As Cloud Computing becomes more pervasive, existing database management systems (DBMS) are offered as cloud data services and new cloud data services are emerging. New data services cater to new web-scale workloads and Big Data. The cost of testing database systems has been traditionally a large part of the total development cost. Building reliable data services, testing, benchmarking and monitoring quality of service in a production environment is at least an equally challenging and costly undertaking. There is a need for novel and revolutionary ideas for the current situation to improve in the future.
The Reliable Data Services & Systems Workshop (RDSS) replaces the Workshop on Testing Database Systems. Similarly to the DBTest workshop, RDSS aims to expose to the academic community the challenges faced by industry practitioners related to building and testing reliable data services. The long term goal is to devise new techniques that reduce the cost and time to build, test and tune data services so that vendors can spend more time and energy on actual innovations. We see the challenges in building and testing data services as a superset of what practitioners face in traditional DBMS testing. RDSS’s goal is to is shift the focus of the conversation and research ideas to data services while remaining open to the areas of interest covered by DBTest in the past.
Topics of Interest
Testing and resilience in service-oriented architectures
Testing issues in multi-tenant database systems and cloud database systems
Testing issues in large-scale analytics systems (e.g., Hadoop)
Testing the reliability and availability of database services and systems
Metrics for quality of service, elasticity, scale, predictability and workload performance
Metrics and validation techniques related to service level agreements
Designs, algorithms and techniques that improve the reliability and testability of data services
Testing the efficiency of adaptive policies and components
Testing data services, database systems, and database applications
Generation of test artifacts (e.g., test data, test queries)
Maximizing code coverage of database systems/services/applications
Improving the user experience of data services and systems
Testing and designing systems that are robust to estimation inaccuracies
Identifying performance bottlenecks
Robust query processing
Security and vulnerability testing
War stories and vision papers