MAIZX: A Carbon-Aware Framework for Optimizing Cloud Computing Emissions

• URL: http://arxiv.org/abs/2506.19972v1
• Date: Tue, 24 Jun 2025 19:40:09 GMT
• Title: MAIZX: A Carbon-Aware Framework for Optimizing Cloud Computing Emissions
• Authors: Federico Ruilova, Ernst Gunnar Gran, Sven-Arne Reinemo,
• Abstract summary: Cloud computing poses significant environmental challenges due to its high-energy consumption and carbon emissions.<n>Data centers account for 2-4% of global energy usage, and the ICT sector's share of electricity consumption is projected to reach 40% by 2040.<n>This study evaluates the MAIZX framework, designed to optimize cloud operations and reduce carbon footprint.
• Score: 0.7127829790714169
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cloud computing drives innovation but also poses significant environmental challenges due to its high-energy consumption and carbon emissions. Data centers account for 2-4% of global energy usage, and the ICT sector's share of electricity consumption is projected to reach 40% by 2040. As the goal of achieving net-zero emissions by 2050 becomes increasingly urgent, there is a growing need for more efficient and transparent solutions, particularly for private cloud infrastructures, which are utilized by 87% of organizations, despite the dominance of public-cloud systems. This study evaluates the MAIZX framework, designed to optimize cloud operations and reduce carbon footprint by dynamically ranking resources, including data centers, edge computing nodes, and multi-cloud environments, based on real-time and forecasted carbon intensity, Power Usage Effectiveness (PUE), and energy consumption. Leveraging a flexible ranking algorithm, MAIZX achieved an 85.68% reduction in CO2 emissions compared to baseline hypervisor operations. Tested across geographically distributed data centers, the framework demonstrates scalability and effectiveness, directly interfacing with hypervisors to optimize workloads in private, hybrid, and multi-cloud environments. MAIZX integrates real-time data on carbon intensity, power consumption, and carbon footprint, as well as forecasted values, into cloud management, providing a robust tool for enhancing climate performance potential while maintaining operational efficiency.

Related papers

• Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization [48.70916202664808]
  DiffCarl is a diffusion-modeled carbon- and risk-aware reinforcement learning algorithm for intelligent operation of multi-microgrid systems.<n>It outperforms classic algorithms and state-of-the-art DRL solutions, with 2.3-30.1% lower operational cost.<n>It also achieves 28.7% lower carbon emissions than those of its carbon-unaware variant and reduces performance variability.
  arXiv  Detail & Related papers  (2025-07-22T03:27:07Z)
• Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models [67.0243099823109]
  Generative AI (GAI) holds immense potential to reduce carbon emissions of Artificial Intelligence of Things (AIoT) In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT. We propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules.
  arXiv  Detail & Related papers  (2024-04-28T05:46:28Z)
• Carbon Footprint Reduction for Sustainable Data Centers in Real-Time [2.794742330785396]
  We propose a Data Center Carbon Footprint Reduction (DC-CFR) multi-agent Reinforcement Learning (MARL) framework to optimize data centers for the objectives of carbon footprint reduction, energy consumption, and energy cost.<n>The results show that the DC-CFR MARL agents effectively resolved the complex interdependencies in optimizing cooling, load shifting, and energy storage in real-time for various locations under real-world dynamic weather and grid carbon intensity conditions.
  arXiv  Detail & Related papers  (2024-03-21T02:59:56Z)
• Heuristics and Metaheuristics for Dynamic Management of Computing and Cooling Energy in Cloud Data Centers [0.0]
  We propose novel power and thermal-aware strategies and models to provide joint cooling and computing optimizations. Our results show that the combined awareness from both metaheuristic and best fit decreasing algorithms allow us to describe the global energy into faster and lighter optimization strategies. This approach allows us to improve the energy efficiency of the data center, considering both computing and cooling infrastructures, in up to a 21.74% while maintaining quality of service.
  arXiv  Detail & Related papers  (2023-12-17T09:40:36Z)
• Full Scaling Automation for Sustainable Development of Green Data Centers [13.448126025186538]
  The rapid rise in cloud computing has resulted in an alarming increase in data centers' carbon emissions.<n>Our proposed Full Scaling Automation (FSA) mechanism is an effective method of dynamically adapting resources to accommodate changing workloads.<n>FSA harnesses the power of deep representation learning to accurately predict the future workload of each service and automatically stabilize the corresponding target CPU usage level.
  arXiv  Detail & Related papers  (2023-05-01T08:11:00Z)
• Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach [48.18355658448509]
  Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption. Scheduling training jobs among geographically distributed cloud data centers unveils the opportunity to optimize the usage of computing capacity powered by inexpensive and low-carbon energy. We propose an algorithm based on multi-agent reinforcement learning and actor-critic methods to learn the optimal collaborative scheduling strategy through interacting with a cloud system built with real-life workload patterns, energy prices, and carbon intensities.
  arXiv  Detail & Related papers  (2023-04-17T02:12:30Z)
• Measuring the Carbon Intensity of AI in Cloud Instances [91.28501520271972]
  We provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions. We evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform.
  arXiv  Detail & Related papers  (2022-06-10T17:04:04Z)
• Performance and Energy-Aware Bi-objective Tasks Scheduling for Cloud Data Centers [0.0]
  Cloud computing enables remote execution of users tasks. The pervasive adoption of cloud computing in smart cities services and applications requires timely execution of tasks adhering to Quality of Services (QoS) The increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. We propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objectives.
  arXiv  Detail & Related papers  (2021-04-25T08:55:57Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning [68.37641996188133]
  We introduce a framework for tracking realtime energy consumption and carbon emissions. We create a leaderboard for energy efficient reinforcement learning algorithms. We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
  arXiv  Detail & Related papers  (2020-01-31T05:12:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.