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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64465
Volume:
10 |
6 |
Jun 2026
Pages:
1-8
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 & engineering
Ashokrao Mane Group Of Institutions
Department of computer science & engineering
Ashokrao Mane Group Of Institutions
Department of computer science & engineering
Ashokrao Mane Group Of Institutions
Department of computer science & engineering
Ashokrao Mane Group Of Institutions
Department of computer science & engineering
Ashokrao Mane Group Of Institutions
Department of computer science & engineering
Ashokrao Mane Group Of Institutions
Cloud services offer flexible infrastructure but variable billing can make cost monitoring difficult. FinOps practices support financial accountability by making cloud expenditure visible and actionable. This paper presents CloudPulse AI, a FinOps prototype implemented with Django and Python. The prototype uses simulated hourly billing records, an Isolation Forest model for unusual cost detection, service-specific recommendation rules, dashboard cost summaries, short-term trend projections and email alerts for manually triggered critical anomalies. The implemented system provides an authenticated dashboard, hourly cost visualisation, anomaly ledger, recommendation ledger, estimated saving values and alert email delivery. Functional demonstration screenshots show the output of these modules. The current prototype has not been validated on production AWS billing exports and does not claim benchmark accuracy. CloudPulse AI demonstrates how a lightweight web application can combine cost visibility, unsupervised anomaly detection and practical recommendations in one FinOps workflow. Further work is required for production billing ingestion, deterministic recommendation estimates and formal model evaluation.
KEYWORDS
This paper presented CloudPulse AI, an AWS-focused FinOps advisory prototype for cost monitoring, anomaly detection and resource optimisation. The application combines simulated hourly billing records, Isolation Forest outlier detection, short-term expenditure projection, rule-based recommendations and critical-anomaly email alerts within an authenticated Django interface. The prototype provides a practical foundation for further evaluation with production billing data and more rigorous financial models.
1. Deochake S. Cloud Cost Optimization: A Comprehensive Review of Strategies and Case Studies. arXiv preprint arXiv:2307.12479. 2023. 2. Khan AQ, Matskin M, Prodan R, Bussler C, Roman D, Soylu A. Cost modelling and optimisation for cloud: a graph-based approach. J Cloud Comput. 2024;13:122. 3. Luong NC, Wang P, Niyato D, Wen Y, Han Z. Resource management in cloud networking using economic analysis and pricing models: a survey. IEEE Commun Surv Tutor. 2017;19(2):954-1001. 4. Srinivasa T. Cloud Cost Optimization Methodologies for Cloud Migrations. Int J Intell Syst Appl Eng. 2024;12(21s):4797-4809. 5. Ahmad S, Iqbal T, Munir EU, Ramzan N. Cost optimization in cloud environment based on task deadline. J Cloud Comput. 2023;12:18. 6. Deochake S. ABACUS: A FinOps-Based Cloud Cost Optimization Service for Budget Monitoring and Deployment Cost Estimation. arXiv preprint. 2024.
Publication Date
6/3/2026
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.
Funding Information
No funding information available for this research.
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