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The Storage Fabric of the Grid: The Network Storage Stack James S. Plank Director: Lo gistical  C omputing and  I nternetworking ( LoCI ) Laboratory Department of Computer Science University of Tennessee Cluster and Computational Grids for Scientific Computing: September 12, 2002, Le Chateau de Faverges de la Tour, France
Grid Research &  The Fabric Layer Middleware Application Resources The “Fabric” Layer
What is the Fabric Layer? ,[object Object],[object Object],[object Object]
What is the Fabric Layer? ,[object Object],[object Object],[object Object],Most Grid research accepts these as givens. (Examples: MPI, GridFTP)
LoCI’s Research Agenda Redefine the fabric layer based on End-to-End Principles Communication Storage Computation Data / Link / Physical Network Transport Application Access / Physical IBP Depot exNode LoRS Application Access / Physical IBP NFU exProc LoRS Application
What Should This Get You? ,[object Object],[object Object],[object Object],[object Object],I.E. Better Grids
LoCI Lab Personnel ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Collaborators ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Network Storage Stack Applications Logistical File System Logistical Tools L-Bone IBP Local Access Physical exNode ,[object Object],[object Object],[object Object]
The Network Storage Stack Applications Logistical File System Logistical Tools L-Bone IBP Local Access Physical exNode ,[object Object],[object Object],[object Object]
The Network Storage Stack The L-bone : Resource discovery & proximity queries IBP (Internet Backplane Protocol) :  Allocating  and managing network storage The exNode : A data structure for aggregation LoRS: The Logistical Runtime System : Aggregation tools and methodologies
IBP: The  I nternet  B ackplane  P rotocol ,[object Object],[object Object],[object Object],[object Object],[object Object],Low-level primitives and software for :
The Byte Array: IBP’s Unit of Storage ,[object Object],[object Object],[object Object],[object Object]
The IBP Client API ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Client API: Allocation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Allocation Attributes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Client API: Data Transfer ,[object Object],[object Object],[object Object],[object Object],2-party: 3-party: N-party/other things:
IBP Client API: Management ,[object Object],[object Object],[object Object],[object Object],[object Object]
IBP Servers ,[object Object],[object Object],[object Object],[object Object]
Typical IBP usage scenario
Logistical Networking Strategies Sender Receiver IBP Network Sender Receiver IBP Network Sender Receiver IBP IBP IBP #2 #1 #3 Sender Receiver IBP #4 IBP IBP
XSuffrage on MCell/APST  Tokyo Institute of Technology NetSolve + NFS NetSolve + IBP APST Daemon APST Client ,[object Object],University of Tennessee, Knoxville NetSolve + IBP University of California, San Diego GRAM + GASS
MCell/APST Experimental Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],workqueue XSufferage Scheduling Heuristics match data and tasks in appropriate locations - Automatic staging with IBP effective - Improved overall performance
The Network Storage Stack The L-bone : Resource Discovery & Proximity queries IBP : Allocating and managing network storage (like a network malloc) The exNode : A data structure for aggregation LoRS: The Logistical Runtime System : Aggregation tools and methodologies
The Logistical Backbone (L-Bone) ,[object Object],[object Object],[object Object],[object Object]
Snapshot: August, 2002 Approximately 1.6 TB of publicly accessible storage (Scaling to a petabyte someday…)
The Network Storage Stack The L-bone : Resource Discovery & Proximity queries IBP : Allocating and managing network storage (like a network malloc) The exNode : A data structure for aggregation LoRS: The Logistical Runtime System : Aggregation tools and methodologies
The exNode ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The exNode (XML-based) A B C 0 300 200 100 IBP Depots Network
The Network Storage Stack The L-bone : Resource Discovery & Proximity queries IBP : Allocating and managing network storage (like a network malloc) The exNode : A data structure for aggregation LoRS: The Logistical Runtime System : Aggregation tools and methodologies
Logistical Runtime System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Logistical Runtime System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Upload
Augment to Tennessee
Augment to Santa Barbara
Stat (ls)
Failures do happen.
Download
Trimming (dead capability removal)
End-To-End Services: ,[object Object],[object Object],[object Object],[object Object]
Parity / Coding IBP Buffers Network = + + = + 2 + 3 ExNode with Coding
Scalability ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Status Applications Logistical File System Logistical Tools L-Bone IBP Local Access Physical exNode ,[object Object],[object Object],[object Object]
What’s Coming Up? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Storage Fabric of the Grid: The Network Storage Stack James S. Plank Director: Lo gistical  C omputing and  I nternetworking ( LoCI ) Laboratory Department of Computer Science University of Tennessee Cluster and Computational Grids for Scientific Computing: September 12, 2002, Le Chateau de Faverges de la Tour, France
Replication: Experiment #1 UCSB UCSD TAMU UTK UNC Harvard Turin, IT Stuttgart, DE 3 MB file 0 3 MB UTK 2 UTK 5 UTK 6 UTK 3 UTK 4 UTK 1 UCSB 1 UCSB 2 UCSB 3 UCSD 1 UCSD 3 Harvard UNC UTK 5 UTK 2 UTK 5 UTK 6 UTK 3 UCSB 1 UCSB 2 UCSB 3
Replication: Experiment #1 Depot Availability at UTK Depot Availability at UCSD Depot Availability at Harvard 860 Download Attempts 100% Success 857 Download Attempts 100% Success 751 Download Attempts 100% Success 0 10 20 30 40 50 70 80 90 100 60 UTK 99.85 UCSD UCSB Harvard UNC 99.71 95.31 59.77 99.88 Fragment Availability (%) 0 10 20 30 40 50 70 80 90 100 60 UTK 96.27 UCSD UCSB Harvard UNC 98.60 88. 60 57.29 97.20 Fragment Availability (%) 0 10 20 30 40 50 70 80 90 100 60 UTK 99.87 UCSD UCSB Harvard UNC 99.80 90.47 57.45 99.87 Fragment Availability (%)
Most Frequent Download Path From UTK From Harvard From UCSD
Replication: Experiment #2 ,[object Object],[object Object],3 MB file 1,225 Attempts 93.88% Success 0 3 MB UTK 2 UTK 5 100% UTK 6 UTK 3 UTK 4  99.84% UTK 1 UCSB 1 93.88% UCSB 2 UCSB 3  UCSD 1 UCSD 3 100% Harvard 48.24% UNC UTK 5 99.78% UTK 2 UTK 5 100% UTK 6 100% UTK 3 UCSB 1 UCSB 2 94.69% UCSB 3

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The Storage Fabric of the Grid: Network Storage Stack

  • 1. The Storage Fabric of the Grid: The Network Storage Stack James S. Plank Director: Lo gistical C omputing and I nternetworking ( LoCI ) Laboratory Department of Computer Science University of Tennessee Cluster and Computational Grids for Scientific Computing: September 12, 2002, Le Chateau de Faverges de la Tour, France
  • 2. Grid Research & The Fabric Layer Middleware Application Resources The “Fabric” Layer
  • 3.
  • 4.
  • 5. LoCI’s Research Agenda Redefine the fabric layer based on End-to-End Principles Communication Storage Computation Data / Link / Physical Network Transport Application Access / Physical IBP Depot exNode LoRS Application Access / Physical IBP NFU exProc LoRS Application
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11. The Network Storage Stack The L-bone : Resource discovery & proximity queries IBP (Internet Backplane Protocol) : Allocating and managing network storage The exNode : A data structure for aggregation LoRS: The Logistical Runtime System : Aggregation tools and methodologies
  • 12.
  • 13.
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  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20. Typical IBP usage scenario
  • 21. Logistical Networking Strategies Sender Receiver IBP Network Sender Receiver IBP Network Sender Receiver IBP IBP IBP #2 #1 #3 Sender Receiver IBP #4 IBP IBP
  • 22.
  • 23.
  • 24. The Network Storage Stack The L-bone : Resource Discovery & Proximity queries IBP : Allocating and managing network storage (like a network malloc) The exNode : A data structure for aggregation LoRS: The Logistical Runtime System : Aggregation tools and methodologies
  • 25.
  • 26. Snapshot: August, 2002 Approximately 1.6 TB of publicly accessible storage (Scaling to a petabyte someday…)
  • 27. The Network Storage Stack The L-bone : Resource Discovery & Proximity queries IBP : Allocating and managing network storage (like a network malloc) The exNode : A data structure for aggregation LoRS: The Logistical Runtime System : Aggregation tools and methodologies
  • 28.
  • 29. The exNode (XML-based) A B C 0 300 200 100 IBP Depots Network
  • 30. The Network Storage Stack The L-bone : Resource Discovery & Proximity queries IBP : Allocating and managing network storage (like a network malloc) The exNode : A data structure for aggregation LoRS: The Logistical Runtime System : Aggregation tools and methodologies
  • 31.
  • 32.
  • 35. Augment to Santa Barbara
  • 40.
  • 41. Parity / Coding IBP Buffers Network = + + = + 2 + 3 ExNode with Coding
  • 42.
  • 43.
  • 44.
  • 45. The Storage Fabric of the Grid: The Network Storage Stack James S. Plank Director: Lo gistical C omputing and I nternetworking ( LoCI ) Laboratory Department of Computer Science University of Tennessee Cluster and Computational Grids for Scientific Computing: September 12, 2002, Le Chateau de Faverges de la Tour, France
  • 46. Replication: Experiment #1 UCSB UCSD TAMU UTK UNC Harvard Turin, IT Stuttgart, DE 3 MB file 0 3 MB UTK 2 UTK 5 UTK 6 UTK 3 UTK 4 UTK 1 UCSB 1 UCSB 2 UCSB 3 UCSD 1 UCSD 3 Harvard UNC UTK 5 UTK 2 UTK 5 UTK 6 UTK 3 UCSB 1 UCSB 2 UCSB 3
  • 47. Replication: Experiment #1 Depot Availability at UTK Depot Availability at UCSD Depot Availability at Harvard 860 Download Attempts 100% Success 857 Download Attempts 100% Success 751 Download Attempts 100% Success 0 10 20 30 40 50 70 80 90 100 60 UTK 99.85 UCSD UCSB Harvard UNC 99.71 95.31 59.77 99.88 Fragment Availability (%) 0 10 20 30 40 50 70 80 90 100 60 UTK 96.27 UCSD UCSB Harvard UNC 98.60 88. 60 57.29 97.20 Fragment Availability (%) 0 10 20 30 40 50 70 80 90 100 60 UTK 99.87 UCSD UCSB Harvard UNC 99.80 90.47 57.45 99.87 Fragment Availability (%)
  • 48. Most Frequent Download Path From UTK From Harvard From UCSD
  • 49.