How consistent is your cloud application?

Proceedings of the 3rd ACM Symposium on Cloud Computing, SoCC 2012

Volumn , Issue , 2012, Pages

  Zellag, Kamal ^a   Kemme, Bettina ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

Cloud datastores; Consistency; Serializability

Indexed keywords

BUSINESS LOGIC; CONSISTENCY; LOW LEVEL; SERIALIZABILITY;

CLOUD COMPUTING;

EID: 84870568376     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2391229.2391235     Document Type: Conference Paper

References (34)

1. A. Adya, B. Liskov, and P. E. O'Neil. Generalized isolation level definitions. In ICDE, pages 67-78, 2000.
2. R. Akerkar. Introduction to Artificial Intelligence. PHI Learning Pvt. Ltd., illustrated edition, 2005.
3. Amazon Relational Database Service. A distributed relational database service. http://aws.amazon.com/rds/.
4. Amazon SimpleDB. A highly available and flexible non-relational data store. http://aws.amazon.com/simpledb/.
5. AspectJ. The Java aspect-oriented extension. http://www.eclipse.org/ aspectj/.
6. C. Bennett, R. L. Grossman, D. Locke, J. Seidman, and S. Vejcik. Malstone: towards a benchmark for analytics on large data clouds. In SIGKDD, pages 145-152, 2010.
7. H. Berenson, P. Bernstein, J. Gray, J. Melton, E. O'Neil, and P. O'Neil. A critique of ANSI SQL isolation levels. In ACM SIGMOD Conf., 1995.
8. A. Bernstein, P. Lewis, and S. Lu. Semantic conditions for correctness at different isolation levels. In Proceedings of IEEE International Conference on Data Engineering, pages 57-66. IEEE, 2000.
9. E. A. Brewer. Towards robust distributed systems (abstract). In PODC, page 7, 2000.
10. Cassandra. A highly scalable, distributed and structured key-value store. http://cassandra.apache.org/.
11. S. Das, D. Agrawal, and A. E. Abbadi. Elastras: An elastic transactional data store in the cloud. In USENIX HotCloud, Boston, MA, 06/2009 2009. USENIX, USENIX.
12. S. Das, D. Agrawal, and A. E. Abbadi. G-store: a scalable data store for transactional multi key access in the cloud. In SoCC, pages 163-174, 2010.
13. K. P. Eswaran, J. N. Gray, R. A. Lorie, and I. L. Traiger. The notions of consistency and predicate locks in a database system. Commun. ACM, 19(11):624-633, 1976.
14. A. Fekete. Serialisability and snapshot isolation. In Proceedings of the Australian Database Conference, pages 201-210, 1999.
15. A. Fekete, S. Goldrei, and J. P. Asenjo. Quantifying isolation anomalies. PVLDB, 2(1):467-478, 2009.
16. A. Fekete, D. Liarokapis, E. J. O'Neil, P. E. O'Neil, and D. Shasha. Making snapshot isolation serializable. ACM Trans. Database Syst., 30(2):492-528, 2005.
17. S. Gilbert and N. Lynch. Brewer's conjecture and the feasibility of consistent, available, partition-tolerant web services. SIGACT News, 33:51-59, June 2002.
18. Google App Engine. A platform as a service cloud computing platform. http://code.google.com/appengine/.
19. J. Gray, P. Helland, P. O'Neil, and D. Shasha. The dangers of replication and a solution. SIGMOD Rec., 25:173-182, 1996.
20. JMeter. A Java based performance measuring tool. http://jakarta.apache. org/jmeter/.
21. S. Jorwekar, A. Fekete, K. Ramamritham, and S. Sudarshan. Automating the detection of snapshot isolation anomalies. In VLDB, pages 1263-1274. VLDB, 2007.
22. D. Kossmann, T. Kraska, and S. Loesing. An evaluation of alternative architectures for transaction processing in the cloud. In Proceedings of the 2010 international conference on Management of data, SIGMOD '10, pages 579-590, New York, NY, USA, 2010. ACM.
23. T. Kraska, M. Hentschel, G. Alonso, and D. Kossmann. Consistency rationing in the cloud: Pay only when it matters. PVLDB, 2(1):253-264, 2009.
24. J. J. Levandoski, D. B. Lomet, M. F. Mokbel, and K. Zhao. Deuteronomy: Transaction support for cloud data. In CIDR, pages 123-133, 2011.
25. A. Li, X. Yang, S. Kandula, and M. Zhang. Cloudcmp: comparing public cloud providers. In Internet Measurement Conference, pages 1-14, 2010.
26. Microsoft SQL Azure. A Microsoft cloud-based service offering data-storage capabilities. http://www.microsoft.com/windowsazure/.
27. W. Sobel, S. Subramanyam, A. Sucharitakul, J. Nguyen, H. Wong, A. Klepchukov, S. Patil, O. Fox, and D. Patterson. Cloudstone: Multi-platform, multi-language benchmark and measurement tools for web 2.0, 2008.
28. A. Thomson, T. Diamond, S.-C. Weng, K. Ren, P. Shao, and D. J. Abadi. Calvin: fast distributed transactions for partitioned database systems. In SIGMOD Conference, pages 1-12, 2012.
29. H. T. Vo, C. Chen, and B. C. Ooi. Towards elastic transactional cloud storage with range query support. PVLDB, 3(1):506-517, 2010.
30. H. Wada, A. Fekete, L. Zhao, K. Lee, and A. Liu. Data consistency properties and the trade-offs in commercial cloud storage: the consumers' perspective. In CIDR, pages 134-143, 2011.
31. WebFilings. A cloud-based company hosting applications for financial and executive teams. http://www.webfilings.com/.
32. K. Zellag and B. Kemme. Real-time quantification and classification of consistency anomalies in multi-tier architectures. In ICDE, pages 613-624, 2011.
33. K. Zellag and B. Kemme. Consad: a real-time consistency anomalies detector. In SIGMOD Conference, pages 641-644, 2012.
34. W. Zhou, G. Pierre, and C.-H. Chi. Cloudtps: Scalable transactions for web applications in the cloud. IEEE Transactions on Services Computing, 99(PrePrints), 2011.