With AI getting pushed down the throats of every software out there, could microservices really benefit from AI?
As Artificial intelligence (AI) permeates every part of technology, companies aren't asking what we can integrate AI with, but rather what can't we integrate it with. Microservices, something we touched on a couple of years ago, haven't escaped this speculation, especially with, you guessed it, the emergence of ChatGPT. With everyone madly scrambling to incorporate AI into their application whether it needs it or not, sometimes you have to critically examine whether something really needs AI. Let's put microservices to the test!
Microservice architecture is a method of designing single-application software systems as a collection of services. It deviates from the traditional structure of "monolithic architecture" which has all aspects of the application tightly woven together. Monolithic applications can make it difficult to update or patch different functions without affecting other areas, while each component of microservice applications can be built, tested, and shipped separately. The transition from monolithic to microservice architecture can be a long and painful road, but it’s a vital part of digital transformation. It helps mitigate risk, promotes the transition from manual to automatic processes, and facilitates agile development. So, why does this type of application architecture need AI?
Even though microservices are independently deployed, there are a lot of moving parts to keep track of as a software increases in complexity. AI algorithms that support scalability by identifying areas that can be optimized can make it easier to grow. AI can also help you keep an eye on the status of all the different services with real-time insights into your data, helping you make faster decisions that are backed by accurate and relevant data.
Microservices and Machine Learning are a match made in heaven. One of the greatest appeals of microservices architecture is that you can patch one part without affecting the others (devs here will know that sometimes when you change code, it breaks a completely unrelated section, like when you make a minor content update and accidentally crash a vital system). But with so many moving parts, even when they're independent, there's a lot that needs monitoring.
Load prediction, well, predicts the load that could be placed on the network of systems so they can compensate before they're hit. The resources they requisition can range from network and the number of rules triggered to more customer support staff available only when they're needed.
The great thing about microservices is that you can tweak them twice a day and thrice on Friday. The not-so-great thing about that is that you have to test each release rigorously. Self-healing makes all that testing easier, taking test debugging out of the equation and focusing on testing major releases.
Proper automation can also help with the thorough testing required for each new deployment. Microservices mean that you only have to test that service instead of the entire application, but it can still take a significant amount of time and effort to test every single release. Test automation paired with AI can take the burden off of your testers. Write a test once — and quickly with Natural Language Programming — and trigger it to run with every release to cut down on the time your testers spend testing and free them up for their application analyses and exploratory testing. And wouldn't you know it, but Virtuoso meets all of these criteria and more. Book a demo to discover how we are the best solution for testing your microservices.
