Microservices is a popular architectural style for the development of distributed software, with an emphasis on modularity, scalability, and flexibility. Indeed, in microservice systems, functionalities are provided by loosely coupled, small services, each focusing on a specific business capability. Building a system according to the microservices architectural style brings a number of challenges, mainly related to how the different microservices are deployed and coordinated and how they interact. In this paper, we provide a survey about how techniques in the area of Artificial Intelligence have been used to tackle these challenges.

Artificial intelligence is one of the technologies with the most transformative potential in business. According to research by McKinsey, 70 per cent of companies are likely to have adopted at least one form of AI by 2030. This will contribute to an additional $13tr of global economic activity. Machine learning – a subset of artificial intelligence – enables machines to get better at executing tasks without human intervention, by finding patterns in data, and learning from their experience. It's no surprise, therefore, that there has been an explosion in the number of machine learning companies worldwide.

The challenge to analyze earthquake signals with optimum precision grows along with the amount of available seismic data. At the Karlsruhe Institute of Technology (KIT), researchers have deployed a neural network to determine the arrival-time of seismic waves and thus precisely locate the epicenter of the earthquake. In their report in the Seismological Research Letters journal, they point out that Artificial Intelligence is able to evaluate the data with the same precision as an experienced seismologist. For precisely locating an earthquake event, it is critical to determine the exact arrival-time of the majority of seismic waves at the seismometer station (the so-called phase arrival). Without this knowledge, further accurate seismological evaluations are not possible.