Application-engaged Dynamic Orchestration of Optical Network Resources
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The document discusses the orchestration of optical network resources, emphasizing dynamic lambda services for business continuity, data storage, and utility computing. It highlights the need for sophisticated scheduling and resource management in grid applications, showcasing a use case where resource requests are efficiently handled. The conclusions suggest a shift from traditional data transfer methods towards more flexible service architectures and advocate for ongoing research in validation and security integration.
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