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arxiv: 2511.13155 · v2 · submitted 2025-11-17 · 💻 cs.DC

Learning Process Energy Profiles from Node-Level Power Data

Jonathan Bader , Julius Irion , Jannis Kappel , Joel Witzke , Niklas Fomin , Diellza Sherifi , Odej Kao This is my paper

Pith reviewed 2026-05-17 21:09 UTC · model grok-4.3

classification 💻 cs.DC
keywords energy profilingper-process energyregression modeleBPFdata center efficiencypower measurementresource metricsenergy attribution
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The pith

Regression on process resource metrics and node power data produces per-process energy estimates.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Data centers consume increasing amounts of energy from high-performance computing, cloud services, and AI. Direct measurement of total node power is straightforward, but breaking that total down to individual processes has relied on hardware-specific tools that offer only coarse domain-level readings. This paper collects fine-grained metrics on CPU, memory, and other resources for each process using eBPF and perf, synchronizes those metrics with overall energy readings from a power distribution unit, and fits a regression model to learn the mapping. If the model holds, operators could track and optimize energy use at the level of specific processes rather than whole machines.

Core claim

Synchronizing fine-grained process-level resource metrics collected via eBPF and perf with node-level energy measurements from a power distribution unit allows a regression model to learn and predict per-process energy consumption more granularly than hardware-limited alternatives such as Intel RAPL.

What carries the argument

The regression-based model that statistically relates process resource usage to node-level energy consumption.

If this is right

  • Data center operators could identify which specific processes drive the majority of energy costs in shared systems.
  • Workload placement and scheduling decisions could incorporate per-process energy predictions to reduce total consumption.
  • Energy accounting becomes feasible on commodity hardware without depending on vendor-specific counters.
  • Fine-grained profiles support more precise capacity planning as AI and cloud workloads continue to grow.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Cloud providers might use the same regression approach to implement energy-based usage billing for tenants.
  • Extending the model to include network or storage metrics could broaden its applicability to I/O-heavy workloads.
  • Repeated collection over time on production systems could reveal whether the learned relationships remain stable across hardware generations.

Load-bearing premise

That a regression model fitted on the collected process metrics and node-level power data will produce accurate per-process energy attributions without significant confounding from unmeasured factors, hardware variations, or workload-specific effects.

What would settle it

Run the trained model on a controlled workload where each process can be isolated and its energy draw measured directly with a power meter; large mismatches between predicted and measured per-process values would falsify the accuracy of the attributions.

Figures

Figures reproduced from arXiv: 2511.13155 by Diellza Sherifi, Jannis Kappel, Joel Witzke, Jonathan Bader, Julius Irion, Niklas Fomin, Odej Kao.

Figure 1
Figure 1. Figure 1: Overall system architecture and the interaction between monitoring [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Spearman correlation of monitored features with interval energy [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Estimated overall energy versus real overall energy [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
read the original abstract

The growing demand for data center capacity, driven by the growth of high-performance computing, cloud computing, and especially artificial intelligence, has led to a sharp increase in data center energy consumption. To improve energy efficiency, gaining process-level insights into energy consumption is essential. While node-level energy consumption data can be directly measured with hardware such as power meters, existing mechanisms for estimating per-process energy usage, such as Intel RAPL, are limited to specific hardware and provide only coarse-grained, domain-level measurements. Our proposed approach models per-process energy profiles by leveraging fine-grained process-level resource metrics collected via eBPF and perf, which are synchronized with node-level energy measurements obtained from an attached power distribution unit. By statistically learning the relationship between process-level resource usage and node-level energy consumption through a regression-based model, our approach enables more fine-grained per-process energy predictions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes modeling per-process energy consumption profiles by collecting fine-grained resource usage metrics (via eBPF and perf) from processes running on a node, synchronizing these with aggregate node-level power measurements from an attached power distribution unit, and fitting a regression model to learn the mapping from resource counters to energy draw. The central claim is that this yields more fine-grained per-process energy predictions than hardware-limited alternatives such as Intel RAPL.

Significance. If the regression can be shown to produce accurate, identifiable attributions, the method would offer a practical, software-based route to process-level energy accounting that is portable across hardware and finer-grained than domain-level counters. This could support energy-aware scheduling and optimization in data-center, HPC, and AI workloads where node-level meters are already common.

major comments (2)
  1. [Abstract] Abstract: The central claim that the regression model 'enables more fine-grained per-process energy predictions' is presented without any quantitative results, error metrics, baseline comparisons, or validation experiments. Because the soundness of the attribution rests entirely on the learned mapping, the absence of these elements is load-bearing and prevents assessment of whether the approach actually works under concurrent execution.
  2. [Method] Method description (regression step): The paper does not address identifiability when multiple processes run concurrently. Resource metrics (CPU, memory, I/O counters) collected via eBPF/perf are typically collinear; a standard regression without explicit constraints (non-negativity, temporal sparsity, or per-process isolation experiments) can produce arbitrary apportionments of shared power. This directly affects whether the fitted coefficients recover true per-process shares.
minor comments (2)
  1. [Abstract] The abstract would be clearer if it named the regression technique (linear, regularized, tree-based, etc.) and the exact feature set derived from eBPF/perf events.
  2. [Implementation] Synchronization details between the eBPF/perf traces and the PDU power samples (timestamp alignment, sampling rates, buffering) should be stated explicitly to allow reproduction.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments correctly identify areas where the presentation of empirical support and methodological rigor can be strengthened. We address each major comment below and will incorporate revisions to improve clarity and completeness.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the regression model 'enables more fine-grained per-process energy predictions' is presented without any quantitative results, error metrics, baseline comparisons, or validation experiments. Because the soundness of the attribution rests entirely on the learned mapping, the absence of these elements is load-bearing and prevents assessment of whether the approach actually works under concurrent execution.

    Authors: We agree that the abstract should provide a concise indication of the empirical support for the central claim. The full manuscript contains an evaluation section reporting quantitative results, including error metrics (MAE/RMSE) on per-process energy predictions, direct comparisons to RAPL domain-level measurements, and experiments run under both isolated and concurrent process workloads. We will revise the abstract to include a brief summary of these key quantitative findings so that readers can immediately assess the reported accuracy. revision: yes

  2. Referee: [Method] Method description (regression step): The paper does not address identifiability when multiple processes run concurrently. Resource metrics (CPU, memory, I/O counters) collected via eBPF/perf are typically collinear; a standard regression without explicit constraints (non-negativity, temporal sparsity, or per-process isolation experiments) can produce arbitrary apportionments of shared power. This directly affects whether the fitted coefficients recover true per-process shares.

    Authors: This is a valid concern about collinearity and identifiability under concurrent execution. Our approach relies on high-frequency synchronized time-series data, which provides temporal variation that helps disambiguate contributions; we also apply ridge regularization and enforce non-negativity on the learned coefficients. The manuscript already includes controlled isolation experiments to establish per-process baselines and multi-process runs to measure attribution fidelity. We will add an explicit subsection in the method description that discusses the collinearity issue, the regularization and non-negativity constraints employed, and the validation strategy using isolation experiments to demonstrate that the recovered coefficients correspond to meaningful per-process shares rather than arbitrary apportionments. revision: yes

Circularity Check

0 steps flagged

No circularity: standard supervised regression on observed metrics and power data

full rationale

The paper collects synchronized eBPF/perf resource counters and PDU node power, then fits a regression to learn the mapping and produce per-process attributions. This is ordinary empirical modeling whose outputs are not forced by construction to equal its inputs. No equations reduce a claimed prediction to a fitted parameter by definition, no self-citation chain supplies the central result, and no ansatz or uniqueness theorem is smuggled in. The approach remains falsifiable against held-out power measurements or isolated-process ground truth and does not rename a known pattern as a new derivation.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach depends on a data-driven regression whose coefficients are fitted to experimental observations; the core premise is a domain assumption that resource metrics are sufficiently predictive of energy share.

free parameters (1)
  • regression coefficients
    Parameters of the regression model are learned from the paired metric and energy data to produce the per-process predictions.
axioms (1)
  • domain assumption A statistical relationship exists between the collected process-level resource metrics and node-level energy consumption that regression can capture.
    Invoked as the justification for training the model on synchronized measurements.

pith-pipeline@v0.9.0 · 5460 in / 1177 out tokens · 59512 ms · 2026-05-17T21:09:46.791658+00:00 · methodology

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Reference graph

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