EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systems

• URL: http://arxiv.org/abs/2404.11411v1
• Date: Wed, 17 Apr 2024 14:12:47 GMT
• Title: EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systems
• Authors: Meghana Tedla, Shubham Kulkarni, Karthik Vaidhyanathan,
• Abstract summary: Self-adaptation techniques, recognized for their potential in energy savings within software systems, have yet to be extensively explored in Machine Learning-Enabled Systems. This research underscores the feasibility of enhancing MLS sustainability through intelligent runtime adaptations.
• Score: 1.0923877073891446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The sustainability of Machine Learning-Enabled Systems (MLS), particularly with regard to energy efficiency, is an important challenge in their development and deployment. Self-adaptation techniques, recognized for their potential in energy savings within software systems, have yet to be extensively explored in Machine Learning-Enabled Systems (MLS), where runtime uncertainties can significantly impact model performance and energy consumption. This variability, alongside the fluctuating energy demands of ML models during operation, necessitates a dynamic approach. Addressing these challenges, we introduce EcoMLS approach, which leverages the Machine Learning Model Balancer concept to enhance the sustainability of MLS through runtime ML model switching. By adapting to monitored runtime conditions, EcoMLS optimally balances energy consumption with model confidence, demonstrating a significant advancement towards sustainable, energy-efficient machine learning solutions. Through an object detection exemplar, we illustrate the application of EcoMLS, showcasing its ability to reduce energy consumption while maintaining high model accuracy throughout its use. This research underscores the feasibility of enhancing MLS sustainability through intelligent runtime adaptations, contributing a valuable perspective to the ongoing discourse on energy-efficient machine learning.

Related papers

• Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights [2.1249213103048414]
  The rapid adoption of large language models (LLMs) has led to significant energy consumption and carbon emissions. This paper explores the integration of energy-efficient optimization techniques in the deployment of LLMs to address these concerns.
  arXiv  Detail & Related papers  (2025-04-07T21:56:59Z)
• Systematic Weight Evaluation for Pruning Large Language Models: Enhancing Performance and Sustainability [1.542607498220242]
  This research focuses on the systematic evaluation of individual weight importance throughout the training process. We propose a method that effectively reduces model size without compromising performance. These findings highlight the critical need for optimized AI models to ensure sustainable development.
  arXiv  Detail & Related papers  (2025-02-24T11:34:49Z)
• DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs [70.91804882618243]
  This paper proposes DSMoE, a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge. Experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches.
  arXiv  Detail & Related papers  (2025-02-18T02:37:26Z)
• Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles [52.43886862287498]
  This paper explores the integration of large language models (LLMs) into energy management. We propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization. We present a case study to demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles.
  arXiv  Detail & Related papers  (2025-01-26T14:31:03Z)
• Addressing the sustainable AI trilemma: a case study on LLM agents and RAG [7.6212949300713015]
  Large language models (LLMs) have demonstrated significant capabilities, but their widespread deployment and more advanced applications raise critical sustainability challenges. We propose the concept of the Sustainable AI Trilemma, highlighting the tensions between AI capability, digital equity, and environmental sustainability.
  arXiv  Detail & Related papers  (2025-01-14T17:21:16Z)
• Impact of ML Optimization Tactics on Greener Pre-Trained ML Models [46.78148962732881]
  This study aims to (i) analyze image classification datasets and pre-trained models, (ii) improve inference efficiency by comparing optimized and non-optimized models, and (iii) assess the economic impact of the optimizations. We conduct a controlled experiment to evaluate the impact of various PyTorch optimization techniques (dynamic quantization, torch.compile, local pruning, and global pruning) to 42 Hugging Face models for image classification. Dynamic quantization demonstrates significant reductions in inference time and energy consumption, making it highly suitable for large-scale systems.
  arXiv  Detail & Related papers  (2024-09-19T16:23:03Z)
• Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inference [2.553456266022126]
  Machine learning (ML) has seen tremendous advancements, but its environmental footprint remains a concern. Acknowledging the growing environmental impact of ML this paper investigates Green ML.
  arXiv  Detail & Related papers  (2024-06-20T13:59:34Z)
• GAISSALabel: A tool for energy labeling of ML models [1.5899411215927992]
  This paper introduces GAISSALabel, a web-based tool designed to evaluate and label the energy efficiency of Machine Learning models. The tool's adaptability allows for customization in the proposed labeling system, ensuring its relevance in the rapidly evolving ML field.
  arXiv  Detail & Related papers  (2024-01-30T16:31:48Z)
• Power Hungry Processing: Watts Driving the Cost of AI Deployment? [74.19749699665216]
  generative, multi-purpose AI systems promise a unified approach to building machine learning (ML) models into technology. This ambition of generality'' comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit. We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models. We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions
  arXiv  Detail & Related papers  (2023-11-28T15:09:36Z)
• Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI [9.918392710009774]
  We argue that efficiency alone is not enough to make ML as a technology environmentally sustainable. We present and argue for systems thinking as a viable path towards improving the environmental sustainability of ML holistically.
  arXiv  Detail & Related papers  (2023-09-05T09:07:24Z)
• Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching [1.2277343096128712]
  We propose the concept of a Machine Learning Model Balancer, focusing on managing uncertainties related to ML models by using multiple models. AdaMLS is a novel self-adaptation approach that leverages this concept and extends the traditional MAPE-K loop for continuous MLS adaptation. Preliminary results suggest AdaMLS surpasses naive and single state-of-the-art models in guarantees.
  arXiv  Detail & Related papers  (2023-08-19T09:33:51Z)
• Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation [82.85015548989223]
  Pentathlon is a benchmark for holistic and realistic evaluation of model efficiency. Pentathlon focuses on inference, which accounts for a majority of the compute in a model's lifecycle. It incorporates a suite of metrics that target different aspects of efficiency, including latency, throughput, memory overhead, and energy consumption.
  arXiv  Detail & Related papers  (2023-07-19T01:05:33Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• Systematic review of deep learning and machine learning for building energy [0.0]
  Building energy (BE) management has an essential role in urban sustainability and smart cities. The machine learning (ML) and deep learning (DL) methods and applications have been promising for the advancement of the accurate and high-performance energy models. The present study provides a comprehensive review of ML and DL-based techniques applied for handling BE systems.
  arXiv  Detail & Related papers  (2022-02-23T13:40:45Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)
• 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)

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.