ENERGY AND CARBON AWARE Sample Clauses

ENERGY AND CARBON AWARE. The objectives of this workstream can be summarized in the following: • Define an energy- and carbon-aware computation model that can shift loads both in time and geography. • Devise cost-effective infrastructure optimisations for industrial environments. • Use Artificial Intelligence and Machine Learning methods for performance prediction and enhancement. This workstream will create an energy- and carbon-aware computing model within the FLUIDOS framework. In addition to presenting its own resources, each FLUIDOS domain will communicate the current carbon intensity of its electricity (measured in grams of CO2 per kilowatt-hour), which varies across locations due to specific electricity mixes and fluctuates hourly at each location. This model goes beyond the inherent energy-saving features of FLUIDOS, providing the capability to optimize energy consumption and carbon emissions in edge computing. FLUIDOS will utilize this model to perform load shifting both in time and geography, capitalizing on nodes close to energy sources or powered by a significant share of renewable energy. The task will design the energy- and carbon- aware model, outlining its main functional elements and additional technical specifications, including functionalities and interfaces. In tandem with the focus on energy and carbon efficiency, this task aims to reduce infrastructure costs. These savings will arise from the inherent resource and energy efficiency of FLUIDOS and additional efficiency measures from the previous task. For example, some nodes may employ less powerful CPUs and offload tasks to nearby nodes, resulting in overall energy and cost reduction. While there is often synergy between OPEX cost savings and energy/carbon savings, depending on electricity prices, conflicts may arise. To address these potentially conflicting goals, the energy- and carbon-aware computing model of FLUIDOS will enable setting fine-granular priorities and reflecting desired trade-offs between the two when relevant. Furthermore, the work package will delve into the training of artificial intelligence and machine learning models for two purposes. Firstly, to learn from past loads and predict the energy demand of future edge computing tasks. Secondly, to compare and learn from strategies used by other nodes for achieving better individual and overall energy efficiency. The outcomes of the energy demand prediction task will contribute to the model from the previously described tasks, which communicat...
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