An Energy Attribution Model for Multi-Tenant AI Workloads in Industrial IoT Edge Computing Environments

The rapid proliferation of AI-enabled Industrial Internet of Things (IIoT) applications has significantly increased the energy consumption of shared edge computing infrastructures. Despite this sustainability challenge, contemporary resource pricing models in edge computing environments remain largely anchored in coarse-grained physical resource allocations rather than the actual energy consumed during workload execution. To support energy-aware resource management in industrial edge computing, this article formalizes an energy attribution model for AI workloads executed in shared edge nodes, where multi-tenant workloads concurrently share computing resources. The primary challenge in such environments stems from the inherent non-separability of localized power consumption among co-located workloads due to dynamic resource sharing and execution interference. To address this technical hurdle without introducing prohibitive monitoring or instrumentation overheads to the industrial environment, our model partitions aggregate system-level energy metrics into baseline platform elements and resource-specific functional components. It then formulates energy shares to individual workloads by solving consistent attribution functions based on observable resource allocation and utilization variables. By categorizing infrastructure hardware into utilization-driven, allocation-centric, and hybrid behavior profiles, the model precisely approximates workload-level energy responsibility within a practical error margin in shared IIoT edge computing environments.