Wed Jul 10, 2024

GreenAnalyzer: A framework for Geo-distRibuted Edge-cloud ENergy consumption ANALYsis towards Zero Emission Rates

Geo-distributed data centers operate continuously, requiring substantial electrical energy and contributing to global carbon emissions. In 2022, the electricity consumption of data centers was estimated to be between 240-340 TWh, accounting for approximately 1-1.3% of global electricity demand. This consumption leads to significant operational costs and environmental impact, with around 330 million metric tons of CO2 emissions in 2020. Driven by the dual pressures of operational expenses and climate change, both large and small data center operators are increasingly focusing on providing carbon-neutral services. This shift is further motivated by initiatives such as the European Green Deal and the UK net-zero strategy.

GreenAnalyzer is a framework provided by the University of Cyprus's Laboratory for Internet Computing (LInC) to address the pressing challenges of energy consumption and carbon emissions in geo-distributed edge data centers (DCs). Recognizing the significant energy demands and environmental impact of data centers, GreenAnalyzer aims to enhance the sustainability of these operations through advanced modeling and predictive analytics. By integrating various web APIs with state-of-the-art machine learning (ML) and artificial intelligence (AI) techniques, GreenAnalyzer focuses on providing detailed insights into the energy needs of edge compute nodes and the forecast energy output from renewable energy sources (RES) like photovoltaic (PV) systems. These predictive capabilities enable data center operators to optimize their energy use, minimize carbon emissions, and reduce operational costs, addressing the need for improved energy efficiency.

Core Functionalities:

  • Energy Consumption Modeling: GreenAnalyzer utilizes pre-trained models to predict the energy consumption of various components within a data center, including individual processes, servers, and racks. These models are based on detailed workload utilization characteristics, allowing for accurate predictions and efficient energy management.

  • Energy Production Forecasting: The framework employs a hybrid science-guided AI approach, combining traditional scientific models with advanced ML techniques to forecast energy production from renewable sources, particularly PV systems. This method ensures accurate and reliable predictions by accounting for various influencing factors such as weather conditions and temporal variations.

  • Integration with External APIs: GreenAnalyzer enriches its predictive models with real-time data from publicly available APIs, including weather conditions, energy costs, and carbon emissions. This integration ensures that the framework can provide comprehensive and up-to-date insights into the energy dynamics of data centers.

  • RESTful API: To facilitate seamless integration with other systems, GreenAnalyzer offers a RESTful API that allows external applications, such as cloud schedulers and dashboards, to access its predictions and analyses. This API enables data center operators to make informed decisions based on accurate and timely data.

  • Open Source Commitment: GreenAnalyzer is developed as an open-source project, with its codebase, models, and datasets made publicly available. This commitment to open-source principles promotes transparency, collaboration, and further research in sustainable data center operations.

GreenAnalyzer represents a significant step forward in the quest for sustainable data center operations, offering a comprehensive, data-driven approach to energy management that benefits both the environment and the economy.

Keywords: Energy Modeling, Green Data Centers, Machine Learning, Renewable Energy, Carbon Emissions, Edge Computing


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