FIWARE represents a new European Cloud platform that aims to land on the international ICT market bringing prominent novel advantages for societies. In fact, it provides new compelling and novel software components, available through APIs, able to give developers new valuable Cloud platform functionalities. The main contribution of this work consists in providing software architects an useful experience regarding the adoption of FIWARE for the design of a Cloud and Internet of Things (IoT) architecture. More specifically, we describe how can be possible to use the FIWARE Cloud platform to speed up the design of a real e-health Remote Patient Monitoring (RPM) architecture with an agile software development methodology. Our architecture aims to allow care givers to improve remote assistance to patients at home, optimizing the management of the workflow of doctors, physicians, medical assistants, and other involved hospital operators. In this paper, we specifically describe the main FIWARE components that we have adopted to design our architecture and how they have been integrated.

The development of IoT (Internet of Things) applications poses a number of scientific and technological challenges which stem from the characteristics of the IoT domain itself. These include the huge and increasing number of connected entities (devices, physical objects, people,...) populating the physical environment, the variety of their types which leads to heterogeneity of the data produced or consumed by those connected entities. In this paper we argue that semantic modeling takes up many of these challenges and explain how the core platform developed by the FI-WARE project supports IoT application developers. After a short introduction of the characteristics and requirements of IoT applications we identify the contribution of semantic technologies to address some of them. We describe the FI-WARE platform enablers which support these technologies and illustrate through a real application how these enablers help developers satisfy these requirements.

Most Infrastructure as a Service clouds present limited capabilities to control how a service behaves at runtime, once it has been deployed, beyond basic low-level scalability rules for VMs. Higher-level approaches fail to provide mechanisms for a fine grained level of control of the service at runtime, being only focused on scaling. These scalability rules are based on an ad hoc ``grammar'' that is not expressive enough to reflect other desired control mechanisms at runtime (e.g. reconfigurations, dynamic changes in the rules or in the components of the application, retiering, etc.). Here, we present an analysis on different alternatives for supporting such features. The Rule Interchange Format (RIF) emerges as a likely candidate to support the required flexibility and so it is proved in a typical use case. Also, a preliminary implementation of a mapping mechanisms is offered to parse RIF rules to widespread rule engines such as Drools and Jess.

Cloud computing [23] is a promising paradigm for the provisioning of IT services. Cloud computing infrastructures, such as those offered by the RESERVOIR project, aim to facilitate the deployment, management and execution of services across multiple physical locations in a seamless manner. In order for service providers to meet their quality of service objectives, it is important to examine how software architectures can be described to take full advantage of the capabilities introduced by such platforms. When dealing with software systems involving numerous loosely coupled components, architectural constraints need to be made explicit to ensure continuous operation when allocating and migrating services from one host in the Cloud to another. In addition, the need for optimising resources and minimising over-provisioning requires service providers to control the dynamic adjustment of capacity throughout the entire service lifecycle. We discuss the implications for software architecture definitions of distributed applications that are to be deployed on Clouds. In particular, we identify novel primitives to support service elasticity, co-location and other requirements, propose language abstractions for these primitives and define their behavioural semantics precisely by establishing constraints on the relationship between architecture definitions and Cloud management infrastructures using a model denotational approach in order to derive appropriate service management cycles.

Currently, virtualization is a proven technology that potentially provides a great opportunity for industry growth and research, due to its advantages in flexibility and cost reduction. However, designing virtual network environments is a complex process that requires great effort. For this, the work proposed in this paper is focused on applying modeling techniques to characterize virtual network environments. Firstly, we have analyzed the existing approaches to model virtualized infrastructures. Based on this analysis, we have designed a generic model to characterize and manage virtual network environments based on the CIM Schema. To asses the feasibility of our approach, we have implemented a CIM client based on this model, which enables to deploy virtual network environments automatically, independently of the used underlying virtualization platform. The test results using this client demonstrated the efficiency of this implementation, which was evaluated with Xen and VMware Server.

Testbeds are controlled experimentation platforms where solutions (software, architectures, etc.) can be developed, deployed and tested in an environment that resembles real utilization conditions. This paper describes a model-driven methodology for automatic testbed reconfiguration which solves the problems of manual interaction and inter-testbed scenario reutilization. It is based in a high-level testbed-independent model of the desired scenario (so it can be applied to any testbed in general) which is particularized to testbed-specific scenario models for automatic deployment and management by model-based tools. The paper also details our practical experiences applying the methodology to two quite different use cases: VNUML-based virtual testbeds and the GMPLS-enabled optical network of ADRENALINE testbed® (each one with its own model-based automatic deployment tools), thus assessing the feasibility and generality of the proposed approach.