Uncovering the Importance of Mean Time Between Failures
Though providers work to ensure system uptime, they are often challenged by unexpected technical issues that impact customer-facing systems. To stay ahead of unplanned outages, providers must establish incident management metrics, such as mean time between failures (MTBF), to calculate how long their applications will run before they experience issues.
To demonstrate how MTBF helps ASPs resolve critical outages before they impact customer experience, this post will examine the following topics:
- Defining Mean Time Between Failures (MTBF)
- Defining What Is an Application Failure
- Consequences of Application Failures
- Using MTBF and MTTR Metrics to Avoid Costly Breakdowns
- Improving Incident Response Management with Automated Alerting
Defining Mean Time Between Failures (MTBF)
MTBF is a critical component of incident response management as it measures the time between total hours of system uptime and the total quantity of issues that occur. MTBF calculations give engineers an estimation of when applications will fail and when maintenance is required. To successfully calculate MTBF, organizations must use the following formula:
MTBF = (Total uptime hours) / (number of failures)
For instance, an application has operated for 900 hours in a year and experienced five failures within the same period. In this case, the mean time between failures for the resource is 180 hours. Organizations must collect actual, precise data to make reliable MTBF calculations and ensure that customers are not impacted by future, possible failures.
Defining What Is an Application Failure
Providers must define what constitutes a failure when calculating MTBF for specific applications. An ASP that offers mission-critical systems, such as customer relationship management (CRM) software, cannot afford to experience even the shortest unplanned outage. CRM software is an integral, key part of sales and opportunity management for businesses.
Major resource failures are often linked to network outages, human errors, server issues and service usage spikes. ASPs must consider these factors when conducting a full MTBF analysis.
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Consequences of Application Failures
Critical IT infrastructure downtime can cost customers up to $100,000 per hour. This tarnishes customer-provider relationships, and dissatisfied customers will switch to other providers that have proven 99.99 percent service availability and reliability.
According to a recent study, most organizations expect mission-critical systems to have a maximum tolerable downtime of less than one hour. These expectations, combined with contractual service-level agreements (SLAs), create insurmountable pressure for ASPs to immediately repair application failures. To alleviate this pressure, providers must calculate MTBF and use the analysis to improve mean time to repair (MTTR) for different system breakdowns.
Using MTBF and MTTR Metrics to Avoid Costly Breakdowns
As previously mentioned, MTBF is a quick, surface-level analysis that estimates the average time between system breakdowns. Organizations can leverage this knowledge to plan preventive maintenance ahead of time and minimize MTTR.
Organizations can improve decision-making and efficiency when correcting low or high-priority failures. Low-priority incidents do not require complex, long-term fixes to ensure the availability of systems. There is a short amount of time required to resolve minor issues, such as when systems are running too slow. In contrast, high-priority incidents, such as complete system outages, directly impact customer operations and require robust, long-term fixes.
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Improving Incident Response Management With Automated Alerting
Automated alerting solutions are an integral component of incident response management. The systems integrate with IT observability software to provide real-time visibility into the status of resources, such as servers and websites, and orchestrate notifications when breakdowns are detected. By automating the incident response process, ASPs can ensure that failures are immediately resolved by DevOps teams and site reliability engineers (SREs).
Key capabilities of an automated alerting platform, such as OnPage, include the following:
- Digital on-call schedules: Schedule the on-call team and ensure that incident alerts are sent to the right person.
- Escalation policies and redundancies: If an alert cannot be handled by a particular member of the IT team, the notification will be forwarded to the next on-call engineer. Message redundancies ensure that alerts are sent to multiple channels when Wi-Fi is unavailable.
- Persistent, prioritized mobile alerts: Audible, intrusive, loud alerts continue until they are acknowledged by the right respondent. Priority alerting ensures that alerts are handled more appropriately and with the correct level of immediacy.
- Post-incident reports: Review the alerting process to see what went right and determine what can be improved for future outages.
- Scalable integrations: Integrate monitoring tools with an effective alerting solution that triggers immediate, real-time mobile notifications when incidents happen.
Conclusion
Application service providers must maximize system uptime and ensure that future, possible outages do not impact customers’ business operations. To achieve this, providers must calculate mean time between failures to gain predictive insights into low and high-priority incidents. This way, providers can implement effective, preventive measures to overcome different failures and improve customer experience (CX) in the process.
