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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM68515

CydroFlux AI

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68515

Volume:

10 |

10 |

Oct 2026

Pages:

1-12

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM68515

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

NS

Nandhini S

Department of Computer Science And Engneering

Sri Shakthi Institute of Engineering and Technology

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NP

Nethra P

Department of Computer Science And Engneering

Sri Shakthi Institute of Engineering and Technology

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MA

Malini A

Department of Computer Science And Engneering

Sri Shakthi Institute of Engineering and Technology

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MM

Monisha M

Department of Computer Science And Engneering

Sri Shakthi Institute of Engineering and Technology

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NV

Netra V

Department of Computer Science And Engneering

Sri Shakthi Institute of Engineering and Technology

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Abstract

CydroFlux AI is a web platform that decides where an AI workload should run, not only whether it can run. It routes inference, batch-training and real-time requests across six AWS-hosted data-centre regions by combining a transparent Green Intelligence Score (GIS) with strict operational guardrails. The GIS is a weighted sum of renewable-energy share (40%), grid carbon intensity (30%), water-usage effectiveness (20%) and server-load headroom (10%). Facilities must first satisfy latency (≤150 ms), load (≤90%), data-residency and telemetry-validity rules; eligible facilities are then ordered by a deterministic tiebreaker hierarchy, and every decision is stored with an audit trail and a human-readable reason. A Gemma-family language model interprets natural-language workload descriptions into structured requirements but cannot invent telemetry or choose the facility; a rule-based parser replaces it when unavailable. The backend uses FastAPI and MongoDB Atlas with an in-memory fallback, and the React interface supports English, Tamil and Hindi. In verification, 118 of 119 automated tests passed; the single failure needs live internet access. An audit log of 1,777 development decisions shows a median routing computation of 0.78 ms, and the GIS ranking was stable across five weight profiles. Carbon, renewable, water and load values are currently modelled from regional baselines, not live feeds, so outputs must not be read as measured emissions or validated savings.

KEYWORDS

carbon-aware routing green AI data-centre scheduling sustainability scoring explainable decisions large language model

Cite This Article

Nandhini S, Nethra P, Malini A, Monisha M, Netra V, "CydroFlux AI," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-12, Oct 2026. https://doi.org/10.55041/IJSREM68515

Full Article/Conclusion

CydroFlux AI implements an end-to-end, sustainability-aware routing engine for AI workloads. It collects and normalises telemetry for six data-centre regions, scores each facility with a transparent Green Intelligence Score, enforces latency, capacity and residency guardrails, chooses a facility by a deterministic hierarchy and records the full reasoning. A language model helps interpret natural-language requests but is confined to intent. . At baseline the facility ranking is robust to the weights and to telemetry noise, and the latency guardrail is what determines the winner for a given client. The contribution is the orchestration and its inspectability, not a new optimisation algorithm.

References

[1]     E. Masanet, A. Shehabi, N. Lei, S. Smith and J. Koomey, “Recalibrating global data center energy-use estimates,” Science, vol. 367, no. 6481, 2020. [2]     D. Patterson et al., “Carbon emissions and large neural network training,” arXiv:2104.10350, 2021. [3]     P. Li, J. Yang, M. A. Islam and S. Ren, “Making AI less ‘thirsty’: Uncovering and addressing the secret water footprint of AI models,” arXiv:2304.03271, 2023. [4]     A. Radovanović et al., “Carbon-aware computing for datacenters,” IEEE Trans. Power Syst., 2023. [5]     The Green Grid, “Water usage effectiveness (WUE): A Green Grid data center sustainability metric,” White Paper, 2011. [6]     L. A. Barroso and U. Hölzle, “The case for energy-proportional computing,” IEEE Computer, vol. 40, no. 12, 2007. [7]     Gemma Team, Google DeepMind, “Gemma: Open models based on Gemini research and technology,” arXiv:2403.08295, 2024. [8]     C. Percival, “Stronger key derivation via sequential memory-hard functions,” BSDCan, 2009. [9]     OWASP Foundation, Password Storage Cheat Sheet. [Online]. Available: https://cheatsheetseries.owasp.org/ [10]  M. Jones, J. Bradley and N. Sakimura, “JSON Web Token (JWT),” RFC 7519, IETF, 2015.

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Publication Date

10/29/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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