## 1 Introduction
On the other hand, the composition of services with different requirements can also be enacted vertically in the hierarchy (from the network to the cloud through the edge), where a reconfiguration of services (and network, if necessary) is even more complex due to the heterogeneity of services in terms of computing needs and requirements (e.g., latency). Network topology is expected to constantly change along with device mobility and variable application requirements, introducing a more dynamic behavior in the system;
* **Data management and portability** at run time involves the design and deployment of policies, architectures and procedures, allowing the accurate management of the full data lifecycle, including strategies for placement and accessing it (e.g., measuring and quantifying the trade-off between placing data and services at the cloud or edge level, etc.) as well as performing the appropriate actions when data need to be ported to assure service continuity. Even if containers-based technologies can support and ease the portability of stateless components, complexity rises when addressing stateful components, where integrity needs to be maintained during the porting process while, at the same time, ensuring business continuity;
* Applying **federation concepts in the Cloud and in the Edge**: The pervasiveness of the technologies introduces a need to manage shared resources in a more intelligent, comprehensive manner that is less ad hoc. This has given rise to the concept of federation [4]. The Cloud Continuum will ultimately need some type of federation to manage how different sets of data producers (e.g., sensors, edge nodes) and data consumers (e.g., application components running on traditional cloud resources) can collaborate and share data. This will also imply the federation of the resources where these different elements are running;* **Deployment and governance** properties will take on additional dimensions when considering heterogeneous distributed environments. While sensors and IoT devices will typically not have any extra capacity for hosting the federation properties, edge nodes could host these functions, depending on their actual capacity and the number of sensors associated with them. Scalability is an issue as an edge node will have a finite capacity that will define how many IoT devices it can manage, and how it can manage the sharing of data with external data consumers;
* **Security Assurance in Cloud/Edge/IoT:** Security Teams and Operators in the Cloud Continuum are challenged today with the task of providing and consuming services in a secure way, but often lack the proper tools to quickly identify, choose and compose the most suitable set of technologies and platforms that offer the ideal trade-off between functionality and security or privacy guarantees. Additionally, they are in a scenario where data or services might be migrated between IoT, Edge and Cloud when security implications (security, privacy, physical tampering or legal policies) are relevant and need to be addressed. Due to the peculiar features of edge computing architectures (i.e., heterogeneity, distributed architecture, massive data processing, location-awareness and volatility), the traditional data security and privacy-preserving mechanisms in cloud computing are no longer suitable. In particular, data security and privacy-preserving in edge computing have to be lightweight, fine-grained and distributed [5]. Application developers and operators in the Cloud Continuum (CloudOps) face today the challenge of embracing the new paradigm but often lack the proper tools and mechanisms to configure, plan, prepare and execute these heterogeneous computational environments.