Microservice Management with AIOps.

The adoption of the digital era lit like fire among every IT organization. There were excellent advantages with this adoption as well as a set of challenges were also faced. One of the concerns faced was with the managing of applications and services. Monolithic application architecture was no longer applicable in the digital era, that is, having a single-tiered application wherein the user interface and data access code resides in one platform. That's where the concept of microservice architecture came into the picture. The microservice architecture breaks down the application into independent components. All of these components work as separate units and perform application-specific tasks. The implementation of microservice brought a potential advantage to the IT organization.
But the happiness of these advantages only persisted to some extent. As tossing a coin has two win-win options, implementation of microservice has the same. Along with benefits come a set of disadvantages. The practical possibility of successfully implementing microservice architecture was low. By dividing an application into small stand-alone services, each performing a single function and communicating over the network increased the complexity. These bought in challenges such as load balancing issues, network communication of services. Apart from that, monitoring microservice became a chaotic task. With the division of an application into independent units, the need for monitoring became extensive. As the number of services to monitor multi-folded, it was difficult to derive proper inference from these components as several alerts kept on generating. There were so many moving components, and the deployment and integration of the service/applications were challenging across multiple platforms. There was high resource utilization which was hard to be recognized manually. With all these challenges, it was difficult for the successful implementation of microservices. There was a need for a much-advanced mechanism to observe and monitor the root cause. Artificial intelligence is the right choice.
Applying AI-based Observability for Solving Monitoring Challenges:
AI in IT Operations(AIOps) is the new normal now, and AI-based observability is one such area of implementation of AIOps. AI-based observability helped in completely automating the process of monitoring the microservice architecture. This method of implementation helped in automatically and intelligently identifying out issues that occurred in the system. That is, observability helped in pointing out the precise problems from a large volume of data. In this way, the IT operators were intelligently alerted to the problems that were occurred in the system. A great advantage was that every microservice monitoring was made possible from one single platform. This method helped to reduce operational cost as the need for the utilization of multiple tools was avoided. Moreover, with AI-based models for observability, unknown issues or anomalies were easily identified from the system. The models automatically identified the issues and pro-actively altered the IT operators.
We have seen how AI-based observability help in automatically identifying unknown issues and intelligently alerting the IT operators. Now we need a mechanism where the system is capable enough to heal/remediate problems on its own automatically. 
Introducing AI in Microservice Management :
AI-based observability was able to identify and point out all the unknown issues from the system. And all of these problems needed a solution where the system auto-remediates them, and that's where AI in microservice management works. The AI-based system helps in automating the orchestration of microservice management. In the above paragraph, we have mentioned the basic challenges that were faced by the implementation of microservice. With the upcome of AI and AI-based observability in microservice management, AI-based systems could target out the real issue and then perform an auto-remediation phase. The implementation of AIOps helped in troubleshooting the unknown issues for the services/components. Let's see some of the areas where AI helped microservice management. 
Fixing Availability Issues
One challenge faced by the microservice architecture was in the availability of service/components. As the application was divided into independent service/components, it was difficult to identify each service's availability. This is used to create chaos in the system. AI-based models were capable enough to predict and identify the availability of resources. This availability identification helped automate the request/quires that needed to be executed to the available components. In this way, the chaos in the system was reduced to a great extent.
Load prediction
Understanding the load on the system and acting on the load unbalance issues is another capability of the AI-based system.  This will allow systems to provide more resources before they're needed. AI-based models are highly intelligent enough to capture the needs and predict if there will be a load increase situation by monitoring multiple KPI, and in case of the situation, new resources will be added up. 
Resource Optimization
With the adoption of microservice management, several unwanted resource utilization came into the picture. These resources just added up the operational cost and did not benefit much on the architecture side. AI-based models were capable enough to identify these under-utilized resources and free them up. The ability to plan using smart resource management could save operational costs while maintaining a stable and robust service. AI-based models ensured this.
Security
Security is a primary concern in any IT organization, and with the implementation of microservice, the tension is more. Leveraging the capabilities of AI will help here. AI helps in discovering attack patterns based on known behaviors. AI enforcement at the network and database level can improve security actions.
The future of application architecture is solely to be governed by the implementation of microservices, and then those architectures will be controlled by AI. The complexity of microservice is just increasing at a drastic rate, and to keep up with this, the need and use of AI is a must.
To learn more about Algomox AIOps, please visit our AIOps Platform Page.