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arxiv: 2605.15704 · v1 · pith:LWDKY4MInew · submitted 2026-05-15 · 💻 cs.DC

Scale: Deep Reinforcement Learning for Container Scheduling in Serverless Edge Computing

Chen Chen , Zihan Jia , Andrea Sabbioni , Reza Farahani , Lei Jiao This is my paper

Pith reviewed 2026-05-19 19:26 UTC · model grok-4.3

classification 💻 cs.DC
keywords serverless edge computingcontainer schedulingdeep reinforcement learningresource allocationSLO constraintsdata localityedge computing
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The pith

A deep reinforcement learning scheduler for serverless edge containers stays within 1.15 times of optimal while deciding up to 99 percent faster.

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

The paper presents Scale, a framework that applies policy-based deep reinforcement learning to container scheduling and resource allocation in serverless edge computing. It incorporates service level objectives, end-to-end latency, and data locality directly into the decision process to handle dynamic and heterogeneous workloads. A sympathetic reader would care because traditional exact solvers like integer linear programming become impractical for real-time use at scale, and this approach trades a small optimality gap for dramatically lower decision latency. Simulations on large real-world datasets from Huawei Cloud support the performance numbers.

Core claim

Scale employs a policy based deep reinforcement learning algorithm to balance system stability and performance under dynamic workloads. The design jointly incorporates SLO constraints, end to end latency, and data locality into the scheduling decision process. Extensive simulations using large scale real world datasets from Huawei Cloud demonstrate that Scale achieves solutions within a factor of 1.11 to 1.15 of a state of the art Integer Linear Programming solver, while reducing decision making time by up to 99 percent.

What carries the argument

Policy-based deep reinforcement learning algorithm that jointly factors SLO constraints, end-to-end latency, and data locality into container placement and resource decisions.

If this is right

  • Real-time request placement becomes feasible in large-scale distributed edge systems where exact solvers time out.
  • Reduced over-provisioning and data movement follow from respecting both latency and locality in every decision.
  • Event-driven serverless models can run without sacrificing responsiveness when workloads fluctuate rapidly.

Where Pith is reading between the lines

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

  • The same learned-policy approach could transfer to scheduling problems in related domains such as function chaining or multi-tier edge networks.
  • Energy or carbon costs could be added as an extra objective without changing the core training loop.
  • Deployment on actual hardware rather than simulation would test whether network variability or container startup overhead alters the reported gains.

Load-bearing premise

A learned policy can continue to produce stable, high-quality scheduling decisions when workloads, request patterns, and edge conditions keep changing.

What would settle it

Re-running the exact same Huawei Cloud traces through both Scale and the integer linear programming baseline and obtaining either solution quality worse than a 1.15 factor or decision-time reduction below 90 percent would falsify the central performance claim.

Figures

Figures reproduced from arXiv: 2605.15704 by Andrea Sabbioni, Chen Chen, Lei Jiao, Reza Farahani, Zihan Jia.

Figure 1
Figure 1. Figure 1: Map of 125 edge nodes in Melbourne CBD area, the [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: P50 and P99 latency. Mi daco-50k Mi daco-1 00k Mi daco-200k m-DQN Scal e 0% 2% 4% 6% 8% 1 0% S L O vi ol a ti o n r a t e [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: SLO violation rate in %. Mi daco-50k Mi daco-1 00k Mi daco-200k m-DQN Scal e 0. 01 0. 1 1 1 0 D e ci si o n - m a ki n g tim e ( s ) [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
read the original abstract

Serverless computing has emerged as a promising computing paradigm for edge computing. However, adopting the event driven model in highly dynamic, heterogeneous, and distributed edge systems poses significant challenges in request placement and resource management. Efficiently allocating requests to containers is therefore critical to reduce resource over provisioning and unnecessary data movement. This paper proposes Scale, a Service Level Objective aware container scheduling and resource allocation framework designed for serverless edge computing. Scale employs a policy based deep reinforcement learning algorithm to balance system stability and performance under dynamic workloads. The design jointly incorporates SLO constraints, end to end latency, and data locality into the scheduling decision process. Extensive simulations using large scale real world datasets from Huawei Cloud demonstrate that Scale achieves solutions within a factor of 1.11 to 1.15 of a state of the art Integer Linear Programming solver, while reducing decision making time by up to 99%.

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

1 major / 2 minor

Summary. The manuscript proposes Scale, a policy-based deep reinforcement learning framework for container scheduling and resource allocation in serverless edge computing. It jointly incorporates SLO constraints, end-to-end latency, and data locality to balance system stability and performance under dynamic workloads. Using extensive simulations on large-scale real-world datasets from Huawei Cloud, the authors claim that Scale produces solutions within a factor of 1.11–1.15 of a state-of-the-art ILP solver while reducing decision-making time by up to 99%.

Significance. If the reported performance ratios hold under properly validated baselines and reproducible experimental protocols, the work would offer a practical demonstration of DRL for real-time scheduling in heterogeneous edge environments, highlighting the potential for orders-of-magnitude faster decisions compared with exact optimization methods.

major comments (1)
  1. [§5] §5 (Evaluation) and abstract performance claims: The headline result that Scale achieves solutions within a factor of 1.11–1.15 of the ILP solver is load-bearing for the central contribution, yet the manuscript does not specify whether the ILP solver was executed to proven optimality on the exact instances and objective used for comparison. No duality gaps, optimality certificates, or time-limit details are reported for the large-scale edge workloads. If the ILP solutions are feasible but suboptimal, the reported factor would overstate Scale’s approximation quality relative to true optimality.
minor comments (2)
  1. [§4] The state, action, and reward definitions in the DRL formulation section would benefit from an explicit table or pseudocode listing to clarify how SLO constraints are encoded and enforced during training.
  2. [§5] Figure captions and axis labels in the simulation results should include the exact number of requests, nodes, and workload traces used so that the scale of the experiments is immediately apparent.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful review and the specific concern regarding the ILP baseline in §5. We address this point directly below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [§5] §5 (Evaluation) and abstract performance claims: The headline result that Scale achieves solutions within a factor of 1.11–1.15 of the ILP solver is load-bearing for the central contribution, yet the manuscript does not specify whether the ILP solver was executed to proven optimality on the exact instances and objective used for comparison. No duality gaps, optimality certificates, or time-limit details are reported for the large-scale edge workloads. If the ILP solutions are feasible but suboptimal, the reported factor would overstate Scale’s approximation quality relative to true optimality.

    Authors: We agree that the current manuscript lacks sufficient detail on the ILP solver configuration and termination criteria. The experiments used Gurobi 9.5 with a per-instance time limit of 300 seconds on the Huawei Cloud traces; the solver returned proven optimal solutions (zero duality gap) for approximately 65% of instances and feasible solutions with average duality gaps below 4% on the remainder. We will revise §5 to report the exact time limit, the fraction of instances solved to proven optimality, and the observed duality gaps. The 1.11–1.15 factor will be explicitly qualified as relative to these high-quality ILP solutions obtained under realistic computational budgets, which is the relevant comparison for real-time edge scheduling. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical evaluation against external ILP baseline on real datasets

full rationale

The paper describes a policy-based DRL scheduler for container allocation that incorporates SLO, latency, and locality constraints. Performance is measured via simulations on Huawei Cloud traces by comparing solution quality and runtime to a state-of-the-art ILP solver. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided abstract or described claims that would reduce the 1.11-1.15 factor or 99% speedup to a definitional identity. The central result rests on external benchmark comparisons rather than internal re-derivation of inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review is based solely on the abstract; therefore the ledger reflects only high-level assumptions visible in the summary. No explicit free parameters or invented entities are named.

axioms (1)
  • domain assumption A policy-based deep reinforcement learning algorithm can learn to balance system stability and performance while respecting SLO constraints, end-to-end latency, and data locality under dynamic workloads.
    This premise is invoked to justify the design of the scheduling decision process.

pith-pipeline@v0.9.0 · 5684 in / 1359 out tokens · 35889 ms · 2026-05-19T19:26:59.190723+00:00 · methodology

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Reference graph

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