Real-time quantification and classification of consistency anomalies in multi-tier architectures

Zellag, Kamal; Kemme, Bettina

doi:10.1109/icde.2011.5767927

Cited by 9 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With this, only the commit operations are sequentially, transaction execution itself can occur concurrently. As shown in [6], the overhead in response time created by colAgent, including the sequential commit order for RC, was less than 3% for all our experiments.…”

Section: The Collector Agentmentioning

confidence: 71%

See 1 more Smart Citation

ConsAD

Zellag

Kemme

2012

Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

Self Cite

View full text Add to dashboard Cite

In this demonstration, we present ConsAD, a tool that detects consistency anomalies for arbitrary multi-tier applications that use lower levels of isolation than serializability. As the application is running, ConsAD detects and quantifies anomalies indicating exactly the transactions and data items involved. Furthermore, it classifies the detected anomalies into patterns showing the business methods involved as well as their occurrence frequency. ConsAD can guide designers to either choose an isolation level for which their application shows few anomalies or change their transaction design to avoid the anomalies. Its graphical interface shows detailed information about detected anomalies as they occur and analyzes their patterns as well as their distribution.

show abstract

Section: The Collector Agentmentioning

confidence: 71%

“…In the following we only shortly describe colAgent and detAgent as they were presented in [6]. In contrast, vis-Agent will be presented for the first time in this demonstration.…”

Section: Introductionmentioning

confidence: 99%

ConsAD

Zellag

Kemme

2012

Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

Self Cite

View full text Add to dashboard Cite

show abstract

“…Wada et al evaluated the staleness of Amazon's SimpleDB using end-user request tracing [81], while Bermbach and Tai evaluated Amazon S3 [22], each quantifying various forms of non-serializable behavior. Golab et al provide algorithms for verifying the linearizability of and sequential consistency arbitrary data stores [51] and Zellag and Kemme provide algorithms for verifying their serializability [85] and other cycle-based isolation anomalies [86]. Probabilistically Bounded Staleness provides time-and version-based staleness predictions for eventually consistent data stores [18].…”

Section: Related Workmentioning

confidence: 98%

Feral Concurrency Control

Bailis

Fekete

Franklin

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

View full text Add to dashboard Cite

The rise of data-intensive "Web 2.0" Internet services has led to a range of popular new programming frameworks that collectively embody the latest incarnation of the vision of Object-Relational Mapping (ORM) systems, albeit at unprecedented scale. In this work, we empirically investigate modern ORM-backed applications' use and disuse of database concurrency control mechanisms. Specifically, we focus our study on the common use of feral, or application-level, mechanisms for maintaining database integrity, which, across a range of ORM systems, often take the form of declarative correctness criteria, or invariants. We quantitatively analyze the use of these mechanisms in a range of open source applications written using the Ruby on Rails ORM and find that feral invariants are the most popular means of ensuring integrity (and, by usage, are over 37 times more popular than transactions). We evaluate which of these feral invariants actually ensure integrity (by usage, up to 86.9%) and which-due to concurrency errors and lack of database support-may lead to data corruption (the remainder), which we experimentally quantify. In light of these findings, we present recommendations for database system designers for better supporting these modern ORM programming patterns, thus eliminating their adverse effects on application integrity.

show abstract

“…In [32,33], we proposed an on-line approach for quantifying consistency anomalies in multi-tier architectures, where multiple application servers access a centralized database. We supported any isolation level that is higher than or equal to read-committed and used its properties to detect data centric anomalies.…”

Section: Related Workmentioning

confidence: 99%

“…Wada et al investigated in [30] consistency properties for some cloud datastores, but they limited their study to a subset of client-centric consistency definitions such as monotonic-reads and read-your-writes. In [32,33], we detect consistency anomalies for multi-tier applications where multiple application servers access a centralized ACID database. Our approach assumed that (1) data items are accessed only in transactional contexts, (2) all transactions execute under the same isolation level and (3) the database is not replicated.…”

Section: Introductionmentioning

confidence: 99%

How consistent is your cloud application?

Zellag

Kemme

2012

Proceedings of the Third ACM Symposium on Cloud Computing

Self Cite

View full text Add to dashboard Cite

Current cloud datastores usually trade consistency for performance and availability. However, it is often not clear how an application is affected when it runs under a low level of consistency. In fact, current application designers have basically no tools that would help them to get a feeling of which and how many inconsistencies actually occur for their particular application. In this paper, we propose a generalized approach for detecting consistency anomalies for arbitrary cloud applications accessing various types of cloud datastores in transactional or non-transactional contexts. We do not require any knowledge on the business logic of the studied application nor on its selected consistency guarantees. We experimentally verify the effectiveness of our approach by using the Google App Engine and Cassandra datastores.

show abstract

Real-time quantification and classification of consistency anomalies in multi-tier architectures

Cited by 9 publications

References 16 publications

ConsAD

ConsAD

Feral Concurrency Control

How consistent is your cloud application?

Contact Info

Product

Resources

About