Over the time, the type of applications has evolved from batch, compute or memory intensive applications to streaming or even interactive applications. As a result, applications are getting more complex and become long-running. Such applications might require frequent-access to multiple distributed data sources. During application deployment and provisioning, the user can face various issues such as (i) where to effectively place both the data and the computation; (ii) how to achieve required objectives while reducing the overall application running cost. Data could be generated from various sources, including a multitude of devices over IoT environments that can generate a huge amount of data, while the applications are running. An application can further produce a large amount of data. In general, data of such size is usually referred to as Big Data. In general, Big Data is characterised by five properties [1, 2]. These are volume, velocity (means rapid update and propagation of data), variety

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The emergence of fog and edge computing has complemented cloud computing in the design of pervasive, computing-intensive applications. The proximity of fog resources to data sources has contributed to minimizing network operating expenditure and has permitted latency-aware processing. Furthermore, novel approaches such as serverless computing change the structure of applications and challenge the monopoly of traditional Virtual Machine (VM)-based applications. However, the efforts directed to the modeling of cloud applications have not yet evolved to exploit these breakthroughs and handle the whole application lifecycle efficiently. In this work, we present a set of Topology and Orchestration Specification for Cloud Applications (TOSCA) extensions to model applications relying on any combination of the aforementioned technologies. Our approach features a design-time “type-level” flavor and a run time “instance-level” flavor. The introduction of semantic enhancements and the use of two TOSCA flavors enables the optimization of a candidate topology before its deployment. The optimization modeling is achieved using a set of constraints, requirements, and criteria independent from the underlying hosting infrastructure (i.e., clouds, multi-clouds, edge devices). Furthermore, we discuss the advantages of such an approach in comparison to other notable cloud application deployment approaches and provide directions for future research.