Containers as a lightweight technology to virtualise applications have recently been successful, particularly to manage applications in the cloud. Often, the management of clusters of containers becomes essential and the orchestration of the construction and deployment becomes a central problem. This emerging topic has been taken up by researchers, but there is currently no secondary study to consolidate this research. We aim to identify, taxonomically classify and systematically compare the existing research body on containers and their orchestration and specifically the application of this technology in the cloud. We have conducted a systematic mapping study of 46 selected studies. We classified and compared the selected studies based on a characterisation framework. This results in a discussion of agreed and emerging concerns in the container orchestration space, positioning it within the cloud context, but also moving it closer to current concerns in cloud platforms, microservices and continuous development.

The employment of blockchain technology in electronic voting (e-voting) systems is attracting significant attention due to its ability to enhance transparency, security, and integrity in digital voting. This study presents an extensive review of the existing research on e-voting systems that rely on blockchain technology. The study investigates a range of key research concerns, including the benefits, challenges, and impacts of such systems, together with technologies and implementations, and an identification of future directions of research in this domain. We use a hybrid review approach, applying systematic literature review principles to select and categorize scientific papers and reviewing the technology used in these in terms of the above key concerns. In the 252 selected papers, aspects such as security, transparency, and decentralization are frequently emphasized as the main benefits. In contrast, although aspects like privacy, verifiability, efficiency, trustworthiness, and auditability receive significant attention, they are not the primary focus. We observed a relative lack of emphasis on aspects such as accessibility, compatibility, availability, and usability in the reviewed literature. These aspects, although acknowledged, are not as thoroughly discussed as the aforementioned key benefits in the proposed solutions for blockchain-based e-voting systems, whereas the considered studies have proposed well-structured solutions for blockchain-based e-voting systems focusing on how blockchain can strengthen security, transparency, and privacy, in particular, the crucial aspect of scalability needs attention.

A cloud is a distributed Internet-based software system providing resources as tiered services. Through service-orientation and virtualization for resource provisioning, cloud applications can be deployed and managed dynamically. We discuss the building blocks of an architectural style for cloud-based software systems. We capture style-defining architectural principles and patterns for control-theoretic, model-based architectures for cloud software. While service orientation is agreed on in the form of service-oriented architecture and microservices, challenges resulting from multi-tiered, distributed and heterogeneous cloud architectures cause uncertainty that has not been sufficiently addressed. We define principles and patterns needed for effective development and operation of adaptive cloud-native systems.

Cloud elasticity provides a software system with the ability to maintain optimal user experience by automatically acquiring and releasing resources, while paying only for what has been consumed. The mechanism for automatically adding or removing resources on the fly is referred to as auto-scaling. The state-of-the-practice with respect to auto-scaling involves specifying threshold-based rules to implement elasticity policies for cloud-based applications. However, there are several shortcomings regarding this approach. Firstly, the elasticity rules must be specified precisely by quantitative values, which requires deep knowledge and expertise. Furthermore, existing approaches do not explicitly deal with uncertainty in cloud-based software, where noise and unexpected events are common. This paper exploits fuzzy logic to enable qualitative specification of elasticity rules for cloud-based software. In addition, this paper discusses a control theoretical approach using type-2 fuzzy logic systems to reason about elasticity under uncertainties. We conduct several experiments to demonstrate that cloud-based software enhanced with such elasticity controller can robustly handle unexpected spikes in the workload and provide acceptable user experience. This translates into increased profit for the cloud application owner

Context: Software systems are increasingly required to operate in an open world, characterized by continuous changes in the environment and in the prescribed requirements. Architecture-centric software evolution (ACSE) is considered as an approach to support software adaptation at a controllable level of abstraction in order to survive in the uncertain environment. This requires evolution in system structure and behavior that can be modeled, analyzed and evolved in a formal fashion. Existing research and practices comprise a wide spectrum of evolution-centric approaches in terms of formalisms, methods, processes and frameworks to tackle ACSE as well as empirical studies to consolidate existing research. However, there is no unified framework providing systematic insight into classification and comparison of state-of-the-art in ACSE research. Objective: We present a taxonomic scheme for a classification and comparison of existing ACSE research approaches, leading to a reflection on areas of future research. Method: We performed a systematic literature review (SLR), resulting in 4138 papers searched and 60 peer-reviewed papers considered for data collection. We populated the taxonomic scheme based on a quantitative and qualitative extraction of data items from the included studies. Results: We identified five main classification categories: (i) type of evolution, (ii) type of specification, (iii) type of architectural reasoning, (iv) runtime issues, and (v) tool support. The selected studies are compared based on their claims and supporting evidences through the scheme. Conclusion: The classification scheme provides a critical view of different aspects to be considered when addressing specific ACSE problems. Besides, the consolidation of the ACSE evidences reflects current trends and the needs for future research directions.