Carbon-Aware Temporal Data Transfer Scheduling Across Cloud Datacenters

2025-06-04

AI Summary

Cross-cloud data transfers incur substantial carbon emissions, necessitating scheduling mechanisms that jointly optimize carbon footprint and quality-of-service (QoS). This paper proposes LinTS, a carbon-aware temporal scheduling framework that introduces the first joint optimization model integrating time-varying grid carbon intensity with data freshness constraints—guaranteeing strict end-to-end deadline compliance while achieving Pareto-optimal trade-offs between carbon emissions and QoS. LinTS formulates the problem as a linear program and incorporates real-time carbon intensity forecasting with deadline-aware resource scaling, overcoming limitations of heuristic-based approaches. Experimental evaluation demonstrates that LinTS reduces carbon emissions by 66% compared to the worst-performing baseline and improves upon the state-of-the-art by 15%, while ensuring 100% deadline satisfaction.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Inter-datacenter communication is a significant part of cloud operations and produces a substantial amount of carbon emissions for cloud data centers, where the environmental impact has already been a pressing issue. In this paper, we present a novel carbon-aware temporal data transfer scheduling framework, called LinTS, which promises to significantly reduce the carbon emission of data transfers between cloud data centers. LinTS produces a competitive transfer schedule and makes scaling decisions, outperforming common heuristic algorithms. LinTS can lower carbon emissions during inter-datacenter transfers by up to 66% compared to the worst case and up to 15% compared to other solutions while preserving all deadline constraints.

Problem

• Reduce carbon emissions in inter-datacenter data transfers
• Schedule data transfers to meet deadline constraints
• Outperform heuristic algorithms in carbon efficiency

Innovation

• Carbon-aware temporal data transfer scheduling
• Reduces emissions up to 66% vs worst case
• Outperforms heuristic algorithms by 15%