Resource Management in AI-Enabled Cloud Native Databases: A Systematic Literature Review Study
Keywords:
Resource Management; Artificial Intelligence; Cloud Native Databases; Digital Transformation.Abstract
In the face of digital transformation, organizations are currently grappling with the differences between new and classic business models, which require agile, adaptable, personalized, and intelligent approaches. Cloud-native, as a novel technological idea, plays a crucial role in assisting organizations in constructing and operating adaptable and expandable applications in modern dynamic environments, including public, private, and hybrid clouds. Cloud-native offers several benefits, including enhanced performance, optimized resource utilization, reduced operational expenses, and improved scalability. The main aim of this work is to investigate resource management in cloud-native databases that are enabled by artificial intelligence. The research methodology utilized in this study is a comprehensive literature review. This study conducted a comprehensive analysis of 30 scholarly papers sourced from various online academic databases spanning the period from 2018 to 2024. Based on this research, efficiently managing resources in AI-enabled cloud-native databases is crucial for enterprises seeking to harness the potential of artificial intelligence while optimizing productivity and reducing expenses. The results of this study assist database developers in choosing suitable objectives and devising strategies for enhancing AI-enabled Cloud-native databases.
Downloads
References
.Li, F. (2019). Cloud-native database systems at Alibaba: Opportunities and challenges. Proceedings of the VLDB Endowment, 12(12), 2263-2272.
.Feng, X., Guo, C., Jiao, T., & Song, J. (2022). A maturity model for AI-empowered cloud-native databases: from the perspective of resource management. Journal of Cloud Computing, 11(1), 39.
.Ton That, D. H., Wagner, J., Rasin, A., & Malik, T. (2019). PLI^++: efficient clustering of cloud databases. Distributed and Parallel Databases, 37, 177-208.
.Cloud-native data patterns. (2022). Retrieved April 27, 2024, from Microsoft.com website: https://learn.microsoft.com/en-us/dotnet/architecture/cloud-native/distributed-data.
.Zeb, S., Rathore, M. A., Mahmood, A., Hassan, S. A., Kim, J., & Gidlund, M. (2021, December). Edge intelligence in softwarized 6G: Deep learning-enabled network traffic predictions. In 2021 IEEE Globecom Workshops (GC Wkshps) (pp. 1-6). IEEE.
.Zhang, R., Li, Y., Li, H., & Wang, Q. (2022). Evolutionary game analysis on cloud providers and enterprises’ strategies for migrating to cloud-native under digital transformation. Electronics, 11(10), 1584.
.Samdanis, K., & Taleb, T. (2020). The road beyond 5G: A vision and insight of the key technologies. IEEE Network, 34(2), 135-141.
.Cardoso, K. V., Both, C. B., Prade, L. R., Macedo, C. J., & Lopes, V. H. L. (2020). A softwarized perspective of the 5G networks. arXiv preprint arXiv:2006.10409.
.Mengist, W., Soromessa, T., & Legese, G. (2020). Method for conducting systematic literature review and meta-analysis for environmental science research. MethodsX, 7, 100777.
.Spillner, J., Toffetti, G., & López, M. R. (2018). Cloud-Native Databases: An Application Perspective. In Advances in Service-Oriented and Cloud Computing: Workshops of ESOCC 2017, Oslo, Norway, September 27-29, 2017, Revised Selected Papers 6 (pp. 102-116). Springer International Publishing.
.Szczyrbowski, M., & Myszor, D. (2015, May). Comparison of the behaviour of local databases and databases located in the cloud. In International Conference: Beyond Databases, Architectures and Structures (pp. 253-261). Cham: Springer International Publishing.
.Li, G., Dong, H., & Zhang, C. (2022). Cloud databases: New techniques, challenges, and opportunities. Proceedings of the VLDB Endowment, 15(12), 3758-3761.
.Bacon, D. F., Bales, N., Bruno, N., Cooper, B. F., Dickinson, A., Fikes, A., ... & Woodford, D. (2017, May). Spanner: Becoming a SQL system. In Proceedings of the 2017 ACM International Conference on Management of Data (pp. 331-343).
.Cao, Y., Dong, Q., Wang, D., Liu, Y., Zhang, P., Yu, X., & Niu, C. (2021). TIDB: a comprehensive database of trained immunity. Database, 2021, baab041.
.Verbitski, A., Gupta, A., Saha, D., Brahmadesam, M., Gupta, K., Mittal, R., ... & Bao, X. (2017, May). Amazon aurora: Design considerations for high throughput cloud-native relational databases. In Proceedings of the 2017 ACM International Conference on Management of Data (pp. 1041-1052).
.Cao, W., Liu, Y., Cheng, Z., Zheng, N., Li, W., Wu, W., ... & Zhang, T. (2020). {POLARDB} meets computational storage: Efficiently support analytical workloads in {Cloud-Native} relational database. In 18th USENIX conference on file and storage technologies (FAST 20) (pp. 29-41).
.Depoutovitch, A., Chen, C., Chen, J., Larson, P., Lin, S., Ng, J., ... & He, Y. (2020, June). Taurus database: How to be fast, available, and frugal in the cloud. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (pp. 1463-1478).
.Toffetti, G., Brunner, S., Blöchlinger, M., Spillner, J., & Bohnert, T. M. (2017). Self-managing cloud-native applications: Design, implementation, and experience. Future Generation Computer Systems, 72, 165-179.
.Li, F. (2023). Modernization of databases in the cloud era: Building databases that run like Legos. Proceedings of the VLDB Endowment, 16(12), 4140-4151.
.Wang, X., Li, N., Zhang, L., Zhang, X., & Zhao, Q. (2021, May). Rapid Trend Prediction for Large-Scale Cloud Database KPIs by Clustering. In 2021 IEEE/ACM International Workshop on Cloud Intelligence (CloudIntelligence) (pp. 1-6). IEEE.
.Tan, J., Zhang, T., Li, F., Chen, J., Zheng, Q., Zhang, P., ... & Zhang, R. (2019). ibtune: Individualized buffer tuning for large-scale cloud databases. Proceedings of the VLDB Endowment, 12(10), 1221-1234.
.Zhang, X., Wu, H., Chang, Z., Jin, S., Tan, J., Li, F., ... & Cui, B. (2021, June). Restune: Resource oriented tuning boosted by meta-learning for cloud databases. In Proceedings of the 2021 international conference on management of data (pp. 2102-2114).
.Salmanian, Z., Izadkhah, H., & Isazadeh, A. (2022). Auto-scale resource provisioning in IaaS clouds. The Computer Journal, 65(2), 297-309.
.Velayutham, S., & Shanmugam, G. (2021). Artificial Intelligence assisted Canary Testing of Cloud Native RAN in a mobile telecom system.
.Abouelyazid, M., & Xiang, C. (2019). Architectures for AI Integration in Next-Generation Cloud Infrastructure, Development, Security, and Management. International Journal of Information and Cybersecurity, 3(1), 1-19.
.Zeb, S., Rathore, M. A., Hassan, S. A., Raza, S., Dev, K., & Fortino, G. (2023). Toward AI-enabled nextG networks with edge intelligence-assisted microservice orchestration. IEEE Wireless Communications, 30(3), 148-156.
.Singh, A. (2023). Optimization of the Cloud-Native Infrastructure using Artificial Intelligence.
.Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., ... & Uhlig, S. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, 100514.
.Schein, S., Arutiunian, G., Burshtein, V., Sadeh, G., Townshend, M., Friedman, B., & Sadr-azodi, S. (2021). Developing Medical AI: a cloud-native audio-visual data collection study. arXiv preprint arXiv:2110.03660.
.Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., ... & Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606-659.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
