In this paper, we propose an effortless way for disaggregating the CPU-memory couple, two of the most important resources in cloud computing. Instead of redesigning each resource board, the disaggregation is done at the power supply domain level. In other words, CPU and memory still share the same board, but their power supply domains are separated. Besides this disaggregation, we make the two following contributions: (1) the prototyping of a new ACPI sleep state (called zombie and noted Sz) which allows to suspend a server (thus save energy) while making its memory remotely accessible; and (2) the prototyping of a rack-level system software which allows the transparent utilization of the entire rack resources (avoiding resource waste). We experimentally evaluate the effectiveness of our solution and show that it can improve the energy efficiency of state-of-the-art consolidation techniques by up to 86%, with minimal additional complexity.

Federated Learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no energy or performance impact on mobile devices, and are therefore not suitable for applications that require frequent ( online ) model updates, such as news recommenders. This paper presents FLeet , the first Online FL system, acting as a middleware between the Android OS and the machine learning application. FLeet combines the privacy of Standard FL with the precision of online learning thanks to two core components: (i) I-Prof , a new lightweight profiler that predicts and controls the impact of learning tasks on mobile devices, and (ii) AdaSGD , a new adaptive learning algorithm that is resilient to delayed updates. Our extensive evaluation shows that Online FL, as implemented by FLeet , can deliver a 2.3 × quality boost compared to Standard FL, while only consuming 0.036% of the battery per day. I-Prof can accurately control the impact of learning tasks by improving the prediction accuracy by up to 3.6 × in terms of computation time, and by up to 19 × in terms of energy. AdaSGD outperforms alternative FL approaches by 18.4% in terms of convergence speed on heterogeneous data.

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract-This paper discusses the design and development efforts made to collect data using an opportunistic crowdsensing mobile application. Relevant issues are underlined, and solutions proposed within the CHIST-ERA Macaco project for the specifics of collecting fine-grained content and context data are highlighted. Global statistics on the data gathered for over a year of collection show its quality: Macaco data provides a longterm and fine-grained sampling of the user behavior and network usage that is relevant to model and analyse for future content and context-aware networking developments.

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