Vol. 5 No. 1 (2025): Issue 5
Articles

Autonomous Cloud Resource Management through DBSCAN-based unsupervised learning

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  • Qinyi Zhu
Qinyi Zhu
Indiana University, 107 S Indiana Ave, Bloomington, IN 47405, USA
DOI: https://doi.org/10.71070/oaml.v5i1.112

Published 2025-06-03

Keywords

  • Autonomous,
  • Resource Management,
  • Cloud Computing,
  • Unsupervised Learning,
  • DBSCAN

How to Cite

Zhu , Q. (2025). Autonomous Cloud Resource Management through DBSCAN-based unsupervised learning. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.112
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Autonomous resource management in cloud computing is crucial for optimizing performance and resource utilization. Current research primarily focuses on supervised learning techniques, which require labeled data and manual intervention. However, unsupervised learning methods have the potential to autonomously adapt to dynamic cloud environments without the need for prior training data. In this context, this paper proposes a novel approach utilizing DBSCAN-based unsupervised learning for autonomous cloud resource management. This innovative method aims to cluster cloud resources based on their utilization patterns, enabling proactive resource allocation and dynamic scaling. By leveraging unsupervised learning, our approach addresses the challenges of scalability and real-time resource management in cloud environments, contributing to the advancement of autonomous cloud computing systems.

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References

  1. F. Zaker, Marin Litoiu, and Mark Shtern, "Formally Verified Scalable Look Ahead Planning For Cloud Resource Management," ACM Transactions on Autonomous and Adaptive Systems, vol. 17, 2022.
  2. K. Cho and J.-F. Baffier, "An Autonomous Resource Management Model towards Cloud Morphing," in EdgeSys@EuroSys, 2023.
  3. H. Xia, "Design and Application of Cloud Computing Human Resource Management Model Using Big Data Technology," in 2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS), 2024.
  4. D. Dong, "Agent-based cloud simulation model for resource management," Journal of Cloud Computing, vol. 12, 2023.
  5. V. Bucur and L. Miclea, "Multi-Cloud Resource Management Techniques for Cyber-Physical Systems," in Italian National Conference on Sensors, 2021.
  6. S. Xiu et al., "Task-Driven Computing Offloading and Resource Allocation Scheme for Maritime Autonomous Surface Ships Under Cloud–Shore–Ship Collaboration Framework," Journal of Marine Science and Engineering, 2024.
  7. M. Hasan et al., "Federated Learning for Computational Offloading and Resource Management of Vehicular Edge Computing in 6G-V2X Network," IEEE Transactions on Consumer Electronics, vol. 70, 2024.
  8. P. Liu et al., "Data-Connector: An Agent-Based Framework for Autonomous ML-Based Smart Management in Cloud-Edge Continuum," in IEEE International Conference on Network Protocols, 2024.
  9. M. Hajihosseinlou, et al., "Intelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches," Journal of Geochemical Exploration, 2024.
  10. J. Qian, et al., "MDBSCAN: A multi-density DBSCAN based on relative density," Neurocomputing, 2024.
  11. M. Al-batah, et al., "Enhancement over DBSCAN Satellite Spatial Data Clustering," Journal of Electrical and Computer Engineering, 2024.
  12. E. Schubert, et al., "DBSCAN Revisited, Revisited," ACM Transactions on Database Systems, 2017.
  13. Zheng Xing, et al., "Block-Diagonal Guided DBSCAN Clustering," IEEE Transactions on Knowledge and Data Engineering, 2024.
  14. X. Deng, L. Li, M. Enomoto, and Y. Kawano, ‘Continuously frequency-tuneable plasmonic structures for terahertz bio-sensing and spectroscopy’, Scientific reports, vol. 9, no. 1, p. 3498, 2019.
  15. X. Deng, M. Simanullang, and Y. Kawano, ‘Ge-core/a-si-shell nanowire-based field-effect transistor for sensitive terahertz detection’, in Photonics, MDPI, 2018, p. 13.
  16. X. Deng and Y. Kawano, ‘Surface plasmon polariton graphene midinfrared photodetector with multifrequency resonance’, Journal of Nanophotonics, vol. 12, no. 2, pp. 026017–026017, 2018.
  17. Y. Zhang and J. D. Hart, ‘The Effect of Prior Parameters in a Bayesian Approach to Inferring Material Properties from Experimental Measurements’, Journal of Engineering Mechanics, vol. 149, no. 3, p. 04023007, Mar. 2023, doi: 10.1061/JENMDT.EMENG-6687.
  18. Y. Zhang and A. Needleman, ‘On the identification of power-law creep parameters from conical indentation’, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 477, no. 2252, p. 20210233, Aug. 2021, doi: 10.1098/rspa.2021.0233.
  19. Z. Luo, H. Yan, and X. Pan, ‘Optimizing Transformer Models for Resource-Constrained Environments: A Study on Model Compression Techniques’, Journal of Computational Methods in Engineering Applications, pp. 1–12, Nov. 2023, doi: 10.62836/jcmea.v3i1.030107.
  20. H. Yan and D. Shao, ‘Enhancing Transformer Training Efficiency with Dynamic Dropout’, Nov. 05, 2024, arXiv: arXiv:2411.03236. doi: 10.48550/arXiv.2411.03236.
  21. H. Yan, ‘Real-Time 3D Model Reconstruction through Energy-Efficient Edge Computing’, Optimizations in Applied Machine Learning, vol. 2, no. 1, 2022.
  22. Y. Shu, Z. Zhu, S. Kanchanakungwankul, and D. G. Truhlar, ‘Small Representative Databases for Testing and Validating Density Functionals and Other Electronic Structure Methods’, J. Phys. Chem. A, vol. 128, no. 31, pp. 6412–6422, Aug. 2024, doi: 10.1021/acs.jpca.4c03137.
  23. C. Kim, Z. Zhu, W. B. Barbazuk, R. L. Bacher, and C. D. Vulpe, ‘Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implications’, Toxicology Letters, vol. 401, pp. 125–138, 2024.
  24. J. Shen et al., ‘Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation’, NeuroImage, vol. 297, p. 120739, 2024.
  25. K. F. Faridi et al., ‘Factors associated with reporting left ventricular ejection fraction with 3D echocardiography in real‐world practice’, Echocardiography, vol. 41, no. 2, p. e15774, Feb. 2024, doi: 10.1111/echo.15774.
  26. Z. Zhu, ‘Tumor purity predicted by statistical methods’, in AIP Conference Proceedings, AIP Publishing, 2022.
  27. Z. Zhao, P. Ren, and Q. Yang, ‘Student self-management, academic achievement: Exploring the mediating role of self-efficacy and the moderating influence of gender insights from a survey conducted in 3 universities in America’, Apr. 17, 2024, arXiv: arXiv:2404.11029. doi: 10.48550/arXiv.2404.11029.
  28. Z. Zhao, P. Ren, and M. Tang, ‘Analyzing the Impact of Anti-Globalization on the Evolution of Higher Education Internationalization in China’, Journal of Linguistics and Education Research, vol. 5, no. 2, pp. 15–31, 2022.
  29. M. Tang, P. Ren, and Z. Zhao, ‘Bridging the gap: The role of educational technology in promoting educational equity’, The Educational Review, USA, vol. 8, no. 8, pp. 1077–1086, 2024.
  30. P. Ren, Z. Zhao, and Q. Yang, ‘Exploring the Path of Transformation and Development for Study Abroad Consultancy Firms in China’, Apr. 17, 2024, arXiv: arXiv:2404.11034. doi: 10.48550/arXiv.2404.11034.
  31. P. Ren and Z. Zhao, ‘Parental Recognition of Double Reduction Policy, Family Economic Status And Educational Anxiety: Exploring the Mediating Influence of Educational Technology Substitutive Resource’, Economics & Management Information, pp. 1–12, 2024.
  32. Z. Zhao, P. Ren, and M. Tang, ‘How Social Media as a Digital Marketing Strategy Influences Chinese Students’ Decision to Study Abroad in the United States: A Model Analysis Approach’, Journal of Linguistics and Education Research, vol. 6, no. 1, pp. 12–23, 2024.
  33. G. Zhang and T. Zhou, ‘Finite Element Model Calibration with Surrogate Model-Based Bayesian Updating: A Case Study of Motor FEM Model’, IAET, pp. 1–13, Sep. 2024, doi: 10.62836/iaet.v3i1.232.
  34. G. Zhang, W. Huang, and T. Zhou, ‘Performance Optimization Algorithm for Motor Design with Adaptive Weights Based on GNN Representation’, Electrical Science & Engineering, vol. 6, no. 1, Art. no. 1, Oct. 2024, doi: 10.30564/ese.v6i1.7532.
  35. T. Zhou, G. Zhang, and Y. Cai, ‘Unsupervised Autoencoders Combined with Multi-Model Machine Learning Fusion for Improving the Applicability of Aircraft Sensor and Engine Performance Prediction’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Feb. 2025, doi: 10.71070/oaml.v5i1.83.
  36. Y. Tang and C. Li, ‘Exploring the Factors of Supply Chain Concentration in Chinese A-Share Listed Enterprises’, Journal of Computational Methods in Engineering Applications, pp. 1–17, 2023.
  37. C. Li and Y. Tang, ‘Emotional Value in Experiential Marketing: Driving Factors for Sales Growth–A Quantitative Study from the Eastern Coastal Region’, Economics & Management Information, pp. 1–13, 2024.
  38. C. Li and Y. Tang, ‘The Factors of Brand Reputation in Chinese Luxury Fashion Brands’, Journal of Integrated Social Sciences and Humanities, pp. 1–14, 2023.
  39. C. Y. Tang and C. Li, ‘Examining the Factors of Corporate Frauds in Chinese A-share Listed Enterprises’, OAJRC Social Science, vol. 4, no. 3, pp. 63–77, 2023.
  40. W. Huang, T. Zhou, J. Ma, and X. Chen, ‘An ensemble model based on fusion of multiple machine learning algorithms for remaining useful life prediction of lithium battery in electric vehicles’, Innovations in Applied Engineering and Technology, pp. 1–12, 2025.
  41. W. Huang and J. Ma, ‘Predictive Energy Management Strategy for Hybrid Electric Vehicles Based on Soft Actor-Critic’, Energy & System, vol. 5, no. 1, 2025, Accessed: Jun. 01, 2025.
  42. J. Ma, K. Xu, Y. Qiao, and Z. Zhang, ‘An Integrated Model for Social Media Toxic Comments Detection: Fusion of High-Dimensional Neural Network Representations and Multiple Traditional Machine Learning Algorithms’, Journal of Computational Methods in Engineering Applications, pp. 1–12, 2022.
  43. W. Huang, Y. Cai, and G. Zhang, ‘Battery Degradation Analysis through Sparse Ridge Regression’, Energy & System, vol. 4, no. 1, Art. no. 1, Dec. 2024, doi: 10.71070/es.v4i1.65.
  44. Z. Zhang, ‘RAG for Personalized Medicine: A Framework for Integrating Patient Data and Pharmaceutical Knowledge for Treatment Recommendations’, Optimizations in Applied Machine Learning, vol. 4, no. 1, 2024, Accessed: Jun. 01, 2025.
  45. Z. Zhang, K. Xu, Y. Qiao, and A. Wilson, ‘Sparse Attention Combined with RAG Technology for Financial Data Analysis’, Journal of Computer Science Research, vol. 7, no. 2, Art. no. 2, Mar. 2025, doi: 10.30564/jcsr.v7i2.8933.
  46. P.-M. Lu and Z. Zhang, ‘The Model of Food Nutrition Feature Modeling and Personalized Diet Recommendation Based on the Integration of Neural Networks and K-Means Clustering’, Journal of Computational Biology and Medicine, vol. 5, no. 1, 2025, Accessed: Mar. 12, 2025.
  47. Y. Qiao, K. Xu, Z. Zhang, and A. Wilson, ‘TrAdaBoostR2-based Domain Adaptation for Generalizable Revenue Prediction in Online Advertising Across Various Data Distributions’, Advances in Computer and Communication, vol. 6, no. 2, 2025, Accessed: Jun. 01, 2025.
  48. K. Xu, Y. Gan, and A. Wilson, ‘Stacked Generalization for Robust Prediction of Trust and Private Equity on Financial Performances’, Innovations in Applied Engineering and Technology, pp. 1–12, 2024.
  49. A. Wilson and J. Ma, ‘MDD-based Domain Adaptation Algorithm for Improving the Applicability of the Artificial Neural Network in Vehicle Insurance Claim Fraud Detection’, Optimizations in Applied Machine Learning, vol. 5, no. 1, 2025, Accessed: Jun. 01, 2025.