arxiv: 2605.02179 · v1 · submitted 2026-05-04 · 💻 cs.NI

Recognition: 3 theorem links

· Lean Theorem

Risk-Budgeted Online Scheduling for Continuous Edge Inference over Evolving Time Horizons

Houyi Qi, Minghui Liwang, Sai Zou, Wei Ni

Pith reviewed 2026-05-08 18:57 UTC · model grok-4.3

classification 💻 cs.NI

keywords edge computingonline schedulingrisk budgetingLSTM predictiondeadline violationcontinuous inferencegame theory

0 comments

The pith

Dynamic risk budgets and LSTM predictions let online schedulers maintain sustained timeliness for continuous edge inference.

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

Continuous edge inference requires not only low latency per time slot but also control over how deadline violations accumulate or cluster across longer periods under changing channels and workloads. Existing methods optimize only the current slot and can allow violation patterns to worsen over time. AEGIS treats the tendency toward deadline violations as an evolving state managed by adjustable user-level risk budgets that update across time. It adds short-term LSTM forecasts to build a smooth risk measure for upcoming decisions and solves the resulting resource competition as a potential-aligned game under bandwidth and compute limits. An asynchronous algorithm reaches convergence in finite steps, and tests show higher timely-inference rates along with shorter violation bursts.

Core claim

AEGIS models deadline-violation tendency as an updatable cross-time control state through dynamic user-level risk budgets, leverages LSTM-based short-term state prediction to construct a smooth deadline-violation risk surrogate, and formulates the time-wise resource competition as a potential-aligned game under coupled feasibility constraints, producing an asynchronous online algorithm with finite-step convergence.

What carries the argument

updatable cross-time user-level risk budgets together with an LSTM risk surrogate inside a potential-aligned game under coupled constraints

Load-bearing premise

That tracking deadline-violation tendency through updatable cross-time user risk budgets and LSTM short-term forecasts will yield stable online decisions under coupled bandwidth and computing constraints without creating fresh instability.

What would settle it

In a live edge deployment, if the fraction of timely inferences does not rise or if average violation-burst lengths increase relative to standard schedulers under the same channel and workload traces, the risk-budget mechanism would be shown not to deliver the claimed cross-time stability.

Figures

Figures reproduced from arXiv: 2605.02179 by Houyi Qi, Minghui Liwang, Sai Zou, Wei Ni.

**Figure 1.** Figure 1: Framework and procedure of AEGIS, including edge inference task view at source ↗

**Figure 2.** Figure 2: Service reliability and long-term stability versus the number of MUs: view at source ↗

**Figure 3.** Figure 3: In Fig. 3(a), AEGIS and AEGISNoBudget achieve view at source ↗

**Figure 3.** Figure 3: Efficiency, utility, and online overhead versus the number of MUs: view at source ↗

read the original abstract

Continuous edge inference necessitates not merely low per-timeslot latency, but sustained timeliness guarantees in the presence of time-varying channels, fluctuating edge workloads, and coupled bandwidth-computing resource constraints. Existing studies predominantly optimize instantaneous delay or per-timeslot utility, while largely overlooking the regulation of cross-time deadline violation dynamics in continuous services. To address this, we propose AEGIS, a prediction-empowered risk-budgeted online scheduling framework for continuous edge inference. AEGIS models deadline-violation tendency as an updatable cross-time control state through dynamic user-level risk budgets, so that online scheduling accounts for both instantaneous efficiency and long-term service stability. To support proactive decision making, AEGIS leverages LSTM-based short-term state prediction to construct a smooth deadline-violation risk surrogate, and formulates the resulting time-wise resource competition as a potential-aligned game under coupled feasibility constraints. An asynchronous online algorithm is then developed with finite-step convergence. Experiments demonstrate that AEGIS improves the timely inference ratio, reduces average violation risk and violation burst length, and achieves a favorable delay--risk--convergence trade-off over representative baselines.

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.

Desk Editor's Note private letter to a colleague

AEGIS adds dynamic risk budgets and LSTM prediction to online edge scheduling for sustained timeliness, but the experimental support stays thin on details.

read the letter

The key point is that this paper shifts edge inference scheduling from per-slot delay minimization to managing cross-time violation risk via updatable user-level budgets. AEGIS layers LSTM short-term prediction on top, casts the resource competition as a potential-aligned game under coupled bandwidth-compute constraints, and supplies an asynchronous algorithm claimed to converge in finite steps. That combination targets a practical gap in continuous services where bursty violations matter more than average latency.

Referee Report

2 major / 1 minor

Summary. The paper proposes AEGIS, a prediction-empowered risk-budgeted online scheduling framework for continuous edge inference. It models deadline-violation tendency as an updatable cross-time control state via dynamic user-level risk budgets, leverages LSTM-based short-term state prediction to construct a smooth deadline-violation risk surrogate, formulates time-wise resource competition as a potential-aligned game under coupled feasibility constraints, develops an asynchronous online algorithm with finite-step convergence, and reports experiments showing improved timely inference ratio, reduced average violation risk and violation burst length, plus a favorable delay-risk-convergence trade-off over baselines.

Significance. If the finite-step convergence holds under inexact predictions and the experimental gains are reproducible, the work could advance edge inference scheduling by incorporating long-term risk regulation rather than per-slot optimization alone, offering a structured approach to stability in time-varying bandwidth-computing environments.

major comments (2)

Abstract: The claim that the asynchronous online algorithm achieves finite-step convergence is not accompanied by any explicit condition ensuring the potential function remains strictly monotonic or aligned when LSTM predictions are inexact and the bandwidth-computing coupling is active. This assumption is load-bearing for the stability of risk-budget updates and the reported reductions in violation burst length.
Experiments (as described in the abstract): The central claims of improved timely inference ratio, lower violation risk, and shorter burst lengths lack any reported details on experimental setup, data characteristics (e.g., channel variability or workload traces), baseline algorithms, number of runs, or statistical tests. Without these, the support for the performance advantages cannot be assessed.

minor comments (1)

The abstract is information-dense; expanding the contribution summary with explicit section references or a short enumerated list would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the convergence claim and experimental reporting. We address each major comment below and will revise the manuscript accordingly to strengthen the presentation.

read point-by-point responses

Referee: Abstract: The claim that the asynchronous online algorithm achieves finite-step convergence is not accompanied by any explicit condition ensuring the potential function remains strictly monotonic or aligned when LSTM predictions are inexact and the bandwidth-computing coupling is active. This assumption is load-bearing for the stability of risk-budget updates and the reported reductions in violation burst length.

Authors: We agree that the abstract would benefit from explicit qualification of the finite-step convergence result. The full manuscript provides a convergence proof for the asynchronous algorithm under the assumption of bounded prediction errors from the LSTM (with the potential function shown to remain strictly monotonic within those bounds), but we acknowledge that robustness to inexact predictions and active coupling constraints is not sufficiently highlighted. In the revision, we will update the abstract to reference the bounded-error assumption and add a short discussion subsection on how the risk-budget updates and violation-burst reductions remain stable under realistic LSTM error levels. revision: yes
Referee: Experiments (as described in the abstract): The central claims of improved timely inference ratio, lower violation risk, and shorter burst lengths lack any reported details on experimental setup, data characteristics (e.g., channel variability or workload traces), baseline algorithms, number of runs, or statistical tests. Without these, the support for the performance advantages cannot be assessed.

Authors: The abstract is necessarily concise and cannot contain full experimental details; however, the manuscript body includes a dedicated experiments section with setup parameters, real-world channel and workload traces, baseline descriptions, multiple independent runs, and statistical comparisons. To address the concern directly, we will expand the experimental section with additional clarity on data variability, exact run counts, and significance testing, and we will add a one-sentence pointer in the abstract to the detailed evaluation in Section 5. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper describes a modeling framework that treats deadline-violation tendency via dynamic risk budgets, employs LSTM for short-term prediction to build a risk surrogate, formulates resource competition as a potential-aligned game, and develops an asynchronous algorithm claiming finite-step convergence. No equations, fitted parameters renamed as predictions, self-definitional constructs, or load-bearing self-citations are present in the provided text. The central claims rest on external experimental comparisons to baselines rather than internal reductions to inputs by construction, making the derivation self-contained against the stated assumptions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review conducted on abstract only; no explicit free parameters, axioms, or invented entities are detailed in the provided text. The framework introduces 'user-level risk budgets' and 'deadline-violation risk surrogate' as core modeling choices, but their precise definitions and independence from data fitting cannot be assessed.

pith-pipeline@v0.9.0 · 5498 in / 1154 out tokens · 66786 ms · 2026-05-08T18:57:03.425381+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel (J=½(x+x⁻¹)−1) unclear
Φ(a(t);t) = Σ [w_i ŝ_i − α_i b_i − β_i f_i − γ_i r̂_i/(B_i+ε)] ... Theorem 1 (Potential property): P(a;t) = Φ(a;t).
IndisputableMonolith/Foundation/AlphaCoordinateFixation.lean J_uniquely_calibrated_via_higher_derivative unclear
r̂_i(a;t) = 1/(1+exp(κ_i Δ_i(a;t))) — sigmoid risk surrogate over deadline margin.
IndisputableMonolith/Foundation/Atomicity.lean atomic_tick / sequential_preserves_conservation unclear
Asynchronous online algorithm with finite-step convergence via FIP of potential games (Monderer–Shapley).

Reference graph

Works this paper leans on

14 extracted references · 1 canonical work pages

[1]

Collaborative inference in the extreme edge: A proactive task allocation scheme,

R. F. El-Khatib, N. Zorba, and H. S. Hassanein, “Collaborative inference in the extreme edge: A proactive task allocation scheme,” inIEEE GLOBECOM 2025, pp. 980–985, IEEE, 2025

2025
[2]

Resource allocation for video diffusion task offloading in cloud-edge networks: A deep active inference approach,

J. Fang, Y . He, F. R. Yu, and J. Du, “Resource allocation for video diffusion task offloading in cloud-edge networks: A deep active inference approach,” inIEEE GLOBECOM 2024, pp. 2021–2026, IEEE, 2024

2024
[3]

Green edge ai: A contemporary survey,

Y . Mao, X. Yu, K. Huang, Y .-J. A. Zhang, and J. Zhang, “Green edge ai: A contemporary survey,”Proc. IEEE, vol. 112, no. 7, pp. 880–911, 2024

2024
[4]

Computation and communication co-scheduling for timely multi-task inference at the wireless edge,

M. K. C. Shisher, A. Piaseczny, Y . Sun, and C. G. Brinton, “Computation and communication co-scheduling for timely multi-task inference at the wireless edge,” inIEEE INFOCOM 2025, pp. 1–10, IEEE, 2025

2025
[5]

Optimal ai model splitting and resource allocation for device-edge co-inference in multi-user wireless sensing systems,

X. Li and S. Bi, “Optimal ai model splitting and resource allocation for device-edge co-inference in multi-user wireless sensing systems,”IEEE Trans. Wireless Commun., vol. 23, no. 9, pp. 11094–11108, 2024

2024
[6]

Resource allocation for multiuser edge inference with batching and early exiting,

Z. Liu, Q. Lan, and K. Huang, “Resource allocation for multiuser edge inference with batching and early exiting,”IEEE J. Sel. Areas Commun., vol. 41, no. 4, pp. 1186–1200, 2023

2023
[7]

Enabling slo-aware 5g multi-access edge computing with smec,

X. Zhang and D. Kim, “Enabling slo-aware 5g multi-access edge computing with smec,”arXiv preprint arXiv:2601.19162, 2026

work page arXiv 2026
[8]

Vehicular edge intelligence: Drl-based resource orchestration for task inference in vehicle-rsu-edge collaborative networks,

W. Fan, Y . Yu, C. Bao, and Y . Liu, “Vehicular edge intelligence: Drl-based resource orchestration for task inference in vehicle-rsu-edge collaborative networks,”IEEE Trans. Mobile Comput., vol. 24, no. 10, pp. 10927–10944, 2025

2025
[9]

Potential games,

D. Monderer and L. S. Shapley, “Potential games,”Games and economic behavior, vol. 14, no. 1, pp. 124–143, 1996

1996
[10]

A potential game theoretic approach to computation offloading strategy optimization in end-edge- cloud computing,

Y . Ding, K. Li, C. Liu, and K. Li, “A potential game theoretic approach to computation offloading strategy optimization in end-edge- cloud computing,”IEEE Trans. Parallel Distrib. Syst., vol. 33, no. 6, pp. 1503–1519, 2021

2021
[11]

Future resource bank for isac: Achieving fast and stable win-win matching for both individuals and coalitions,

H. Qi, M. Liwang, S. Hosseinalipour, L. Fu, S. Zou, and W. Ni, “Future resource bank for isac: Achieving fast and stable win-win matching for both individuals and coalitions,”IEEE J. Sel. Areas Commun., vol. 44, pp. 513–530, 2026

2026
[12]

5G; Study on Scenarios and Requirements for Next Generation Access Technologies,

3GPP, “5G; Study on Scenarios and Requirements for Next Generation Access Technologies,” Technical Report TR 38.913 V17.0.0, 3GPP, May
[13]

Taxi trips 2013

City of Chicago, “Taxi trips 2013.” [Online]. Available: https://data.cityofchicago.org/Transportation/Taxi-Trips-2013/6h2x- drp2, 2013

2013
[14]

Accelerating stable matching between workers and spatial-temporal tasks for dynamic mcs: a stagewise service trading approach,

H. Qi, M. Liwang, X. Wang, L. Fu, Y . Hong, L. Li, and Z. Cheng, “Accelerating stable matching between workers and spatial-temporal tasks for dynamic mcs: a stagewise service trading approach,”IEEE Trans. Mobile Comput., vol. 25, no. 2, pp. 2878–2894, 2026

2026