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DWDM 
RAM 
Data@LIGHTspeed 
Application-engaged 
Dynamic Orchestration of 
Optical Network Resources 
NNTTOONNCC 
National Transparent Optical 
Network Consortium 
Defense Advanced Research 
Projects Agency BUSINESS WITHOUT BOUNDARIES
Grids & C. freed us from the cuffs 
of bit-blasting races 
Apps such as Grids call for a complex mix of: 
Bit-blasting 
Finesse (granularity of control) 
Virtualization (access to diverse knobs) 
Resource bundling (network AND …) 
Security (AAA to start) 
Free from GUIs, any human intervention 
++ 
++ 
++ 
++ 
++ 
== λ’s are worth a whole new look!
Business Drivers for next-gen λ services 
• Business Continuity/Disaster Recovery 
– Remote file storage/back-up 
– Recovery after equipment or path failure 
– Alternate site operations due to natural or man-made disaster 
• Storage and data on demand 
– Rapid expansion of network attached storage capacity 
– Archival storage and retrievals 
– Logistical networking – pre-fetch and cache 
• Grids 
– Financial: portfolio risk analysis; Manufacturing: CAD; Entertainment: 
digital rendering; Lifesciences: drug discovery 
– Utility computing for pay-as-you-go business models
We pick role-model dynamics… 
Under-constrained window use-case 
• Request for 1/2 hour between 4:00 and 
5:30 on Segment D granted to Bob’s Grid 
at 4:00 
• New request from Alice’s Grid for same 
segment for 1 hour between 3:30 and 
5:00. Alice’s credentials support the 
request 
• Reschedule Bob’s Grid to 4:30; Alice’s 
Grid stays at 3:30. Everyone is happy. 
Bob’s Grid 
3:30 4:00 4:30 5:00 5:30 
Alice’s Grid 
3:30 4:00 4:30 5:00 5:30 
Bob 
Alice 
3:30 4:00 4:30 5:00 5:30 
Route allocated for a time slot; new request comes in; former route can 
be rescheduled for a later slot within window to accommodate new 
request
… and map them over dynamic λs 
Applications 
Data Transfer Scheduling 
Network Resource Scheduling 
Communication Protocols 
ODIN 
OMNInet 
Application 
Collective 
Resource 
Connectivity 
Fabric 
DD 
WWD 
D 
MM- 
- 
RR 
AA 
MM
DWDM-RAM Architecture 
Data-Intensive Applications 
Data 
Transfer 
Service 
Network Resource 
Service 
Basic Network 
Resource 
Service 
Network 
Resource 
Scheduler 
Dynamic Lambda, Optical Burst, etc., 
Data 
Center 
l1 
ln 
l1 
ln 
Data 
Center 
Grid services 
Data 
Handler 
Service 
Information Service 
DTS API 
Application 
Middleware 
Layer 
NRS Grid Service API 
Network Resource 
Middleware 
Layer 
l  OGSI-ification API 
Connectivity and 
Fabric Layers 
Optical path control
Finesse Task 2: Characterize 
overall utilization 
#1 Transfer #1 Transfer 
#2 Transfer #2 Transfer 
-30 0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600 630 660 
allocate path 
de-allocate path 
time (sec) 
Demonstrator, a Notional View 
NORTEL NETWORKS CONFIDENTIAL 
100s of 
transactions 
per second 
100s of 
transactions 
per second 
100s of 
transactions 
per second 
Remote 
storage / 
processing 
location 
Remote 
storage / 
processing 
location 
Remote 
storage / 
processing 
location 
Dynamically provisioned 
carrier or large 
enterprise network – 
switched lambdas and 
sub-lambdas
Current Demonstrator (Supercomm, 0604) 
Carrier 
POP POP 
NORTEL NETWORKS CONFIDENTIAL 
BIGBANK 
Records/second: 16K records/second 
Record size: 4 KB 
Queue Load: 1,245,726 records 
4,982,904,000 bytes 
Queue Fill rate: 64 Mbytes/sec 
Next Sched. Queue Delivery: 2:35 pm 
Delivery countdown: 33 sec 
Burst duration: 150 sec 
Last burst throughput: 610 Mbps 
Accumulation period: constant 
Stocks-R-Us 
Records/second: 14K records/second 
Record size: 2 KB 
Queue Load: 70 records 
140,000 bytes 
Queue Fill rate: 28 Mbytes/sec 
Next Sched. Queue Delivery: IN PROCESS 
Delivery countdown: IN PROCESS 
Burst duration: 90 sec 
Last burst throughput: 580 Mbps 
Accumulation period: constant 
Dynamically 
Provisioned 
Lambda Mesh 
POP POP
Conclusions 
• A journey past “bit-blasting” has begun 
• For rich λ semantics, it’s key to define proper layers 
–Web Service mechanisms dominate the upper layers 
– The ASTN-based OMNInet control plane proves solid foundation 
and handy separation of concerns 
– The combination gives preliminary confirmation of scaling 
• New work items 
– Simulation + empirical validation with large(r) populations 
– Integration with workflow languages 
–AAA and data path security 
– Integration with a Grid Community Scheduler

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Application-engaged Dynamic Orchestration of Optical Network Resources

  • 1. DWDM RAM Data@LIGHTspeed Application-engaged Dynamic Orchestration of Optical Network Resources NNTTOONNCC National Transparent Optical Network Consortium Defense Advanced Research Projects Agency BUSINESS WITHOUT BOUNDARIES
  • 2. Grids & C. freed us from the cuffs of bit-blasting races Apps such as Grids call for a complex mix of: Bit-blasting Finesse (granularity of control) Virtualization (access to diverse knobs) Resource bundling (network AND …) Security (AAA to start) Free from GUIs, any human intervention ++ ++ ++ ++ ++ == λ’s are worth a whole new look!
  • 3. Business Drivers for next-gen λ services • Business Continuity/Disaster Recovery – Remote file storage/back-up – Recovery after equipment or path failure – Alternate site operations due to natural or man-made disaster • Storage and data on demand – Rapid expansion of network attached storage capacity – Archival storage and retrievals – Logistical networking – pre-fetch and cache • Grids – Financial: portfolio risk analysis; Manufacturing: CAD; Entertainment: digital rendering; Lifesciences: drug discovery – Utility computing for pay-as-you-go business models
  • 4. We pick role-model dynamics… Under-constrained window use-case • Request for 1/2 hour between 4:00 and 5:30 on Segment D granted to Bob’s Grid at 4:00 • New request from Alice’s Grid for same segment for 1 hour between 3:30 and 5:00. Alice’s credentials support the request • Reschedule Bob’s Grid to 4:30; Alice’s Grid stays at 3:30. Everyone is happy. Bob’s Grid 3:30 4:00 4:30 5:00 5:30 Alice’s Grid 3:30 4:00 4:30 5:00 5:30 Bob Alice 3:30 4:00 4:30 5:00 5:30 Route allocated for a time slot; new request comes in; former route can be rescheduled for a later slot within window to accommodate new request
  • 5. … and map them over dynamic λs Applications Data Transfer Scheduling Network Resource Scheduling Communication Protocols ODIN OMNInet Application Collective Resource Connectivity Fabric DD WWD D MM- - RR AA MM
  • 6. DWDM-RAM Architecture Data-Intensive Applications Data Transfer Service Network Resource Service Basic Network Resource Service Network Resource Scheduler Dynamic Lambda, Optical Burst, etc., Data Center l1 ln l1 ln Data Center Grid services Data Handler Service Information Service DTS API Application Middleware Layer NRS Grid Service API Network Resource Middleware Layer l OGSI-ification API Connectivity and Fabric Layers Optical path control
  • 7. Finesse Task 2: Characterize overall utilization #1 Transfer #1 Transfer #2 Transfer #2 Transfer -30 0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600 630 660 allocate path de-allocate path time (sec) 
  • 8. Demonstrator, a Notional View NORTEL NETWORKS CONFIDENTIAL 100s of transactions per second 100s of transactions per second 100s of transactions per second Remote storage / processing location Remote storage / processing location Remote storage / processing location Dynamically provisioned carrier or large enterprise network – switched lambdas and sub-lambdas
  • 9. Current Demonstrator (Supercomm, 0604) Carrier POP POP NORTEL NETWORKS CONFIDENTIAL BIGBANK Records/second: 16K records/second Record size: 4 KB Queue Load: 1,245,726 records 4,982,904,000 bytes Queue Fill rate: 64 Mbytes/sec Next Sched. Queue Delivery: 2:35 pm Delivery countdown: 33 sec Burst duration: 150 sec Last burst throughput: 610 Mbps Accumulation period: constant Stocks-R-Us Records/second: 14K records/second Record size: 2 KB Queue Load: 70 records 140,000 bytes Queue Fill rate: 28 Mbytes/sec Next Sched. Queue Delivery: IN PROCESS Delivery countdown: IN PROCESS Burst duration: 90 sec Last burst throughput: 580 Mbps Accumulation period: constant Dynamically Provisioned Lambda Mesh POP POP
  • 10. Conclusions • A journey past “bit-blasting” has begun • For rich λ semantics, it’s key to define proper layers –Web Service mechanisms dominate the upper layers – The ASTN-based OMNInet control plane proves solid foundation and handy separation of concerns – The combination gives preliminary confirmation of scaling • New work items – Simulation + empirical validation with large(r) populations – Integration with workflow languages –AAA and data path security – Integration with a Grid Community Scheduler