The REliable CApacity Provisioning and enhanced remediation for distributed cloud applications (RECAP) project aims to advance cloud and edge computing technology, to develop mechanisms for reliable capacity provisioning, and to make application placement, infrastructure management, and capacity provisioning autonomous, predictable and optimized. This paper presents the RECAP vision for an integrated edge-cloud architecture, discusses the scientific foundation of the project, and outlines plans for toolsets for continuous data collection, application performance modeling, application and component auto-scaling and remediation, and deployment optimization. The paper also presents four use cases from complementing fields that will be used to showcase the advancements of RECAP.

The database landscape has significantly evolved over the last decade as cloud computing enables to run distributed databases on virtually unlimited cloud resources. Hence, the already non-trivial task of selecting and deploying a distributed database system becomes more challenging. Database evaluation frameworks aim at easing this task by guiding the database selection and deployment decision. The evaluation of databases has evolved as well by moving the evaluation focus from performance to distribution aspects such as scalability and elasticity. This paper presents a cloud-centric analysis of distributed database evaluation frameworks based on evaluation tiers and framework requirements. It analysis eight well adopted evaluation frameworks. The results point out that the evaluation tiers performance, scalability, elasticity and consistency are well supported, in contrast to resource selection and availability. Further, the analysed frameworks do not support cloud-centric requirements but support classic evaluation requirements.

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Cloud computing offers a flexible pay-as-you-go model for provisioning application resources, which enables applications to scale on-demand based on the current workload. In many cases, though, users face the single vendor lock-in effect, missing opportunities for optimal and adaptive application deployment across multiple clouds. Several cloud modelling languages have been developed to support multi-cloud resource management, but still they lack holistic cloud management of all aspects and phases. This work defines the Cloud Application Modelling and Execution Language (CAMEL), which (i) allows users to specify the full set of design time aspects for multi-cloud applications, and (ii) supports the models@runtime paradigm that enables capturing an application's current state facilitating its adaptive provisioning. CAMEL has been already used in many projects, domains and use cases due to its wide coverage of cloud management features. Finally, CAMEL has been positively evaluated in this work in terms of its usability and applicability in several domains (e.g., data farming, flight scheduling, financial services) based on the technology acceptance model (TAM).

The age of cloud computing has introduced all the mechanisms needed to elastically scale distributed, cloudenabled applications. At roughly the same time, NoSQL databases have been proclaimed as the scalable alternative to relational databases. Since then, NoSQL databases are a core component of many large-scale distributed applications. This paper evaluates the scalability and elasticity features of the three widely used NoSQL database systems Couchbase, Cassandra and MongoDB under various workloads and settings using throughput and latency as metrics. The numbers show that the three database systems have dramatically different baselines with respect to both metrics and also behave unexpected when scaling out. For instance, while Couchbase's throughput increases by 17% when scaled out from 1 to 4 nodes, MongoDB's throughput decreases by more than 50%. These surprising results show that not all tested NoSQL databases do scale as expected and even worse, in some cases scaling harms performances.

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