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Documents authored by Gifford, Robert

Document
DOI: 10.4230/LIPIcs.ECRTS.2026.17
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, and Ricardo G. Sanfelice

Published in: LIPIcs, Volume 375, 38th European Conference on Real-Time Systems (ECRTS 2026)

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.
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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)

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@InProceedings{eisenklam_et_al:LIPIcs.ECRTS.2026.17,
  author =	{Eisenklam, Abigail and Montenegro G., Carlos A. and Wang, Xian and Cai, Yifan and Gifford, Robert and Phan, Linh Thi Xuan and Sanfelice, Ricardo G.},
  title =	{{Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions}},
  booktitle =	{38th European Conference on Real-Time Systems (ECRTS 2026)},
  pages =	{17:1--17:26},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-429-1},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{375},
  editor =	{Kritikakou, Angeliki},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2026.17},
  URN =		{urn:nbn:de:0030-drops-266095},
  doi =		{10.4230/LIPIcs.ECRTS.2026.17},
  annote =	{Keywords: multicore, resource allocation, optimal control, learning, multi-path programs}
}
Document
Artifact
DOI: 10.4230/DARTS.12.2.9
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, and Ricardo G. Sanfelice

Published in: DARTS, Volume 12, Issue 2, Special Issue of the 38th European Conference on Real-Time Systems (ECRTS 2026)

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

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)

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@Article{eisenklam_et_al:DARTS.12.2.9,
  author =	{Eisenklam, Abigail and Montenegro G., Carlos A. and Wang, Xian and Cai, Yifan and Gifford, Robert and Phan, Linh Thi Xuan and Sanfelice, Ricardo G.},
  title =	{{Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions (Artifact)}},
  pages =	{9:1--9:4},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2026},
  volume =	{12},
  number =	{2},
  editor =	{Eisenklam, Abigail and Montenegro G., Carlos A. and Wang, Xian and Cai, Yifan and Gifford, Robert and Phan, Linh Thi Xuan and Sanfelice, Ricardo G.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.12.2.9},
  URN =		{urn:nbn:de:0030-drops-266261},
  doi =		{10.4230/DARTS.12.2.9},
  annote =	{Keywords: multicore, resource allocation, optimal control, learning, multi-path programs}
}
Document
DOI: 10.4230/OASIcs.NINeS.2026.26
Running Distributed Systems like Clockwork

Authors: Karan Newatia, Robert Gifford, Qingjie Lu, Andreas Haeberlen, and Linh Thi Xuan Phan

Published in: OASIcs, Volume 139, 1st New Ideas in Networked Systems (NINeS 2026)

Abstract
Distributed Systems are commonly built using a set of standard assumptions: we assume that message delays are unbounded, that any packet can be lost in the network, and that clocks cannot be closely synchronized. On the one hand, these conservative assumptions result in robust systems that can operate reliably in a wide variety of conditions. On the other hand, they also force the system to do a lot of complex ad-hoc coordination and thus limit the performance it can achieve. In this paper, we take a look at what lies beyond this standard model. We observe that, on modern hardware in a single-tenant data center, distributed systems are able to closely coordinate and essentially "run like clockwork" with very little effort. If we are willing to additionally rule out some worst-case failure scenarios, this results in a large performance improvement, both in practice and even in theory. We demonstrate this effect using state-machine replication (SMR) as a case study: our SMR protocol, Watchmaker, exceeds the throughput of state-of-the-art algorithms by two orders of magnitude, and it requires only half as many replicas to tolerate the same number of faults.
Cite as

Karan Newatia, Robert Gifford, Qingjie Lu, Andreas Haeberlen, and Linh Thi Xuan Phan. Running Distributed Systems like Clockwork. In 1st New Ideas in Networked Systems (NINeS 2026). Open Access Series in Informatics (OASIcs), Volume 139, pp. 26:1-26:31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{newatia_et_al:OASIcs.NINeS.2026.26,
  author =	{Newatia, Karan and Gifford, Robert and Lu, Qingjie and Haeberlen, Andreas and Phan, Linh Thi Xuan},
  title =	{{Running Distributed Systems like Clockwork}},
  booktitle =	{1st New Ideas in Networked Systems (NINeS 2026)},
  pages =	{26:1--26:31},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-414-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{139},
  editor =	{Argyraki, Katerina and Panda, Aurojit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NINeS.2026.26},
  URN =		{urn:nbn:de:0030-drops-256115},
  doi =		{10.4230/OASIcs.NINeS.2026.26},
  annote =	{Keywords: State-machine replication, distributed systems, data centers, clock synchronization, fault tolerance, synchrony}
}
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