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arxiv: 2604.24548 · v1 · submitted 2026-04-27 · 💻 cs.DC

Recognition: unknown

SpotVista: Availability-Aware Recommendation System for Reliable and Cost-Efficient Multi-Node Spot Instances

Taeyoon Kim , Kyumin Kim , Kyunghwan Kim , Hayoung Kim , Seungwoo Jeong , Moohyun Song , Kyungyong Lee
Authors on Pith no claims yet

Pith reviewed 2026-05-08 01:38 UTC · model grok-4.3

classification 💻 cs.DC
keywords spot instancescloud computingavailabilityrecommendation systemmulti-nodecost efficiencyreliabilityAWS
0
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The pith

SpotVista recommends pools of multi-node spot instances that deliver substantially higher availability and lower costs than previous methods.

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

The paper introduces SpotVista, a recommendation system for choosing reliable and cost-efficient groups of multi-node spot cloud instances. Cloud providers offer cheap spot instances but they can be interrupted without warning, and modern applications often need multiple such instances working together. Existing tools focus on single instances and rely on outdated pricing signals, so SpotVista instead gathers large-scale data on actual multi-node availability despite query limits, analyzes the patterns, and builds a method to pick better configurations. If successful, this would let users run multi-node workloads on spot instances with much less risk of failure and still save money compared to on-demand or less optimized spot choices.

Core claim

SpotVista establishes a methodology for recommending cost-efficient and reliable multi-node spot instance configurations by collecting and analyzing a large-scale multi-node availability dataset. Through real-world interruption experiments, it shows this approach outperforms SpotVerse by achieving 81.28% greater availability and 2.84% more cost savings in multi-region setups, and surpasses AWS SpotFleet with 21.6% higher stability and 26.3% greater cost savings.

What carries the argument

The SpotVista recommendation methodology, which uses collected multi-node availability datasets to identify stable and low-cost instance pools.

Load-bearing premise

The large-scale multi-node availability dataset collected is representative of real-world behaviors across workloads, regions, and cloud providers, allowing the methodology to generalize.

What would settle it

A replication experiment on a different cloud provider or with a different workload type that shows no improvement or worse performance than SpotVerse or SpotFleet would falsify the claim of general superiority.

Figures

Figures reproduced from arXiv: 2604.24548 by Hayoung Kim, Kyumin Kim, Kyunghwan Kim, Kyungyong Lee, Moohyun Song, Seungwoo Jeong, Taeyoon Kim.

Figure 1
Figure 1. Figure 1: Success rate of spot instance allocation for types with a single-node view at source ↗
Figure 2
Figure 2. Figure 2: Example of quantifying spot instance availability score by using the view at source ↗
Figure 3
Figure 3. Figure 3: Implementation of the proposed system recommendation module. The files are saved with a Year-Month￾Date-Time naming convention to include historical data. The recommendation module is designed using a serverless architecture [42] to flexibly handle irregular recommendation requests from users. When users provide their resource require￾ment, such as minimum number of CPU cores or memory size, along with opt… view at source ↗
Figure 4
Figure 4. Figure 4: Multi-node SPS dataset collection overhead and completeness of the proposed sampling query heuristic view at source ↗
Figure 5
Figure 5. Figure 5: Mean Absolute Error of the USQS heuristic as a function of step size view at source ↗
Figure 6
Figure 6. Figure 6: Spatial, temporal, and seasonal characteristics of multi-node view at source ↗
Figure 9
Figure 9. Figure 9: T3 differences for the same instance type across regions and AZs 1 5 10 15 20 25 30 35 40 45 50 T3 10 20 30 40 50 60 70 80 24-hour T3 sustatining ratio (%) view at source ↗
Figure 8
Figure 8. Figure 8: SPS value distribution diversity to build a compute resource pool view at source ↗
Figure 11
Figure 11. Figure 11: Superb real availability modeling capability of the proposed reliability view at source ↗
Figure 15
Figure 15. Figure 15: High correlations of T3 and T2 0.0 0.5 1.0 Weight 0 20 40 60 80 100 Availability Score (a) Availability score 0.0 0.5 1.0 Weight 40 50 60 70 80 90 100 Cost Score (b) Cost score view at source ↗
Figure 16
Figure 16. Figure 16: Impact of weight (W) to score distribution and T2 are highly synchronized, and an availability score derived solely from T3 is sufficient for assessing instance stability. Impact of Weight Parameter, W. To analyze the effect of W on ranking, 360 synthetic scenarios were constructed with vary￾ing vCPU (80–640) and memory (160–1280 GB) requirements across different instance categories, families, and types. … view at source ↗
Figure 17
Figure 17. Figure 17: Average total scores as we diversify a set of recommended instance view at source ↗
Figure 18
Figure 18. Figure 18: Comparing the spot instance recommendation quality of SpotVista with SpotVerse view at source ↗
Figure 19
Figure 19. Figure 19: Comparing SpotVista with AWS SpotFleet and simple approaches without using historical dataset view at source ↗
read the original abstract

Cloud vendors offer discounted spot instances to maximize surplus resource utilization, but these instances are subject to the risk of sudden interruption. Traditional pricing datasets have been employed to predict this risk, yet recent policy changes by cloud vendors have diminished their effectiveness. To promote spot instance usage, public cloud vendors provide instant availability datasets to help users mitigate interruption risks. While existing research utilizing this data has proposed methods to reduce interruptions, these studies have primarily focused on single-node instances, overlooking the stability of multi-node environments widely adopted for modern cloud workloads. This paper proposes SpotVista, a system that recommends a resource pool of reliable and cost-efficient multi-node spot instances by leveraging various publicly available datasets. To achieve this, SpotVista collects a large-scale multi-node availability dataset while overcoming significant query limitations. Through a thorough analysis of multi-node spot instance availability behavior, SpotVista establishes a methodology for recommending cost-efficient and reliable multi-node configurations. To evaluate how effectively the proposed methodology reflects multi-node availability and cost efficiency, extensive real-world interruption experiments were conducted. The results demonstrate that SpotVista outperforms the state-of-the-art work, SpotVerse, achieving 81.28% greater availability and 2.84\% more cost savings in a multi-region setup. When compared to a publicly available service, AWS SpotFleet, SpotVista provides 21.6\% higher stability and 26.3% greater cost savings.

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 paper introduces SpotVista, a recommendation system for selecting reliable and cost-efficient multi-node spot instance pools. It collects a large-scale multi-node availability dataset despite query limitations, performs an analysis of joint availability behavior across nodes, derives a recommendation methodology, and validates it via real-world interruption experiments. The abstract reports that SpotVista achieves 81.28% greater availability and 2.84% more cost savings than SpotVerse in multi-region setups, and 21.6% higher stability plus 26.3% greater cost savings than AWS SpotFleet.

Significance. If the dataset proves representative and the experimental results hold under scrutiny, the work would address a genuine gap: existing spot-instance research has focused on single-node cases while modern workloads are multi-node. A validated methodology could improve practical adoption of spot instances by providing concrete, availability-aware configuration advice that balances reliability and cost.

major comments (2)
  1. [Abstract / Evaluation] Abstract and Evaluation section: the headline claims (81.28% availability gain vs. SpotVerse, 21.6% stability gain vs. SpotFleet) rest on real-world interruption experiments, yet the manuscript supplies no description of experimental design, statistical methods, data-exclusion rules, how availability was measured, or the number of trials. Without these details the quantitative results cannot be assessed for robustness or reproducibility.
  2. [Data Collection / Methodology] Data-collection and methodology sections: the paper states that a large-scale multi-node availability dataset was gathered “while overcoming significant query limitations,” but provides no sampling strategy, bias-mitigation steps, coverage across regions/workloads/providers, or validation against independent traces. Because all subsequent recommendation rules and performance claims derive from this dataset, its representativeness is load-bearing and must be demonstrated.
minor comments (2)
  1. [Abstract] Abstract: the percentage “2.84%” appears with an escaped backslash (“2.84%”); this is a minor typesetting artifact.
  2. [Discussion / Conclusion] The manuscript would benefit from an explicit limitations subsection that discusses the scope of the collected dataset and the conditions under which the recommendation rules may not generalize.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important areas for improving transparency in our work. We address each major comment below and will incorporate the suggested details into the revised manuscript to strengthen the presentation of our experimental results and data collection process.

read point-by-point responses

  1. Referee: [Abstract / Evaluation] Abstract and Evaluation section: the headline claims (81.28% availability gain vs. SpotVerse, 21.6% stability gain vs. SpotFleet) rest on real-world interruption experiments, yet the manuscript supplies no description of experimental design, statistical methods, data-exclusion rules, how availability was measured, or the number of trials. Without these details the quantitative results cannot be assessed for robustness or reproducibility.

    Authors: We acknowledge that the current manuscript does not provide sufficient detail on the experimental design supporting the headline claims. Although the Evaluation section notes that real-world interruption experiments were conducted, it lacks explicit descriptions of the setup. In the revised version, we will add a dedicated subsection in the Evaluation section that specifies the experimental design, including the number of trials performed, the precise method for measuring availability (monitoring instance uptime and interruptions over defined periods), the statistical methods applied (e.g., computation of means, standard deviations, and confidence intervals), and any data-exclusion rules (such as filtering transient network-related events). These additions will improve reproducibility and allow readers to assess the robustness of the reported gains versus SpotVerse and AWS SpotFleet. revision: yes

  2. Referee: [Data Collection / Methodology] Data-collection and methodology sections: the paper states that a large-scale multi-node availability dataset was gathered “while overcoming significant query limitations,” but provides no sampling strategy, bias-mitigation steps, coverage across regions/workloads/providers, or validation against independent traces. Because all subsequent recommendation rules and performance claims derive from this dataset, its representativeness is load-bearing and must be demonstrated.

    Authors: We agree that the representativeness of the multi-node availability dataset is critical, given that it forms the basis for our analysis and recommendations. The manuscript currently mentions overcoming query limitations but omits the supporting methodological details. We will expand the Data Collection section to describe the sampling strategy (periodic, systematic queries across instance types and regions to capture temporal patterns), bias-mitigation steps (use of multiple accounts and varied query schedules to reduce rate-limiting effects), coverage (specific regions, instance families, and time span), and validation steps (comparisons with available single-node public traces where feasible). These revisions will better demonstrate the dataset's suitability for deriving the joint-availability methodology. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on independent data collection and external experiments

full rationale

The paper's chain proceeds from public availability datasets (explicitly external) to collection of a multi-node trace, analysis to form a recommendation methodology, and validation via separate real-world interruption experiments. No equations or steps reduce a prediction to a fitted input by construction, no self-definitional loops appear, and no load-bearing uniqueness or ansatz is imported via self-citation. Performance deltas (81.28% availability, 26.3% cost savings) are reported from those independent experiments rather than being statistically forced by the input data itself. The derivation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The contribution is empirical and systems-oriented; the abstract describes data collection, behavioral analysis, and experimental validation with no evident mathematical axioms, free parameters, or newly postulated entities.

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Reference graph

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