Real-Time Scheduling with Predictions

Tianming Zhao, Wei Li, Albert Y. Zomaya

Abstract: The recent revival in learning theory gives us improved capabilities for accurate predictions and increased opportunities for performance enhancement. This work extends the research agenda of augmenting algorithms with predictions to one of the central scheduling problems – soft real-time scheduling on single and parallel machines to minimize the mean response time. We design an algorithm, PEDRMLF (Predictions Enhanced Dynamic Randomized MultiLevel Feedback), that incorporates job size predictions, achieving an optimal competitive ratio under perfect predictions and the best-known competitive ratio under any predictions. PEDRMLF is the first algorithm that simultaneously achieves optimal consistency and bounded robustness. Simulations show that the proposed algorithm performs close to the theoretically optimal bound while consistently outperforming state-of-the-art benchmarks.