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.
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.
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