Polish Infrastructure for Supporting Computational Science in the European Research Space FiVO/QStorMan: toolkit for supporting data-oriented applications in PL-Grid R. Slota (1,2), D. Król (1), K. Skalkowski (1), B. Kryza (1), D. Nikolow (1,2), and J. Kitowski (1,2) (1) ACC Cyfronet AGH, Kraków, Poland (2) Institute of Computer Science AGH-UST, Krakow, Poland KU KDM 2011 Zakopane,

Agenda 1. Data intensive applications 2. Research and implementation goals 3. Non-functional requirements in data management 4. FiVO/QStorMan toolkit components and architecture 5. FiVO/QStorMan usage 6. Testing scenarios 7. Results 8. Conclusions

Data intensive applications Main features:  Generate gigabytes (or more) of data per day.  Different types of data which require different types of storage.  Heavily uses read/write operations.  The run time of an application heavily depends on storage access time and transfer speed rather than the computation time. Scientific examples (from wikipedia):  The LHC experiment produces 15 PB/year = ~42 TB/day = ~1 GB/s  The German Climate Computing Center (DKRZ) has a storage capacity of 60 petabytes of climate data.

Research and implementation goals The main objective of the presented research is to manage the data coming from Grid applications using the following concepts:  allowing users to define non-functional requirements for storage devices explicitly,  exploiting a knowledge base of the VO extended with descriptions of storage elements  exploiting information from storage monitoring systems and VO knowledge base to find the most suitable storage device complient with the defined requirements

Non-functional requirements in data management  Data intensive applications may have different requirements, e.g. important data should be replicated  Abstraction of storage elements prevents users from influencing the actual location of data  Distribution of data among available storage elements according to the defined requirements Sample non-functional requirements: freeCapacity currentReadTransferRate averageWriteTransferRate

FiVO/QStorMan toolkit

FiVO/QStorMan usage 1. Using QStorMan portal: Declare your non-functional requirements in the QStorMan portlet. Copy and paste the returned text from the portlet to your JDL file. 2. Using C++ programming library (libses): #include using namespace lustre_api_library; LustreManager manager; StoragePolicy policy; policy.setAverageReadTransferRate(50); policy.setCapacity(100); int descriptor = manager.createFile(„nazwa_pliku.dat”, &policy); 3. Using system C library: declare your non-functional requirements in the GOM knowledge base export LD_PRELOAD=

FiVO/QStorMan testing environment ACC Cyfronet AGH (Cracow) Scientific Linux SL release 5.5 (Boron) 2x Intel(R) Xeon(R) CPU 2.50GHz (4 cores, 1 thread per core) MB RAM ~ 12 TB storage capacity, ~150 MB/s read transfer rate, ~70 MB/s write transfer rate PCSS (Poznan): Scientific Linux CERN SLC release 5.5 (Boron) Intel(R) Xeon(R) CPU 3.00GHz (2 cores, 1 thread per core) 1000 MB RAM ~ 14 TB storage capacity, ~55 MB/s read trasfer rate, ~46 MB/s write trasfer rate ICM (Warsaw): CentOS release 5.5 (Final) Intel(R) Xeon(R) CPU 2.40GHz (4 cores, 1 thread per core) 7975 MB RAM ~ 5 TB storage capacity, ~50 MB/s read trasfer rate, ~27 MB/s write trasfer rate

Testing scenario Scenario: Aims to simulate a Grid job which is scheduled to run in the most suitable data center. The job performs computation and then writes data. Scenario parameters: Number of users – 3 (2 users used QStorMan) and 4 File size – 512 MB Number of files to write – 20, 30, 40 Scenario: Aims to simulate a Grid job which is scheduled to run in the most suitable data center. The job performs computation and then writes data. Scenario parameters: Number of users – 3 (2 users used QStorMan) File size – 512 MB Number of files to write – 20, 30, 40

Results

Conclusions and Future work  The presented research goal is to develop new approaches to issues of storage management in the Grid environment  Explicit definitions of non-functional requirements are necessary in data intensive applications  Allowing to accelerate data-oriented Grid applications by ~45% without any modifications in source code Future work:  Integration with Grid Queuing Systems  Integration with Virtual Organizations

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