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Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions

Authors Abigail Eisenklam , Carlos A. Montenegro G. , Xian Wang , Yifan Cai , Robert Gifford , Linh Thi Xuan Phan , Ricardo G. Sanfelice

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Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time operating systems
Keywords
  • multicore
  • resource allocation
  • optimal control
  • learning
  • multi-path programs

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Abstract

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.

Cite As Get BibTex

Abigail Eisenklam, Carlos A. Montenegro G., Xian Wang, Yifan Cai, Robert Gifford, Linh Thi Xuan Phan, and Ricardo G. Sanfelice. Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions. In 38th European Conference on Real-Time Systems (ECRTS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 375, pp. 17:1-17:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.ECRTS.2026.17

Author Details

Abigail Eisenklam
  • University of Pennsylvania, Philadelphia, PA, USA
Carlos A. Montenegro G.
  • University of California, Santa Cruz, CA, USA
Xian Wang
  • University of Pennsylvania, Philadelphia, PA, USA
Yifan Cai
  • University of Pennsylvania, Philadelphia, PA, USA
Robert Gifford
  • University of Pennsylvania, Philadelphia, PA, USA
Linh Thi Xuan Phan
  • University of Pennsylvania, Philadelphia, PA, USA
Ricardo G. Sanfelice
  • University of California, Santa Cruz, CA, USA

Funding

Partially supported by NSF Grants no. CNS-1955670, CNS-2039054, CNS-2111688, and CCF-2326606, by AFOSR Grants nos. FA9550-23-1-0145, FA9550-23-1-0313, and FA9550-23-1-0678, by AFRL Grant nos. FA8651-22-1-0017 and FA8651-23-1-0004, by ARO Grant no. W911NF-20-1-0253, by DoD Grant no. W911NF-23-1-0158, and by UC Alianza MX Grant no. SRPUCMX24-01.

Supplementary Materials

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