The Internet of Things (IoT) leads to an evergrowing presence of ubiquitous networked computing devices in public, business, and private spaces. These devices do not simply act as sensors, but feature computational, storage, and networking resources. Being located at the edge of the network, these resources can be exploited to execute IoT applications in a distributed manner. This concept is known as fog computing. While the theoretical foundations of fog computing are already established, there is a lack of resource provisioning approaches to enable the exploitation of fogbased computational resources. To resolve this shortcoming, we present a conceptual fog computing framework. Then, we model the service placement problem for IoT applications over fog resources as an optimization problem, which explicitly considers the heterogeneity of applications and resources in terms of Quality of Service attributes. Finally, we propose a genetic algorithm as a problem resolution heuristic and show, through experiments, that the service execution can achieve a reduction of network communication delays when the genetic algorithm is used, and a better utilization of fog resources when the exact optimization method is applied.

Benchmarking the performance of public cloud providers is a common research topic. Previous work has already extensively evaluated the performance of different cloud platforms for different use cases, and under different constraints and experiment setups. In this paper, we present a principled, large-scale literature review to collect and codify existing research regarding the predictability of performance in public Infrastructure-as-a-Service (IaaS) clouds. We formulate 15 hypotheses relating to the nature of performance variations in IaaS systems, to the factors of influence of performance variations, and how to compare different instance types. In a second step, we conduct extensive real-life experimentation on four cloud providers to empirically validate those hypotheses. We show that there are substantial differences between providers. Hardware heterogeneity is today less prevalent than reported in earlier research, while multi-tenancy indeed has a dramatic impact on performance and predictability, but only for some cloud providers. We were unable to discover a clear impact of the time of the day or the day of the week on cloud performance.

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The COVID-19 crisis influenced universities worldwide in early 2020. In Austria, all universities were closed in March 2020 as a preventive measure, and meetings with over 100 people were banned and a curfew was imposed. This development also had a massive impact on teaching, which in Austria takes place largely face-to-face. In this paper we would like to describe the situation of an Austrian university regarding e-learning before and during the first three weeks of the changeover of the teaching system, using the example of Graz University of Technology (TU Graz). The authors provide insights into the internal procedures, processes and decisions of their university and present figures on the changed usage behaviour of their students and teachers. As a theoretical reference, the article uses the e-learning readiness assessment according to Alshaher (2013), which provides a framework for describing the status of the situation regarding e-learning before the crisis. The paper concludes with a description of enablers, barriers and bottlenecks from the perspective of the members of the Educational Technology department.

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