Jacopo Soldani

Sergio Moreschini, Shahrzad Pour, Ivan Lanese et al.

The use of AI in microservices (MSs) is an emerging field as indicated by a substantial number of surveys. However these surveys focus on a specific problem using specific AI techniques, therefore not fully capturing the growth of research and the rise and disappearance of trends. In our systematic mapping study, we take an exhaustive approach to reveal all possible connections between the use of AI techniques for improving any quality attribute (QA) of MSs during the DevOps phases. Our results include 16 research themes that connect to the intersection of particular QAs, AI domains and DevOps phases. Moreover by mapping identified future research challenges and relevant industry domains, we can show that many studies aim to deliver prototypes to be automated at a later stage, aiming at providing exploitable products in a number of key industry domains.

Jacopo Soldani, Antonio Brogi

The momentum gained by microservices and cloud-native software architecture pushed nowadays enterprise IT towards multi-service applications. The proliferation of services and service interactions within applications, often consisting of hundreds of interacting services, makes it harder to detect failures and to identify their possible root causes, which is on the other hand crucial to promptly recover and fix applications. Various techniques have been proposed to promptly detect failures based on their symptoms, viz., observing anomalous behaviour in one or more application services, as well as to analyse logs or monitored performance of such services to determine the possible root causes for observed anomalies. The objective of this survey is to provide a structured overview and a qualitative analysis of currently available techniques for anomaly detection and root cause analysis in modern multi-service applications. Some open challenges and research directions stemming out from the analysis are also discussed.

Francisco Ponce, Jacopo Soldani, Hernán Astudillo et al.

Context: Securing microservice-based applications is crucial, as many IT companies are delivering their businesses through microservices. If security smells affect microservice-based applications, they can possibly suffer from security leaks and need to be refactored to mitigate the effects of security smells therein. Objective: As the currently available knowledge on securing microservices is scattered across different pieces of white and grey literature, our objective here is to distill well-known smells for securing microservices, together with the refactorings enabling to mitigate the effects of such smells. Method: To capture the state of the art and practice in securing microservices, we conducted a multivocal review of the existing white and grey literature on the topic. We systematically analyzed 58 studies published from 2014 until the end of 2020. Results: Ten bad smells for securing microservices are identified, which we organized in a taxonomy, associating each smell with the security properties it may violate and the refactorings enabling to mitigate its effects. Conclusions: The security smells and the corresponding refactorings have pragmatic value for practitioners, who can exploit them in their daily work on securing microservices. They also serve as a starting point for researchers wishing to establish new research directions on securing microservices.

Vladimir Yussupov, Jacopo Soldani, Uwe Breitenbücher et al.

Function-as-a-Service (FaaS) is a cloud service model enabling developers to offload event-driven executable snippets of code. The execution and management of such functions becomes a FaaS provider's responsibility, hereby included their on-demand provisioning and automatic scaling. Key enablers for this cloud service model are FaaS platforms, e.g., AWS Lambda, Microsoft Azure Functions or OpenFaaS. At the same time, the choice of the most appropriate FaaS platform for deploying and running a serverless application is not trivial, as various organizational and technical aspects have to be taken into account. In this work, we present (i) a FaaS platform classification framework derived using a mixed method study and (ii) a systematic technology review of the ten most prominent FaaS platforms, based on the proposed classification framework. Moreover, we present (iii) a FaaS platform selection support system, called \faastener, which helps researchers and practitioners to choose the FaaS platform most suited for their requirements.