type of average (like how a median is a different type of average). The mean is the typical average that you think of in math. For the mean time to reduction (MTTR), your goal is to reduce an issue to a target metric.
You would take the sum of the durations of all the events you’re calculating, and then you divide that by the total number of events during that period. You then work to reduce that target metric to increase performance in your Java application, and in the case of the cloud, you’ll also be looking to decrease your resource costs.
Azul Intelligence Cloud and MTTR
Azul Intelligence Cloud (IC) will help you with MTTR by providing you with the duration and instance data that you need to build out your different metrics. You’ll find several advantages in using IC.
IC and root-cause analysis. IC provides detailed information about your garbage collection activity, thread utilization, and method execution times which can aid in issue diagnosis and root cause analysis. Azul’s global tech support team can also provide application triage and root cause analysis based on an average industry experience of 20+ years per support engineer.
IC verifies your remediation. After you make your changes and deploy a performance fix to reduce the MTTR metric, IC can help you monitor your metric to quickly confirm that you achieved the issue has been resolved. This greatly reduces that time you spend verifying that your fix closed the loop on your incident or issue.
AVD – detects vulnerabilities in code that’s running in production to help reduce MTTI/MTTD compared to most tools that detect vulnerabilities in non-production code. This eliminates any verification step that you may have to go through to verify that the vulnerability found in non-prod is also present in production code, significantly reducing MTTI/MTTD.
CPUs – stabilized quarterly updates focused on delivered security fixes help reduce Meant Time To Resolution/Remediation by being ready to deploy. Compare CPUs to PSUs, which include hundreds of bug fixes, enhancements, new features, etc in addition to security fixes. If you’re relying on PSUs, you’ll need to wait for extensive testing to be performed before you can patch your applications, increasing MTTR.
Out of Cycle Patches – when zero-day vulnerabilities are discovered, if you don’t have access to out of cycle patches from a commercial support vendor, you’ll need to wait up to 3 months for a PSU, increasing MTTR. With out of cycle patches, you can resolve zero-day events far quicker, decreasing MTTR.
To learn more about how IC can help you achieve your MTTR goals and other key incident metrics, see Azul Intelligence Cloud and contact an Azul Java expert.
