Optimizing Workload Migration for Carbon and Cost Reductions Under Grid Constraints: New Insights and a Practical Evaluation Framework

As cloud-scale artificial intelligence (AI) and data processing workloads continue to surge, data center operators are increasingly exploring workload migration strategies to optimize energy costs and reduce environmental impact. While prior work has addressed cost- or carbon-aware migration independently, fewer studies have incorporated power grid strain as an operational constraint alongside these objectives. This article introduces a multiobjective optimization framework for cross-regional workload migration that minimizes electricity cost and carbon emissions, while enforcing strict constraints on grid strain and workload performance. Our approach uses real-world data from U.S. independent system operators (ISOs) to evaluate the feasibility and effectiveness of migrating workloads across regions under these combined considerations. We model migration overheads, including penalties from latency and runtime extension, and show how they influence the net benefit of shifting workloads. Our experiments show that up to 13% carbon and up to 15% cost reductions can be achieved while ensuring that no destination region exceeds safe grid operating thresholds. Furthermore, we highlight temporal and seasonal patterns that impact migration opportunities. This work provides new insights for sustainable workload migration strategies in geodistributed infrastructures and introduces a practical framework for evaluating carbon- and cost-aware migration decisions under explicit grid strain constraints.