Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS

Eyhab Al-Masri; Preethika Pradeep

REVIEW 3 major objections 4 minor 1 cited by

Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A TOPSIS-based Kubernetes scheduler cuts scheduling-energy use by up to 39.1%.

desk verdict A plausible narrow result about scheduling-decision energy is inflated into cluster-wide savings by a unit mismatch in the extrapolation; worth a look but needs major revision. read the letter →

arxiv 2506.04902 v1 pith:ZVQ7NNVS submitted 2025-06-05 cs.DC cs.PFcs.SYeess.SY

Preethika Pradeep , Eyhab Al-Masri This is my paper · ORCID

classification cs.DCcs.PFcs.SYeess.SY

keywords KubernetesTOPSISenergy-awareschedulingAIoTcontainerorchestrationpodplacementmulti-criteriadecisionanalysisedgecomputing

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

GreenPod is a custom Kubernetes scheduler that uses TOPSIS, a multi-criteria decision analysis method, to rank nodes on five weighted criteria: execution time, energy consumption, core availability, memory availability, and resource balance. The paper claims that in a heterogeneous Google Kubernetes Engine cluster this approach reduces the energy consumed by scheduling decisions by up to 39.1% relative to the default Kubernetes scheduler, with the largest savings under an energy-centric weighting profile and medium-complexity workloads. The motivation is that the default scheduler optimizes only basic resource availability, which is inadequate for energy-sensitive AIoT workloads spanning cloud and edge. The paper also extrapolates the measured savings to annual energy, CO2, and cost reductions for a production-scale cluster, concluding that energy-aware scheduling is a viable sustainability lever for container orchestration.

What carries the argument

The central object is TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), a multi-criteria decision analysis method that ranks each candidate node by its distances to an ideal positive solution and an ideal negative solution, then orders nodes by the resulting closeness coefficient. GreenPod wraps this scoring engine in a scheduling pipeline that profiles workload energy, adaptively weights the five criteria, normalizes the decision matrix, and binds the pod to the top-ranked node through the Kubernetes API. This machinery carries the argument by collapsing five competing objectives into a single node ranking that the scheduler can optimize directly.

What would settle it

Measure whole-cluster energy (idle, scheduling, and pod execution) over a full day on the same heterogeneous GKE node pool with GreenPod energy-centric scheduling and the default scheduler, using identical workloads; if the total-energy difference is far below the 19.38% used in Section V.E, the annual 10.70 MWh and 3.99-ton CO2 estimates do not follow.

Watch

Extended reading notes

Core claim

GreenPod replaces the default kube-scheduler's single-objective scoring with a TOPSIS ranking over five weighted criteria (execution time, energy consumption, core availability, memory availability, and resource balance), selecting the node with the highest closeness to an ideal solution. In a heterogeneous Google Kubernetes Engine cluster, the energy-centric weighting profile reduced scheduling-decision energy by up to 39.1% compared with the default Kubernetes scheduler, with the largest gains on medium-complexity workloads and under medium competition. The paper further extrapolates these savings to annual cluster-level energy, CO2, and cost reductions using SURF Lisa job statistics and a blade-server power model.

Load-bearing premise

The broad annual savings claims depend on applying the 19.38% average optimization, which was measured on scheduling-decision energy, to the 0.024 kWh per-job total-energy estimate, and the paper does not show that scheduling overhead scales to total workload energy.

Editorial extensions

If this is right

With energy-centric weighting, GreenPod reports 37.96%, 39.13%, and 33.82% energy optimization over the default scheduler at low, medium, and high competition levels.
Medium competition is the sweet spot: the average optimization across profiles is 24.03%, compared with 18.98% at low and 15.12% at high competition.
Performance-centric weighting produces the smallest energy gains (2.22% to 8.29%), so prioritizing execution speed alone undercuts the sustainability objective.
If the Section V.E extrapolation holds, one SURF Lisa-scale cluster would save about 10.70 MWh and 3.99 metric tons of CO2 per year, and about $1,380 in direct electricity costs.
Energy-centric scheduling allocates work preferentially to energy-efficient node category A, which is why medium-complexity workloads benefit most while light workloads show variable results due to scheduling overhead.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper's measurements, the headline 39.1% figure describes the energy consumed by the scheduling process itself; whether that translates to total cluster energy (including pod execution) is a separate experiment the paper does not report.
Because the resource-efficient profile collapses to 4.86% optimization under high competition, an adaptive scheduler that switches weighting profiles as contention rises could preserve savings where any single fixed profile degrades.
The same TOPSIS pipeline could be tested on GPU-bound or memory-bound AIoT services rather than batch-style linear-regression tasks to see whether the 39% ceiling generalizes to other workload shapes.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear colleague,

The thing to know about this paper is that the central empirical result is probably real but the headline is not. GreenPod is a TOPSIS-based Kubernetes scheduler, and the authors measure the energy consumed by scheduling decisions across three competition levels and four weighting schemes on a heterogeneous GKE cluster. The measured 39.1% improvement for the energy-centric weighting at medium competition is a reduction in scheduling-decision energy, not a reduction in total workload or cluster energy. The paper itself says so in Table IV: 'Energy Consumed (kJ) quantifies the efficiency of scheduling decisions.' The abstract and conclusion drop that qualification.

What is new: TOPSIS for Kubernetes scheduling is not new (KCSS, K-Tahp), but the systematic comparison of weighting schemes on a heterogeneous cluster with different competition levels is a legitimate extension. The experimental setup is reasonable, and the direction of the result makes sense: if you weight energy heavily, you save scheduling energy.

The soft spot is load-bearing. Section V.E takes the average 19.38% optimization, which applies to scheduling-decision energy of about 0.5 kJ per decision, and multiplies it by an estimate of total job energy (0.024 kWh/job) from a blade-server power model. That calculation assumes the scheduling-energy improvement transfers to total job energy. It does not. The actual scheduling-energy saving is about 0.000025 kWh per decision, roughly 0.1% of the per-job estimate. The annual MWh, CO2, and cost figures are inflated by about two orders of magnitude. There is also a smaller inconsistency: the abstract says 3.39 metric tons of CO2, the body says 3.99.

The paper lacks error bars or any statistical analysis, and the TOPSIS implementation details are thin (how exactly are weights set? how is energy measured?). The claimed open-source artifact is not verifiable from the paper.

Who is this for? Readers interested in energy-aware Kubernetes scheduling might learn from the experimental design, but the results as presented are not trustworthy. The narrow claim, that a TOPSIS scheduler can reduce scheduling energy, could be a valid small contribution if properly qualified.

Recommendation: send to peer review, because the topic is relevant and the empirical setup is real, but the authors need to fix the extrapolation, qualify the claims, and provide implementation details. As it stands, a reject is defensible; the paper is not publishable without major revision.

Referee Report

3 major / 4 minor

Summary. The paper presents GreenPod, a TOPSIS-based Kubernetes scheduler that ranks candidate nodes on five weighted criteria: execution time, energy consumption, core availability, memory availability, and resource balance. The authors evaluate GreenPod against the default Kubernetes scheduler on a heterogeneous Google Kubernetes Engine cluster under three competition levels and four weighting schemes, reporting energy savings of up to 39.1%. They then extrapolate these savings to a production-scale cluster using SURF Lisa job statistics and a blade-server power model, claiming annual savings of 10.70 MWh, 3.99 metric tons of CO2, and about $1,380 per cluster. The paper also releases an open-source scheduler. The core experimental comparison is real, but the headline claim and the real-world impact analysis rest on a conflation between scheduling-decision energy and total workload energy, which is a load-bearing error.

Significance. If the experimental results were properly scoped to scheduling-decision energy only, GreenPod would be a modest engineering contribution: it demonstrates that a TOPSIS-based custom scheduler can be integrated into Kubernetes and can reduce the energy spent in scheduling decisions in a heterogeneous cluster. However, the paper's stated significance—energy-efficient orchestration for AIoT workloads with meaningful cluster-wide savings—depends entirely on the unsupported extrapolation in Section V.E. The paper explicitly defines its measured 'Energy Consumed (kJ)' as quantifying the efficiency of scheduling decisions (Table IV), yet the abstract and conclusion make unqualified claims about reducing energy consumption, and the impact analysis applies the scheduling-decision savings percentage to total per-job energy. Because the extrapolation inflates the savings by roughly two orders of magnitude, the environmental and economic claims (MWh, CO2, cost, carbon credits) are not credible as presented. The underlying experimental comparison may be salvageable, but only with a major reframing of the claims and new measurements of total workload energy.

major comments (3)

[Table VI] Section V.E contains a unit mismatch that undermines the real-world impact claims. Table IV defines 'Energy Consumed (kJ)' as quantifying the efficiency of scheduling decisions, and Table VI reports values around 0.5 kJ per decision for the default scheduler. The average optimization of 19.38% is therefore the reduction in scheduling-decision energy, which is roughly 0.0886 kJ (about 0.000025 kWh) per decision. Section V.E multiplies a per-job total-energy estimate of 0.024 kWh (derived from the Dayarathna blade-server power model with assumed parameters) by 0.1938 to obtain daily savings of 0.0293 MWh and annual savings of 10.70 MWh. This assumes, without any justification, that the 19.38% scheduling-decision energy improvement transfers to the total energy consumed by the job. The paper's own numbers show that scheduling-decision energy is about 0.1% of the 0.024 kWh per-job estimate, so the extrapolated MWh, CO2, and cost savings are inflated by approximately two orders of magnitude. This error is load-bearing because the paper's conclusion and contribution claims are based on these extrapolated savings.
[V.E] The abstract and conclusion state that GreenPod 'reduces energy consumption by up to 39.1% compared to the default scheduler.' Since the experimental metric in Table VI is explicitly defined in Table IV as 'energy consumed' by scheduling decisions, the claim should be scoped to 'scheduling-decision energy' or similar. As written, the unqualified phrasing overstates the result by implying a 39.1% reduction in total workload or cluster energy. This is not merely a wording issue: the unsupported extrapolation in Section V.E is a direct consequence of treating the scheduling-only percentage as a workload-level energy saving.
[IV.C] The paper does not specify how the energy consumption values in Table VI were measured. Section III.A mentions 'monitoring agents that collect fine-grained energy data via hardware interfaces or calibrated power models,' but Section IV never states which method was used, what hardware or sensors were involved, how the energy of a single scheduling decision was isolated from the energy of the running workloads, or whether any statistical replication was performed. Without this information, the numerical values in Table VI (e.g., identical 0.5036 kJ for all low-competition default-scheduler rows) cannot be independently assessed, which is a serious problem for the paper's central empirical claim.

minor comments (4)

[Abstract] The abstract reports CO2 reductions of '~3.39 metric tons per cluster annually,' while Section V.F and Table VII state 3.99 metric tons per cluster. These numbers are inconsistent and should be reconciled.
[V.F] Table VII lists Total Savings (1 Yr, Min) as $1,381, while the text in Section V.F says the combined range is '$1,380 to $2,047.' The $1 versus $0 difference is a minor arithmetic inconsistency.
[V.E] The text writes '0.8 88 MWh monthly' with a stray space; Table VII correctly shows 0.88 MWh. Also, 'approximately 0.8 8 MWh monthly' should be '0.88 MWh.'
[II.B] Reference [37] is titled 'K-Tahp: A Kubernetes Load Balancing Strategy base on TOPSIS+AHP'; the acronym should likely be 'K-TAHP' and the word 'based' is misspelled. In addition, the open-source link in reference [39] points to 'aeris-lab/GreenCube,' which does not match the paper's project name 'GreenPod'; the reproducibility claim should be verified.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: GreenPod's central energy-savings claim is an empirical comparison against the default Kubernetes scheduler, and the extrapolation issue in Section V.E is a validity concern, not a circular derivation.

full rationale

GreenPod's central claim is an empirical comparison against the default Kubernetes scheduler on a GKE testbed, with energy savings reported in Table VI. TOPSIS ranking with energy-centric weights is a design choice, not a fitted parameter, and the 39.1% figure is a measured outcome rather than a quantity defined into existence. No load-bearing argument rests on a self-citation; the reference to the authors' own repository [39] only supports reproducibility of the implementation. The Section V.E extrapolation applies the measured 19.38% scheduling-energy optimization to a per-job total-energy estimate derived from an external blade-server power model by Dayarathna et al. This is a questionable unit and scope transfer, since the measured quantity is scheduling-decision energy while the extrapolation target is total workload energy, but it is not circular: the 19.38% is empirically measured, the 0.024 kWh value is externally parameterized, and neither quantity is defined in terms of the claimed savings. Consequently, the derivation chain does not reduce to its own inputs, and no circular step is present. Concerns about the Section V.E extrapolation should be treated as correctness or external-validity risks rather than circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 1 invented entities

The central claim depends on hand-chosen TOPSIS weights and experimental settings, plus an unsupported extrapolation that applies scheduling-only energy savings to total job energy. These are the key free parameters and assumptions that determine the quantitative results.

free parameters (3)

TOPSIS criterion weights = unspecified
The four weighting schemes (general, energy-centric, performance-centric, resource-efficient) are defined qualitatively in Section IV.D, but the exact weights assigned to execution time, energy, cores, memory, and balance are never stated. The results depend on these hand-chosen weights.
Power model parameters for extrapolation = 60% CPU, 8M mem accesses/s, 350 I/O ops/s, 3M net ops/s, 34-min runtime, PUE 1.45
In Section V.E, these values are used to compute the average job energy of 0.024 kWh. They are chosen without empirical basis or sensitivity analysis, and they directly determine the annual savings estimates.
Competition level pod counts = Low: 4/2/2, Medium: 8/4/2, High: 12/6/4
The experimental design in Table V sets the number of light, medium, and complex pods at each competition level. These hand-set values define the tested scenarios and affect the measured optimization percentages.

assumptions (4)

standard math TOPSIS is a valid multi-criteria decision method.
The paper uses TOPSIS as the ranking algorithm without justification; it is an established technique that is accepted as a standard approach.
domain assumption Energy profiling data are accurate.
Section III.A states monitoring agents collect energy data via hardware interfaces or calibrated power models, but no calibration or validation of these measurements is provided.
domain assumption The default Kubernetes scheduler does not consider energy.
The comparison in Section V relies on the well-known behavior that the default scheduler only considers CPU and memory requests, not energy consumption.
ad hoc to paper SURF Lisa SLURM job statistics can be combined with a blade-server power model and the measured optimization percentage.
Section V.E assumes that HPC job counts from Chu et al. [31] and the power model from Dayarathna et al. [32] are representative of a containerized AIoT cluster, and that the 19.38% scheduling-energy optimization applies to total job energy. This is specific to the paper's impact analysis and is not justified.

invented entities (1)

GreenPod scheduler independent evidence
purpose: A custom Kubernetes scheduler using TOPSIS to place pods based on execution time, energy consumption, core availability, memory availability, and resource balance.
The paper references a public GitHub repository (aeris-lab/GreenCube) as a potential source of verification, but the repository contents are not described and we cannot confirm its existence or completeness from the paper alone.

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Cite this review

Pith. "Pith review of Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS." pith.science (2026). https://pith.science/paper/ZVQ7NNVS

@misc{pith2026250604902,
  author       = {Pith},
  title        = {Pith review of: Energy-Optimized Scheduling for AIoT Workloads Using TOPSIS},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZVQ7NNVS}},
  note         = {Machine review of arXiv:2506.04902}
}

read the original abstract

AIoT workloads demand energy-efficient orchestration across cloud-edge infrastructures, but Kubernetes' default scheduler lacks multi-criteria optimization for heterogeneous environments. This paper presents GreenPod, a TOPSIS-based scheduler optimizing pod placement based on execution time, energy consumption, processing core, memory availability, and resource balance. Tested on a heterogeneous Google Kubernetes cluster, GreenPod improves energy efficiency by up to 39.1% over the default Kubernetes (K8s) scheduler, particularly with energy-centric weighting schemes. Medium complexity workloads showed the highest energy savings, despite slight scheduling latency. GreenPod effectively balances sustainability and performance for AIoT applications.

Figures

Figures reproduced from arXiv: 2506.04902 by the authors.

**Figure 1.** , the system consists of three primary tiers: heterogeneous edge devices, an intelligent edge gateway hosting the TOPSIS-based scheduler within the Kubernetes (K8s) environment, and an elastic cloud environment for workload offloading. This design ensures workload-aware placement that balances performance and energy efficiency—critical for sustainable AIoT operations. A. GreenPod System Components • Edge Devices: Th… view at source ↗

**Figure 2.** visualizes the energy optimization achieved by each scheduling strategy across different competition levels. C. Impact of Competition Levels [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

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