A Survey of Emerging Trends and Algorithmic Solutions inCloud-Native Data Management
DOI:
https://doi.org/10.66279/71rygy73Keywords:
Emerging Trends, Algorithmic Solutions, Cloud-Native, Data ManagementAbstract
This research paper aims to provide a comprehensive and systematic survey of architectural evolution, implementation frameworks, and empirical evaluation measures for modern cloud database systems. It explores the paradigm shift from traditional on-premise deployments to highly scalable elastic and cloud native data platforms. This work provides a comprehensive architectural analysis of fundamental decoupling mechanisms including storage-compute disaggregation with dynamic partitioning, sharding and geo-replication protocols for Relational, NoSQL and NewSQL models. We build a strong multi-dimensional taxonomy to assess leading enterprise platforms on dimensions of data models, consistency guarantees and operational tradeoffs. We also provide a detailed survey of performance benchmarking techniques (employing standard workloads such as YCSB and TPC) and introduce a new metric, "Throughput-per-Cost," to measure the economic efficiency of cloud. A critical analysis of security infrastructures is provided, covering modern frameworks like Zero-Trust Architectures, homomorphic encryption and post-quantum cryptographic migration pathways. In this paper, we present a forward-looking research roadmap based on real-world case studies on distributed SQL migration and serverless IoT scaling. It points to emerging trends such as AI-driven autonomous database tuning and decentralized Data Mesh architectures. Database practitioners and researchers in the evolving cloud data management need this as a must-have reference. It highlights key emerging trends, including AI-driven autonomous database tuning and decentralized Data Mesh architectures. It offers a definitive guide for both database practitioners and researchers navigating the evolving landscape of cloud data management.
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[1] K. Grolinger, W. A. Higashino, A. Tiwari, and M. A. Capretz, “Data management in cloud environments: Nosql and newsql data stores,” Journal of Cloud Computing: advances, systems and applications, vol. 2, no. 1, p. 22, 2013. DOI: https://doi.org/10.1186/2192-113X-2-22
[2] H. Dong, C. Zhang, G. Li, and H. Zhang, “Cloud-native databases: A survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 12, pp. 7772–7791, 2024. DOI: https://doi.org/10.1109/TKDE.2024.3397508
[3] G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels, “Dynamo: Amazon’s highly available key-value store,” ACM SIGOPS operating systems review, vol. 41, no. 6, pp. 205–220, 2007. DOI: https://doi.org/10.1145/1323293.1294281
[4] A. Davoudian, L. Chen, and M. Liu, “A survey on nosql stores,” ACM Computing Surveys (CSUR), vol. 51, no. 2, pp. 1–43, 2018. DOI: https://doi.org/10.1145/3158661
[5] A. Pavlo and M. Aslett, “What’s really new with newsql?,” ACM Sigmod Record, vol. 45, no. 2, pp. 45–55, 2016. DOI: https://doi.org/10.1145/3003665.3003674
[6] J. C. Corbett, J. Dean, M. Epstein, A. Fikes, C. Frost, J. J. Furman, S. Ghemawat, A. Gubarev, C. Heiser, P. Hochschild, et al., “Spanner: Google’s globally distributed database,” ACM Transactions on Computer Systems (TOCS), vol. 31, no. 3, pp. 1–22, 2013. DOI: https://doi.org/10.1145/2491245
[7] E. Jonas, J. Schleier-Smith, V. Sreekanti, C.-C. Tsai, A. Khandelwal, Q. Pu, V. Shankar, J. Carreira, K. Krauth, N. Yadwadkar, et al., “Cloud programming simplified: A berkeley view on serverless computing,” arXiv preprint arXiv:1902.03383, 2019.
[8] X. Zhou, C. Chai, G. Li, and J. Sun, “Database meets artificial intelligence: A survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 3, pp. 1096–1116, 2020. DOI: https://doi.org/10.1109/TKDE.2020.2994641
[9] D. Abadi, A. Ailamaki, D. Andersen, P. Bailis, M. Balazinska, P. Bernstein, P. Boncz, S. Chaudhuri,
A. Cheung, A. Doan, et al., “The seattle report on database research,” ACM Sigmod Record, vol. 48, no. 4, pp. 44–53, 2020. DOI: https://doi.org/10.1145/3385658.3385668
[10] R. Marcus, P. Negi, H. Mao, N. Tatbul, M. Alizadeh, and T. Kraska, “Bao: Making learned query optimization practical,” in Proceedings of the 2021 International Conference on Management of Data, pp. 1275–1288, 2021. DOI: https://doi.org/10.1145/3448016.3452838
[11] C. Wang, K. Ren, J. Wang, and K. M. R. Urs, “Harnessing the cloud for securely solving large-scale systems of linear equations,” in 2011 31st International conference on distributed computing systems, pp. 549–558, IEEE, 2011. DOI: https://doi.org/10.1109/ICDCS.2011.41
[12] C. Liu, C. Yang, X. Zhang, and J. Chen, “External integrity verification for outsourced big data in cloud and iot: A big picture,” Future generation computer systems, vol. 49, pp. 58–67, 2015. DOI: https://doi.org/10.1016/j.future.2014.08.007
[13] J. Singh, J. Cobbe, and C. Norval, “Decision provenance: Harnessing data flow for accountable systems,” IEEE Access, vol. 7, pp. 6562–6574, 2018. DOI: https://doi.org/10.1109/ACCESS.2018.2887201
[14] P. K. Erdelt and J. Jestel, “Dbms-benchmarker: Benchmark and evaluate dbms in python,” Journal of Open Source Software, vol. 7, no. 79, p. 4628, 2022. DOI: https://doi.org/10.21105/joss.04628
[15] S. Werner, J. Kuhlenkamp, M. Klems, J. Müller, and S. Tai, “Serverless big data processing using matrix
multiplication as example,” in 2018 IEEE international conference on big data (Big Data), pp. 358–365, IEEE, 2018.
[16] P. Castro, V. Ishakian, V. Muthusamy, and A. Slominski, “The rise of serverless computing,” Communications of the ACM, vol. 62, no. 12, pp. 44–54, 2019. DOI: https://doi.org/10.1145/3368454
[17] G. Banegas, D. J. Bernstein, I. Van Hoof, and T. Lange, “Concrete quantum cryptanalysis of binary elliptic curves,” Cryptology ePrint Archive, 2020. DOI: https://doi.org/10.46586/tches.v2021.i1.451-472
[18] H. Allam and S. Trivedi, “Ai-guided grover search for simulation-based evaluation of post-quantum security in ckks homomorphic encryption,” Journal of Smart Algorithms and Applications (JSAA), vol. 3, no. 1, pp. 22–35, 2026. DOI: https://doi.org/10.66279/jaksw134
[19] O. Babaoglu, M. Marzolla, and M. Tamburini, “Design and implementation of a p2p cloud system,” in Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 412–417, 2012. DOI: https://doi.org/10.1145/2245276.2245357
[20] A. Nambiar and D. Mundra, “An overview of data warehouse and data lake in modern enterprise data management,” Big data and cognitive computing, vol. 6, no. 4, p. 132, 2022. DOI: https://doi.org/10.3390/bdcc6040132
[21] M. Mukherjee, L. Shu, and D. Wang, “Survey of fog computing: Fundamental, network applications, and research challenges,” IEEE communications surveys & tutorials, vol. 20, no. 3, pp. 1826–1857, 2018. DOI: https://doi.org/10.1109/COMST.2018.2814571
[22] M. Armbrust, A. Ghodsi, R. Xin, M. Zaharia, et al., “Lakehouse: a new generation of open platforms that unify data warehousing and advanced analytics,” in Proceedings of CIDR, vol. 8, p. 28, sn, 2021.
[23] K. Kaur and R. Rani, “Modeling and querying data in nosql databases,” in 2013 IEEE international conference on big data, pp. 1–7, IEEE, 2013. DOI: https://doi.org/10.1109/BigData.2013.6691765
[24] A. Verbitski, A. Gupta, D. Saha, M. Brahmadesam, K. Gupta, R. Mittal, S. Krishnamurthy, S. Maurice, T. Kharatishvili, and X. Bao, “Amazon aurora: Design considerations for high throughput cloud-native relational databases,” in Proceedings of the 2017 ACM International Conference on Management of Data, DOI: https://doi.org/10.1145/3035918.3056101
pp. 1041–1052, 2017.
[25] M. Vuppalapati, J. Miron, R. Agarwal, D. Truong, A. Motivala, and T. Cruanes, “Building an elastic query engine on disaggregated storage,” in 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20), pp. 449–462, 2020.
[26] Y. Fouad, A. N. Ghareeb, E. Selem, et al., “Evolution of routing protocols in wireless sensor networks considering challenges advances and drone-assisted innovations,” Computational Discovery and Intelligent Systems (CDIS), vol. 2, no. 2, pp. 22–41, 2026. DOI: https://doi.org/10.66279/mwz42k06
[27] D. Ongaro and J. Ousterhout, “In search of an understandable consensus algorithm,” in 2014 USENIX annual technical conference (USENIX ATC 14), pp. 305–319, 2014.
[28] S. Eismann, J. Scheuner, E. Van Eyk, M. Schwinger, J. Grohmann, N. Herbst, C. L. Abad, and A. Iosup, “Serverless applications: Why, when, and how?,” IEEE software, vol. 38, no. 1, pp. 32–39, 2020. DOI: https://doi.org/10.1109/MS.2020.3023302
[29] B. Varghese and R. Buyya, “Next generation cloud computing: New trends and research directions,” Future generation computer systems, vol. 79, pp. 849–861, 2018. DOI: https://doi.org/10.1016/j.future.2017.09.020
[30] B. F. Cooper, A. Silberstein, E. Tam, R. Ramakrishnan, and R. Sears, “Benchmarking cloud serving systems with ycsb,” in Proceedings of the 1st ACM symposium on Cloud computing, pp. 143–154, 2010. DOI: https://doi.org/10.1145/1807128.1807152
[31] T. P. P. Council, “Tpc-h benchmark specification,” Published at http://www. tcp. org/hspec. html, vol. 21, pp. 592–603, 2008.
[32] R. Taft, I. Sharif, A. Matei, N. VanBenschoten, J. Lewis, T. Grieger, K. Niemi, A. Woods, A. Birzin, R. Poss, et al., “Cockroachdb: The resilient geo-distributed sql database,” in Proceedings of the 2020 ACM SIGMOD international conference on management of data, pp. 1493–1509, 2020. DOI: https://doi.org/10.1145/3318464.3386134
[33] J. M. Hellerstein, J. Faleiro, J. E. Gonzalez, J. Schleier-Smith, V. Sreekanti, A. Tumanov, and C. Wu, “Serverless computing: One step forward, two steps back,” arXiv preprint arXiv:1812.03651, 2018.
[34] C. Curino, E. P. C. Jones, R. A. Popa, N. Malviya, E. Wu, S. R. Madden, H. Balakrishnan, and N. Zeldovich, “Relational cloud: A database-as-a-service for the cloud,” 2011.
[35] J. Liu, “Evaluating standard-based self-virtualizing devices: A performance study on 10 gbe nics with sr-iov support,” in 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS), pp. 1–12, IEEE, 2010. DOI: https://doi.org/10.1109/IPDPS.2010.5470365
[36] A. Agache, M. Brooker, A. Iordache, A. Liguori, R. Neugebauer, P. Piwonka, and D.-M. Popa, “Firecracker: Lightweight virtualization for serverless applications,” in 17th USENIX symposium on networked systems design and implementation (NSDI 20), pp. 419–434, 2020.
[37] S. Ruj, M. Stojmenovic, and A. Nayak, “Decentralized access control with anonymous authentication of data stored in clouds,” IEEE transactions on parallel and distributed systems, vol. 25, no. 2, pp. 384–394, 2013. DOI: https://doi.org/10.1109/TPDS.2013.38
[38] A. Acar, H. Aksu, A. S. Uluagac, and M. Conti, “A survey on homomorphic encryption schemes: Theory
and implementation,” ACM Computing Surveys (Csur), vol. 51, no. 4, pp. 1–35, 2018.
[39] S. Rose, O. Borchert, S. Mitchell, and S. Connelly, “Zero trust architecture,” NIST special publication,
vol. 800, no. 207, pp. 1–52, 2020. DOI: https://doi.org/10.1111/anti.12614
[40] A. Verbitski, A. Gupta, D. Saha, J. Corey, K. Gupta, M. Brahmadesam, R. Mittal, S. Krishnamurthy, S. Maurice, T. Kharatishvilli, et al., “Amazon aurora: On avoiding distributed consensus for i/os, commits, and membership changes,” in Proceedings of the 2018 International Conference on Management of Data, pp. 789–796, 2018. DOI: https://doi.org/10.1145/3183713.3196937
[41] benchANT GmbH, “benchant database ranking: Cloud database performance benchmarking,” 2026. https://benchant.com/ranking/database-ranking, accessed June 2026.
[42] J. Zhang, Y. Liu, K. Zhou, G. Li, Z. Xiao, B. Cheng, J. Xing, Y. Wang, T. Cheng, L. Liu, et al., “An end-to-end automatic cloud database tuning system using deep reinforcement learning,” in Proceedings of the 2019 international conference on management of data, pp. 415–432, 2019. DOI: https://doi.org/10.1145/3299869.3300085
[43] D. Van Aken, A. Pavlo, G. J. Gordon, and B. Zhang, “Automatic database management system tuning through large-scale machine learning,” in Proceedings of the 2017 ACM international conference on management of data, pp. 1009–1024, 2017. DOI: https://doi.org/10.1145/3035918.3064029
[44] C. Witt, M. Bux, W. Gusew, and U. Leser, “Predictive performance modeling for distributed batch processing using black box monitoring and machine learning,” Information Systems, vol. 82, pp. 33–52, 2019. DOI: https://doi.org/10.1016/j.is.2019.01.006
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Data Availability Statement
The data used in this study are derived from publicly available sources, benchmarking reports, and previously published research. No proprietary or confidential datasets were used. All relevant data supporting the findings of this study are included within the article or referenced appropriately.
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