In modern software development, microservices are crucial. Instead of a monolithic application, where everything is tightly coupled, microservice architecture offers a way to develop, deploy and maintain services independently. This enhances agility, resilience, and scalability, but it also introduces challenges concerning for example resource allocation and performance optimization.
In his doctoral thesis in Computer Science, Michel Gokan Khan addresses such challenges in large scale microservice chains, specifically in cloud native computing. In our conversation, he explains some of the key contributions of his research. Two of them being the PerfSim, a tool designed by Michel that is a performance simulator for cloud native system, and NFV-Inspector, another tool designed by him to be able to profile and analyse microservices specifically in network functions virtualization (NFV) environments. Michel’s research is also able to show how machine learning can be used for optimizing microservice chains in cloud environments. By contributions such as these and more, Michel’s research helps to reveal the true potential of artificial intelligence in profiling, modelling, simulating, and most importantly optimizing the performance and cost of running microservice chains.
Michel Gokan Khan’s doctoral thesis can be downloaded from DiVA: Unchaining Microservice Chains: Machine Learning Driven Optimization in Cloud Native Systems
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