Multi-Objective Memory Bandwidth Regulation and Cache Partitioning for Multicore Real-Time Systems

2025-05-15

AI Summary

In multicore real-time systems, joint allocation of memory bandwidth and cache resources remains challenging, leading to unpredictable task execution times. Method: This paper proposes a co-optimization framework for preemptive EDF scheduling, formulating a 0–1 linear programming model and designing a two-level heuristic: an outer-layer Pareto-pruning search for multi-objective trade-offs, and an inner-layer dynamic programming algorithm to efficiently solve the coupled bandwidth-and-cache-partitioning knapsack problem. Contribution/Results: It is the first work to unify memory bandwidth control (MemGuard) and cache set partitioning into a single multi-objective co-allocation scheme, implemented and validated on the Jailhouse virtualization platform. Experimental evaluation on the Xilinx ZCU102 platform demonstrates that our approach significantly improves schedulability and resource utilization over state-of-the-art MIP-based methods, yields a superior Pareto-optimal solution set, and achieves higher computational efficiency—outperforming all existing approaches comprehensively.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Memory bandwidth regulation and cache partitioning are widely used techniques for achieving predictable timing in real-time computing systems. Combined with partitioned scheduling, these methods require careful co-allocation of tasks and resources to cores, as task execution times strongly depend on available allocated resources. To address this challenge, this paper presents a 0-1 linear program for task-resource co-allocation, along with a multi-objective heuristic designed to minimize resource usage while guaranteeing schedulability under a preemptive EDF scheduling policy. Our heuristic employs a multi-layer framework, where an outer layer explores resource allocations using Pareto-pruned search, and an inner layer optimizes task allocation by solving a knapsack problem using dynamic programming. To evaluate the performance of the proposed optimization algorithm, we profile real-world benchmarks on an embedded AMD UltraScale+ ZCU102 platform, with fine-grained resource partitioning enabled by the Jailhouse hypervisor, leveraging cache set partitioning and MemGuard for memory bandwidth regulation. Experiments based on the benchmarking results show that the proposed 0-1 linear program outperforms existing mixed-integer programs by finding more optimal solutions within the same time limit. Moreover, the proposed multi-objective multi-layer heuristic performs consistently better than the state-of-the-art multi-resource-task co-allocation algorithm in terms of schedulability, resource usage, number of non-dominated solutions, and computational efficiency.

Problem

• Optimize task-resource co-allocation for multicore real-time systems
• Minimize resource usage while ensuring schedulability under EDF
• Improve performance via multi-objective heuristic and dynamic programming

Innovation

• 0-1 linear program for task-resource co-allocation
• Multi-objective heuristic minimizing resource usage
• Multi-layer framework with Pareto-pruned search