ABSTRACT
Enterprises are moving their business-critical workloads to public clouds at an accelerating pace. Multi-tenancy is a crucial tenet for cloud data service providers allowing them to provide services in cost-effective manner by sharing of resources among tenants of the service. In this tutorial we review architectures of today's cloud data services and identify trends and challenges that arise in multi-tenant cloud data services. We discuss techniques that have been developed for enabling elasticity, providing SLAs, ensuring performance isolation, and reducing cost. We conclude with open research problems in cloud data services.
- A Technical Overview of Azure Cosmos DB. 2020. https://azure.microsoft.com/en-us/blog/a-technical-overview-of-azure-cosmos-db/. Accessed 20 January 2021.Google Scholar
- Daniel Abadi. 2012. Consistency tradeoffs in modern distributed database system design: CAP is only part of the story. Computer, Vol. 45, 2 (2012), 37--42.Google ScholarDigital Library
- Daniel Abadi, Anastasia Ailamaki, David Andersen, Peter Bailis, Magdalena Balazinska, Philip Bernstein, Peter Boncz, Surajit Chaudhuri, Alvin Cheung, AnHai Doan, et al. 2020. The Seattle Report on Database Research. ACM SIGMOD Record, Vol. 48, 4 (2020), 44--53.Google ScholarDigital Library
- Sanjay Agrawal, Surajit Chaudhuri, and Vivek R Narasayya. 2000. Automated selection of materialized views and indexes in SQL databases. In VLDB, Vol. 2000. 496--505.Google Scholar
- Josep Aguilar-Saborit, Raghu Ramakrishnan, Krish Srinivasan, Kevin Bocksrocker, Yannis Ioalagia, Mahadevan Sankara, and Moe Shafiei. 20120. POLARIS: The Distributed SQL Engine in Azure Synapse. Proceedings of the VLDB Endowment (20120).Google Scholar
- Mumtaz Ahmad, Ashraf Aboulnaga, Shivnath Babu, and Kamesh Munagala. 2008. Modeling and exploiting query interactions in database systems. In Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 183--192.Google ScholarDigital Library
- Mert Akdere, Ugur Cetintemel, Matteo Riondato, Eli Upfal, and Stanley B Zdonik. 2012. Learning-based query performance modeling and prediction. In Data Engineering (ICDE), 2012 IEEE 28th International Conference on. IEEE, 390--401.Google ScholarDigital Library
- Mohammad Al-Fares, Alexander Loukissas, and Amin Vahdat. 2008. A scalable, commodity data center network architecture. ACM SIGCOMM computer communication review, Vol. 38, 4 (2008), 63--74.Google Scholar
- Mohammad Alizadeh and Tom Edsall. 2013. On the data path performance of leaf-spine datacenter fabrics. In 2013 IEEE 21st annual symposium on high-performance interconnects. IEEE, 71--74.Google Scholar
- Amazon Athena. 2020. https://aws.amazon.com/athena/. Accessed 20 January 2021.Google Scholar
- Amazon Aurora Serverless. 2020. https://aws.amazon.com/rds/aurora/serverless/. Accessed 20 January 2021.Google Scholar
- Amazon Dynamo DB On-Demand. 2020. https://aws.amazon.com/about-aws/whats-new/2018/11/announcing-amazon-dynamodb-on-demand/. Accessed 20 January 2021.Google Scholar
- Amazon Firecracker. 2020. https://aws.amazon.com/about-aws/whats-new/2018/11/firecracker-lightweight-virtualization-for-serverless-computing/. Accessed 20 January 2021.Google Scholar
- Amazon RDS Multi-AZ. 2020. https://aws.amazon.com/rds/features/multi-az/. Accessed 20 January 2021.Google Scholar
- An Updated Performance Comparison of Virtual Machines and Linux Containers. 2014. https://dominoweb.draco.res.ibm.com/reports/rc25482.pdf. Accessed 20 January 2021.Google Scholar
- Ganesh Ananthanarayanan, Christopher Douglas, Raghu Ramakrishnan, Sriram Rao, and Ion Stoica. 2012. True elasticity in multi-tenant data-intensive compute clusters. In Proceedings of the Third ACM Symposium on Cloud Computing. ACM, 24.Google ScholarDigital Library
- Panagiotis Antonopoulos, Alex Budovski, Cristian Diaconu, Alejandro Hernandez Saenz, Jack Hu, Hanuma Kodavalla, Donald Kossmann, Sandeep Lingam, Umar Farooq Minhas, Naveen Prakash, et al. 2019. Socrates: The New SQL Server in the Cloud. In Proceedings of the 2019 International Conference on Management of Data. ACM, 1743--1756.Google ScholarDigital Library
- Apache Hadoop. 2020. http://hadoop.apache.org. Accessed 20 January 2021.Google Scholar
- Raja Appuswamy, Goetz Graefe, Renata Borovica-Gajic, and Anastasia Ailamaki. 2019. The five-minute rule 30 years later and its impact on the storage hierarchy. Commun. ACM, Vol. 62, 11 (2019), 114--120.Google ScholarDigital Library
- Arvind Arasu, Spyros Blanas, Ken Eguro, Raghav Kaushik, Donald Kossmann, Ravishankar Ramamurthy, and Ramarathnam Venkatesan. 2013. Orthogonal Security with Cipherbase.. In CIDR .Google Scholar
- Michael Armbrust, Tathagata Das, Liwen Sun, Burak Yavuz, Shixiong Zhu, Mukul Murthy, Joseph Torres, Herman van Hovell, Adrian Ionescu, Alicja Łuszczak, et al. 2020. Delta lake: high-performance ACID table storage over cloud object stores. Proceedings of the VLDB Endowment, Vol. 13, 12 (2020), 3411--3424.Google ScholarDigital Library
- Michael Armbrust, Reynold S Xin, Cheng Lian, Yin Huai, Davies Liu, Joseph K Bradley, Xiangrui Meng, Tomer Kaftan, Michael J Franklin, Ali Ghodsi, et al. 2015. Spark sql: Relational data processing in spark. In Proceedings of the 2015 ACM SIGMOD international conference on management of data. 1383--1394.Google ScholarDigital Library
- AtScale. 2020. https://www.atScale.com/. Accessed 20 January 2021.Google Scholar
- Automatic Plan Correction. 2020. https://docs.microsoft.com/en-us/sql/relational-databases/automatic-tuning/automatic-tuning. Accessed 20 January 2021.Google Scholar
- AWS Nitro System. 2020. https://aws.amazon.com/ec2/nitro/. Accessed 20 January 2021.Google Scholar
- AWS Redshift. 2020. https://aws.amazon.com/redshift/. Accessed 20 January 2021.Google Scholar
- Azure Cosmos DB Serverless. 2020. https://azure.microsoft.com/en-us/blog/build-apps-of-any-size-or-scale-with-azure-cosmos-db/. Accessed 20 January 2021.Google Scholar
- Azure SQL Data Warehouse. 2020. https://azure.microsoft.com/en-us/services/sql-data-warehouse/. Accessed 20 January 2021.Google Scholar
- Azure SQL DB Automatic Tuning. 2020. https://docs.microsoft.com/en-us/sql/relational-databases/automatic-tuning/automatic-tuning. Accessed 20 January 2021.Google Scholar
- Azure SQL DB Serverless. 2020. https://docs.microsoft.com/en-us/azure/sql-database/sql-database-serverless. Accessed 20 January 2021.Google Scholar
- Azure Synapse Analytics. 2020. https://docs.microsoft.com/en-us/azure/synapse-analytics/overview-what-is. Accessed 20 January 2021.Google Scholar
- David F Bacon, Nathan Bales, Nico Bruno, Brian F Cooper, Adam Dickinson, Andrew Fikes, Campbell Fraser, Andrey Gubarev, Milind Joshi, Eugene Kogan, et al. 2017. Spanner: Becoming a sql system. In Proceedings of the 2017 ACM International Conference on Management of Data. ACM, 331--343.Google ScholarDigital Library
- Ishan Banerjee, Fei Guo, Kiran Tati, and Rajesh Venkatasubramanian. 2013. Memory overcommitment in the ESX server. VMware Technical Journal, Vol. 2, 1 (2013), 2--12.Google Scholar
- Paul Barham, Rebecca Isaacs, Richard Mortier, and Dushyanth Narayanan. 2003. Magpie: Online Modelling and Performance-aware Systems.. In HotOS. 85--90.Google ScholarDigital Library
- R. Bringhurst. 2012. The Elements of Typographic Style fourth ed.). Hartley & Marks: Vancouver, BC.Google Scholar
- Brendan Burns, Brian Grant, David Oppenheimer, Eric Brewer, and John Wilkes. 2016. Borg, Omega, and Kubernetes. Queue, Vol. 14, 1 (2016), 10.Google ScholarDigital Library
- Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C Hsieh, Deborah A Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E Gruber. 2008. Bigtable: A distributed storage system for structured data. ACM Transactions on Computer Systems (TOCS), Vol. 26, 2 (2008), 1--26.Google ScholarDigital Library
- Surajit Chaudhuri. 1998. An overview of query optimization in relational systems. In Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems. ACM, 34--43.Google ScholarDigital Library
- Surajit Chaudhuri, Umeshwar Dayal, and Vivek Narasayya. 2011. An overview of business intelligence technology. Commun. ACM, Vol. 54, 8 (2011), 88--98.Google ScholarDigital Library
- Surajit Chaudhuri and Vivek Narasayya. 2007. Self-tuning database systems: a decade of progress. In Proceedings of the 33rd international conference on Very large data bases. VLDB Endowment, 3--14.Google ScholarDigital Library
- Surajit Chaudhuri, Vivek Narasayya, and Ravishankar Ramamurthy. 2004. Estimating progress of execution for SQL queries. In Proceedings of the 2004 ACM SIGMOD international conference on Management of data. ACM, 803--814.Google ScholarDigital Library
- Surajit Chaudhuri and Vivek R Narasayya. 1997. An efficient, cost-driven index selection tool for Microsoft SQL server. In VLDB, Vol. 97. Citeseer, 146--155.Google Scholar
- Yun Chi, Hyun Jin Moon, and Hakan Hacigümücs. 2011a. iCBS: incremental cost-based scheduling under piecewise linear SLAs. Proceedings of the VLDB Endowment, Vol. 4, 9 (2011), 563--574.Google ScholarDigital Library
- Yun Chi, Hyun Jin Moon, Hakan Hacigümücs, and Junichi Tatemura. 2011b. SLA-tree: a framework for efficiently supporting SLA-based decisions in cloud computing. In Proceedings of the 14th International Conference on Extending Database Technology. ACM, 129--140.Google ScholarDigital Library
- Kristina Chodorow. 2013. MongoDB: the definitive guide: powerful and scalable data storage ." O'Reilly Media, Inc.".Google Scholar
- Christopher Clark, Keir Fraser, Steven Hand, Jacob Gorm Hansen, Eric Jul, Christian Limpach, Ian Pratt, and Andrew Warfield. 2005. Live migration of virtual machines. In Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation-Volume 2. USENIX Association, 273--286.Google ScholarDigital Library
- Neil Conway, William R Marczak, Peter Alvaro, Joseph M Hellerstein, and David Maier. 2012. Logic and lattices for distributed programming. In Proceedings of the Third ACM Symposium on Cloud Computing. 1--14.Google ScholarDigital Library
- James C Corbett, Jeffrey Dean, Michael Epstein, Andrew Fikes, Christopher Frost, Jeffrey John Furman, Sanjay Ghemawat, Andrey Gubarev, Christopher Heiser, Peter Hochschild, et al. 2013. Spanner: Google's globally distributed database. ACM Transactions on Computer Systems (TOCS), Vol. 31, 3 (2013), 8.Google ScholarDigital Library
- Carlo Curino, Djellel E Difallah, Chris Douglas, Subru Krishnan, Raghu Ramakrishnan, and Sriram Rao. 2014. Reservation-based scheduling: If you're late don't blame us!. In Proceedings of the ACM Symposium on Cloud Computing. ACM, 1--14.Google ScholarDigital Library
- Carlo Curino, Evan PC Jones, Samuel Madden, and Hari Balakrishnan. 2011a. Workload-aware database monitoring and consolidation. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data. ACM, 313--324.Google ScholarDigital Library
- Carlo Curino, Evan PC Jones, Raluca Ada Popa, Nirmesh Malviya, Eugene Wu, Sam Madden, Hari Balakrishnan, and Nickolai Zeldovich. 2011b. Relational cloud: A database-as-a-service for the cloud. (2011).Google Scholar
- Benoit Dageville, Thierry Cruanes, Marcin Zukowski, Vadim Antonov, Artin Avanes, Jon Bock, Jonathan Claybaugh, Daniel Engovatov, Martin Hentschel, Jiansheng Huang, et al. 2016. The snowflake elastic data warehouse. In Proceedings of the 2016 International Conference on Management of Data. ACM, 215--226.Google ScholarDigital Library
- William James Dally and Brian Patrick Towles. 2004. Principles and practices of interconnection networks .Elsevier.Google ScholarDigital Library
- Sudipto Das, Miroslav Grbic, Igor Ilic, Isidora Jovandic, Andrija Jovanovic, Vivek R Narasayya, Miodrag Radulovic, Maja Stikic, Gaoxiang Xu, and Surajit Chaudhuri. 2019. Automatically indexing millions of databases in microsoft azure sql database. In Proceedings of the 2019 International Conference on Management of Data. ACM, 666--679.Google ScholarDigital Library
- Sudipto Das, Feng Li, Vivek R Narasayya, and Arnd Christian Konig. 2016. Automated demand-driven resource scaling in relational database-as-a-service. In Proceedings of the 2016 International Conference on Management of Data. ACM, 1923--1934.Google ScholarDigital Library
- Sudipto Das, Vivek R Narasayya, Feng Li, and Manoj Syamala. 2013. CPU sharing techniques for performance isolation in multi-tenant relational database-as-a-service. Proceedings of the VLDB Endowment, Vol. 7, 1 (2013), 37--48.Google ScholarDigital Library
- Sudipto Das, Shoji Nishimura, Divyakant Agrawal, and Amr El Abbadi. 2010. Live database migration for elasticity in a multitenant database for cloud platforms. CS, UCSB, Santa Barbara, CA, USA, Tech. Rep, Vol. 9 (2010), 2010.Google Scholar
- Sudipto Das, Shoji Nishimura, Divyakant Agrawal, and Amr El Abbadi. 2011. Albatross: lightweight elasticity in shared storage databases for the cloud using live data migration. Proceedings of the VLDB Endowment, Vol. 4, 8 (2011), 494--505.Google ScholarDigital Library
- Jeffrey Dean and Luiz André Barroso. 2013. The tail at scale. Commun. ACM, Vol. 56, 2 (2013), 74--80.Google ScholarDigital Library
- Jeffrey Dean and Sanjay Ghemawat. 2008. MapReduce: simplified data processing on large clusters. Commun. ACM, Vol. 51, 1 (2008), 107--113.Google ScholarDigital Library
- Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash Lakshman, Alex Pilchin, Swaminathan Sivasubramanian, Peter Vosshall, and Werner Vogels. 2007. Dynamo: amazon's highly available key-value store. In ACM SIGOPS operating systems review, Vol. 41. ACM, 205--220.Google Scholar
- Bailu Ding, Sudipto Das, Ryan Marcus, Wentao Wu, Surajit Chaudhuri, and Vivek R Narasayya. 2019. Ai meets ai: Leveraging query executions to improve index recommendations. In Proceedings of the 2019 International Conference on Management of Data. 1241--1258.Google ScholarDigital Library
- Songyun Duan, Shivnath Babu, and Kamesh Munagala. 2009. Fa: A system for automating failure diagnosis. In Data Engineering, 2009. ICDE'09. IEEE 25th International Conference on. IEEE, 1012--1023.Google ScholarDigital Library
- Jennie Duggan, Ugur Cetintemel, Olga Papaemmanouil, and Eli Upfal. 2011. Performance prediction for concurrent database workloads. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data. ACM, 337--348.Google ScholarDigital Library
- Anshuman Dutt, Chi Wang, Vivek Narasayya, and Surajit Chaudhuri. 2020. Efficiently approximating selectivity functions using low overhead regression models. Proceedings of the VLDB Endowment, Vol. 13, 12 (2020), 2215--2228.Google ScholarDigital Library
- Anshuman Dutt, Chi Wang, Azade Nazi, Srikanth Kandula, Vivek Narasayya, and Surajit Chaudhuri. 2019. Selectivity estimation for range predicates using lightweight models. Proceedings of the VLDB Endowment, Vol. 12, 9 (2019), 1044--1057.Google ScholarDigital Library
- Elastic Pools in Azure SQL Database. 2020. https://docs.microsoft.com/en-us/azure/sql-database/sql-database-elastic-pool. Accessed 20 January 2021.Google Scholar
- Aaron J Elmore, Sudipto Das, Divyakant Agrawal, and Amr El Abbadi. 2011. Zephyr: live migration in shared nothing databases for elastic cloud platforms. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data. ACM, 301--312.Google ScholarDigital Library
- Extended Events Overview. 2019. https://docs.microsoft.com/en-us/sql/relational-databases/extended-events/. Accessed 20 January 2021.Google Scholar
- Fauna DB. 2020. https://fauna.com. Accessed 20 January 2021.Google Scholar
- Rodrigo Fonseca, George Porter, Randy H Katz, and Scott Shenker. 2007. X-trace: A pervasive network tracing framework. In 4th USENIX Symposium on Networked Systems Design & Implementation (NSDI 07) .Google Scholar
- Archana Ganapathi, Harumi Kuno, Umeshwar Dayal, Janet L Wiener, Armando Fox, Michael Jordan, and David Patterson. 2009. Predicting multiple metrics for queries: Better decisions enabled by machine learning. In Data Engineering, 2009. ICDE'09. IEEE 25th International Conference on. IEEE, 592--603.Google ScholarDigital Library
- Anshul Gandhi, Mor Harchol-Balter, Ram Raghunathan, and Michael A Kozuch. 2012. Autoscale: Dynamic, robust capacity management for multi-tier data centers. ACM Transactions on Computer Systems (TOCS), Vol. 30, 4 (2012), 14.Google ScholarDigital Library
- Anshul Gandhi, Sidhartha Thota, Parijat Dube, Andrzej Kochut, and Li Zhang. 2016. Autoscaling for hadoop clusters. In 2016 IEEE International Conference on Cloud Engineering (IC2E). IEEE, 109--118.Google ScholarCross Ref
- Gartner DBMS Future. 2019. https://www.gartner.com/document/3941821. Accessed 20 January 2021.Google Scholar
- Rahul Ghosh and Vijay K Naik. 2012. Biting off safely more than you can chew: Predictive analytics for resource over-commit in iaas cloud. In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on. IEEE, 25--32.Google ScholarDigital Library
- Zhenhuan Gong, Xiaohui Gu, and John Wilkes. 2010. Press: Predictive elastic resource scaling for cloud systems. In Network and Service Management (CNSM), 2010 International Conference on. Ieee, 9--16.Google ScholarCross Ref
- Google BigQuery. 2020. https://cloud.google.com/bigquery. Accessed 20 January 2021.Google Scholar
- Google Persistent Disk. 2020. https://cloud.google.com/persistent-disk/. Accessed 20 January 2021.Google Scholar
- Abel Gordon, Michael Hines, Dilma Da Silva, Muli Ben-Yehuda, Marcio Silva, and Gabriel Lizarraga. 2011. Ginkgo: Automated, application-driven memory overcommitment for cloud computing. Proc. RESoLVE (2011).Google Scholar
- Robert Grandl, Ganesh Ananthanarayanan, Srikanth Kandula, Sriram Rao, and Aditya Akella. 2015. Multi-resource packing for cluster schedulers. ACM SIGCOMM Computer Communication Review, Vol. 44, 4 (2015), 455--466.Google ScholarDigital Library
- Robert Grandl, Srikanth Kandula, Sriram Rao, Aditya Akella, and Janardhan Kulkarni. 2016. G: Packing and Dependency-aware Scheduling for Data-Parallel Clusters. In Proceedings of OSDI'16: 12th USENIX Symposium on Operating Systems Design and Implementation. 81.Google Scholar
- Jim Gray and Franco Putzolu. 1987. The 5 minute rule for trading memory for disc accesses and the 10 byte rule for trading memory for CPU time. In Proceedings of the 1987 ACM SIGMOD international conference on Management of data. 395--398.Google ScholarDigital Library
- Albert Greenberg, James R Hamilton, Navendu Jain, Srikanth Kandula, Changhoon Kim, Parantap Lahiri, David A Maltz, Parveen Patel, and Sudipta Sengupta. 2009. VL2: a scalable and flexible data center network. In Proceedings of the ACM SIGCOMM 2009 conference on Data communication. 51--62.Google ScholarDigital Library
- Ajay Gulati, Arif Merchant, and Peter J Varman. 2010. mClock: handling throughput variability for hypervisor IO scheduling. In Proceedings of the 9th USENIX conference on Operating systems design and implementation. USENIX Association, 437--450.Google Scholar
- Hakan Hacigümücs, Bala Iyer, Chen Li, and Sharad Mehrotra. 2002. Executing SQL over encrypted data in the database-service-provider model. In Proceedings of the 2002 ACM SIGMOD international conference on Management of data. ACM, 216--227.Google ScholarDigital Library
- Joseph M Hellerstein, Jose Faleiro, Joseph E Gonzalez, Johann Schleier-Smith, Vikram Sreekanti, Alexey Tumanov, and Chenggang Wu. 2018. Serverless computing: One step forward, two steps back. arXiv preprint arXiv:1812.03651 (2018).Google Scholar
- Herodotos Herodotou, Harold Lim, Gang Luo, Nedyalko Borisov, Liang Dong, Fatma Bilgen Cetin, and Shivnath Babu. 2011. Starfish: A Self-tuning System for Big Data Analytics.. In Cidr, Vol. 11. 261--272.Google Scholar
- Kelsey Hightower, Brendan Burns, and Joe Beda. 2017. Kubernetes: up and running: dive into the future of infrastructure ." O'Reilly Media, Inc.".Google Scholar
- Benjamin Hindman, Andy Konwinski, Matei Zaharia, Ali Ghodsi, Anthony D Joseph, Randy H Katz, Scott Shenker, and Ion Stoica. 2011. Mesos: A platform for fine-grained resource sharing in the data center.. In NSDI, Vol. 11. 22--22.Google ScholarDigital Library
- Botong Huang, Matthias Boehm, Yuanyuan Tian, Berthold Reinwald, Shirish Tatikonda, and Frederick R Reiss. 2015a. Resource elasticity for large-scale machine learning. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM, 137--152.Google ScholarDigital Library
- Botong Huang, Nicholas WD Jarrett, Shivnath Babu, Sayan Mukherjee, and Jun Yang. 2015b. Cümülön: Matrix-based data analytics in the cloud with spot instances. Proceedings of the VLDB Endowment, Vol. 9, 3 (2015), 156--167.Google ScholarDigital Library
- Jiamin Huang, Barzan Mozafari, Grant Schoenebeck, and Thomas F Wenisch. 2017b. A top-down approach to achieving performance predictability in database systems. In Proceedings of the 2017 ACM International Conference on Management of Data. ACM, 745--758.Google ScholarDigital Library
- Jiamin Huang, Barzan Mozafari, and Thomas F Wenisch. 2017a. Statistical analysis of latency through semantic profiling. In Proceedings of the Twelfth European Conference on Computer Systems. ACM, 64--79.Google ScholarDigital Library
- Hyperspace: An indexing subsystem for Apache Spark. 2020. https://docs.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-performance-hyperspace?pivots=programming-language-csharp. Accessed 20 January 2021.Google Scholar
- IBM Netezza. 2020. "https://www.ibmbigdatahub.com/sites/default/files/document/redguide_2011.pdf". Accessed 20 January 2021.Google Scholar
- Calin Iorgulescu, Reza Azimi, Youngjin Kwon, Sameh Elnikety, Manoj Syamala, Vivek Narasayya, Herodotos Herodotou, Paulo Tomita, Alex Chen, Jack Zhang, et al. [n.,d.]. PerfIso: Performance Isolation for Commercial Latency-Sensitive Services. In 2018 USENIX Annual Technical Conference USENIX ATC 18), pages=519--532, year=2018, organization=USENIX Association .Google Scholar
- Navendu Jain, Ishai Menache, and Ohad Shamir. 2014. On-demand, spot, or both: Dynamic resource allocation for executing batch jobs in the cloud. (2014).Google Scholar
- Virajith Jalaparti, Chris Douglas, Mainak Ghosh, Ashvin Agrawal, Avrilia Floratou, Srikanth Kandula, Ishai Menache, Joseph Seffi Naor, and Sriram Rao. 2018. Netco: Cache and I/O Management for Analytics over Disaggregated Stores. In Proceedings of the ACM Symposium on Cloud Computing. ACM, 186--198.Google ScholarDigital Library
- Alekh Jindal, Konstantinos Karanasos, Sriram Rao, and Hiren Patel. 2018. Selecting subexpressions to materialize at datacenter scale. Proceedings of the VLDB Endowment, Vol. 11, 7 (2018), 800--812.Google ScholarDigital Library
- Eric Jonas, Johann Schleier-Smith, Vikram Sreekanti, Chia-Che Tsai, Anurag Khandelwal, Qifan Pu, Vaishaal Shankar, Joao Carreira, Karl Krauth, Neeraja Yadwadkar, et al. 2019. Cloud programming simplified: A berkeley view on serverless computing. arXiv preprint arXiv:1902.03383 (2019).Google Scholar
- Chris Jones and John Wilkes. 2016. Service Level Objectives. Site Reliability Engineering: How Google Runs Production Systems.Google Scholar
- Gopal Kakivaya, Lu Xun, Richard Hasha, Shegufta Bakht Ahsan, Todd Pfleiger, Rishi Sinha, Anurag Gupta, Mihail Tarta, Mark Fussell, Vipul Modi, et al. 2018. Service fabric: a distributed platform for building microservices in the cloud. In Proceedings of the Thirteenth EuroSys Conference. ACM, 33.Google ScholarDigital Library
- Konstantinos Karanasos, Sriram Rao, Carlo Curino, Chris Douglas, Kishore Chaliparambil, Giovanni Matteo Fumarola, Solom Heddaya, Raghu Ramakrishnan, and Sarvesh Sakalanaga. 2015. Mercury: Hybrid centralized and distributed scheduling in large shared clusters. In 2015 USENIX Annual Technical Conference (USENIXATC 15). 485--497.Google Scholar
- David Karger, Eric Lehman, Tom Leighton, Rina Panigrahy, Matthew Levine, and Daniel Lewin. 1997. Consistent hashing and random trees: Distributed caching protocols for relieving hot spots on the world wide web. In Proceedings of the twenty-ninth annual ACM symposium on Theory of computing. 654--663.Google ScholarDigital Library
- Nodira Khoussainova, Magdalena Balazinska, and Dan Suciu. 2012. Perfxplain: debugging mapreduce job performance. Proceedings of the VLDB Endowment, Vol. 5, 7 (2012), 598--609.Google ScholarDigital Library
- Andreas Kipf, Thomas Kipf, Bernhard Radke, Viktor Leis, Peter Boncz, and Alfons Kemper. 2018. Learned cardinalities: Estimating correlated joins with deep learning. arXiv preprint arXiv:1809.00677 (2018).Google Scholar
- D. E. Knuth and D. Bibby. 1986. The TeX book .Addison-Wesley: Reading, MA.Google Scholar
- Paraschos Koutris, Prasang Upadhyaya, Magdalena Balazinska, Bill Howe, and Dan Suciu. 2013. Toward practical query pricing with QueryMarket. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. ACM, 613--624.Google ScholarDigital Library
- Paraschos Koutris, Prasang Upadhyaya, Magdalena Balazinska, Bill Howe, and Dan Suciu. 2015. Query-based data pricing. Journal of the ACM (JACM), Vol. 62, 5 (2015), 43.Google ScholarDigital Library
- Kubernetes Documentation. 2020. https://kubernetes.io/docs. Accessed 20 January 2021.Google Scholar
- L. Lamport. 1994. ŁaTeX: A Document Preparation System second ed.). Addison-Wesley.Google Scholar
- Willis Lang, Karthik Ramachandra, David J DeWitt, Shize Xu, Qun Guo, Ajay Kalhan, and Peter Carlin. 2016. Not for the Timid: On the Impact of Aggressive Over-booking in the Cloud. Proceedings of the VLDB Endowment, Vol. 9, 13 (2016), 1245--1256.Google ScholarDigital Library
- Pedro Las-Casas, Giorgi Papakerashvili, Vaastav Anand, and Jonathan Mace. 2019. Sifter: Scalable Sampling for Distributed Traces, without Feature Engineering. In Proceedings of the ACM Symposium on Cloud Computing. 312--324.Google ScholarDigital Library
- Jiexing Li, Arnd Christian König, Vivek Narasayya, and Surajit Chaudhuri. 2012. Robust estimation of resource consumption for sql queries using statistical techniques. Proceedings of the VLDB Endowment, Vol. 5, 11 (2012), 1555--1566.Google ScholarDigital Library
- David Lo, Liqun Cheng, Rama Govindaraju, Parthasarathy Ranganathan, and Christos Kozyrakis. 2015. Heracles: improving resource efficiency at scale. In ACM SIGARCH Computer Architecture News, Vol. 43. ACM, 450--462.Google ScholarDigital Library
- Jiaheng Lu, Yuxing Chen, Herodotos Herodotou, and Shivnath Babu. 2019. Speedup Your Analytics: Automatic Parameter Tuning for Databases and Big Data Systems. Proceedings of the VLDB Endowment, Vol. 12, 12 (2019), 1970--1973.Google ScholarDigital Library
- Gang Luo, Jeffrey F Naughton, Curt J Ellmann, and Michael W Watzke. 2004. Toward a progress indicator for database queries. In Proceedings of the 2004 ACM SIGMOD international conference on Management of data. ACM, 791--802.Google ScholarDigital Library
- Lin Ma, Bailu Ding, Sudipto Das, and Adith Swaminathan. 2020. Active learning for ML enhanced database systems. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 175--191.Google ScholarDigital Library
- Ryan Marcus, Parimarjan Negi, Hongzi Mao, Chi Zhang, Mohammad Alizadeh, Tim Kraska, Olga Papaemmanouil, and Nesime Tatbul. 2019. Neo: A learned query optimizer. arXiv preprint arXiv:1904.03711 (2019).Google Scholar
- Ryan Marcus and Olga Papaemmanouil. 2018. Deep reinforcement learning for join order enumeration. In Proceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management. ACM, 3.Google ScholarDigital Library
- MarketResearch. 2019. https://www.marketsandmarkets.com/Market-Reports/cloud-database-as-a-service-dbaas-market-1112.html. Accessed 20 January 2021.Google Scholar
- Sergey Melnik, Andrey Gubarev, Jing Jing Long, Geoffrey Romer, Shiva Shivakumar, Matt Tolton, and Theo Vassilakis. 2010. Dremel: interactive analysis of web-scale datasets. Proceedings of the VLDB Endowment, Vol. 3, 1--2 (2010), 330--339.Google ScholarDigital Library
- Sergey Melnik, Andrey Gubarev, Jing Jing Long, Geoffrey Romer, Shiva Shivakumar, Matt Tolton, Theo Vassilakis, Hossein Ahmadi, Dan Delorey, Slava Min, et al. 2020. Dremel: a decade of interactive SQL analysis at web scale. Proceedings of the VLDB Endowment, Vol. 13, 12 (2020), 3461--3472.Google ScholarDigital Library
- Microsoft Power BI. 2020. https://powerbi.microsoft.com. Accessed 20 January 2021.Google Scholar
- Pulkit A Misra, Í nigo Goiri, Jason Kace, and Ricardo Bianchini. [n.,d.]. Scaling distributed file systems in resource-harvesting datacenters. In 2017 USENIX Annual Technical Conference USENIX ATC 17), pages=799--811, year=2017, organization=USENIX Association .Google Scholar
- MongoDB Atlas. 2020. https://www.mongodb.com/. Accessed 20 January 2021.Google Scholar
- Hyun Jin Moon, Yun Chi, and Hakan Hacigümüs. 2010. SLA-aware profit optimization in cloud services via resource scheduling. In Services (SERVICES-1), 2010 6th World Congress on. IEEE, 152--153.Google Scholar
- Vivek Narasayya and Surajit Chaudhuri. 2021. Cloud Data Services: Workloads, Architectures and Multi-Tenancy. Foundations and Trends® in Databases, Vol. 10, 1 (2021), 1--107. https://doi.org/10.1561/1900000060Google ScholarDigital Library
- Vivek Narasayya, Sudipto Das, Manoj Syamala, Badrish Chandramouli, and Surajit Chaudhuri. 2013. Sqlvm: Performance isolation in multi-tenant relational database-as-a-service. (2013).Google Scholar
- Vivek Narasayya, Ishai Menache, Mohit Singh, Feng Li, Manoj Syamala, and Surajit Chaudhuri. 2015. Sharing buffer pool memory in multi-tenant relational database-as-a-service. Proceedings of the VLDB Endowment, Vol. 8, 7 (2015), 726--737.Google ScholarDigital Library
- Olga Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, and Manuel Costa. 2016. Oblivious Multi-Party Machine Learning on Trusted Processors.. In USENIX Security Symposium. 619--636.Google Scholar
- Oracle MultiTenant. 2013. https://www.oracle.com/technetwork/database/multitenant-wp-12c-1949736.pdf. Accessed 20 January 2021.Google Scholar
- Oracle SQL Trace. 2020. https://docs.oracle.com/database/121/TGSQL/tgsql_trace.htm. Accessed 20 January 2021.Google Scholar
- Jennifer Ortiz, Victor Teixeira De Almeida, and Magdalena Balazinska. 2015. Changing the Face of Database Cloud Services with Personalized Service Level Agreements.. In CIDR .Google Scholar
- Andrew Pavlo, Gustavo Angulo, Joy Arulraj, Haibin Lin, Jiexi Lin, Lin Ma, Prashanth Menon, Todd C Mowry, Matthew Perron, Ian Quah, et al. 2017. Self-Driving Database Management Systems.. In CIDR .Google Scholar
- Matthew Perron, Raul Castro Fernandez, David DeWitt, and Samuel Madden. 2020. Starling: A Scalable Query Engine on Cloud Functions. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 131--141.Google ScholarDigital Library
- Raluca Ada Popa, Catherine Redfield, Nickolai Zeldovich, and Hari Balakrishnan. 2011. CryptDB: protecting confidentiality with encrypted query processing. In Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles. ACM, 85--100.Google ScholarDigital Library
- Adrian Daniel Popescu, Andrey Balmin, Vuk Ercegovac, and Anastasia Ailamaki. 2013. PREDIcT: towards predicting the runtime of large scale iterative analytics. Proceedings of the VLDB Endowment, Vol. 6, 14 (2013), 1678--1689.Google ScholarDigital Library
- Jeff Rasley, Konstantinos Karanasos, Srikanth Kandula, Rodrigo Fonseca, Milan Vojnovic, and Sriram Rao. 2016. Efficient queue management for cluster scheduling. In Proceedings of the Eleventh European Conference on Computer Systems. ACM, 36.Google ScholarDigital Library
- Mendel Rosenblum and Tal Garfinkel. 2005. Virtual machine monitors: Current technology and future trends. Computer, Vol. 38, 5 (2005), 39--47.Google ScholarDigital Library
- Sudip Roy, Arnd Christian König, Igor Dvorkin, and Manish Kumar. 2015. Perfaugur: Robust diagnostics for performance anomalies in cloud services. In Data Engineering (ICDE), 2015 IEEE 31st International Conference on. IEEE, 1167--1178.Google ScholarCross Ref
- Felix Schuster, Manuel Costa, Cédric Fournet, Christos Gkantsidis, Marcus Peinado, Gloria Mainar-Ruiz, and Mark Russinovich. 2015. VC3: Trustworthy data analytics in the cloud using SGX. In Security and Privacy (SP), 2015 IEEE Symposium on. IEEE, 38--54.Google ScholarDigital Library
- Pat Selinger. 2017. Optimizer Challenges in a Multi-Tenant World. In High Performance Transaction Systems, HPTS 2017 .Google Scholar
- Raghav Sethi, Martin Traverso, Dain Sundstrom, David Phillips, Wenlei Xie, Yutian Sun, Nezih Yegitbasi, Haozhun Jin, Eric Hwang, Nileema Shingte, et al. 2019. Presto: SQL on Everything. In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 1802--1813.Google Scholar
- Zhiming Shen, Sethuraman Subbiah, Xiaohui Gu, and John Wilkes. 2011. Cloudscale: elastic resource scaling for multi-tenant cloud systems. In Proceedings of the 2nd ACM Symposium on Cloud Computing. ACM, 5.Google ScholarDigital Library
- Benjamin H Sigelman, Luiz Andre Barroso, Mike Burrows, Pat Stephenson, Manoj Plakal, Donald Beaver, Saul Jaspan, and Chandan Shanbhag. 2010. Dapper, a large-scale distributed systems tracing infrastructure. (2010).Google Scholar
- Rohit Sinha, Manuel Costa, Akash Lal, Nuno P Lopes, Sriram Rajamani, Sanjit A Seshia, and Kapil Vaswani. 2016. A design and verification methodology for secure isolated regions. In ACM SIGPLAN Notices, Vol. 51. ACM, 665--681.Google ScholarDigital Library
- Vikram Sreekanti, Chenggang Wu Xiayue Charles Lin, Jose M Faleiro, Joseph E Gonzalez, Joseph M Hellerstein, and Alexey Tumanov. 2020. Cloudburst: Stateful Functions-as-a-Service. arXiv preprint arXiv:2001.04592 (2020).Google Scholar
- Tableau Online. 2020. https://www.tableau.com/products/cloud-bi. Accessed 20 January 2021.Google Scholar
- Alexander Thomson, Thaddeus Diamond, Shu-Chun Weng, Kun Ren, Philip Shao, and Daniel J Abadi. 2012. Calvin: fast distributed transactions for partitioned database systems. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. 1--12.Google ScholarDigital Library
- Stephen Tu, M Frans Kaashoek, Samuel Madden, and Nickolai Zeldovich. 2013. Processing analytical queries over encrypted data. In Proceedings of the VLDB Endowment, Vol. 6. VLDB Endowment, 289--300.Google ScholarDigital Library
- Prasang Upadhyaya, Magdalena Balazinska, and Dan Suciu. 2016. Price-optimal querying with data apis. Proceedings of the VLDB Endowment, Vol. 9, 14 (2016), 1695--1706.Google ScholarDigital Library
- Bhuvan Urgaonkar, Prashant Shenoy, and Timothy Roscoe. 2009. Resource overbooking and application profiling in a shared internet hosting platform. ACM Transactions on Internet Technology (TOIT), Vol. 9, 1 (2009), 1.Google ScholarDigital Library
- Leslie G Valiant and Gordon J Brebner. 1981. Universal schemes for parallel communication. In Proceedings of the thirteenth annual ACM symposium on Theory of computing. 263--277.Google ScholarDigital Library
- Dana Van Aken, Andrew Pavlo, Geoffrey J Gordon, and Bohan Zhang. 2017. Automatic database management system tuning through large-scale machine learning. In Proceedings of the 2017 ACM International Conference on Management of Data. ACM, 1009--1024.Google ScholarDigital Library
- Vinod Kumar Vavilapalli, Arun C Murthy, Chris Douglas, Sharad Agarwal, Mahadev Konar, Robert Evans, Thomas Graves, Jason Lowe, Hitesh Shah, Siddharth Seth, et al. 2013. Apache hadoop yarn: Yet another resource negotiator. In Proceedings of the 4th annual Symposium on Cloud Computing. ACM, 5.Google ScholarDigital Library
- Alexandre Verbitski, Anurag Gupta, Debanjan Saha, Murali Brahmadesam, Kamal Gupta, Raman Mittal, Sailesh Krishnamurthy, Sandor Maurice, Tengiz Kharatishvili, and Xiaofeng Bao. 2017. Amazon aurora: Design considerations for high throughput cloud-native relational databases. In Proceedings of the 2017 ACM International Conference on Management of Data. ACM, 1041--1052.Google ScholarDigital Library
- Alexandre Verbitski, Anurag Gupta, Debanjan Saha, James Corey, Kamal Gupta, Murali Brahmadesam, Raman Mittal, Sailesh Krishnamurthy, Sandor Maurice, Tengiz Kharatishvilli, et al. 2018. Amazon aurora: On avoiding distributed consensus for i/os, commits, and membership changes. In Proceedings of the 2018 International Conference on Management of Data. 789--796.Google ScholarDigital Library
- Ben Verghese, Anoop Gupta, and Mendel Rosenblum. 1998. Performance isolation: sharing and isolation in shared-memory multiprocessors. In ACM SIGPLAN Notices, Vol. 33. ACM, 181--192.Google ScholarDigital Library
- Abhishek Verma, Luis Pedrosa, Madhukar Korupolu, David Oppenheimer, Eric Tune, and John Wilkes. 2015. Large-scale cluster management at Google with Borg. In Proceedings of the Tenth European Conference on Computer Systems. ACM, 18.Google ScholarDigital Library
- ESX VMware. [n.,d.]. Understanding Memory Resource Management in VMware ESX 4.1. ( [n.,d.]).Google Scholar
- William Voorsluys, James Broberg, Srikumar Venugopal, and Rajkumar Buyya. 2009. Cost of virtual machine live migration in clouds: A performance evaluation. In IEEE International Conference on Cloud Computing. Springer, 254--265.Google ScholarDigital Library
- Carl A Waldspurger. 2002. Memory resource management in VMware ESX server. ACM SIGOPS Operating Systems Review, Vol. 36, SI (2002), 181--194.Google ScholarDigital Library
- Gerhard Weikum, Axel Moenkeberg, Christof Hasse, and Peter Zabback. 2002. Self-tuning database technology and information services: from wishful thinking to viable engineering. In VLDB'02: Proceedings of the 28th International Conference on Very Large Databases. Elsevier, 20--31.Google ScholarCross Ref
- Craig D Weissman and Steve Bobrowski. 2009. The design of the force. com multitenant internet application development platform. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data. 889--896.Google ScholarDigital Library
- Chenggang Wu, Jose Faleiro, Yihan Lin, and Joseph Hellerstein. 2019. Anna: A kvs for any scale. IEEE Transactions on Knowledge and Data Engineering (2019).Google Scholar
- Wentao Wu, Yun Chi, Hakan Hacigümücs, and Jeffrey F Naughton. 2013. Towards predicting query execution time for concurrent and dynamic database workloads. Proceedings of the VLDB Endowment, Vol. 6, 10 (2013), 925--936.Google ScholarDigital Library
- Wentao Wu, Xi Wu, Hakan Hacigümücs, and Jeffrey F Naughton. 2014. Uncertainty aware query execution time prediction. Proceedings of the VLDB Endowment, Vol. 7, 14 (2014), 1857--1868.Google ScholarDigital Library
- Pengcheng Xiong, Yun Chi, Shenghuo Zhu, Hyun Jin Moon, Calton Pu, and Hakan Hacgümücs. 2015. SmartSLA: Cost-sensitive management of virtualized resources for CPU-bound database services. IEEE Transactions on Parallel and Distributed Systems, Vol. 26, 5 (2015), 1441--1451.Google ScholarDigital Library
- Pengcheng Xiong, Yun Chi, Shenghuo Zhu, Junichi Tatemura, Calton Pu, and Hakan HacigümücS. 2011. ActiveSLA: a profit-oriented admission control framework for database-as-a-service providers. In Proceedings of the 2nd ACM Symposium on Cloud Computing. ACM, 15.Google ScholarDigital Library
- Yin Yang, Dimitris Papadias, Stavros Papadopoulos, and Panos Kalnis. 2009. Authenticated join processing in outsourced databases. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data. ACM, 5--18.Google ScholarDigital Library
- Dong Young Yoon, Ning Niu, and Barzan Mozafari. 2016. DBSherlock: A performance diagnostic tool for transactional databases. In Proceedings of the 2016 International Conference on Management of Data. ACM, 1599--1614.Google ScholarDigital Library
- Heechul Yun, Gang Yao, Rodolfo Pellizzoni, Marco Caccamo, and Lui Sha. 2013. Memguard: Memory bandwidth reservation system for efficient performance isolation in multi-core platforms. In Real-Time and Embedded Technology and Applications Symposium (RTAS), 2013 IEEE 19th. IEEE, 55--64.Google Scholar
- Matei Zaharia, Mosharaf Chowdhury, Michael J Franklin, Scott Shenker, and Ion Stoica. 2010. Spark: Cluster computing with working sets. HotCloud, Vol. 10, 10--10 (2010), 95.Google ScholarDigital Library
- Zhi-Hui Zhan, Xiao-Fang Liu, Yue-Jiao Gong, Jun Zhang, Henry Shu-Hung Chung, and Yun Li. 2015. Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Computing Surveys (CSUR), Vol. 47, 4 (2015), 1--33.Google ScholarDigital Library
- Ning Zhang, Peter J Haas, Vanja Josifovski, Guy M Lohman, and Chun Zhang. 2005. Statistical learning techniques for costing XML queries. In Proceedings of the 31st international conference on Very large data bases. VLDB Endowment, 289--300.Google ScholarDigital Library
- Yunqi Zhang, George Prekas, Giovanni Matteo Fumarola, Marcus Fontoura, Ínigo Goiri, and Ricardo Bianchini. 2016. History-based harvesting of spare cycles and storage in large-scale datacenters. In Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation. 755--770.Google ScholarDigital Library
Index Terms
- Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities
Recommendations
Cloud Multi-Tenancy: Issues and Developments
UCC '17 Companion: Companion Proceedings of the10th International Conference on Utility and Cloud ComputingCloud Computing (CC) is a computational paradigm that provides pay-per use services to customers from a pool of networked computing resources that are provided on demand. Customers therefore does not need to worry about infrastructure or storage. Cloud ...
Elastic Multi-tenant Business Process Based Service Pattern in Cloud Computing
CLOUDCOM '14: Proceedings of the 2014 IEEE 6th International Conference on Cloud Computing Technology and ScienceElasticity is an essential property of cloud computing. It helps service providers to efficiently exploit cloud resources and reduce servicing costs. Therefore, the multitenant business processes are long-running and they are concurrently accessed by ...
Multi-tenant SOA Middleware for Cloud Computing
CLOUD '10: Proceedings of the 2010 IEEE 3rd International Conference on Cloud ComputingEnterprise IT infrastructure incurs many costs ranging from hardware costs and software licenses/maintenance costs to the costs of monitoring, managing, and maintaining IT infrastructure. The recent advent of cloud computing offers some tangible ...
Comments