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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68515
Volume:
10 |
10 |
Oct 2026
Pages:
1-12
Publication Frequency:
Monthly (12 issues per year)
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.
Department of Computer Science And Engneering
Sri Shakthi Institute of Engineering and Technology
Department of Computer Science And Engneering
Sri Shakthi Institute of Engineering and Technology
Department of Computer Science And Engneering
Sri Shakthi Institute of Engineering and Technology
Department of Computer Science And Engneering
Sri Shakthi Institute of Engineering and Technology
Department of Computer Science And Engneering
Sri Shakthi Institute of Engineering and Technology
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
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.
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Publication Date
10/29/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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