September 16, 2010
The use of consolidated storage systems requires deep understanding of performance requirements as well as suitable tools to manage such requirements for configuring such systems. As advanced features such as Quality-of-Service (QoS) are introduced, most customers will have difficulties to configure these features. This is mainly due to the fact, that state-of-the-art storage products integrate various kinds of devices and offer customers several interfaces to seamlessly access file system space in their infrastructure. Common features include high availability, overbooking, de-duplication, and even support for configuring priorities for achieving desired levels of Quality-of-Service for individual workloads sharing the same system. This results in multiple QoS criteria to be taken in account and to be optimized in order to guarantee proper operation of the consolidated storage environment.However, the business interest does not lie in mere storage management, but in fulfilling the requirements of applications and higher-level services, from which it is in general difficult to deduce specific storage QoS parameters. Here, this fellowship proposal is aiming at creating one essential pillar linking business objectives and storage-level QoS parameters through an automated mapping approach. Instead of setting priorities on certain parameters, we propose research and development tasks for describing the desired Quality-of-Service at a higher level (called Service Level Objectives) and automatically map those to storage requirements including the configuration parameters necessary to configure the storage system. This approach simplifies the configuration of the system and also allows administrators more easily to verify whether the target desired QoS level is has been achieved.This research work will create suitable application models based on workload analyses of typical business applications. As a result, tools will be provided which allow workload modeling on this application level and to automatically create suitable storage QoS/SLO configurations for this workload. The fellowship will, in addition, cover the verification of the models through simulation and testing on real systems. Based on these results, adaption mechanism will be evaluated to dynamically verify whether the workload for the forecasted application setting fits the original modeling and to propose or initiate changes to the QoS/SLO configuration of storage systems.