No abstract

Cloud Platform as a Service (PaaS) is a rapidly growing IT paradigm which enables software developers to deploy applications without the burden of software platform maintenance. Currently, the PaaS market is dominated by a few providers that promote incompatible standards. This introduces adoption barriers that prevent the interoperability between heterogeneous PaaS offerings, so software developers are not able to manage distributed applications spanning multiple public/private clouds. In this paper we present a multi-PaaS application management solution as a result of the Cloud4SOA European project that addresses these challenges. To clarify this approach a distributed deployment and cloud bursting scenarios are used.

The technological leap of smart technologies and the Internet of Things has advanced the conventional model of the electrical power and energy systems into a new digital era, widely known as the Smart Grid. The advent of Smart Grids provides multiple benefits, such as self-monitoring, self-healing and pervasive control. However, it also raises crucial cybersecurity and privacy concerns that can lead to devastating consequences, including cascading effects with other critical infrastructures or even fatal accidents. This paper introduces a novel architecture, which will increase the Smart Grid resiliency, taking full advantage of the Software-Defined Networking (SDN) technology. The proposed architecture called SDN-microSENSE architecture consists of three main tiers: (a) Risk assessment, (b) intrusion detection and correlation and (c) self-healing. The first tier is responsible for evaluating dynamically the risk level of each Smart Grid asset. The second tier undertakes to detect and correlate security events and, finally, the last tier mitigates the potential threats, ensuring in parallel the normal operation of the Smart Grid. It is noteworthy that all tiers of the SDN-microSENSE architecture interact with the SDN controller either for detecting or mitigating intrusions.

Surveillance systems that capture video and audio in enterprise facilities and public places produce massive amounts of data while operating at a 24/7 mode. There is an increasing need to process, on the fly, such huge video and audio data streams to enable a quick summary of "interesting" events that are happening during a specified time frame in a particular location. Concepts like fog computing based on localisation of data processing will relax the need of existing cloud-based solutions from extensive bandwidth and processing needs at remote cloud resources, however, the abilities of data processing on the extreme edge are limited by the hardware capabilities of the devices. In this paper, we describe a novel, adaptive architecture and that builds on top of a distributed computing paradigm and is ideal for smart surveillance systems that can utilize resources at cloud, fog and edge. We provide the main architectural components, the hardware options and key software components of the system. The proposed architecture uses cloud, edge and fog computing concepts. Edge computing is realized by a camera embedded system, cloud computing with the usage of public accessible infrastructure for data processing and fog computing for the processing and data fusion of video streams in small areas.

The rapid development of Smart Cities is aided by the convergence of information and communication technologies (ICT). Data is a key component of Smart City applications as well as a serious worry. Data is the critical factor that drives the whole development life-cycle in most Smart City use-cases, according to an exhaustive examination of several Smart City use-cases. Mishandling data, on the other hand, can have severe repercussions for programs that get incorrect data and users whose privacy may be compromised. As a result, we believe that an integrated ICT solution in Smart Cities is key to achieve the highest levels of scalability, data integrity, and secrecy within and across Smart Cities. As a result, this paper discusses a variety of modern technologies for Smart Cities and proposes our integrated architecture, which connects Blockchain technologies with modern data analytic techniques (e.g., Federated Learning) and Edge/Fog computing to address the current data privacy issues in Smart Cities. Finally, we discuss and present our proposed architectural framework in detail, taking into account an online marketing campaign and an e-Health application use-cases.