Research Paper

Core Innovation

This paper introduces a three-layer geo-distributed inference placement model incorporating electricity prices, carbon intensity, latency, and migration frictions. It formalizes digital relocation of electricity demand as a constrained optimization problem and defines new operational metrics to evaluate energy and carbon returns on latency relaxation.

Why It Matters

AI inference is a growing electricity demand source with flexibility to shift computation geographically. Efficient relocation reduces energy costs and carbon emissions while respecting latency and regulatory limits, enabling scalable, sustainable AI services globally.

Potential Customers & Pain Points

• Cloud providers – High energy costs and carbon footprint
• Data center operators – Capacity and regulatory constraints
• Enterprises with AI workloads – Need latency-compliant cost-efficient inference
• Sustainability-focused tech firms – Demand carbon-efficient AI operations

Business Model

Subscription-based SaaS platform integrated with cloud providers and enterprise AI infrastructure for continuous inference workload optimization and reporting.

Competitive Landscape

• Google Carbon-Aware Computing
• Microsoft Azure Sustainability Tools
• AWS Compute Optimizer

Implementation Challenges

• Complexity of integrating latency and regulatory constraints
• Data privacy and state locality limitations
• Migration friction and network egress costs reducing benefits

Validation Strategy

• Pilot with cloud providers to measure energy and latency trade-offs
• Simulate workload relocation scenarios with real-world data
• Partner with sustainability teams to quantify carbon impact reductions