A Survey on Task Scheduling in Carbon-Aware Container Orchestration

2025-08-07

AI Summary

Addressing the escalating energy consumption and carbon emissions from large-model training and cloud service expansion, this paper investigates carbon-aware container scheduling. Through a systematic literature review, we present the first taxonomy of cloud-native schedulers—particularly Kubernetes—from an environmental sustainability perspective, integrating both hardware-centric and software-centric strategies. We propose the first comprehensive classification framework for cloud task scheduling explicitly targeting carbon reduction, explicitly characterizing each algorithm by its sustainability objective, optimization dimension, and technical approach. Our analysis identifies emerging trends—including dynamic carbon intensity awareness and multi-objective co-optimization—and highlights critical open challenges, such as real-time data-driven closed-loop control and cross-domain coordinated scheduling. This work provides theoretical foundations and practical guidelines for designing and standardizing low-carbon cloud systems.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Machine Learning: Efficient ML / Green AI
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Sustainability and climate impact of web technologies
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Reducing carbon emissions in cloud computing via task scheduling
• Surveying Kubernetes strategies for sustainable container orchestration
• Analyzing trends in eco-friendly cloud scheduling algorithms

Innovation

• Kubernetes scheduling strategies categorized
• Taxonomy for cloud task scheduling
• Sustainable scheduling solutions design