Hybrid Elastic ARM&Cloud HPC Collaborative Platform for generic tasks

Compute-heavy workloads are currently run on Hybrid HPC structures using x86 CPUs and GPUs from Intel, AMD, or NVidia, which have extremely high energy and financial costs. However, thanks to the incredible progress made on CPUs and GPUs based on the ARM architecture and their ubiquity in today’s mobile devices, it’s possible to conceive of a low-cost solution for our world’s data processing needs. Every year ARM-based mobile devices become more powerful, efficient, and come in ever smaller packages with ever growing storage. At the same time, smartphones waste these capabilities at night while they’re charging. This represents billions of idle devices whose processing power is not being utilized. For that reason, the objective of this paper is to evaluate and develop a hybrid, distributed, scalable, and redundant platform that allows for the utilization of these idle devices through a cloud-based administration service. The system would allow for massive improvements in terms of efficiency and cost for com-pute-heavy workload. During the evaluation phase, we were able to establish savings in power and cost significant enough to justify exploring it as a serious alternative to traditional computing architectures.Instituto de Investigación en Informátic

Distributed Resource Allocation in Cloud Computing Using Multi-Agent Systems

The Infrastructure-as-a-Service model of cloud computing allocates resources in the form of virtual machines that can be resized and live migrated at runtime. This paper presents a novel distributed resource allocation approach for VM consolidation relying on multi-agent systems. Our approach uses a utility function based on host CPU utilization to drive live migration actions. Experimental results show reduced service level agreement violations and a better overall performance compared to a centralized approach and a threshold-based distributed approach

Hohe Fehlwahrnehmungen zu wichtigen politischen Themen in der Bevölkerung

Anhand von groß angelegten Umfragen in vier europäischen Ländern untersuchen wir die Determinanten von Fehlwahrnehmungen bei politischen Themen, die insbesondere bei links- und rechtspopulistischen Parteien typischerweise hoch auf der Agenda stehen. Unsere Ergebnisse zeigen, dass die Menschen den Anteil von Einwander*innen und Muslim*innen überschätzen. Auch der Prozentsatz der Menschen unter der Armutsgrenze und der Einkommensanteil der Reichsten werden deutlich zu hoch eingeschätzt. Weibliche, einkommensschwache und weniger gebildete Befragte haben höhere Fehlwahrnehmungen. Außerdem beleuchten wir, wie die COVID-19-Pandemie verzerrte Wahrnehmungen beeinflusst hat: insbesondere rechte Fehlwahrnehmungen hinsichtlich des Anteils der Muslim*innen wurden leicht verstärkt

Misperceptions and Fake News During the COVID-19 Pandemic

By conducting large-scale surveys in four European countries, we investigate the determinants of right- and left-wing misperceptions as well as fake news exposure and sharing. We also shed light on how the COVID-19 pandemic influenced both misperceptions and fake news. Our results indicate that people substantially overestimate the share of immigrants, Muslims, people under the poverty line, and the income share of the richest. Female, lower-income, and lower-educated respondents have higher misperceptions, whereas the higher-educated, male, married, right-wing and, younger respondents share fake news more often, both intentionally and unintentionally. The COVID-19 pandemic increased fake news sharing and amplified right-wing misperceptions

Hohe Fehlwahrnehmungen zu wichtigen politischen Themen in der Bevölkerung

Anhand von groß angelegten Umfragen in vier europäischen Ländern untersuchen wir die Determinanten von Fehlwahrnehmungen bei politischen Themen, die insbesondere bei links- und rechtspopulistischen Parteien typischerweise hoch auf der Agenda stehen. Unsere Ergebnisse zeigen, dass die Menschen den Anteil von Einwander*innen und Muslim*innen überschätzen. Auch der Prozentsatz der Menschen unter der Armutsgrenze und der Einkommensanteil der Reichsten werden deutlich zu hoch eingeschätzt. Weibliche, einkommensschwache und weniger gebildete Befragte haben höhere Fehlwahrnehmungen. Außerdem beleuchten wir, wie die COVID-19-Pandemie verzerrte Wahrnehmungen beeinflusst hat: insbesondere rechte Fehlwahrnehmungen hinsichtlich des Anteils der Muslim*innen wurden leicht verstärkt

An architecture for resource management in a fog-to-cloud framework

Fog-to-cloud (F2C) platforms provide an excellent framework for the efficient resource management in the context of smart cities. In such a scenario, a vast number of heterogeneous resources, including computing devices and IoT sensors, are considered in coordination to provide the best facilities. One of the most critical and challenging tasks in this framework is appropriately managing the set of resources available in the smart city. Many devices with different features should be efficiently classified, organized, and selected, to fulfill the requirements during services execution. In this paper, we present the design of an architecture for resource management as part of a core module in an F2C system. In this architecture, we classify both, the system resources and services and, based on the users’ preferences and sharing policies; we discuss the process of resource selection according to a predefined cost model. The cost model could consider any cost dimension, such as performance, energy consumption, or any eventual business model associated with the F2C system.Peer ReviewedPostprint (published version