Continuous Deployment of Trustworthy Smart IoT Systems.

While the next generation of IoT systems need to perform distributed processing and coordinated behaviour across IoT, Edge and Cloud infrastructures, their development and operation are still challenging. A major challenge is the high heterogeneity of their infrastructure, which broadens the surface for security attacks and increases the complexity of maintaining and evolving such complex systems. In this paper, we present our approach for Generation and Deployment of Smart IoT Systems (GeneSIS) to tame this complexity. GeneSIS leverages model-driven engineering to support the DevSecOps of Smart IoT Systems (SIS). More precisely, GeneSIS includes: (i) a domain specific modelling language to specify the deployment of SIS over IoT, Edge and Cloud infrastructure with the necessary concepts for security and privacy; and (ii) a [email protected] engine to enact the orchestration, deployment, and adaptation of these SIS. The results from our smart building case study have shown that GeneSIS can support security by design from the development (via deployment) to the operation of IoT systems and back again in a DevSecOps loop. In other words, GeneSIS enables IoT systems to keep up security and adapt to evolving conditions and threats while maintaining their trustworthiness.The research leading to these results has received funding from the European Commission’s H2020 Programme under grant agreement numbers 780351 (ENACT)

Orchestration in the Cloud-to-Things Compute Continuum: Taxonomy, Survey and Future Directions

IoT systems are becoming an essential part of our environment. Smart cities, smart manufacturing, augmented reality, and self-driving cars are just some examples of the wide range of domains, where the applicability of such systems has been increasing rapidly. These IoT use cases often require simultaneous access to geographically distributed arrays of sensors, and heterogeneous remote, local as well as multi-cloud computational resources. This gives birth to the extended Cloud-to-Things computing paradigm. The emergence of this new paradigm raised the quintessential need to extend the orchestration requirements i.e., the automated deployment and run-time management) of applications from the centralised cloud-only environment to the entire spectrum of resources in the Cloud-to-Things continuum. In order to cope with this requirement, in the last few years, there has been a lot of attention to the development of orchestration systems in both industry and academic environments. This paper is an attempt to gather the research conducted in the orchestration for the Cloud-to-Things continuum landscape and to propose a detailed taxonomy, which is then used to critically review the landscape of existing research work. We finally discuss the key challenges that require further attention and also present a conceptual framework based on the conducted analysis.Comment: Journal of Cloud Computing Pages: 2

A Case Study of Edge Computing Implementations: Multi-access Edge Computing, Fog Computing and Cloudlet

With the explosive growth of intelligent and mobile devices, the current centralized cloud computing paradigm is encountering difficult challenges. Since the primary requirements have shifted towards implementing real-time response and supporting context awareness and mobility, there is an urgent need to bring resources and functions of centralized clouds to the edge of networks, which has led to the emergence of the edge computing paradigm. Edge computing increases the responsibilities of network edges by hosting computation and services, therefore enhancing performances and improving quality of experience (QoE). Fog computing, multi-access edge computing (MEC), and cloudlet are three typical and promising implementations of edge computing. Fog computing aims to build a system that enables cloud-to-thing service connectivity and works in concert with clouds, MEC is seen as a key technology of the fifth generation (5G) system, and Cloudlet is a micro-data center deployed in close proximity. In terms of deployment scenarios, Fog computing focuses on the Internet of Things (IoT), MEC mainly provides mobile RAN application solutions for 5G systems, and cloudlet offloads computing power at the network edge. In this paper, we present a comprehensive case study on these three edge computing implementations, including their architectures, differences, and their respective application scenario in IoT, 5G wireless systems, and smart edge. We discuss the requirements, benefits, and mechanisms of typical co-deployment cases for each paradigm and identify challenges and future directions in edge computing

The Computing Fleet: Managing Microservices-based Applications on the Computing Continuum

In this paper we propose the concept of "Computing Fleet" as an abstract entity representing groups of heterogeneous, distributed, and dynamic infrastructure elements across the Computing Continuum (covering the Edge- Fog-Cloud computing paradigms). In the process of using fleets, stakeholders obtain the virtual resources from the fleet, deploy software applications to the fleet, and control the data flow, without worrying about what devices are used in the fleet, how they are connected, and when they may join and exit the fleet. We propose a three-layer reference architecture for the Computing Fleet capturing key elements for designing and operating fleets. We discuss key aspects related to the management of microservices-based applications on the Computing Fleet and propose an approach for deployment and orchestration of microservices-based applications on fleets. Furthermore, we present a software prototype as a preliminary evaluation of the Computing Fleet concept in a concrete Cloud- Edge scenario related to remote patients monitoring.acceptedVersio

Model-based Continuous Deployment of SIS

This chapter is organized as follows. Section 4.2 provides an overview of the current state of the art and of the practice for the automatic deployment of SIS. Section 4.3 introduces our solutions for the automatic deployment of SIS, first describing how they can be integrated in order to form a coherent deployment bundle and then detailing each our two enablers: GENESIS and DivENACT. Section 4.4 focus on the support offered by our solutions to ensure the trustworthiness deployment of SIS. Finally, Section 4.5 draws some conclusions.publishedVersio