Given nowadays increasing complexity of software systems, their architectural design prior to their actual implementation becomes quite complicated.

In today’s ever-changing technological landscape, companies are constantly looking for innovative ways to increase efficiency and minimize costs, and even more nowadays with the advent of artificial intelligence and large language models.

The feature extraction capabilities of Machine Learning (ML) models have led to their wide adoption in a large variety of sectors: from anomaly detection for machinery, to user clustering and behavioral prediction, market trends predictions, or the analysis of text, sound, and image data.

Artificial intelligence helps coordinate precision agriculture tasks

In the world of finance, where every decision can have significant ramifications, the possibility of predicting market movements is invaluable. Traditionally, analysts have relied on a combination of data analysis, market trends, and expert insights to make informed predictions.

As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land.

Due to its multivariate and multipurpose use and reuse, data’s worth is dramatically increasing, leading to an era characterized by the generation of data marketplaces towards accessing, selling, sharing, and trading data and data assets.

The Cloud offers enhanced flexibility in the management of resources while it promises the reduction of its cost as well as its infinite scalability. In this way, due to these advantages, there is a recent move towards migrating business processes (BPs) in the Cloud.

Industry and academia shift from the single cloud provider paradigm to cloud federations and alternative models, which orchestrate heterogeneous resources, such as Mobile Edge Computing and Fog Computing.

With the development of new system solutions that integrate traditional cloud computing with the edge/fog computing paradigm, dynamic optimization of service execution has become a challenge due to the edge computing resources being more distributed and dynamic.