Orchestrating Composite Applications at the Edge

Edge computing plays a pivotal role in enabling time-critical Machine Learning (ML) by bringing computational capabilities closer to end users. However, satisfying stringent inference latency and quality constraints under varying task complexity and limited edge resources remains challenging. We tackle this by proposing a novel architectural approach for ML- based edge deployments and introducing CARE, our orchestra- tion framework that jointly configures composite applications and compute resources to meet inference latency and quality targets, while minimizing energy consumption. Experimental results in the context of Multi-Object Tracking (MOT) demonstrate that CARE improves inference quality by up to 50% and reduces latency by up to 2× over monolithic baselines.