Structural coupling for microservices

Cloud-native Applications are “distributed, elastic and horizontal-scalable systems composed of (micro) services which isolates states in a minimum of stateful components”. Hence, an important property is to ensure a low coupling and a high cohesion among the (micro)services composing the cloud-native application. Loosely coupled and highly cohesive services allow development teams to work in parallel, reducing the communication overhead between teams. However, despite both practitioners and researchers agreement on the importance of this general property, there are no validated metrics to effectively measure or test the actual coupling level between services. In this work, we propose ways to compute and to visualize the coupling between microservices, this by extending and adapting the concepts behind the computation of the traditional structural coupling. We validate these measures with a case study involving 17 open source projects and we provide an automatic approach to measure them. The results of this study highlight how these metrics provide to practitioners a quantitative and visual views of services compositions, which can be useful to conceive advanced systems to monitor the services evolution

Anomaly Detection in Cloud-Native systems

In recent years, microservices have gained popularity due to their benefits such as increased maintainability and scalability of the system. The microservice architectural pattern was adopted for the development of a large scale system which is commonly deployed on public and private clouds, and therefore the aim is to ensure that it always maintains an optimal level of performance. Consequently, the system is monitored by collecting different metrics including performancerelated metrics. The first part of this thesis focuses on the creation of a dataset of realistic time series with anomalies at deterministic locations. This dataset addresses the lack of labeled data for training of supervised models and the absence of publicly available data, in fact the data are not usually shared due to privacy concerns. The second part consists of an empirical study on the detection of anomalies occurring in the different services that compose the system. Specifically, the aim is to understand if it is possible to predict the anomalies in order to perform actions before system failures or performance degradation. Consequently, eight different classification-based Machine Learning algorithms were compared by collecting accuracy, training time and testing time, to figure out which technique might be most suitable for reducing system overload. The results showed that there are strong correlations between metrics and that it is possible to predict the anomalies in the system with approximately 90% of accuracy. The most important outcome is that performance-related anomalies can be detected by monitoring a limited number of metrics collected at runtime with a short training time. Future work includes the adoption of prediction-based approaches and the development of some tools for the prediction of anomalies in cloud native environments

A Decomposition and Metric-Based Evaluation Framework for Microservices

Migrating from monolithic systems into microservice is a very complex task. Companies are commonly decomposing the monolithic system manually, analyzing dependencies of the monolith and then assessing different decomposition options. The goal of our work is two-folded: 1) we provide a microservice measurement framework to objectively evaluate and compare the quality of microservices-based systems; 2) we propose a decomposition system based on business process mining. The microservice measurement framework can be applied independently from the decomposition process adopted, but is also useful to continuously evaluate the architectural evolution of a system. Results show that the decomposition framework helps companies to easily identify the different decomposition options. The measurement framework can help to decrease the subjectivity of the decision between different decomposition options and to evaluate architectural erosion in existing systems.acceptedVersionPeer reviewe