Reliable and Efficient Grid Data Placement using Stork and DiskRouter

1. Reliable and Efficient Grid Data Placement using Stork and DiskRouter Tevfik Kosar University of Wisconsin-Madison kosart@cs.wisc.edu April 15th, 2004

2. A Single Project.. • LHC (Large Hadron Collider) • Comes online in 2006 • Will produce 1 Exabyte data by 2012 • Accessed by ~2000 physicists, 150 institutions, 30 countries

3. And Many Others.. • Genomic information processing applications • Biomedical Informatics Research Network (BIRN) applications • Cosmology applications (MADCAP) • Methods for modeling large molecular systems • Coupled climate modeling applications • Real-time observatories, applications, and data-management (ROADNet)

4. The Same Big Problem.. • Need for data placement: • Locate the data • Send data to processing sites • Share the results with other sites • Allocate and de-allocate storage • Clean-up everything • Do these reliably and efficiently

5. Outline • Introduction • Stork • DiskRouter • Case Studies • Conclusions

6. Stork • A scheduler for data placement activities in the Grid • What Condor is for computational jobs, Stork is for data placement • Stork comes with a new concept: “Make data placement a first class citizen in the Grid.”

7. Stage-in • Execute the Job • Stage-out Stage-in Execute the job Stage-out Release input space Release output space Allocate space for input & output data Individual Jobs The Concept

8. Stage-in • Execute the Job • Stage-out Stage-in Execute the job Stage-out Release input space Release output space Allocate space for input & output data Data Placement Jobs Computational Jobs The Concept

9. A B D E F The Concept Condor Job Queue DaP A A.submit DaP B B.submit Job C C.submit ….. Parent A child B Parent B child C Parent C child D, E ….. DAG specification C DAGMan Stork Job Queue C E

10. Why Stork? • Stork understands the characteristics and semantics of data placement jobs. • Can make smart scheduling decisions, for reliable and efficient data placement.

11. Failure Recovery and Efficient Resource Utilization • Fault tolerance • Just submit a bunch of data placement jobs, and then go away.. • Control number of concurrent transfers from/to any storage system • Prevents overloading • Space allocation and De-allocations • Make sure space is available

12. Support for Heterogeneity Protocol translation using Stork memory buffer.

13. Support for Heterogeneity Protocol translation using Stork Disk Cache.

14. Flexible Job Representation and Multilevel Policy Support [ Type = “Transfer”; Src_Url = “srb://ghidorac.sdsc.edu/kosart.condor/x.dat”; Dest_Url = “nest://turkey.cs.wisc.edu/kosart/x.dat”; …… …… Max_Retry = 10; Restart_in = “2 hours”; ]

15. Run-time Adaptation • Dynamic protocol selection [ dap_type = “transfer”; src_url = “drouter://slic04.sdsc.edu/tmp/test.dat”; dest_url = “drouter://quest2.ncsa.uiuc.edu/tmp/test.dat”; alt_protocols = “nest-nest, gsiftp-gsiftp”; ] [ dap_type = “transfer”; src_url = “any://slic04.sdsc.edu/tmp/test.dat”; dest_url = “any://quest2.ncsa.uiuc.edu/tmp/test.dat”; ]

16. Run-time Adaptation • Run-time Protocol Auto-tuning [ link = “slic04.sdsc.edu – quest2.ncsa.uiuc.edu”; protocol = “gsiftp”; bs = 1024KB; //block size tcp_bs = 1024KB; //TCP buffer size p = 4; ]

17. Outline • Introduction • Stork • DiskRouter • Case Studies • Conclusions

18. DiskRouter • A mechanism for high performance, large scale data transfers • Uses hierarchical buffering to aid in large scale data transfers • Enables application-level overlay network for maximizing bandwidth • Supports application-level multicast

19. Store and Forward C A With DiskRouter DiskRouter B Without DiskRouter Improves performance when bandwidth fluctuation between A and B is independent of the bandwidth fluctuation between B and C

20. DiskRouter Overlay Network 90 Mb/s B A

21. DiskRouter Overlay Network 90 Mb/s B A 400 Mb/s 400 Mb/s DiskRouter C Add a DiskRouter Node C which is not necessarily on the path from A to B, to enforce use of an alternative path.

22. Data Mover/Distributed Cache Sourcewrites to the closestDiskRouterandDestinationreceives it up from itsclosestDiskRouter Source Destination DiskRouter Cloud

23. Outline • Introduction • Stork • DiskRouter • Case Studies • Conclusions

24. Submit Site SRB Server UniTree Server SDSC Cache NCSA Cache Case Study I: SRB-UniTree Data Pipeline • Transfer ~3 TB of DPOSS data from SRB @SDSC to UniTree @NCSA • A data pipeline created with Stork and DiskRouter

25. Failure Recovery Diskrouter reconfigured and restarted UniTree not responding SDSC cache reboot & UW CS Network outage Software problem

26. Case Study -II

27. Dynamic Protocol Selection

28. Runtime Adaptation • Before Tuning: • parallelism = 1 • block_size = 1 MB • tcp_bs = 64 KB • After Tuning: • parallelism = 4 • block_size = 1 MB • tcp_bs = 256 KB

29. Conclusions • Regard data placement as first class citizen. • Introduce a specialized scheduler for data placement. • Introduce a high performance data transfer tool. • End-to-end automation, fault tolerance, run-time adaptation, multilevel policy support, reliable and efficient transfers.

30. Future work • Enhanced interaction between Stork, DiskRouter and higher level planners • co-scheduling of CPU and I/O • Enhanced authentication mechanisms • More run-time adaptation

31. You don’t have to FedEx your data anymore.. We deliver it for you! • For more information • Stork: • Tevfik Kosar • Email: kosart@cs.wisc.edu • http://www.cs.wisc.edu/condor/stork • DiskRouter: • George Kola • Email: kola@cs.wisc.edu • http://www.cs.wisc.edu/condor/diskrouter