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Data Management, Storage and Access Optimization in High Performance Distributed Environment

Data Management, Storage and Access Optimization in High Performance Distributed Environment. Xiaohui Shen Department of Electrical and Computer Engineering Northwestern University Jan 17, 2001. Outline. Problem Definition Solutions Meta-data Management System

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Data Management, Storage and Access Optimization in High Performance Distributed Environment

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  1. Data Management, Storage and Access Optimization in High Performance Distributed Environment Xiaohui Shen Department of Electrical and Computer Engineering Northwestern University Jan 17, 2001 Xiaohui Shen

  2. Outline • Problem Definition • Solutions • Meta-data Management System • Remote Storage Access Optimizations • Multi-Storage I/O System • Distributed Parallel File System • I/O performance prediction and evaluation • Integrated working environment Xiaohui Shen

  3. Motivation Xiaohui Shen

  4. Current Solutions • Parallel File System and runtime libraries: smart I/O optimizations, caching, prefetching, parallel I/O • User interfaces are low-level • No portable • Hard-coded • I/O selection is difficult for runtime systems • Database Systems: high-level, easy-to-use, portable • lack of power I/O optimizations Xiaohui Shen

  5. System Architecture Xiaohui Shen

  6. Tasks • Meta-data Management System • Remote Storage Access Optimizations • Efficient Storage Organization • Multi-Storage I/O System • Distributed Parallel File System • I/O performance prediction and evaluation • Integrated working environment Xiaohui Shen

  7. Part 1: Meta-data Management System (MDMS) • Abstract Storage Devices (ASDs) • Storage patterns & access patterns • Access History and trail of navigation Xiaohui Shen

  8. MDMS Tables Xiaohui Shen

  9. MDMS Internal Representation Xiaohui Shen

  10. MDMS I/O Flow (API) Xiaohui Shen

  11. Optimizations inside MDMS Xiaohui Shen

  12. Part 2: Remote Storage Access Optimization for HSS • Secondary Storage Access techniques: collective-I/O, data sieving, caching, prefetching etc • Tertiary Storage Systems directly interacts with applications • Remote environment Xiaohui Shen

  13. Optimizations • Remote Collective I/O • Remote Data sieving • Asynchronous I/O • Subfile • Superfile • Migration, Stage and Purge, SRB Container Xiaohui Shen

  14. Optimization: Subfile Xiaohui Shen

  15. Optimization: Superfile • Create: One large file • Access: first access brings the whole large file into memory, subsequent accesses can be directly serviced from memory Xiaohui Shen

  16. Other Optimizations • Migration • Stage • Purge • SRB Container Xiaohui Shen

  17. Part 3: MS-I/O: A Multi-storage I/O System • Further performance improvement is limited by the nature of storage media. • The problem is rooted in the traditional Single-storage resource architecutre. Xiaohui Shen

  18. Solution: Multi-storage Resource Architecture • Increases logical storage capacity • Provides a more flexible and reliable computing environment • Provides new opportunities for further performance improvement Xiaohui Shen

  19. Multi-storage Resource Architecture Xiaohui Shen

  20. Experimental Environment • Local Postgres Database • Local Disks • Remote Disks • Remote Tapes • Compute resource: Argonne SP2 Xiaohui Shen

  21. Multi-storage I/O System Xiaohui Shen

  22. Database Tables and I/O Routines • Run table • Dataset table • Access pattern table • Storage pattern table • Execution table Xiaohui Shen

  23. User Access Pattern (write) Xiaohui Shen

  24. User Access Pattern (read) Xiaohui Shen

  25. Optimization decision Flow Xiaohui Shen

  26. Applications and Tools Xiaohui Shen

  27. Experimental Environment • Applications: IBM SP2 at Argonne • Multiple Storage Resources: • Local Disks: Argonne SP2 • Remote Disks: SDSC • Remote Tapes: SDSC HPSS • Local Database: Postgres at NWU Xiaohui Shen

  28. MS-I/O Experiments:Data Analysis on Astrophysics data • No access pattern then Remote Tape • DataPartition=‘BBB’ then Remote Tape + Colletive I/O • WhenUse=‘soon’ & Size =‘ medium’ then Remote Disk • Plus DataPartion=‘BBB” then Remote Disk + Collective I/O • Plus UseFrequency=‘frequent’ then Local Disk • Plus DataPartion=‘BBB” then Local Disk + Collective I/O Xiaohui Shen

  29. MS-I/O Experiments: Volume Rendering • No Access Pattern then Remote Tape • ComputeTime=‘large’ then Remote Tape + Asyn- I/O • WhenUse=‘soon’ & Size =‘ medium’ then Remote Disk • Plus ComputeTime=‘large’ then Remote Disk + Asyn - I/O • Plus UseFrequency=‘frequent’ then Local Disk • Plus ComputeTime=‘large’ then Local Disk + Asyn - I/O Xiaohui Shen

  30. WriteSize=‘huge’ & FutureReadSize = ‘partial’ WriteSize=‘small’ & WriteSequence=‘y’ & FutureReadSequence=‘y’ MS-I/O Experiments: Subfile and Superfile Xiaohui Shen

  31. Dataset was first placed at Remote site Read.UseFrequency =‘frequent’ Dataset being frequently used is detected. MS-I/O Experiments: Replication and Access History Xiaohui Shen

  32. Part 4: DPFS: A Distributed Parallel File System • Collect idle distributed storage as supplement to native storage of parallel computing systems • Characteristics • Distributed • Parallel • File System • Database Xiaohui Shen

  33. System Architecture of DPFS Xiaohui Shen

  34. Software Architecture of DPFS • Parallelism • Concurrency Xiaohui Shen

  35. DPFS BSU and File view • A Basic Striping Unit (BSU) is called brick in DPFS. Size is 64K. Xiaohui Shen

  36. Striping Methods • Lineal Striping • Multi-dimensional Striping • Array Striping Xiaohui Shen

  37. Lineal Striping Xiaohui Shen

  38. Problems of Linear Striping Xiaohui Shen

  39. Multi-dimensional Striping Xiaohui Shen

  40. Array Striping Xiaohui Shen

  41. Striping Algorithms • Round - Robin • Greedy Algorithm Xiaohui Shen

  42. P0: 0-7 P1:8-15 P2:16-23 P3:24-31 P0(0,4) P1(9,13) P2(18,22)P3(27,31) P0(1,5) P1(10,14) P2(19,23) P3(24,28) ... Request Combination Xiaohui Shen

  43. Meta-data and Database Xiaohui Shen

  44. Tree Structure Xiaohui Shen

  45. Application Programming Interface • DPFS-Open () • DPFS-Write () • DPFS-Read () • DPFS-Close () Xiaohui Shen

  46. User Interface • File system commands: cp, mkdir, rm, ls etc • File transfer between DPFS and general sequential file system. Example: cp local:my.data DPFS:/home/xhshen:4:greedy Xiaohui Shen

  47. Experimental Environment • Compute Resource: Argonne IBM SP2 • Storage Resources: • Class 1: Argonne Linux machines (Fast Ethernet and ATM) • Class 2: NWU Workstations (155M ATM) • Class 3: NWU Workstations (10 M Eithernet) Xiaohui Shen

  48. DPFS Performance Numbers: File Level Comparison Xiaohui Shen

  49. DPFS Performance Numbers: Striping Algorithm Comparison Xiaohui Shen

  50. Part 5: I/O Performance Prediction and Evaluation • Performance Model • Performance Prediction Algorithm Xiaohui Shen

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