In recent years, a plethora of deployment technologies evolved, many following a declarative approach to automate the delivery of software components. Even if such technologies share the same purpose, they differ in features and supported mechanisms. Thus, it is difficult to compare and select deployment automation technologies as well as to migrate from one technology to another. Hence, we present a systematic review of declarative deployment technologies and introduce the essential deployment metamodel (EDMM) by extracting the essential parts that are supported by all these technologies. Thereby, the EDMM enables a common understanding of declarative deployment models by facilitating the comparison, selection, and migration of technologies. Moreover, it provides a technology-independent baseline for further deployment automation research.

Function-as-a-Service (FaaS) is a novel cloud service model allowing to develop fine-grained, provider-managed cloud applications. In this work, we investigate which challenges motivate researchers to introduce or enhance FaaS platforms and tools. We use a systematic mapping study method to collect and analyze the relevant scientific literature, which helps us answering the three clearly-defined research questions. We design our study using well-established guidelines and systematically apply it to 62 selected publications. The collected and synthesized data provides useful insights into the main challenges that motivate researchers to work on this topic and can be helpful in identifying research gaps for future research.

The introduction of smart contracts has expanded the applicability of blockchains to many domains beyond finance and cryptocurrencies. Moreover, different blockchain technologies have evolved that target special requirements. As a result, in practice, often a combination of different blockchain systems is required to achieve an overall goal. However, due to the heterogeneity of blockchain protocols, the execution of distributed business transactions that span several blockchains leads to multiple interoperability and integration challenges. Therefore, in this article, we examine the domain of Cross-Chain Smart Contract Invocations (CCSCIs), which are distributed transactions that involve the invocation of smart contracts hosted on two or more blockchain systems. We conduct a systematic multi-vocal literature review to get an overview of the available CCSCI approaches. We select 20 formal literature studies and 13 high-quality gray literature studies, extract data from them, and analyze it to derive the CCSCI Classification Framework. With the help of the framework, we group the approaches into two categories and eight subcategories. The approaches differ in multiple characteristics, e.g., the mechanisms they follow, and the capabilities and transaction processing semantics they offer. Our analysis indicates that all approaches suffer from obstacles that complicate real-world adoption, such as the low support for handling heterogeneity and the need for trusted third parties.

The serverless computing paradigm brings multiple benefits to application developers who are interested in consuming computing resources as services without the need to manage physical capacities or limits. There are several deployment technologies and languages available suitable for deploying applications to a single cloud provider. However, for multi-cloud application deployments, multiple technologies have to be used and orchestrated. In addition, the event-driven nature of serverless computing imposes further requirements on modeling such application structures in order to automate their deployment. In this paper, we tackle these issues by introducing an event-driven deployment modeling approach using the standard Topology and Orchestration Specification for Cloud Applications (TOSCA) that fully employs the suggested standard lifecycle to provision and manage multi-cloud serverless applications. To show the feasibility of our approach, we extended the existing TOSCA-based ecosystem OpenTOSCA.