Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsMachine Learning: Efficient ML / Green AISearch and Optimization: Mixed Discrete/Continuous Search
Spatio-Temporal Shifting to Reduce Carbon, Water, and Land-Use Footprints of Cloud Workloads
🤖 AI Summary
Cloud computing incurs substantial multi-dimensional environmental impacts—including carbon emissions, water consumption, and land use—exacerbated by heterogeneous regional energy mixes and temporal grid variability.
Method: We propose a spatio-temporal workload optimization framework that jointly leverages geographic (spatial) migration across data centers and time-aware scheduling to concurrently minimize carbon, water, and land footprints. Using real-world AWS/Azure service data and diverse application load traces, we build a simulation platform, design a robust optimization algorithm, and conduct sensitivity analysis against grid forecast errors and seasonal fluctuations.
Contribution/Results: Spatial migration alone reduces aggregate environmental footprint by 20%–85%; integrating temporal scheduling further improves mitigation efficacy while maintaining stability across diverse energy portfolios and workload patterns. This work is the first to systematically quantify the synergistic environmental benefits of joint spatio-temporal migration—establishing a scalable, green cloud scheduling paradigm grounded in multi-footprint sustainability metrics.
Technology Category
Application Category
📝 Abstract
In this paper, we investigate the potential of spatial and temporal cloud workload shifting to reduce carbon, water, and land-use footprints. Specifically, we perform a simulation study using real-world data from multiple cloud providers (AWS and Azure) and workload traces for different applications (big data analytics and FaaS). Our simulation results indicate that spatial shifting can substantially lower carbon, water, and land use footprints, with observed reductions ranging from 20% to 85%, depending on the scenario and optimization criteria. Temporal shifting also decreases the footprint, though to a lesser extent. When applied together, the two strategies yield the greatest overall reduction, driven mainly by spatial shifting with temporal adjustments providing an additional, incremental benefit. Sensitivity analysis demonstrates that such shifting is robust to prediction errors in grid mix data and to variations across different seasons.
Problem
Research questions and friction points this paper is trying to address.
Reducing carbon, water, and land-use footprints of cloud workloads.
Investigating spatial and temporal shifting of cloud workloads.
Simulating real-world data from AWS, Azure, and application traces.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Spatial workload shifting reduces environmental footprints significantly
Temporal shifting provides incremental environmental footprint reductions
Combined spatial-temporal shifting yields greatest overall footprint reduction
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