Towards scalable, semantic-based virtualized storage resources provisioning

1. Towards scalable, semantic-based virtualized storage resources provisioning Kornel Skałkowski, Renata Słota, Dariusz Król, Michał Orzechowski, Bartosz Kryza, Jacek Kitowski ACC Cyfronet AGH, Krakow, Poland KU KDM 2012 : fifth ACC Cyfronet AGH users' conference : Zakopane, March 07–09, 2012

2. Outline • Introduction • The QStorMan toolkit overview • The QStorMan toolkit architecture • QStorMan usage • Recent improvements • Current status of QStorMan • Test results • Future Work

3. Introduction • Data intensive applications and the 4th science paradigm • Resources virtualization becomes ubiquitous • Storage resources virtualization is often provided by cluster file systems like Lustre • IT infrastructure users expect more and more computing and storage power as well as an appropriate QoS level

4. TheQStorMantoolkit • Main goal is to provide virtualized storage resources with QoS warrianties for data intensive applications • Users can define QoS requirements concerning storage resources on three levels: application, user, virtual organization • Currently we support the following non-functional requirements: • Average Read/Write transfer rate, • Current Read/Write transfer rate, • Free capacity, • Result cachability – dedicated for application, which generates a large number of small files. • The toolkit consists of three components: • Knowledge base (GOM) which stores semantic descriptions concerning storage resources and synchronizes the descriptions with a grid middleware • Dedicated monitoring service (SMED) which performs continuous, real-time monitoring of virtualized storage resources with semantic support • Intelligent resources matching service (SES) which combines information obtained from the GOM and SMED services as well as advanced semantic support in order to perfectly match a virtualized resource from the resources mesh

5. TheQStorMantoolkitarchitecture

6. QStorManusage • Using system C library (libses-wrapper): • declare your non-functional requirements in the GOM knowledge base • export LD_PRELOAD=<path_to_libses_wrapper_librart> 2. Using C++ programming library (libses): #include <LustreManager.h> #include <StoragePolicyFactory.h> using namespace lustre_api_library; LustreManager manager; StoragePolicy policy; policy.setAverageReadTransferRate(50); policy.setCapacity(100); int descriptor = manager.createFile(„nazwa_pliku.dat”, &policy);

7. Recentimprovements • General purpose of the improvements is to provide a scalable, fully semantic-based solution for efficient provisioning of virtualized storage resources • SMED improvements: • Utilization of the enhanced C2MS storage resources semantic model for description of high-level QoS parameters • Application of semanatic reasoners on the monitoring level • SES improvements • Cache mechanism on demand – supporting large number of files generation • Automatic registration of users in knowledge base – decrease required administration effort • GOM improvements • Security enhancements • Scripts for administration

8. The QStorMantoolkitcurrent status • Test installation is running at ACC Cyfronet AGH from over 1 year now • A lot of tests were performed and no major bugs were found • We have passed operational and security audits in PL-Grid succesfully • We now waiting for official deployment in ACC Cyfronet, PCSS Poznan, TASK Gdansk, and ICM Warsaw • Official tutorials, workshops and other material are on the way • Integrated with QoSCosGrid middleware from PCSS • We are willing to cooperate with anyone, who would like to test QStorMan in practice with an exisiting data intensive application

9. Test description Synthetic test • The toolkit evaluation was performed by simulation of 8 users which were executing their applications on the Grid infrastructure • 3 users used the QStorMan toolkit during the applications execution, the others used plain Lustre file system • Every user periodically saved and read a 60 GB file with random sleep periods between the succeeding operations (10 reads and 10 writes) • Users started their applications with random delays in order to simulate real conditions in a Grid environment Test with real user’s application • Simulation of sound wave propagation inside human head • Out-of-core computations • No source code modifications • 5 instances of application running in parallel in order to generate enough load for storage system

10. Synthethic test results • 12% speedup between two fastest applications • 26% speedup on average (~7:20 h vs ~10 h) • No source code modification

11. Real user’s application test result • 15% speedup on average • Running on production infrastructure • No source code modification

12. Futurework • Support for domain-oriented virtualized computing environments • Implementation of new storage resources selection strategies • Orientation toward Cloud computing environments • Dissemination and exploitation among possible users

13. Questions?dkrol@agh.edu.plrena@agh.edu.pl