WG2020

Arthur Chevalier: Optimization of software license placement in the Cloud for economical and efficient deployment

Arthur Chevalier
November 17th 2020, 14:30–15:30

Title: Optimization of software license placement in the Cloud for economical and efficient deployment

Abstract: Today, the use of software is generally regulated by licenses, whether they are free, paid for and with or without access to their sources. The world of licensing is very vast and poorly understood. Often we only know the version most widely used by the general public (a software purchase is equal to a license). The reality is much more complex, especially for large publishers. In this presentation I will present the impact and importance of managing these licenses when using software in a cloud architecture. I will show a case study to demonstrate the impact of dynamic license management and the need to propose new ways to manage and optimize software assets.

Titre: Optimisation du placement des licences logicielles dans le Cloud pour un déploiement économique et efficient

Résumé: Aujourd’hui, l’utilisation des logiciels est généralement réglementée par des licences, qu’elles soient gratuites, payantes et avec ou sans accès à leurs sources. L’univers des licences est très vaste et mal connu. Souvent on ne connaît que la version la plus répandue au grand public (un achat de logiciel est égale à une licence). La réalité est bien plus complexe surtout chez les grands éditeurs. Dans cette présentation je présenterai l’impact et l’importance de la gestion de ces licences lors de l’utilisation de logiciels dans une architecture Cloud. Je montrerai un cas d’étude pour prouver l’impact de la gestion dynamique des licences et la nécessité de proposer de nouvelles façons de gérer et optimiser un patrimoine logiciel.

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WG Avalon : Analysis of energy consumption in a precision beekeeping system

Hugo Hadjur (joint work with Laurent Lefevre and Doreid Ammar (Aivancity group))
October 13, 2020 – 2.30pm – 3.30pm

https://dl.acm.org/doi/10.1145/3410992.3411010
Abstract:
Honey bees have been domesticated by humans for several thousand years and mainly provide honey and pollination, which is fundamental for plant reproduction. Nowadays, the work of beekeepers is constrained by external factors that stress their production (parasites and pesticides among others). Taking care of large numbers of beehives is time-consuming, so integrating sensors to track their status can drastically simplify the work of beekeepers. Precision beekeeping complements beekeepers’ work thanks to the Internet of Things (IoT) technology. If used correctly, data can help to make the right diagnosis for honey bees colony, increase honey production and decrease bee mortality. Providing enough energy for on-hive and in-hive sensors is a challenge. Some solutions rely on energy harvesting, others target usage of large batteries. Either way, it is mandatory to analyze the energy usage of embedded equipment in order to design an energy efficient and autonomous bee monitoring system. This paper relies on a fully autonomous IoT framework that collects environmental and image data of a beehive. It consists of a data collecting node (environmental data sensors, camera, Raspberry Pi and Arduino) and a solar energy supplying node. Supported services are analyzed task by task from an energy profiling and efficiency standpoint, in order to identify the highly pressured areas of the framework. This first step will guide our goal of designing a sustainable precision beekeeping system, both technically and energy-wise.