Periodic Reporting for period 1 - LIGHTAIDGE (Light-weight, emissions aware, simulation and orchestration of Edge Computing and Edge Intelligence)
Periodo di rendicontazione: 2023-05-01 al 2025-04-30
The annual growth of the global energy consumption of digital technologies is 9%, hindering the EU Green Deal objective of reducing 55% greenhouse gas (GHG) emission reduction by 2030. With the ever-increasing deployment of Internet of Things (IoT) devices, Edge Computing (EC), and more specifically Edge Intelligence (EI) – which seeks to exploit these IoT (Edge) devices to process Artificial Intelligence algorithms – has risen as a technology with booming demand potential, but which can also negatively contribute to the global energy consumption and GHG emissions of digital technologies. Regarding EC and EI, emissions-aware (in CO2 equivalent) simulation and orchestration solutions are still under-explored. The LIGHTAIDGE project therefore focuses on light-weight, CO2 emissions-aware EI simulation and orchestration. It proposes significant advances by (i) creating a bridge between High-Performance Computing (HPC) and EC communities through the development of a novel, fast and scalable, CO2 emissions aware simulation framework for EC, and (ii) by producing light-weight, CO2 emissions aware Edge Intelligence orchestrators for low-CO2 EI model training. Foreseen impacts are, at scientific level: the project will establish a bridge between HPC and EC/EI scientific communities, and will pave the path to future, CO2 emissions aware EC and EI research. At technological, economical and societal levels: the project will reduce R&D costs by enabling an economically viable EC and EI prototyping through simulations, will help to drive EI companies in the climate transition by reducing the EI's CO2 emissions through better orchestration, and will contribute to reduce the CO2 emissions due to digital technologies, participating in the European Union Green Deal's objective. The project also proposes training, transfer of knowledge, and dissemination/communication activities for the researcher, constituting a solid path to develop his skills and experience.
The project started with an evaluation of the existing data (e.g. traces, datasets) to perform CO2 aware simulation of computing platforms. None of the existing data directly reports CO2 emissions, but some traces report energy related data. As such, simulations have to rely on energy related data as a proxy for estimating the emissions. Therefore, the work can be divided into two aspects: (1) how to exploit energy related data in computing platforms simulation, as a prerequisite to perform CO2 aware simulation, and (2) how to predict the energy consumption of computing applications, as a prerequisite to give more realistic orchestration scenarios (objective 2).
The project resulted in a new method for managing computing platforms (schedulers) that adapt to the available power (electricity) and deal with the power constraints as lightweight and simple as possible. Such schedulers may be used for managing computing platforms (data-centers, supercomputers), so any data-center wishing to adapt to variations in the power availability may benefit from the results of this project.