2026-09-30T21:36:32Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:26609 2026-09-29T14:37:30Z ddc:004 open_access

Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions Eisenklam, Abigail Montenegro G., Carlos A. Wang, Xian Cai, Yifan Gifford, Robert Phan, Linh Thi Xuan Sanfelice, Ricardo G. multicore resource allocation optimal control learning multi-path programs Predictable timing on multicore systems requires careful management of shared resources such as the last-level cache and memory bandwidth. This paper presents MPORA, an uncertainty-aware dynamic resource allocation framework for multi-path, input-dependent real-time tasks on multicore platforms. MPORA models each job as a discrete-time dynamical system that captures execution dynamics and resource-dependent performance indicators. At runtime, MPORA monitors job execution states and predicts short-term instruction rates and remaining execution times under candidate allocations using predictive models trained offline. It then solves a receding-horizon optimization problem to compute resource allocations that maximize system-wide progress while meeting job deadlines. To address prediction uncertainty, MPORA integrates weighted conformal prediction into the optimization formulation, enabling uncertainty-aware deadline constraints. We implement MPORA as a Linux kernel module with microsecond-scale inference overhead. Experimental results on SPEC CPU benchmarks show that MPORA delivers accurate predictions under unseen inputs and distribution shifts with low overhead, while improving schedulability and response times over existing methods. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Abigail Eisenklam and Carlos A. Montenegro G. and Xian Wang and Yifan Cai and Robert Gifford and Linh Thi Xuan Phan and Ricardo G. Sanfelice 2026 Is Part Of LIPIcs, Volume 375, 38th European Conference on Real-Time Systems (ECRTS 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ECRTS.2026.17 urn:nbn:de:0030-drops-266095 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2026.17 eng https://creativecommons.org/licenses/by/4.0/legalcode