Cloud computing, one of the most
promising concepts in information technology, is drastically
transforming our perception of how web-based data-centric
applications are deployed. By providing a suite of
virtualized resources, it reduces data processing services
to commodities that could be acquired and paid-for on-demand
yielding improved cost efficiencies and at the same time
delivering elastic scalability. Despite the relatively fast
growth and increased adoption of cloud data services,
challenges related to application management still exist.
Provisioning cloud resources to meet application-specific
Quality-of-Service (QoS) goals, assigning incoming query
processing workloads to the reserved resources to optimize
resource utilization, monitoring performance factors to
ensure acceptable QoS levels, are some of the critical tasks
that are still addressed through custom tools at the
application level which drastically increase the application
development and maintenance overhead. In this project, we
argue that the management of data processing applications
should itself be offered to developers as a cloud-based
This project introduces performance extensibility as the key feature of next generation cloud data processing services that could help address the aforementioned challenges. Performance extensibility refers to the ability to express customized performance models and constraints and use them to automatically extend the functionality of core application tasks, such as performance optimization and resource and workload management, towards meeting the application's performance objectives. This project presents a comprehensive research agenda to support performance extensibility for cloud databases by (a) enabling performance criteria and performance Service-Level-Agreements (SLAs) to be defined at the application level and (b) developing a suite of application management services, including workload, resource and SLA management, that can seamlessly customize their functionality towards satisfying application-specific performance objectives.
Papaemmanouil (Brandeis University)
Dimitriadou (Ph.D. student, Brandeis University)
Dimokritos Stamatakis (Ph.D.
student, Brandeis University)
Ryan Marcus (Ph.D. student, Brandeis
Sofiya Semenova (Undergraduate, Brandeis University)
This project is sponsored by the NSF Career Award IIS-1253196.