AI for Carbon-Aware Workload Scheduling

Shift compute to greener grids, cooler regions, and off-peak windows while respecting latency, availability, and cost constraints.

Carbon-aware scheduling leverages carbon intensity forecasts, temperature, and pricing to pick regions, timing, and resource types for jobs. AI can co-optimize emissions, cost, and SLA by predicting demand, modeling grid carbon, and orchestrating queues across clouds/regions.

Autoscaling with carbon-aware thresholds

Workload spillover to regions with cleaner mix.

Thermal-aware placement to reduce cooling load.

live demo · related simulation● LIVE

Carbon/price/temperature feeds; region availability

Workload descriptors (priority, runtime distribution, SLO).

Telemetry for utilization, queue depth, SLA violations.

Frequently asked questions

What is the implementation roadmap for deploying a carbon-aware scheduler?

Implementation Roadmap

How do I instrument workloads with relevant descriptors?

Instrument workloads with descriptors; ingest carbon/price forecasts.

What baseline scheduler configuration should I use initially, considering emissions, cost, and latency?

Baseline scheduler with emissions/cost/latency weights; start with batch jobs.

How can I incorporate checkpointing and preemption to simulate different scenarios?

Add checkpointing/preemption; simulate scenarios; tune weights.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation