Multi-Objective Memory Bandwidth Regulation and Cache Partitioning for Multicore Real-Time Systems
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913844008321024 |
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| author | Sun, Binqi Wei, Zhihang Bastoni, Andrea Roy, Debayan Theile, Mirco Kloda, Tomasz Pellizzoni, Rodolfo Caccamo, Marco |
| author_facet | Sun, Binqi Wei, Zhihang Bastoni, Andrea Roy, Debayan Theile, Mirco Kloda, Tomasz Pellizzoni, Rodolfo Caccamo, Marco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11554 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Objective Memory Bandwidth Regulation and Cache Partitioning for Multicore Real-Time Systems Sun, Binqi Wei, Zhihang Bastoni, Andrea Roy, Debayan Theile, Mirco Kloda, Tomasz Pellizzoni, Rodolfo Caccamo, Marco Optimization and Control Hardware Architecture Distributed, Parallel, and Cluster Computing Operating Systems 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. |
| title | Multi-Objective Memory Bandwidth Regulation and Cache Partitioning for Multicore Real-Time Systems |
| topic | Optimization and Control Hardware Architecture Distributed, Parallel, and Cluster Computing Operating Systems |
| url | https://arxiv.org/abs/2505.11554 |