Building a High Uptime Strategy Using DevOps

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Most organizations that adopt DevOps do so to improve collaboration between teams, so everyone works towards the goal of developing (and delivering) high-quality software to end-users. 

However, many organizations also embrace DevOps to build systems with high uptime. These are primarily organizations that use mission-critical applications and need extremely high availability and uptime so drive business outcomes. 

Why uptime is crucial for businesses

Despite all the advances in technology, outages are a common phenomenon across industries. While a few seconds of downtime is usually not a problem, several instances of prolonged downtime can wreak havoc for businesses – especially those that require 24×7 availability of the mission-critical applications. Such unplanned downtime can not only lead to substantial costs, it can also severely impact customer experience, business reputation, and market position.

Take the example of a healthcare institution. Doctors and nurses need to constantly be able to access stored patient records – including medical history, prescribed medication, lab results, dietary restrictions, allergy information and more – to provide the right quality of care. Even the slightest technical disruption or sluggish performance can lead to delayed treatments – which not only impacts the business of healthcare institutions but also puts patients’ lives at risk. The same can be said about the financial trading sector, where organizations need round-the-clock availability and uptime of trading systems – especially when volumes swell and volatility spikes. 

What uptime means in the DevOps context

Although there is no foolproof way for companies to prevent outages, embracing DevOps can greatly improve uptime. DevOps can not only help detect and manage planned (and unplanned) downtimes, but it can also help teams build a robust backup and disaster recovery strategy while enabling them to carry out end-to-end application performance management. 

By strengthening the incident management process, teams can enable redundancy, minimize alert noise, and rollback bad releases – before they impact customer experience. 

Uptime in the DevOps context has a lot to do with determining what measurements and thresholds for uptime are sufficient for the company. By finalizing metrics to quantify and laying down a process to measure and monitor them across the DevOps lifecycle, teams can monitor (and maintain) uptime and take preventive actions to reduce the frequency of failures as well as the time between two failures. 

Metrics also allow teams to implement tools to reduce coding issues and thus bring the time to repair or resolve issues – while greatly bringing down error rates. They are a great way to track quality problems, performance, and uptime-related issues, and ensure deployments do not cause outages or major issues for users. 

Uptime is a valuable metric that can enable teams to understand the availability of their service or application which is key to sustaining customer satisfaction. It also indicates how quickly teams can respond to issues and resolve them – without affecting application performance or availability. If teams can quantify the amount of planned + unplanned downtime, they can take steps to proactively deal with issues and ensure a 99.95% or equivalent SLAs. That said, here are some business metrics correlated to uptime that DevOps teams can capture to understand how often incidents occur and how quickly they can respond to and resolve those incidents to maintain uptime: 

  • MTTF or mean time to failure can help measure the amount of time the software or application works as intended – before a failure occurs. It can be calculated by adding up the total operating time of the product or application and dividing it by the number of failures.
  • MTBF or mean time between failure can help DevOps teams calculate the time between two successive failures – so the right steps can be taken to resolve them. It is calculated by taking data from a specific period of time and dividing total operational time by the number of failures.
  • MTTA or mean time to acknowledge is the average time it takes for teams to begin working on an issue – after an alert has been triggered. MTTA can be calculated by adding up the time between alert and acknowledgement and dividing the sum by the number of incidents. 
  • MTTR or mean time to repair/resolve can help calculate the time required to resolve issues and improve the uptime of applications.  To calculate this metric, teams need to add up the full resolution time during a specified period of time and divide it by the total number of incidents.

Considerations to design a high uptime implementation strategy

DevOps teams trying to achieve high uptime often end up spending an immense amount of time and cost – which tends to delay time-to-market. Therefore, while designing processes to ensure high uptime, teams should learn to find the right balance between quality and cost in a way that best meets their needs. 

Most failures that DevOps teams experience are because the underlying infrastructure is unable to scale that causes the application to crash. Integrated Infrastructure as code (Category 2 and 3 – DevOps) is a great approach to overcome failures caused due to infrastructure limitations. Such an approach allows team members to write code to create and manage the infrastructure as well as control changes using updated code.

Here are some considerations to keep in mind while designing a high uptime implementation strategy: 

  • Preventing errors before they occur: One of the first steps in building a high uptime strategy is to validate code, so errors in production can be prevented. Such validation, when done early in the development lifecycle using automated testing techniques. These techniques can help teams minimize MTBF. It can also help save testing time while constantly increasing the efficiency of code. 
  • Ensuring quick detection through continuous monitoring: Another critical aspect of any uptime strategy is to set the foundation of continuous monitoring. When done correctly, continuous monitoring can help DevOps teams check if the application is alive and functioning well and track vitals at operating system layer like CPU usage, memory usage, cache memory etc. It can also help teams quickly detect anomalies and issues, allowing them to take timely remediation steps. 
  • Being highly responsive to issues: It is also important for DevOps teams to set alerts, so they can improve their responsiveness to issues. With automated real-time insights into when errors occur and the impact they have on uptime, teams can understand how their service is performing while being highly responsive to issues, thus minimizing MTTR. Integrated and automated knowledge-based articles on DevOps issues can help support teams reach resolution faster. For instance, using Confluence knowledge base, organizations can harness teams’ collective knowledge into easy-to-find answers for everyone and save time in planning tasks or resolving issues. 

Take the right steps 

For several industries, high uptime of systems and applications can mean the difference between success and failure. Although modern-day code is extremely complex and fragile, it is critical for certain industries to ensure code works as intended – without causing any downtime or unavailability issues. 

Since even a few seconds of downtime can have a far-reaching impact on reputation and revenue, embracing DevOps is a sure-shot way of enhancing (and ensuring) high uptime of applications. Using DevOps, teams can quantify an array of uptime metrics and take the right steps to improve uptime.