Chameleon: A Resource Scheduler in A Data Grid Environment

1. Chameleon: A Resource Scheduler in A Data Grid Environment Sang Min Park  Jai-Hoon Kim Ajou University South Korea

2. Contents • Introduction to Data Grid • Related Works • Scheduling Model • Scheduler Implementation • Testbed and Application • Results • Conclusions

3. Introduction to Data Grid • Data Grid Motivations • Petabyte scale data production • Distributed data storage to store parts of data • Distributed computing resources which process the data • Two Most Important Approaches for Data Grid • Secure, reliable, and efficient data transport protocol (ex. GridFTP) • Replication (ex. Replica catalog) • Replication • Large size files are partially replicated among sites • Reduce data access time • Application Scheduling, Dynamic replication issues are emerging

4. Related Works • Data Grid • Replica catalog – mapping from logical file name to physical instance • GridFTP – Secure, reliable, and efficient file transfer protocol • Job Scheduling • Various scheduling algorithms for computational Grid • Application Level Scheduling (AppLes) • Large data collection has not been concerned • Job Scheduling in Data Grid • Roughly analytical and simulation studies are presented • Our works define more in-depth scheduling model

5. Scheduling Model - Assumptions • Assumptions • Site has both data storage and computing facilities • Files are replicated at part of Grid sites • Each site has different amount of computational capability • Grid users request job execution through Job schedulers

6. Scheduling Model - System Factors • Dynamic system factors - Factors change over time • Network bandwidth • Data transfer time is proportional to network bandwidth • NWS- tool for measuring and forecasting network bandwidth • Available computing nodes • Determines execution time of jobs • Decided according to job load on a site • System attributes • Machine architecture (clusters, MPPs, etc) • Processor speed, Available memory, I/O performance, etc.

7. Scheduling Model - System Factors • Application specific factors - Unique factors Data Grid applications have • Size of input data (replica) • If not in the computing site, data fetch is needed • Much time will be consumed to transfer large size data • Size of application code • Application code should be migrated to sites which perform computation • Not critical to the overall performance (small size) • Size of produced output data • When the computing job takes place at the remote site, result data should be returned back to the local • Strongly related to the size of input data

8. Scheduling Model - application scenarios • The model consists of 5 distinct application scenarios • Local Data and Local Execution • Local Data and Remote Execution • Remote Data and Local Execution • Remote Data and Same Remote Execution • Remote Data and Different Remote Execution

9. Scheduling Model - application scenarios • Terms in the scenarios

10. Scheduling Model - application scenarios • Local Data and Local Execution • Input data (replica) is located in local, and processing is performed with local available processors • Data in move consists of • Input data (replica) • Application code • Output data • Cost consists of • Data transfer time between master and computing nodes via LAN • Job execution time using local processors

11. Scheduling Model - application scenarios 2. Local Data and Remote Execution • Locally copied replica is transferred to remote computation site • Cost consists of • Data (input+codes+output) movement time via WAN between local and remote site • Data movement time via LAN in a remote site • Job execution time on a remote site

12. Scheduling Model - application scenarios 3. Remote Data and Local Execution • Remote replica is copied into local site, and processing is performed on local • Cost consists of • Input data movement time via WAN between local and remote site • Data movement time via LAN in a local site • Job execution time on a local processors

13. Scheduling Model - application scenarios 4. Remote Data and Same Remote Execution • Remote site having replica performs computation • Cost consists of • Data (code+output) movement time via WAN between local and remote site • Data movement time via LAN in a remote site • Job execution time on a remote site

14. Scheduling Model - application scenarios 5. Remote Data and Different Remote Execution • Remote site j performs computation with replica copied from remote site i • Cost consists of • Input replica movement time via WAN between remote site i and j • Data (codes + output) movement time via WAN between local and remote j • Data movement time via LAN in a remote site j • Job execution time in a remote site j

15. Scheduling Model - scheduler • Operations of the scheduler • Predict the response time of each scenario • Compare the response time of scenarios • Choose the best scenario and sites holding data and to perform job execution • Requests data movement and job execution

16. Scheduler Implementation • Develop scheduler prototype, called Chameleon, for evaluating the scheduling model • Built on top of services provided by Globus • GRAM • MDS • GridFTP • Replica Catalog • NWS is used for measuring and forecasting network bandwidth • Scheduling algorithms are based on the scheduling models presented

17. Testbed for experiments

18. Applications • Gene sequence comparison applications (Bioinformatics) • Computationally intensive analysis on the large size protein database • Bio-scientists predict structure and functions of newly found protein by comparing it with well known protein database • The size of database reaches over 500 MB • There are various versions of protein database • Large databases are replicated in Data Grid • Two well-known applications, Blast and FASTA, are executed

19. Applications - parameters

20. Experimental Results (1) Results when executing PSI-BLAST Replication scenario

21. Experimental Results (2) Results on the previous slide Results when executing FASTA in the above replication scenario

22. Experimental Results (3) No replication takes place Results when executing PSI-BLAST

23. Experimental Results (4) Increasing the number of replica Decreasing response time

24. Conclusions • Job scheduling models for Data Grid • The models consist of 5 distinct scenarios • Scheduler prototype, called Chameleon, is developed which is based on the presented scheduling models • Perform meaningful experiments with Chameleon on a constructed Grid testbed • We achieve better performance by considering data locations as well as computational capabilities

25. References • ANTZ: http://www.antz.or.kr • ApGrid: http://www.apgrid.org • B. Allcock, J. Bester, J. Bresnahan, A. Chervenak, I. Foster, C. Kesselman, S. Meder, V. Nefedova, D. Quesnel, S. Tuecke. “Secure, Efficient Data Transport and Replica Management for High-Performance Data-Intensive Computing,” IEEE Mass Storage Conference, 2001. • Mark Baker, Rajkumar Buyya and Domenico Laforenza. “The Grid: International Efforts in Global Computing,” International Conference on Advances in Infrastructure for E-Business, Science, and Education on the Internet, SSGRR2000, L'Aquila, Italy, July 2000. • F. Berman and R. Wolski. “The AppLes project: A status report,” Proceedings of the 8th NEC Research Symposium, Berlin, Germany, May 1997. • Rajkumar Buyya, Kim Branson, Jon Giddy and David Abramson. “The Virtual Laboratory: A Toolset for Utilising the World-Wide Grid to Design Drugs,” 2nd IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2002), Berlin, Germany, May 2002. • CERN DataGrid Project: http://www.cern.ch/grid/ • Ann Chervenak, Ian Foster, Carl Kesselman, Charles Salisbury and Steven Tuecke. “The Data Grid: Towards an Architecture for the Distributed Management and Analysis of Large Scientific Datasets,” Journal of Network and Computer Applications, 23:187-200, 2001. • Dirk Düllmann, Wolfgang Hoschek, Javier Jean-Martinez, Asad Samar, Heinz Stockinger and Kurt Stockinger. “Models for Replica Synchronisation and Consistency in a Data Grid,” 10th IEEE Symposium on High Performance and Distributed Computing (HPDC-10), San Francisco, California, August 2001. • I. Foster and C. Kesselman. “The Grid: Blueprint for a New Computing Infrastructure,” Morgan Kaufmann, 1999. • I. Foster, C. Kesselman and S. Tuecke. “The Anatomy of the Grid: Enabling Scalable Virtual Organizations,” International J. Supercomputer Applications, 15(3), 2001. • Cynthia Gibas. “Developing Bioinformatics Computer Skills,” O’REILLY, April 2001. • The Globus Project: http://www.globus.org

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