A Survey on Task Scheduling in Carbon-Aware Container Orchestration

• URL: http://arxiv.org/abs/2508.05949v1
• Date: Fri, 08 Aug 2025 02:20:16 GMT
• Title: A Survey on Task Scheduling in Carbon-Aware Container Orchestration
• Abstract summary: Soaring energy demands of software ecosystems and cloud data centers have driven energy consumption and carbon footprint to unprecedented levels.<n>Industry and academia are increasing efforts to reduce the carbon emissions associated with cloud computing through more efficient task scheduling and infrastructure orchestration.<n>We propose a comprehensive taxonomy for cloud task scheduling studies, with a particular focus on the environmental sustainability aspect.
• Score: 15.001784538833425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The soaring energy demands of large-scale software ecosystems and cloud data centers, accelerated by the intensive training and deployment of large language models, have driven energy consumption and carbon footprint to unprecedented levels. In response, both industry and academia are increasing efforts to reduce the carbon emissions associated with cloud computing through more efficient task scheduling and infrastructure orchestration. In this work, we present a systematic review of various Kubernetes scheduling strategies, categorizing them into hardware-centric and software-centric, annotating each with its sustainability objectives, and grouping them according to the algorithms they use. We propose a comprehensive taxonomy for cloud task scheduling studies, with a particular focus on the environmental sustainability aspect. We analyze emerging research trends and open challenges, and our findings provide critical insight into the design of sustainable scheduling solutions for next-generation cloud computing systems.

Related papers

• A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design [71.32556809242666]
  The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains.<n>It has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.<n>This survey aims to offer a holistic roadmap toward sustainable, scalable, and socially responsible development of large models.
  arXiv  Detail & Related papers  (2026-07-10T04:02:22Z)
• DCcluster-Opt: Benchmarking Dynamic Multi-Objective Optimization for Geo-Distributed Data Center Workloads [14.834687262955585]
  We present DCcluster-Opt: an open-source, high-fidelity simulation benchmark for sustainable, geo-temporal task scheduling.<n>It combines curated real-world datasets, including AI workload traces, grid carbon intensity, electricity markets, weather across 20 global regions, cloud transmission costs, and empirical network delay parameters.<n>A modular reward system enables an explicit study of trade-offs among carbon emissions, energy costs, service level agreements, and water use.
  arXiv  Detail & Related papers  (2025-10-31T03:07:12Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [59.52058740470727]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review [10.015735252600793]
  Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges.<n>DRL enables systems to learn and adapt policies based on continuous observations of the environment.<n>This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing.
  arXiv  Detail & Related papers  (2025-01-02T02:08:00Z)
• Transforming the Hybrid Cloud for Emerging AI Workloads [82.21522417363666]
  This white paper envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads.<n>The proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness.<n>This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms.
  arXiv  Detail & Related papers  (2024-11-20T11:57:43Z)
• Computation-efficient Deep Learning for Computer Vision: A Survey [121.84121397440337]
  Deep learning models have reached or even exceeded human-level performance in a range of visual perception tasks. Deep learning models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. New research focus is computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference.
  arXiv  Detail & Related papers  (2023-08-27T03:55:28Z)
• Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency [0.0]
  In recent years, the integration of artificial intelligence (AI) and cloud computing has emerged as a promising avenue for addressing the growing computational demands of AI applications. This paper presents a comprehensive study of scalable, distributed AI frameworks leveraging cloud computing for enhanced deep learning performance and efficiency.
  arXiv  Detail & Related papers  (2023-04-26T15:38:00Z)
• 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)
• HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing [26.48962351761643]
  We propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem. Experiments on simulated and physical cloud environments show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively.
  arXiv  Detail & Related papers  (2021-10-11T18:11:26Z)
• 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.