Continuously update the Cloud Database papers. Please inform us if there are any great papers missed :)
[Monitor] Curino, C., Jones, E. P. C., Madden, S., & Balakrishnan, H. (2011). Workload-aware database monitoring and consolidation. Proceedings of the ACM SIGMOD International Conference on Management of Data, 313–324. [paper ]
[Survey] Armbrust, A. Fox, and R. Griffith, M. (2009). Above the clouds: A Berkeley view of cloud computing. University of California, Berkeley, Tech. Rep. UCB, 07–013. [paper ]
[Survey] Jonas, E., Schleier-Smith, J., Sreekanti, V., Tsai, C.-C., Khandelwal, A., Pu, Q., Shankar, V., Carreira, J., Krauth, K., Yadwadkar, N., Gonzalez, J. E., Popa, R. A., Stoica, I., & Patterson, D. A. (2019). Cloud Programming Simplified: A Berkeley View on Serverless Computing. [paper ]
[Survey] Li, F. (2018). Cloud native database systems at Alibaba: Opportunities and challenges. Proceedings of the VLDB Endowment, 12(12), 2263–2272. [paper ]
[DBaaS] Depoutovitch, A., Chen, C., Chen, J., Larson, P., Lin, S., Ng, J., Cui, W., Liu, Q., Huang, W., Xiao, Y., & He, Y. (2020). Taurus Database: How to be Fast, Available, and Frugal in the Cloud. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1463–1478. [paper ]
[DBaaS] Taft, R., Lang, W., Duggan, J., Elmore, A. J., Stonebraker, M., & De Witt, D. (2016). STeP: Scalable tenant placement for managing database-as-a-service deployments. Proceedings of the 7th ACM Symposium on Cloud Computing, SoCC 2016, 388–400. [paper ]
[DBaaS] Das, S., Li, F., Narasayya, V. R., & König, A. C. (2016). Automated demand-driven resource scaling in relational database-as-a-service. Proceedings of the ACM SIGMOD International Conference on Management of Data, 26-June-2016, 1923–1934. [paper ]
[DBaaS] Narasayya, V., Menache, I., Singh, M., Li, F., Syamala, M., & Chaudhuri, S. (2015). Sharing Buffer Pool Memory in Multi-Tenant Relational. Proceedings of the VLDB Endowment, 8(7), 726-737. [paper ]
[Auto-scaling] Perron, M., Castro Fernandez, R., Dewitt, D., & Madden, S. (2020). Starling: A Scalable Query Engine on Cloud Functions. Proceedings of the ACM SIGMOD International Conference on Management of Data, 131–141. [paper ]
[Auto-scaling] Shen, Z., Subbiah, S., Gu, X., & Wilkes, J. (2011). CloudScale: Elastic resource scaling for multi-tenant cloud systems. Proceedings of the 2nd ACM Symposium on Cloud Computing, SOCC 2011. [paper ]
[Auto-scaling] Wu, C., Sreekanti, V., & Hellerstein, J. M. (2021). Autoscaling tiered cloud storage in Anna. VLDB Journal, 30(1), 25–43. [paper ]
[Auto-scaling] [Disaggregation] Zhang, Y., Ruan, C., Li, C., Yang, J., Cao, W., Li, F., Wang, B., Fang, J., Wang, Y., Huo, J., & Bi, C. (2021). Towards Cost-Effective and Elastic Cloud Database Deployment via Memory Disaggregation. Proc. VLDB Endow., 14(1), 1900–1912. [paper ]
[Partition] Hilprecht, B., Binnig, C., & Röhm, U. (2020). Learning a Partitioning Advisor for Cloud Databases. Proceedings of the ACM SIGMOD International Conference on Management of Data, 143–157. [paper ]
[Disaggregation] Shan, Y., Huang, Y., Chen, Y., & Zhang, Y. (2018). LegoOS : A Disseminated , Distributed OS for Hardware Resource Disaggregation. Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’18)., 69–87. [paper]
[Disaggregation] Angel, S., Nanavati, M., & Sen, S. (2020). Disaggregation and the application. HotCloud 2020 - 12th USENIX Workshop on Hot Topics in Cloud Computing, Co-Located with USENIX ATC 2020.[paper]
[Disaggregation] Klimovic, A., Kozyrakis, C., Thereska, E., John, B., & Kumar, S. (2016). Flash storage disaggregation. Proceedings of the 11th European Conference on Computer Systems, EuroSys 2016. [paper]
[Disaggregation] Zhang, Q., Cai, Y., Chen, X., Angel, S., Chen, A., Liu, V., & Loo, B. T. (2020). Understanding the effect of data center resource disaggregation on production DBMSs. Proceedings of the VLDB Endowment, 13(9), 1568–1581. [paper]
[Optimizer] Wu, C., Jindal, A., Amizadeh, S., Patel, H., & Le, W. (2018). Towards a learning optimizer for shared clouds. Proceedings of the VLDB Endowment, 12(3), 210–222. [paper]
[Optimizer] Leis, V., & Kuschewski, M. (2021). Towards Cost-Optimal Query Processing in the Cloud. Proc. {VLDB} Endow., 14(9), 1606–1612. [paper]
[Safety] Antonopoulos, P., Arasu, A., Singh, K. D., Eguro, K., Gupta, N., Jain, R., Kaushik, R., Kodavalla, H., Kossmann, D., Ogg, N., Ramamurthy, R., Szymaszek, J., Trimmer, J., Vaswani, K., Venkatesan, R., & Zwilling, M. (2020). Azure SQL Database Always Encrypted. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1, 1511–1525. [paper]
[Safety] Arasu, A., Eguro, K., Kaushik, R., & Ramamurthy, R. (2014). Querying encrypted data. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1259–1261. [paper]
[Recovery] Yang, Y., Youill, M., Woicik, M., Liu, Y., Yu, X., Serafini, M., Aboulnaga, A., & Stonebraker, M. (2021). FlexPushdownDB: Hybrid Pushdown and Caching in a Cloud DBMS. FlexPushdownDB: Hybrid Pushdown and Caching in a Cloud DBMS. PVLDB, 14(11), 2101–2113. [paper]
[System] Ortiz, J., Lee, B., Balazinska, M., Gehrke, J., & Hellerstein, J. L. (2020). SLAOrchestrator: Reducing the cost of performance SLAs for cloud data analytics. Proceedings of the 2018 USENIX Annual Technical Conference, USENIX ATC 2018, 547–560.[paper]
[Hardware] Do, J., Sengupta, S., & Swanson, S. (2019). Programmable solid-state storage in future cloud datacenters. Communications of the ACM, 62(6), 54–62. [paper]
[Hardware] Xue, S., Zhao, S., Chen, Q., Deng, G., Liu, Z., Zhang, J., Song, Z., Ma, T., Yang, Y., Zhou, Y., Niu, K., Sun, S., & Guo, M. (2020). Spool: Reliable virtualized NVMe storage pool in public cloud infrastructure. Proceedings of the 2020 USENIX Annual Technical Conference, ATC 2020, 97–110.
[Memory] Kalia, A., Andersen, D., & Kaminsky, M. (2020). Challenges and solutions for fast remote persistent memory access. SoCC 2020 - Proceedings of the 2020 ACM Symposium on Cloud Computing, 105–119. [paper]
[Memory] Wei, X., Chen, R., Chen, H., Jiao, S., Wei, X., Chen, R., & Chen, H. (2020). Fast RDMA-based Ordered Key-Value Store using Remote Learned Cache. Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation Fast RDMA-Based Ordered Key-Value Store Using Remote Learned Cache.[paper]
[Memory] Nelson, J., Holt, B., Myers, B., Briggs, P., Ceze, L., Kahan, S., & Oskin, M. (2015). Latency-Tolerant Software Distributed Shared Memory. Proceedings of the 2015 USENIX Annual Technical Conference, USENIX ATC 2015, 291–305.[paper]
[Memory] Shan, Y., Tsai, S. Y., & Zhang, Y. (2017). Distributed shared persistent memory. SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing, 323–337. [paper]
[Memory] Fent, P., Renen, A. Van, Kipf, A., Leis, V., Neumann, T., & Kemper, A. (2020). Low-latency communication for fast DBMS Using RDMA and shared memory. Proceedings - International Conference on Data Engineering, 2020-April, 1477–1488. [paper]
[Memory] Aguilera, M. K., Amit, N., Calciu, I., Deguillard, X., Gandhi, J., Subrahmanyam, P., Suresh, L., Tati, K., Venkatasubramanian, R., & Wei, M. (2017). Remote memory in the age of fast networks. SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing, 121–127. [paper]
[Memory] Lagar-Cavilla, A., Ahn, J., Souhlal, S., Agarwal, N., Burny, R., Butt, S., Chang, J., Chaugule, A., Deng, N., Shahid, J., Thelen, G., Yurtsever, K. A., Zhao, Y., & Ranganathan, P. (2019). Software-Defined Far Memory in Warehouse-Scale Computers. International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 317–330. [paper]
[Network] Ziegler, T., Vani, S. T., Binnig, C., Fonseca, R., & Kraska, T. (2019). Designing distributed tree-based index structures for fast RDMA-capable networks. Proceedings of the ACM SIGMOD International Conference on Management of Data, 741–758. [paper]
[Network] Tirmazi, M., Ben Basat, R., Gao, J., & Yu, M. (2020). Cheetah: Accelerating Database Queries with Switch Pruning. Proceedings of the ACM SIGMOD International Conference on Management of Data, 2407–2422. [paper]
[Network] Craddock, H., Konudula, L. P., Cheng, K., & Kul, G. (2019). The case for physical memory pools: A vision paper. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11513 LNCS(Vm), 208–221. [paper]
[Application] Müller, I., Marroquín, R., & Alonso, G. (2020). Lambada: Interactive Data Analytics on Cold Data Using Serverless Cloud Infrastructure. Proceedings of the ACM SIGMOD International Conference on Management of Data, 115–130. [paper]
[Application] Yu, X., Youill, M., Woicik, M., Ghanem, A., Serafini, M., Aboulnaga, A., & Stonebraker, M. (2020). PushdownDB: Accelerating a DBMS Using S3 Computation. Proceedings - International Conference on Data Engineering, 2020-April, 1802–1805. [paper]
[Application] Antonopoulos, P., Budovski, A., Diaconu, C., Saenz, A. H., Hu, J., Kodavalla, H., Kossmann, D., Lingam, S., Minhas, U. F., Prakash, N., Purohit, V., Qu, H., Ravella, C. S., Reisteter, K., Shrotri, S., Tang, D., & Wakade, V. (2019). Socrates: The new SQL server in the cloud. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1743–1756. [paper]
[Industry] Verbitski, A., Gupta, A., Saha, D., Corey, J., Gupta, K., Brahmadesam, M., Mittal, R., Krishnamurthy, S., Maurice, S., Kharatishvilli, T., & Bao, X. (2018). Amazon Aurora. 789–796. [paper]
[Industry] Li, F. (2018). Cloud native database systems at Alibaba: Opportunities and challenges. Proceedings of the VLDB Endowment, 12(12), 2263–2272. [paper]
[Industry] Dobrescu, M., Argyraki, K., & Argyraki EPFL, K. (2014). Millions of Tiny Databases. Proceedings of the 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’14). [paper]
[Industry] Cao, W., Liu, Y., Cheng, Z., Zheng, N., Li, W., Wu, W., Ouyang, L., Wang, P., Wang, Y., Kuan, R., Liu, Z., Zhu, F., & Zhang, T. (2020). POLARDB Meets Computational Storage : Efficiently Support Analytical Workloads in Cloud-Native Relational Database. 18th USENIX Conference on File and Storage Technologies (FAST 20). 2020. [paper]
[Industry] Cao, W., Zhang, Y., Yang, X., Li, F., Wang, S., Hu, Q., Cheng, X., Chen, Z., Liu, Z., Fang, J., Wang, B., Wang, Y., Sun, H., Yang, Z., Cheng, Z., Chen, S., Wu, J., Hu, W., Zhao, J., … Tong, J. (2021). PolarDB Serverless: A Cloud Native Database for Disaggregated Data Centers. Proceedings of the ACM SIGMOD International Conference on Management of Data, 2477–2489. [paper]
[Industry] Cao, W., Liu, Z., Wang, P., Chen, S., Zhu, C., Zheng, S., Wang, Y., & Ma, G. (2018). PolarFS: An ultralow latency and failure resilient distributed file system for shared storage cloud database. Proceedings of the VLDB Endowment, 11(12), 1849–1862. [paper]
[Industry] Dageville, B., Cruanes, T., Zukowski, M., Antonov, V., Avanes, A., Bock, J., Claybaugh, J., Engovatov, D., Hentschel, M., Huang, J., Lee, A. W., Motivala, A., Munir, A. Q., Pelley, S., Povinec, P., Rahn, G., Triantafyllis, S., & Unterbrunner, P. (2016). The snowflake elastic data warehouse. Proceedings of the ACM SIGMOD International Conference on Management of Data, 26-June-20, 215–226. [paper]
[Industry] Mattson, T., Rogers, J., & Elmore, A. J. (2018). The BigDAWG polystore system. Making Databases Work: The Pragmatic Wisdom of Michael Stonebraker, 44(2), 279–289. [paper]
[Industry] Huang, D., Liu, Q., Cui, Q., Fang, Z., Ma, X., Xu, F., Shen, L., Tang, L., Zhou, Y., Huang, M., Wei, W., Liu, C., Zhang, J., Li, J., Wu, X., Song, L., Sun, R., Yu, S., Zhao, L., … Tang, X. (2020). TiDB: a Raft-based HTAP database. Proceedings of the VLDB Endowment, 13(12), 3072–3084. [paper]
[Challenges] Zhang, Q., Cai, Y., Angel, S., Liu, V., Chen, A., & Loo, B. T. (2020). Rethinking Data Management Systems for Disaggregated Data Centers. CIDR 2019 - 9th Biennial Conference on Innovative Data Systems Research.[paper]
[Challenges] Hellerstein, J. M., Faleiro, J., Gonzalez, J. E., Schleier-Smith, J., Sreekanti, V., Tumanov, A., & Wu, C. (2019). Serverless computing: One step forward, two steps back. CIDR 2019 - 9th Biennial Conference on Innovative Data Systems Research.[paper]