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

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

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DARTS.12.2.9.pdf
  • Filesize: 0.65 MB
  • 4 pages

Document Identifiers

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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Artifact

Abstract

This artifact accompanies the paper Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions appearing in ECRTS 2026. It is distributed as a Docker image and provides the scripts and datasets needed to reproduce the paper’s core empirical results across six SPEC CPU 2017 benchmarks. The artifact trains and evaluates gradient boosting regression trees (GBRT) that predict two fine-grained execution properties of each benchmark (remaining execution time and next-window instruction rate) under different resource allocations, exports each model to a compact Q16.16 fixed-point binary, evaluates the speed, accuracy, and size of the models using a fixed-point C inference library, and applies weighted conformal prediction (WCP) to produce high-probability upper bounds on the prediction errors under different operating scenarios. The in-kernel decision logic, which implements MPORA: Multi-Path Online Resource Allocation, is included as source for inspection only, as it requires a custom kernel and specialized hardware to run.

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 (Artifact). In Special Issue of the 38th European Conference on Real-Time Systems (ECRTS 2026). Dagstuhl Artifacts Series (DARTS), Volume 12, Issue 2, pp. 9:1-9:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/DARTS.12.2.9

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.

Related Article

  • 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), LIPIcs, Vol. 375, pp. 17:1-17:26, 2026. https://doi.org/10.4230/LIPIcs.ECRTS.2026.17

References

  1. Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785-794, 2016. URL: https://doi.org/10.1145/2939672.2939785.
  2. Standard Performance Evaluation Corporation. SPEC CPU® 2017 Benchmark. https://www.spec.org/cpu2017/, 2017.
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