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The focus of research into 5G networks to date has been largely on the required advances in network architectures, technologies, and infrastructures. Less effort has been put on the applications and services that will make use of and exploit the flexibility of 5G networks built upon the concept of software-defined networking (SDN) and network function virtualization (NFV). Media-based applications are amongst the most demanding services, requiring large bandwidths for high Manuscript

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.

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The on-line processing of high volume data streams is a prerequisite for many modern applications relying on real-time data such as global sensor networks or multimedia streaming. In order to achieve efficient data processing and scalability w.r.t. the number of distributed data sources and applications, in-network processing of data streams in an overlay network of data processing operators has been proposed. For such stream processing overlay networks, the placement of operators onto physical hosts plays an important role for the resulting quality of service-in particular, the endto-end latency-and network load. To this end, we present an enhanced placement algorithm that minimizes the network load put onto the system by a stream processing task under userdefined delay constraints in this paper. Our algorithm finds first the optimal solution in terms of network load and then degrades this solution to find a constrained optimum. In order to reduce the overhead of the placement algorithm, we included mechanisms to reduce the search space in terms of hosts that are considered during operator placement. Our evaluations show that this approach leads to an operator placement of high quality solution while inducing communication overhead proportional only to a small percentage of the total hosts.