Providing Edge to Cloud Continuum With Adaptive Model Selection and Operational Score

Advancements in Deep Neural Network (DNN) models and hardware accelerators have made edge intelligence a practical alternative to cloud-based intelligence. However, application-specific requirements, such as accuracy, latency, security, and privacy, as well as workload fluctuations, necessitate dynamic allocation of edge and cloud resources. To facilitate such dynamic allocation, we propose an adaptive model selection and switching framework that leverages operational performance scores. We evaluate the approach using various object classification models, demonstrating its ability to balance accuracy and inference time while ensuring scalability and efficient resource utilization.