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https://doi.org/10.62836/jcmea.v4i1.04010429
Qinyi Zhu. (2024). An Innovative Approach for Distributed Cloud Computing through Dynamic Bayesian Networks. Journal of Computational Methods in Engineering Applications, 4(1), 1–16. https://doi.org/10.62836/jcmea.v4i1.04010429
Volume 4, Issue 1, 2024

An Innovative Approach for Distributed Cloud Computing through Dynamic Bayesian Networks

This paper addresses the growing complexity and challenges present in distributed cloud computing systems. As the demand for cloud services continues to rise, there is a critical need for innovative solutions to optimize resource allocation and improve overall system performance. Current research in this field faces obstacles such as scalability, resource management, and fault tolerance. To overcome these challenges, this study proposes an innovative approach utilizing dynamic Bayesian networks to facilitate efficient resource allocation and workload management in distributed cloud environments. The research aims to enhance system performance, minimize resource wastage, and improve overall user experience within cloud computing infrastructures.

complexity resource allocation fault tolerance workload management system performance

References

  1. Pham X-Q, Nguyen TD. Huynh-The T,et al. Distributed Cloud Computing: Architecture, Enabling Technologies, and Open Challenges. IEEE Consumer Electronics Magazine 2023; 12(3): 98–106.
  2. Pham X-Q, Huynh-The T, Kim D-S. A Vision of Distributed Cloud Computing. Information and Communication Technology Convergence. In Proceedings of the 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Republic of Korea, 19–21 October 2022.
  3. Yan S, He L, Seo J, et al. Concurrent Healthcare Data Processing and Storage Framework Using Deep-Learning in Distributed Cloud Computing Environment. IEEE Transactions on Industrial Informatics 2021; 17(4): 2794–2801.
  4. Amin R, Kumar N, Biswas GP, et al. A light weight authentication protocol for IoT-enabled devices in distributed Cloud Computing environment. Future Generation Computer Systems 2018; 78: 1005–1019.
  5. Liu G. Coordinated Control of Networked Multiagent Systems via Distributed Cloud Computing Using Multistep State Predictors. IEEE Transactions on Cybernetics 2020; 52(2): 810–820.
  6. Huang H, Lu S, Wu Z, et al. An efficient authentication and key agreement protocol for IoT-enabled devices in distributed cloud computing architecture. EURASIP Journal on Wireless Communications and Networking 2020; 2021: 150.
  7. Rashid ZN, Zebari SR, Sharif KH, et al. Distributed Cloud Computing and Distributed Parallel Computing: A Review. In Proceedings of the 2018 International Conference on Advanced Science and Engineering (ICOASE), Duhok, Iraq, 9–11 October 2018.
  8. Duan S, Wang D, Ren J, et al. Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey. IEEE Communications Surveys & Tutorials 2023; 25: 591–624.
  9. Wu F, Li X, Xu L, et al. Authentication Protocol for Distributed Cloud Computing: An Explanation of the Security Situations for Internet-of-Things-Enabled Devices. IEEE Consumer Electronics Magazine 2018; 7: 38–44.
  10. Murphy KP, Russell SJ. Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. Thesis, University of California, Berkeley, CA, USA, 2002.
  11. Doucet A, de Freitas N, Murphy KP, et al. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. In Proceedings of the Conference on Uncertainty in Artificial Intelligence, Stanford, CA, USA, 30 June–3 July 2000.
  12. Caetano HO, Desuó L, Fogliatto MS, et al. Resilience assessment of critical infrastructures using dynamic Bayesian networks and evidence propagation. Reliability Engineering & System Safety 2023; 241: 109691.
  13. Kammouh O, Gardoni P, Cimellaro GP. Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian Networks. Reliability Engineering & System Safety 2020; 198: 106813.
  14. Tong Q, Gernay T. Resilience Assessment of Process Industry Facilities Using Dynamic Bayesian Networks. Chemical Engineering Research & Design 2022; 169: 547–563.
  15. Choi Y, McClenen C. Development of Adaptive Formative Assessment System Using Computerized Adaptive Testing and Dynamic Bayesian Networks. Applied Sciences 2020; 10: 8196.
  16. Jafari MJ, Pouyakian M, Hanifi SM. Reliability Evaluation of Fire Alarm Systems Using Dynamic Bayesian Networks and Fuzzy Fault Tree Analysis. Journal of Loss Prevention in The Process Industries 2020; 67: 104229.
  17. Gomes IP, Wolf D. Health Monitoring System for Autonomous Vehicles Using Dynamic Bayesian Networks for Diagnosis and Prognosis. Journal of Intelligent and Robotic Systems 2020; 101(1): 19.
  18. Cai BP, Zhang YP, Yuan, XB, et al. A Dynamic-Bayesian-Networks-Based Resilience Assessment Approach of Structure Systems: Subsea Oil and Gas Pipelines as A Case Study. China Ocean Engineering 2020; 34(5): 597–607.
  19. Liu H, Zhang H, Zhang Y, et al. Modeling of Wastewater Treatment Processes Using Dynamic Bayesian Networks Based on Fuzzy PLS. IEEE Access 2020; 8: 92129–92140.
  20. Zhang Y, Bhattacharya K. Iterated Learning and Multiscale Modeling of History-Dependent Architectured Metamaterials. Mechanics of Materials 2024; 197: 105090. https://doi.org/10.1016/j.mechmat.2024.105090.
  21. Zhang Y, Hart JD. The Effect of Prior Parameters in a Bayesian Approach to Inferring Material Properties from Experimental Measurements. Journal of Engineering Mechanics 2023; 149: 04023007. https://doi.org/10.1061/JENMDT.EMENG-6687.
  22. Zhang Y, Needleman A. On the Identification of Power-Law Creep Parameters from Conical Indentation. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 2021; 477: 20210233. https://doi.org/10.1098/rspa.2021.0233.
  23. Luo Z, Yan H, Pan X. Optimizing Transformer Models for Resource-Constrained Environments: A Study on Model Compression Techniques. Journal of Computational Methods in Engineering Applications 2023; 1–12. https://doi.org/10.62836/jcmea.v3i1.030107.
  24. Yan H, Shao D. Enhancing Transformer Training Efficiency with Dynamic Dropout. arXiv 2024; arXiv:2411.03236. https://doi.org/10.48550/arXiv.2411.03236.
  25. Yan H. Real-Time 3D Model Reconstruction through Energy-Efficient Edge Computing. Optimizations in Applied Machine Learning 2022; 2(1).
  26. Shu Y, Zhu Z, Kanchanakungwankul, S, et al. Small Representative Databases for Testing and Validating Density Functionals and Other Electronic Structure Methods. The Journal of Physical Chemistry A 2024; 128: 6412–6422. https://doi.org/10.1021/acs.jpca.4c03137.
  27. Kim C, Zhu Z, Barbazuk WB, et al. Time-Course Characterization of Whole-Transcriptome Dynamics of HepG2/C3A Spheroids and Its Toxicological Implications. Toxicology Letters 2024; 401: 125–138.
  28. Shen J, Zhang Y, Zhu Z, et al. Joint Modeling of Human Cortical Structure: Genetic Correlation Network and Composite-Trait Genetic Correlation. NeuroImage 2024; 297: 120739.
  29. Faridi KF, Zhu Z, Shah NN, et al. Factors Associated with Reporting Left Ventricular Ejection Fraction with 3D Echocardiography in Real-World Practice. Echocardiography 2024; 41: e15774. https://doi.org/10.1111/echo.15774.
  30. Zhu Z. Tumor Purity Predicted by Statistical Methods. In AIP Conference Proceedings; AIP Publishing: Melville, NY, USA, 2022.
  31. Zhao Z, Ren P, Yang Q. 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. arXiv 2024; arXiv:2404.11029.
  32. Zhao Z, Ren P, Tang M. Analyzing the Impact of Anti-Globalization on the Evolution of Higher Education Internationalization in China. Journal of Linguistics and Education Research 2022; 5: 15–31.
  33. Tang M, Ren P, Zhao Z. Bridging the Gap: The Role of Educational Technology in Promoting Educational Equity. The Educational Review, USA 2024; 8: 1077–1086.
  34. Ren P, Zhao Z, Yang Q. Exploring the Path of Transformation and Development for Study Abroad Consultancy Firms in China. arXiv 2024; arXiv:2404.11034.
  35. Ren P, Zhao Z. Parental Recognition of Double Reduction Policy, Family Economic Status and Educational Anxiety: Exploring the Mediating Influence of Educational Technology Substitutive Resource. Economics & Management Information 2024; 3(1): 1–12.
  36. Zhao Z, Ren P, Tang M. 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 2024; 6: 12–23.
  37. Zhang G, Zhou T. Finite Element Model Calibration with Surrogate Model-Based Bayesian Updating: A Case Study of Motor FEM Model. IAET 2024; 3(1): 1–13. https://doi.org/10.62836/iaet.v3i1.232.
  38. Zhang G, Huang W, Zhou T. Performance Optimization Algorithm for Motor Design with Adaptive Weights Based on GNN Representation. Electrical Science & Engineering 2024; 6(1): 1–13. https://doi.org/10.30564/ese.v6i1.7532.
  39. Tang Y, Li C. Exploring the Factors of Supply Chain Concentration in Chinese A-Share Listed Enterprises. Journal of Computational Methods in Engineering Applications 2023; 3(1): 1–17.
  40. Li C, Tang Y. Emotional Value in Experiential Marketing: Driving Factors for Sales Growth–A Quantitative Study from the Eastern Coastal Region. Economics & Management Information 2024; 1–13.
  41. Li C, Tang Y. The Factors of Brand Reputation in Chinese Luxury Fashion Brands. Journal of Integrated Social Sciences and Humanities 2023; 1–14.
  42. Tang CY, Li C. Examining the Factors of Corporate Frauds in Chinese A-share Listed Enterprises. OAJRC Social Science 2023; 4: 63–77.
  43. Ma J, Xu K, Qiao Y, et al. 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 2022; 2(1): 1–12.
  44. Huang W, Cai Y, Zhang G. Battery Degradation Analysis through Sparse Ridge Regression. Energy & System 2024; 4. https://doi.org/10.71070/es.v4i1.65.
  45. Xu K, Gan Y, Wilson A. Stacked Generalization for Robust Prediction of Trust and Private Equity on Financial Performances. Innovations in Applied Engineering and Technology 2024; 3(1); 1–12.

Supporting Agencies

  1. Funding: This research received no external funding.