Application and software development has become an integral part of enterprises all over the world. Numerous technologies and architectural patterns have emerged over the years and have made its impact on making software development and maintenance easier than before. One such architectural pattern very much used in enterprises these days is the Microservice architecture.
Microservice architecture, in general, is the breaking down of components for software into individual services. These individual services can work as standalone functions and at the same time, can work together with the support of APIs. By dividing it into such services, the microservice structure makes scaling easier and development faster than before.
While Microservices provide ease and flexibility in maintaining and scaling, it has its limitations. One of the major concerns of microservice architecture is the network communication and load balancing. Proper management of multiple services requires deep structures, dependency graphs and much more. It becomes difficult to handle and manage the independence of microservice architecture, especially when you’re working on a smaller team scale.
The future of Microservice architecture
Adding to the present scenario, AI is taking over the microservice architecture and thus making the management of the multiple services easier for humans. It helps in monitoring and managing the tasks and thereby making it possible to tackles all challenges.
Prediction of load: One of the major concerns when working with a microservice structure is the management and understanding of the loads. With advanced AI technology, resources can be delivered before they are needed. With the help of AI, it is possible to handle multiple tasks on multiple networks.
Advanced security: With the help of Artificial Intelligence it is easier to understand patterns and behaviors at the network and database level. This not just improves the security, but also keeps track of the actions and tasks performed by authorized people.
Anomaly Detection: Being subjected to constant change and updates, it is always necessary for the microservice structure to keep constant monitoring of the behavior pattern of the services. With the help of AI, detection can be handled more easily in the microservice structure. AI can help the system identify whether services are reacting differently over time and also can analyze the reason behind the variation. It can identify bugs based on studying the clusters of microservices having continuous issues.
Planning: With the constant upgradations of various services of the software, it is important to be able to accommodate this in the cloud at the time of need. Planning for resources can be cost-effective and can also assure stable services.