Performance-Aware Scheduling and Load Balancing for Edge/Cloud Systems

Edge computing enables time-sensitive applications by bringing computation closer to data sources. However, existing orchestration frameworks such as Kubernetes often overlook performance variability, leading to inefficient resource management. While recent advances have introduced performance-aware scheduling, load balancing remains largely reactive. In particular, performance-aware load balancers are still missing from mainstream systems, limiting their ability to meet subsecond latency requirements. This paper presents a unified extension to orchestration frameworks that integrates both performance-aware scheduling and load balancing. We design lightweight performance predictors, trained on historical monitoring data, to estimate performance fluctuations. Variability predictors inform the scheduler, while Round-Trip Time (RTT) predictors guide load balancing decisions. Our approach is validated using a Single Particle Analysis (SPA) application deployed on a Kubernetes-based edge computing testbed. Results demonstrate that the predictors achieve approximately 85% accuracy in capturing RTT variability. Simulation results show reductions in application completion times ranging from 50% to 78% for scheduling and 68% for load balancing, highlighting the effectiveness of predictive orchestration in dynamic edge/cloud environments.