CARSS: Leveraging Online Arrival Forecasting and Co-Simulation for Adaptive Scheduling

Coupled simulation, also known as co-simulation, has been proposed to support task schedulers by simulating, at runtime, the Quality of Service (QoS) resulting from scheduling actions. However, existing co-simulation methods typically assume a static arrival time series. This assumption limits the diversity of traffic scenarios. To address this, we propose an online adaptive arrival forecasting framework that integrates a change-point detection module and a probabilistic transformer model to couple co-simulators with arrival series forecasting. This framework also updates the prediction model in response to detected changes. Additionally, we introduce the Co-simulated Adaptive Recurrent Surrogate Scheduler (CARSS), which uses simulated QoS metrics from the co-simulator to make scheduling decisions that enhance system performance. Experiments show that our online adaptive forecasting framework has lower forecasting errors than traditional models and reduces co-simulator prediction error by 11% in average response time and 22% in average service-level agreement (SLA) violation on real-world traces. Furthermore, CARSS improves QoS metrics, achieving 2.5% reduction in average energy consumption and reductions of 8.2% and 82.7% in average response time and average SLA violation, respectively, compared to the best baseline scheduler.