Enhancing Cloud Service Selection and Orchestration with DALMOCS: A Dynamic Adaptive Learning and Multi-Criteria Decision Analysis Approach
Article Sidebar
Main Article Content
Abstract
This paper introduces the Dynamic Adaptive Learning Model for Optimized Cloud Service Selection (DALMOCS), an innovative framework designed to enhance cloud service selection and orchestration by leveraging adaptive learning techniques and a specialized Multi-Criteria Decision Analysis (MCDA) approach. DALMOCS dynamically adjusts selection parameters and criteria weights in real-time, significantly improving the decision-making process. Our quantitative analysis demonstrates the model's efficacy, with DALMOCS achieving a 92% accuracy in service selection and a 95% user satisfaction rate under baseline conditions. It maintained high adaptability with indices of 0.75 and 0.80 under dynamic market conditions and varying user requirements, respectively, alongside consistent execution times, showcasing its efficiency and resilience. These results highlight DALMOCS's potential to offer a robust, adaptable, and efficient solution for navigating the complexities of the cloud services market, marking a significant advancement in cloud computing research and application.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
IJCERT Policy:
The published work presented in this paper is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means that the content of this paper can be shared, copied, and redistributed in any medium or format, as long as the original author is properly attributed. Additionally, any derivative works based on this paper must also be licensed under the same terms. This licensing agreement allows for broad dissemination and use of the work while maintaining the author's rights and recognition.
By submitting this paper to IJCERT, the author(s) agree to these licensing terms and confirm that the work is original and does not infringe on any third-party copyright or intellectual property rights.
References
Arianyan, E., Ahmadi, M. R., & Maleki, D. (2016). A novel taxonomy and comparison method for ranking cloud computing software products. International Journal of Grid and Distributed Computing, 9(3), 173-190.
Raj, P., Raman, A., Raj, P., & Raman, A. (2018). Automated multi-cloud operations and container orchestration. Software-Defined Cloud Centers: Operational and Management Technologies and Tools, 185-218.
Kolb, S., & Wirtz, G. (2014, April). Towards application portability in platform as a service. In 2014 IEEE 8th international symposium on service oriented system engineering (pp. 218-229). IEEE.
Alabool, H., Kamil, A., Arshad, N., & Alarabiat, D. (2018). Cloud service evaluation method-based Multi-Criteria Decision-Making: A systematic literature review. Journal of Systems and Software, 139, 161-188.
Koehler, S., Desamsetti, H., Ballamudi, V. K. R., & Dekkati, S. (2020). Real World Applications of Cloud Computing: Architecture, Reasons for Using, and Challenges. Asia Pacific Journal of Energy and Environment, 7(2), 93-102.
Godse, M., & Mulik, S. (2009). An approach for selecting software-as-a-service (SaaS) product. IEEE International Conference on Cloud Computing, 155-158. https://doi.org/10.1109/CLOUD.2009.84
Garg, S. K., Versteeg, S., & Buyya, R. (2013). A framework for ranking of cloud computing services. Future Generation Computer Systems, 29(4), 1012-1023. https://doi.org/10.1016/j.future.2012.06.006
Zardari, S., & Bahsoon, R. (2015). Cloud adoption: A goal-oriented requirements engineering approach. Journal of Cloud Computing, 4(1), 1-15. https://doi.org/10.1186/s13677-015-0037-6
Li, A., Yang, X., Kandula, S., & Zhang, M. (2018). CloudCmp: Comparing public cloud providers. Proceedings of the ACM SIGCOMM 2010 Conference, 1-14. https://doi.org/10.1145/1851182.1851198.
Tiwari, R. K., & Kumar, R. (2021). A robust and efficient MCDM-based framework for cloud service selection using modified TOPSIS. International Journal of Cloud Applications and Computing, 11(1), 21–51.
Pahl, C., Gammoudi, M. M., Risso, F., & Tricomi, G. (2024). Adaptive Learning and Advanced Multi-Criteria Decision Analysis: Enhancing Cloud Service Selection and Orchestration for Optimal Performance Efficiency and Flexibility. International Journal of Computer Engineering Research and Trends, 11(2), 18–25. https://doi.org/10.22362/ijcert/2024/v11/i2/v11i203
Yamuna, R., & Rani, M. U. (2022). Priority Based Task Scheduling and Delay Optimization in Mobile Edge Computing. International Journal of Computer Engineering Research and Trends, 9(1), 1–6. https://doi.org/10.22362/ijcert/2022/v9/i01/v9i0101
Swathi, V. N. V. L. S., Kumar, G. S., & Vathsala, A. V. (2023). Cloud service selection system approach based on QoS model: A systematic review. International Journal of Recent Innovations in Trends in Computing and Communication, 11(2), 05–13.
Kumar, G. S., Snehalatha, N., Swathi, V. N. V. L. S., Vatsa, S., & Singh, A. (2022). Seamless inter-cloud data transfer. In 2nd International Conference on Mathematical Techniques and Applications: ICMTA2021.
Pasha, M. J., Rao, K. P., MallaReddy, A., & Bande, V. (2023). LRDADF: An AI-enabled framework for detecting low-rate DDoS attacks in cloud computing environments. Measurement and Sensing, 28(100828), 100828.
Ravikumar, G., Begum, Z., Kumar, A. S., Kiranmai, V., Bhavsingh, M., & Kumar, O. K. (2022). Cloud host selection using iterative particle-swarm optimization for dynamic container consolidation. International Journal of Recent Innovations in Trends in Computing and Communication, 10(1s), 247–253.
Krishnan, V. G., Rao, P. V., Raja, S., Bhargav, S., Balaji, S., & Divya, V. (2022). An improved firefly algorithm based task scheduling in cloud computing for effective resource utilization. In 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS).
Jaiswal, A., & Mishra, R. B. (2017). Cloud service selection using TOPSIS and fuzzy TOPSIS with AHP and ANP. In Proceedings of the 2017 International Conference on Machine Learning and Soft Computing.
Hu, Y., Wu, L., Shi, C., Wang, Y., & Zhu, F. (2020). Research on optimal decision-making of cloud manufacturing service provider based on grey correlation analysis and TOPSIS. International Journal of Production Research, 58(3), 748–757.
Kumar, R. R., Mishra, S., & Kumar, C. (2017). Prioritizing the solution of cloud service selection using integrated MCDM methods under Fuzzy environment. The Journal of Supercomputing, 73(11), 4652–4682.