Optimal Dynamic Resource Allocation for Multicore Real-time Systems

2026-10-04

AI Summary

This study addresses the challenge of dynamic resource allocation under hard deadlines and resource constraints in multicore real-time systems by proposing a data-driven optimal control framework that explicitly accounts for timing requirements. The method pioneers the integration of real-time scheduling constraints into optimal control modeling, rigorously proves the existence of feasible solutions, and develops an efficient solving algorithm to achieve optimized resource configuration. Evaluations across four benchmarks on a physical multicore platform demonstrate that the proposed approach significantly enhances overall system performance. This work provides a solid theoretical and methodological foundation for intelligent resource management in real-time systems.

Abstract

We formulate a novel optimal control problem for data-driven dynamic resource allocation in multicore real-time systems. The proposed formulation accounts for hard deadline constraints and resource bounds. Under mild assumptions, we prove several results on the feasibility and existence of optimal solution for the proposed formulation. Building on these analyses, we design an algorithm to solve the multi-core dynamic resource allocation problem. We demonstrate the practical use of this new algorithm by experimentally evaluating it on a real multicore platform using four benchmarks from the literature.

Problem

• Multicore Real-time Systems
• Dynamic Resource Allocation
• Optimal Control
• Hard Deadline Constraints

Innovation

• Dynamic Resource Allocation
• Multicore Real-time Systems
• Optimal Control
• Data-driven
• Hard Deadline Constraints