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        <identifier>oai:drops-oai.dagstuhl.de:23580</identifier>
        <datestamp>2025-10-27T10:21:41Z</datestamp>
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          <dc:title>Multi-Objective Memory Bandwidth Regulation and Cache Partitioning for Multicore Real-Time Systems</dc:title>
          <dc:creator>Sun, Binqi</dc:creator>
          <dc:creator>Wei, Zhihang</dc:creator>
          <dc:creator>Bastoni, Andrea</dc:creator>
          <dc:creator>Roy, Debayan</dc:creator>
          <dc:creator>Theile, Mirco</dc:creator>
          <dc:creator>Kloda, Tomasz</dc:creator>
          <dc:creator>Pellizzoni, Rodolfo</dc:creator>
          <dc:creator>Caccamo, Marco</dc:creator>
          <dc:subject>Multi-objective optimization</dc:subject>
          <dc:subject>memory bandwidth regulation</dc:subject>
          <dc:subject>cache partitioning</dc:subject>
          <dc:subject>partitioned scheduling</dc:subject>
          <dc:subject>real-time systems</dc:subject>
          <dc:description>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.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Binqi Sun and Zhihang Wei and Andrea Bastoni and Debayan Roy and Mirco Theile and Tomasz Kloda and Rodolfo Pellizzoni and Marco Caccamo</dc:contributor>
          <dc:date>2025</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 335, 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ECRTS.2025.2</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-235807</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2025.2</dc:identifier>
          <dc:language>eng</dc:language>
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