Consistency in Cloud-based database systems

Zohra Mahfoud, Nadia Nouali-Taboudjemat


Cloud computing covers the large spectrum of services available on the internet. Cloud services use replication to ensure high availability. Within database replication; various copies of the same data item are stored in different sites, this situation requires managing the consistency of the multiple copies. In fact, the requirement for consistency level can be different following to application natures and other metrics; a delay of some minutes in visualizing latest posts in social networks can be tolerated, while some seconds can make a loss of a bid in an auction system. Wide variety of database management systems are used actually by cloud services. They support different level of consistency to meet the diverse needs of consistency levels.

This paper draws a presentation of the main characteristics of cloud computing and data management systems and describes different consistency models. Then it discusses the most famous cloud-based database management systems from the point of view of their data and consistency models.

Full Text:



Sakr, Sherif, et al. "A survey of large scale data management approaches in cloud environments." Communications Surveys & Tutorials, IEEE 13.3 (2011), pg. 311-336.

A. Elzeiny, A. Abo Elfetouh ,and A Riad: “Cloud Storage: A Survey”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 2, Issue 4, July – August 2013 ISSN 2278-6856: 342- 349

Mohammad, Siba, Sebastian Breß, and Eike Schallehn. "Cloud Data Management: A Short Overview and Comparison of Current Approaches." Grundlagen von Datenbanken. 2012.

Donald Kossmann, Tim Kraska, Simon Loesing: An evaluation of alternative architectures for transaction processing in the cloud. SIGMOD Conference 2010, pg. 579-590

Saeed K. Rahimi, Frank S. Haug: Distributed Database Management Systems_ A Practical Approach-Wiley-IEEE Computer Society Pr (2010)

M.T Özsu, P. Valduriez, "Principles of Distributed Database Systems”, Springer Science+ Business Media, 3rd ed. 2011 Edition, ISBN: 978-1441988331.

V.K. Pallaw, Concept of Database Management Systems, Asian Books Pvt. Ltd., (2010), ISBN : 978-81-8412-119-3

M. Wiesmann, F. Pedone, A. Schiper, B. Kemme, G. Alonso, Understanding Replication in Databases and Distributed Systems. In IEEE Int. Conf on Distributed Computing Systems, ICDCS, pp.464-474 (2000)

M. Wiesmann, F. Pedone, A. Schiper, Database Replication Techniques: a Three Parameter Classification. In the Proceedings of 19th IEEE Symposium on Reliable Distributed Systems (SRDS2000), pages 206-215, Nürnberg, Germany, October 2000. IEEE Computer Society.

SH. Navathe, S. Ceri, G. Wiederhold, J. Dou, “Vertical Partitioning Algorithms for Database Design”, ACM Transactions on Database Systems, Vol. 9, No.4, December 1984.

Codd, E.F. (1970). "A Relational Model of Data for Large Shared Data Banks". Communications of the ACM. 13 (6): 377–387. doi:10.1145/362384.362685

J. Gray, “The Transaction Concept: Virtues and Limitations”. In

proceedings of the 7th VLDB, Cannes, 144-154. 1981.

Francesca Bugiotti, Luca Cabibbo, Paolo Atzeni, Riccardo Torlone, Database Design for NoSQL Systems. ER 2014: 223-231

P. J. Sadalage and M. J. Fowler. NoSQL Distilled. Addison-Wesley, 2012.

Guy Harrison, Next Generation Databases: NoSQL, NewSQL, and Big Data, Apress (2015), ISBN(e): 978-1-4842-1329-2

E. A. Brewer. (Invited Talk) Towards Robust Distributed Systems. In Proc. of PODC, page 7, 2000.

Nancy Lynch and Seth Gilbert, “Brewer's conjecture and the feasibility of consistent, available, partition-tolerant web services”, ACM SIGACT News, Volume 33 Issue 2 (2002), pg. 51-59.

Daniel J. Abadi. Consistency tradeoffs in modern distributed database system design: Cap is only part of the story. Computer, 45(2):37–42, 2012.

Sathiya Prabhu Kumar: Adaptive Consistency Protocols for Replicated Data in Modern Storage Systems with a High Degree of Elasticity. PHD thesis, Conservatoire national des arts et métiers, Paris, France 2016

David Mosberger, Memory Consistency Models, ACM SIGOPS Operating Systems Review Homepage archive. Volume 27 Issue 1, Jan. 1993, Pages 18-26

Adve, Sarita V and Gharachorloo, Kourosh, Shared Memory Consistency Models: A Tutorial, journal of Computer, 1996, volume 29, Issue 12. Pages: 66-76.

W. Vogels, “Eventually consistent”, Communications of the ACM, v.52 n.1: 40-44 (2009), DOI:10.1145/1435417.1435432.

Big Data: Concepts, Methodologies, Tools, and Applications, Management Association Information Resources, published by IGI Global, Release Date: April 2016, ISBN: 9781466698406.

“DB-Engines Ranking”, Available Online [Aug2018]:

“Amazon Simple Storage Service Documentation”. Available Online [Aug2018]:

“Amazon SimpleDB Documentation”. Available Online [Aug2018]:

“Amazon DynamoDB Documentation”. Available Online [Aug2018]:

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. In SOSP, pages 205–220, 2007

D. Bermbach and S. Tai, “Eventual consistency: How soon is eventual? an evaluation of amazon s3’s consistency behavior”, in Proceedings of the 6th Workshop on Middleware for Service Oriented Computing. ACM, 2011, p. 1

“Amazon Amazon Aurora”. Available Online [Aug2018]:

“Amazon Relational Database Service Documentation”. Available Online [Feb2017]:

“Google Cloud Platform: Cloud Storage Products”. Available Online [Aug2018]:

Fay Chang, Jeffrey Dean, Sanjay Ghemawat, et al. “Bigtable: A Distributed Storage System for Structured Data”. ACM TOCS 26.2 (June 2008), 4:1–4:26.

Jason Baker et al. “Megastore: Providing Scalable, Highly Available Storage for Interactive Services”. Proc. of CIDR. 2011, pp. 223–234.

J. Corbett, J. Dean, M. Epstein, et al. Spanner: Google’s globally-distributed database. Proceedings of OSDI, pages 251–264, 2012

“CLOUD SQL”. Available Online [Feb2017]:

“Google Cloud Datastore Documentation”. Available Online [Aug2018]:

Brad Calder, Ju Wang, Aaron Ogus and all, “Windows Azure Storage: A Highly Available Cloud Storage Service with Strong Consistency”, proceeding in the 23rd ACM Symposium on Operating Systems Principles SOSP '11, October 23-26, 2011, Cascais, Portugal.

“Azure Cosmos DB Documentation”. Available Online [Aug2018]:

A. Singla, U. Ramachandran, and J. Hodgins. Temporal Notions of Synchronization and Consistency in Beehive. In Proc. of the 9th Annual ACM Symp. on Parallel Algorithms and Architectures, pages 211–220, Newport, RI, June 1997.

“Microsoft Azure SQL Database”. Available Online [Aug2018]:

“Apache Cassandra”. Available Online [Aug2018]:

Avinash Lakshman, Prashant Malik: Cassandra: a decentralized structured storage system. Operating Systems Review 44(2): 35-40 (2010)

B. Cooper, R. Ramakrishnan, U. Srivastava, A. Silberstein, P. Bohannon, H. Jacobsen, N. Puz, D. Weaver, and R. Yerneni. Pnuts: Yahoo!’s hosted data serving platform.PVLDB, 1(2):1277–1288 (2008)

A. Silberstein, J. Chen, D. Lomax, B. McMillan, M. Mortazavi, P. P. S. Narayan, R. Ramakrishnan, R. Sears: PNUTS in Flight: Web-Scale Data Serving at Yahoo. IEEE Internet Computing 16(1): 13-23 (2012)

“Neo4j”. Available Online [Aug2018]:

Leslie Lamport: Paxos Made Simple, Fast, and Byzantine. OPODIS 2002: 7-9


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.