Automation Frameworks and Best Practices for Cloud-Native Database Lifecycle Management
DOI:
https://doi.org/10.32628/IJSRST2415412Abstract
The migration to cloud-native frameworks makes it imperative to effectively manage database lifecycle across organizations. Also, well developed and automated applications and web systems are needed for effectiveness and scalability of database that meets global standard. This study attempts a comprehensive review of best practices and automation frameworks for effective management of cloud-native database lifecycle. These include scaling, decommissioning, configuration, monitoring and provisioning. The study analyzes orchestration of container and the place of infrastructure as Code (IaC) in the automation of database operations in both public and private spheres of hybrid cloud-native environment. It also analyzes prominent managed services like Google Cloud SQL and Amazon RDS and tools like Kubernetes Operators and Terraform. In the course of the analysis, their strengths and weaknesses are highlighted. The article presents some practicable best practices capable of optimizing performance, ensuring regulatory compliance and safety, and minimizing operational bottlenecks. In all, the study has uniquely offered a realizable guidance to the concerned professionals on how to effectively streamline and manage database operations in changing cloud-native ecological spaces.
Downloads
References
Ahmad, W., Rasool, A., Javed, A. R., Baker, T., & Jalil, Z. (2021). Cyber security in IoT-based cloud computing: A comprehensive survey. Electronics, 11(1), 16. DOI: https://doi.org/10.3390/electronics11010016
Akinloye, A., & Shawana, T. A. (2023). Gas caps detection, hydrocarbon recovery and environmental sustainability. Journal of Applied Ecology and Environmental Design (JAEED), 2(2), 118-130.
Alsamhi, S. H., Shvetsov, A.V., Kumar, S., Shvetsova, S. V., Alhartomi, M. A., Hawbani, A., Rajput, N. S., Srivastava, S., Saif, A., & Nyangaresi, V. O. (2022). UAV computing- assisted search and rescue mission framework for disaster and harsh environment. Drones, 6(7), 154. DOI: https://doi.org/10.3390/drones6070154
Alsuwaie, M. A., Habibnia, B., & Gladyshev, P. (2021). Data leakage prevention adoption model & DLP maturity level assessment. In 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC), 396-405. DOI: https://doi.org/10.1109/ISCSIC54682.2021.00077
Ang'udi, J. J. (2023). Security challenges in cloud computing: A comprehensive analysis. World Journal of Advanced Engineering Technology and Sciences, 10(02), 155–181. DOI: https://doi.org/10.30574/wjaets.2023.10.2.0304 DOI: https://doi.org/10.30574/wjaets.2023.10.2.0304
Astyrakakis, N., Nikoloudakis,Y., Kefaloukos, I., Skianis, C., Pallis, E., & Markakis, E. K. (2019). Cloud-Native application validation & stress testing through a framework for auto-cluster deployment. In 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 1–5. doi: 10.1109/CAMAD.2019.8858164. DOI: https://doi.org/10.1109/CAMAD.2019.8858164
Atmaca,T., Begin,T., Brandwajn, A., & Castel-Taleb, H. (2016). Performance evaluation of cloud computing centers with general arrivals and service. IEEE Trans. Parallel Distrib. Syst., 27(8), 2341–2348. doi: 10.1109/TPDS.2015.2499749. DOI: https://doi.org/10.1109/TPDS.2015.2499749
Baldini, I. et al., (2017). Serverless computing: Current trends and open problems. In Research Advances in Cloud Computing, doi: 10.1007/978-981-10-5026-8_1. DOI: https://doi.org/10.1007/978-981-10-5026-8_1
Boddu, B. (2022). Cloud DBA strategies for SQL and NOSQL data management for business- critical applications. International Journal of Core Engineering & Management, 7(1), 113-127.
Brunner, S., Blochlinger, M., Toffetti, G., Spillner, J., & Bohnert,T. M. (2015). Experimental evaluation of the cloud-native application design. Proc. - 2015 IEEE/ACM 8th Int. Conf. Util. Cloud Comput. UCC, 488–493. doi: 10.1109/UCC.2015.87. DOI: https://doi.org/10.1109/UCC.2015.87
Cappaert, P., & Redei, A. (2020). A scalable cloud native platform for interactive museum exhibits. In EPiC Series in Computing, doi: 10.29007/6b3j. DOI: https://doi.org/10.29007/6b3j
Chang, K. S. P., & Fink, S. J. (2017).Visualizing serverless cloud application logs for program understanding. In Proceedings of IEEE Symposium on Visual Languages and Human- Centric Computing, VL/HCC, doi: 10.1109/VLHCC.2017.8103476. DOI: https://doi.org/10.1109/VLHCC.2017.8103476
Chippagiri, S., & Ravula, P. (2021). Cloud-native development: Review of best practices and frameworks for scalable and resilient web applications. International Journal of New Media Studies (IJNMS), 8(2), 13-21.
Chowdhury, R., Talhi, C., Ould-Slimane, H., & Mourad, A. (2020). A framework for automated monitoring and orchestration of cloud-native applications. In 2020 International Symposium on Networks, Computers and Communications (ISNCC), IEEE, 1–6. doi: 10.1109/ISNCC49221.2020.9297238. DOI: https://doi.org/10.1109/ISNCC49221.2020.9297238
Dhanagari, M. R. (2023). MongoDB in the cloud: Leveraging cloud-native features for modern applications. International Journal of Science and Research Archive, 10(02), 1297-1313. DOI: https://doi.org/10.30574/ijsra.2023.10.2.1089 DOI: https://doi.org/10.30574/ijsra.2023.10.2.1089
DInh-Tuan, H., Mora-Martinez, M., Beierle, F., & Garzon, S. R. (2020). Development frameworks for microservice-based applications: Evaluation and comparison. In ACM International Conference Proceeding Series, doi: 10.1145/3393822.3432339. DOI: https://doi.org/10.1145/3393822.3432339
Dittakavi, R. S. Evaluating the Efficiency and Limitations of Configuration Strategies in Hybrid Cloud Environments. International Journal of Intelligent Automation and Computing, 5(2), 29-45.
Eshwari, H. M., Rekha, B. S., & Srinivasan, G. N. (2020). Hybrid cloud technologies: Dockers, containers and kubernetes. Int. Res. J. Eng. Technol., 7(6), 7628–7634.
Ganesan, P. (2020). DevOps automation for cloud native distributed applications. Journal of Scientific and Engineering Research, 7(2), 342-347.
Ghandour, O., El Kafhali, S., & Hanini, M. (2023). Computing resources scalability performance analysis in cloud computing data center. Journal of Grid Computing, 21(4), 61. DOI: https://doi.org/10.1007/s10723-023-09696-5
Hakli, A., Taibi, D., & Systa, K. (2018). Towards cloud native continuous delivery: An industrial experience report. 2018 Proc. - 11th IEEE/ACM Int. Conf. Util. Cloud Comput. Companion, UCC Companion, 314–320 doi: 10.1109/UCC-Companion.2018.00074. DOI: https://doi.org/10.1109/UCC-Companion.2018.00074
Hasan, M. Z., Fink, R. M., Suyambu, R., & Baskaran, M. K. (2012). Assessment and improvement of intelligent controllers for elevator energy efficiency. In IEEE International Conference on Electro Information Technology, doi: 10.1109/EIT.2012.6220727. DOI: https://doi.org/10.1109/EIT.2012.6220727
Hasan, M. Z., Fink, R., Suyambu, M. R., Baskaran, M. K., James, D., & Gamboa, J. (2015). Performance evaluation of energy efficient intelligent elevator controllers. In IEEE International Conference on Electro Information Technology, doi: 10.1109/EIT.2015.7293320. DOI: https://doi.org/10.1109/EIT.2015.7293320
Hussien, Z. A., Abdulmalik, H. A., Hussain, M. A., Nyangaresi, V. O., Ma, J., Abduljabbar, Z. A., & Abduljaleel, I. Q. (2023). lightweight integrity preserving scheme for secure data exchange in cloud-based Iot systems. Applied Sciences, 13(2), 691. DOI: https://doi.org/10.3390/app13020691
Imadali, S., & Bousselmi, A. (2018). Cloud native 5g virtual network functions: Design principles and use cases. In Proceedings - 8th IEEE International Symposium on Cloud and Services Computing, SC2. doi: 10.1109/SC2.2018.00019. DOI: https://doi.org/10.1109/SC2.2018.00019
Ionescu, S. A., & Diaconita, V. (2023). Transforming Financial decision-making: The interplay of AI, cloud computing and advanced data management technologies. International Journal of Computers Communications & Control, 18(6). DOI: https://doi.org/10.15837/ijccc.2023.6.5735
Jana, I., & Oprea, A. (2019). AppMine: behavioral analytics for web application vulnerability detection. arxiv, doi: 10.48550/arXiv.1908.01928. DOI: https://doi.org/10.1145/3338466.3358923
Kosińska, J., Baliś, B., Konieczny, M., Malawski, M., & Zieliński, S. (2023). Toward the observability of cloud-native applications: the overview of the state-of-the-art. IEEE Access, 11, 73036- 73052. Doi: 10.1109/ACCESS.2023.3281860 DOI: https://doi.org/10.1109/ACCESS.2023.3281860
Kratzke, N., & Peinl, R. (2016). ClouNS-a cloud-native application reference model for enterprise architects. In Proceedings – 2016, IEEE International Enterprise Distributed Object Computing Workshop, EDOCW, doi: 10.1109/EDOCW.2016.7584353. DOI: https://doi.org/10.1109/EDOCW.2016.7584353
Kumar, V. V. (2014). An interactive product development model in remanufacturing environment : Achaos-based artificial bee colony approach. Missouri University of Science and Technology, [Online]. https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=8243&context=masters_theses
Kumar, V. V., Yadav, S. R., Liou, F. W., & Balakrishnan, S. N. (2013). A digital interface for the part designers and the fixture designers for a reconfigurable assembly system. Math. Probl. Eng., doi: 10.1155/2013/943702. DOI: https://doi.org/10.1155/2013/943702
Kumar, V., & Chan, F. T. S. (2011). A superiority search and optimisation algorithm to solve RFID and an environmental factor embedded closed loop logistics model. Int. J. Prod. Res., 49(16). doi: 10.1080/00207543.2010.503201. DOI: https://doi.org/10.1080/00207543.2010.503201
Kumar,V. V., Pandey, M. K., Tiwari, M. K., & Ben-Arieh, D. (2010). Simultaneous optimization of parts and operations sequences in SSMS: A chaos embedded Taguchi particle swarm optimization approach. J. Intell. Manuf., doi: 10.1007/s10845-008-0175-4. DOI: https://doi.org/10.1007/s10845-008-0175-4
Kumar,V. V., Sahoo, A., & Liou, F. W. (2019). Cyber-enabled product lifecycle management: A multi-agent framework. In Procedia Manufacturing, doi: 10.1016/j.promfg.2020.01.247. DOI: https://doi.org/10.1016/j.promfg.2020.01.247
Kumar,V. V., Tripathi, M., Pandey, M. K., & Tiwari, M. K. (2009). Physical programming and conjoint analysis-based redundancy allocation in multistate systems: A Taguchi embedded algorithm selection and control (TAS&C) approach. Proc. Inst. Mech. Eng. Part O J. Risk Reliab., 223(3), 215–232. doi: 10.1243/1748006XJRR210. DOI: https://doi.org/10.1243/1748006XJRR210
L’Esteve, R. C. (2023). New horizons in distributed cloud computing. In The cloud leader’s handbook: Strategically innovate, transform, and scale organizations (123-134). Apress. DOI: https://doi.org/10.1007/978-1-4842-9526-7_8
Malallah, H. S., Qashi, R., Abdulrahman, L. M., Omer, M. A., & Yazdeen, A. A. (2023). Performance analysis of enterprise cloud computing: A review. Journal of Applied Science and Technology Trends, 4(01), 01-12. DOI: https://doi.org/10.38094/jastt401139
Mwiinga, P. (2023). Privacy-preserving technologies: Balancing security and user privacy in the digital age. Int. J. Sci. Res. Publ, [Online]. https://zenodo.org/records/10406538
Nyangaresi, V. O. (2022). Lightweight anonymous authentication protocol for resource- constrained smart home devices based on elliptic curve cryptography. Journal of Systems Architecture, 133, 102763. DOI: https://doi.org/10.1016/j.sysarc.2022.102763
Ogirri, K. O., Akinloye, A., & Oguntona, T. I. (2024). Machine learning techniques, gas emission mitigation, and environmental safety. Journal of Science Innovation & Technology Research (JSITR), 5(9), 255-267.
Oguntona, T. I. (2023, March). Mitigating the impact of high energy prices on UK households using machine learning techniques. International Journal of Modelling & Applied Science Research, 27(9), 215-230.
Peram, P. (2024). Optimizing cloud computing performance: a comprehensive framework of strategies and best practices. International Journal of Engineering and Technology Research (IJETR), 9(2), 387–396. DOI: https://doi.org/10.5281/zenodo.13851330
Peram, P. (2024). Optimizing cloud computing performance: a comprehensive framework of strategies and best practices. International Journal of Engineering and Technology Research (IJETR), 9(2), 387–396. DOI: https://doi.org/10.5281/zenodo.13851330
Qiu, J., Du, Q., Yin, K., Zhang, S., & Qian, C. (2020). Applied sciences: A causality mining and knowledge graph based method of root cause diagnosis for performance. Anomaly in Cloud Applications, doi: 10.3390/app10062166. DOI: https://doi.org/10.3390/app10062166
Saranya, N., Sakthivadivel, M., Karthikeyan, G., Rajkumar, R.. (2023). Securing the cloud: An empirical study on best practices for ensuring data privacy and protection. International Journal of Engineering and Management Research, 13(2), 46-9.
Selvan, M. P., Sowmith, R. S., Dheeraj, P., & Jancy, S.(2021). High secured data access and leakage detection using attribute-based encryption. In Advances in Electronics, Communication and Computing: Select Proceedings of ETAEERE 2020 2021 (433-445). DOI: https://doi.org/10.1007/978-981-15-8752-8_44
Stallings, W. (2020). Data loss prevention as a privacy-enhancing technology. Journal of Data Protection & Privacy, 3(3), 323-33. DOI: https://doi.org/10.69554/GXRO7494
Sunday, A. E., & Olufunminiyi, O. E. (2023). An efficient data protection for cloud storage through encryption. International Journal of Advanced Networking and Applications, 14(5), 609-18. DOI: https://doi.org/10.35444/IJANA.2023.14505
Theodoropoulos, T., Rosa, L., Benzaid, C., Gray, P., Marin, E., Makris, A., Cordeiro, L., Diego, F., Sorokin, P., Girolamo, M.D., et al. (2023). Security in Cloud-Native Services: A Survey. J. Cybersecur. Priv., 3, 758–793. https://doi.org/10.3390/jcp3040034
Theodoropoulos, T., Rosa, L., Benzaid, C., Gray, P., Marin, E., Makris, A., Cordeiro, L., Diego, F., Sorokin, P., Girolamo, M. D. et al. (2023). Security in cloud-native services: a survey. J. Cybersecur. Priv., 3, 758–793. https://doi.org/10.3390/jcp3040034 DOI: https://doi.org/10.3390/jcp3040034
Tiwari, S., & Bhatt, C. (2022). A comprehensive study on cloud computing: Architecture, load balancing, task scheduling and meta-heuristic optimization. In International Conference on Intelligent Cyber Physical Systems and Internet of Things, (137-162). International Publishing. DOI: https://doi.org/10.1007/978-3-031-18497-0_11
Toffetti, G., Brunner, S., Blöchlinger, M., Spillner, J., & Bohnert, T. M. (2017). Self-managing cloud-native applications: Design, implementation, and experience. Futur. Gener. Comput. Syst., doi: 10.1016/j.future.2016.09.002. DOI: https://doi.org/10.1016/j.future.2016.09.002
Turobova, G. O., Djangazova, Q. A., & Ganikhodjayeva, D. Z. (2021). Data loss prevention and challenges faced in their deployments. Oriental renaissance: Innovative, Educational, Natural and Social Sciences, 1(9), 176-82.
Ugwueze, V. U. (2024). Cloud native application development: Best practices and challenges. International Journal of Research Publication and Reviews, 5(12), 2399-2412. DOI: https://doi.org/10.55248/gengpi.5.1224.3533 DOI: https://doi.org/10.55248/gengpi.5.1224.3533
Umran, S. M., Lu, S., Abduljabbar, Z. A., & Nyangaresi, V. O. (2023). Multi-chain blockchain based secure data-sharing framework for industrial IoTs smart devices in petroleum industry. Internet of Things. 24, 100969. DOI: https://doi.org/10.1016/j.iot.2023.100969
Wurster et al., M. (2020). The essential deployment metamodel: A systematic review of deployment automation technologies. In Software-Intensive Cyber-Physical Systems, doi: 10.1007/s00450-019-00412-x. DOI: https://doi.org/10.1007/s00450-019-00412-x
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0
