QoS provisioning for data-oriented applications in PL-Grid

1. QoS provisioning for data-oriented applications in PL-Grid D. Król, B. Kryza, K. Skalkowski, D. Nikolow, R. Slota, and J. Kitowski ACC Cyfronet AGH, Kraków, Poland Institute of Computer Science AGH-UST, Krakow, Poland Cracow Grid Workshop 2010 Kraków, 11-13.10.2010

2. Agenda Research and implementation goals Data intensive applications Non-functional requirements in data management FiVO/QStorMan toolkit Architecture overview Main use case Implementation status Conclusions

3. Research and implementationgoals • The main objective of the presented research is automation of creation and management a Virtual Organization (VO) using knowledge, especially in the data storage area using the following concepts: • allowing users to define non-functional requirements for storage devices explicitly, • exploiting a knowledge base of the VO extendedwith 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

4. Data intensive applications • 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. 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.

5. 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 ofdata among available storage elements according to the defined requirements • Choose possible ways to check a fulfilling ratio of requirements for each storage element

6. FiVO/QStorMan toolkit • On the user side – a programming library (libSES) which provides functions for managing files in a distributed storage environment • On the server side : • A service (Storage Element Selection service) which finds the most suitable storage element according to the defined requirements and current workload • A knowledge base (GOM) which stores a configuration of the storage environment along with defined non-functional requirements from the users • A monitoring system (SMED) which monitors storage resources and provides information about current or average values of different QoS parameters • A portal where a user can define nonfunctional requirements for the storage enviornment.

7. Architecture overview

8. Main use case User Application Defining non-functional requirements Storing requirements definition Classical „write” operation to concrete SE Getting requirements for the application along with configuration of a Lustre installation Portal GOM Getting configuration information of a Lustre installation SES library SMED Monitoring system „Write” operation to the most suitable SE Operation interception Getting actual storage system parameter values Monitoring information SE SE SE ..... Distributed storage system

9. Implementation status – the user side (libSES) • The library is implemented as a shared library in the C++ language. • Communication with the server side is implemented with the libcurl library (exploiting the REST model). • For file storaging we use the Lustre filesystem and its pool mechanism. • The most important part of the library API is : • createFile(fileName : char*, policy : StoragePolicy*) : int • openFile(fileName : char*) : int • closeFile(fileName : char*) : void • changeStoragePolicy(fileName : char*, policy : StoragePolicy*) : void

10. Implementation status – Storage Element Selection service • The finder service is implemented in Python. • Function that determines similarity is as follows: • Communication with the monitoring system is implemented with the REST model • Integration with the VOknowledge base is implemented with the SOAP-based web service – required value of an attribute i • if < the current value of an attribute => • else the current value of an attribute

11. Implementation status – GOM • Allows to use multiple available storage and reasoning mechanisms. • Provides several interfaces for querying and modifying the managed ontologies. • The communication protocols supported by GOM currently include Java RMI and SOAP.

12. Implementation status – SMED • SMED architecture is based on the Enterprise Service Bus. • Current version of the system supports monitoring of : • local system hard drives, • disk arrays, • hierarchical storage management systems • distributed file systems, e.g. Lustre

13. Implementation status – non-functional requirements • Current implementation of the StoragePolicy class includes: • preferredDeviceType – a device type which is preferred by the user, e.g. a fast writable device or high available device • capacity –free storage space required • averageReadTransferRate – mean read time from the last monitoring serie • averageWriteTransferRate – mean write time from the last monitoring serie • throughput – numerical value of the required throughput • throughputLevel – an abstract level of required throughput, e.g. LOW, MEDIUM or HIGH The user can choose only these aspects of a policy which are important from their point of view.

14. Conclusions • 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 find the most suitable storage element within a distributed file system or a server where a grid job should be scheduled based on the given requirements • The presented solution is easy to use (standard C++ shared library), extend (description of storage elements is in an ontological form) and understand (clean algorithm of finding a storage element)

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