Jun 17, 2021. By Aleena Mathew
The emergence of the digital era increased the competition among IT organizations. IT team started developing new/latest applications, which consisted of new technology and IT architecture. All of these applications were a must for IT organizations to lay their foot strong. Apart from the IT team, the demand kept increasing from the user side, where fulfilling customer requests became a high priority. To meet these dynamic changes of end-user, applications needed to be stable, reliable, scalable, and should be available without any runtime issues or crashes. Ensuring this availability with traditional and legacy applications is challenging. As legacy applications were not able to meet the above requirements. Thats where applications were developed with the use of modern technologies. But even though with the upcome of modern applications there was a lot of challenges. Most of the applications were deployed as microservices, and these were hybrid, complex, and distributed in nature. There were thousands plus transactions and API calls among the application component. This became a tough job of IT operators to spot out issues in these calls and resolve them. This requires a lot of time and operational cost to backtrack and check the issues and ensure high application availability. This required a much more advanced mechanism where automated ensuring of application availability can be ensured. Artificial intelligence is the right solution here.
AIOps enabled Application Availability Check:
The emergence of AIOps is widespread among every IT organization. The implementation of AIOps enabled IT organizations to automate most of the IT operations which were done manually. This enabled in saving up time and also operational cost. With that, let's see how AIOps enables in ensuring application availability without the need for manual intervention. As mentioned before, most applications are developed as distributed microservices and this requires a lot of API calls and transitions which are needed for successful communication. These API calls created a lot of chaos when interaction among applications increase and IT operators were not able to properly monitor. That's API monitoring came into the picture. API Monitoring refers to the practice of monitoring Application Programming Interfaces, most commonly in production, to gain visibility into performance, availability, and functional correctness. API monitoring is a key factor to gain visibility into application insight. Monitoring API calls will enable understanding if any application failure has occurred. Therefore, continuous monitoring of API calls is a must. This enables, IT operators to keep track of the transaction calls among hybrid applications. With the implementation of AIOps, API monitoring became simple. The AI-based models were capable to automatically keep track and proactively alert the IT team if any application faced downtime. Application availability is now completely automated with the use of AIOps, where the AI-bases system automaticaly analyzes every application's present irrespective of their underlying infrastructure. With AI-based observability in place, deployed AI models will proactively capture unknown problems where manual effort is not much required. This will save IT operators time as well as the end-user side availability is also ensured. Identifying the issues in availability won't alone solve our problem. We need an intelligent mechanism that is capable of automatically remediating the problem ensuring the 24/7 availability of an application. That's where AI-based auto-remediation takes place.
AI-enabled Auto-remediation for Continuous Application Availability
We have seen how AI-based observability helps in continuous application availability by API monitoring and proactively identify events and anomalies. Now the need is for having an automated mechanism for auto-remediating the issues that occurred and AI-based auto-remediation is the answer. With AI-based auto-remediation, there will be automated workflows created. These workflows will be automatically triggered when an event/anomaly occurs, and in our case, when an application availability is affected. When an event occurs due to application failure, automatically workflow is initiated in such a way the application is made to restart or creating a load balancer in which another application instance is made to start. AI-based models extensively work here to bring the auto-remediation process into action. With Auto-remediation, the IT team does not need to monitor every API-calls and check the availability of the application. This method will continually ensure that application availability is not affected on the end-user side.
AIOps Usecase Example of Continuous Application Availability:
Let's see a real scenario in which AIOps monitor application availability. There are many applications up and running which support end-users and IT team. Moreover, there are scenarios in which some applications may crash, continuously log errors or not work due to several reasons. Identifying these issues and resolving them manually requires a lot of time and effort. By the time the issues are identified, it might be too late and the issues may cause a bigger problem and great loss. AIOps enables the complete automation of application performance monitoring. Every application present in the system will continuously be monitored and checks for any application crashes. The AI-based models will check the log file for continuous error logging. High API calls at a single point in time can cause the application to crash due to an overload of requests. The AI-based systems will automatically trigger the crash and perform an auto-remediation to start another instance of the application, in such a way end-user side is not affected.
To learn more about Algomox AIOps, please visit our AIOps Platform Page.