Contemporary solutions for cloud-supported, edge-data analytics mostly apply analytics techniques in a rigid bottom-up approach, regardless of the data's origin. Typically, data are generated at the edge of the infrastructure and transmitted to the cloud, where traditional data analytics techniques are applied. Currently, developers are forced to resort to ad hoc solutions specifically tailored for the available infrastructure (for example, edge devices) when designing, developing, and operating the data analytics applications. Here, a novel approach implements cloud-supported, real-time data analytics in edge-computing applications. The authors introduce their serverless edge-data analytics platform and application model and discuss their main design requirements and challenges, based on real-life healthcare use case scenarios.

The ongoing transition towards a networked society requires reliable and secure network infrastructure and services. As networks evolve from simple point to point systems towards complex, software-defined, ultra-high capacity and reach, and distributed cloud environments, new security challenges emerge. The EU-funded RECODIS project aims at coordinating and fostering research collaboration in Europe on disaster resiliency in communication networks. One of the disaster types, considered by RECODIS Working Group (WG) 4, are deliberate human-made attacks aimed at gaining unauthorized access to the network or disrupting the service. In order to develop methods for increasing network security in the presence of attacks, it is crucial to first identify the security vulnerabilities and attack methods that exploit them, as well as the capabilities and shortcomings of existing security schemes. To this end, the members of RECODIS WG4 performed a comprehensive overview of attack methods and security approaches from the literature. This overview covers the security vulnerabilities inherent to the underlying physical layer, the implications of software-defined networking to security, and security challenges in cloud networks spanning geographically distributed data centers.

All e-Assessment systems have several challenges, such as accurate evaluation, security and data privacy, performance, scalability etc. In this paper we focus on e-Assessment scalability and performance. We propose a SOA architecture of a cloud hosted e-Assessment system which uses scalability and elasticity in order to achieve sustainable performance. Our solution consists of three subsystems, the first for management, the second for reports, and the third for on-demand activities during the assessments. It reduces the overall costs since it uses minimum resources utilized only during the e-Assessment. Better performance is expected to be achieved since the active subsystem for each assessment works with much smaller data compared to the centralized one.

Hardware-based courses in computer science studies require much effort from both students and teachers. The most important part of students' learning is attending in person and actively working on laboratory exercises on hardware equipment. This paper deals with a specific group of students, those who are marginalized by not being able to regularly attend lessons and perform the laboratory exercises. This group of “busy students” includes older students, and those who travel, are employed or are parents. In particular it describes how such students achieved impressive results in a hardware-based course by using Open Educational Resources (OER). These facilitated students' learning process and gave busy students the opportunity to organize their learning time so as to work on the hardware courses remotely from home when they had time and opportunity. The results of the evaluation show significant improvements, both in grade distribution and course throughput, after implementing the new Open Educational Resources approach.