1. LIDS MIT Outline Motivation Simulation Study Scheduled OFS Experimental Results Discussion

2. LIDS MIT Optical Flow Switching Motivation OFS reduces the amount of electronic processing by switching long sessions at the WDM layer –Lower costs, reduced delays, increased switch capacity –Provide specific QoS for advanced services

3. LIDS MIT OFS Motivation (cont) Flow Size 1KB1MB100MB10MB Number of Flows Total Bytes Flow Size 1MB100MB10MB1KB Optical DomainElect. DomainOptical DomainElect. Domain -Internet displays a “heavy-tail” distribution of connections -More efficient optics => more transactions in optical domain (red line moves left)

4. LIDS MIT Optical Flow Switching Study Short-duration optical connections –Access area –Wide area Network architecture issues –Connection setup –Route/wavelength assignment –Goal: efficient use of network resources I.e. high throughput Previous work: “probabilistic” approaches –Difficulty: high-arrival rate leads to high blocking probability –Problem: lack of timely network state information Our proposed solution: Use of timing information in network –Schedule connections –Gather timely network state information This demonstration –Demonstrate flow switching –Demonstrate viability of timing and scheduling connections –Investigate key sources of overhead –High efficiency

5. LIDS MIT Connection Setup Investigation Key issue: –How to learn optical resource availability? –Distribution problem –“Wavelength continuity” problem makes it worse Previous work –Addresses issues one at a time –Assumes perfect network state information –Will these results be useful for ONRAMP, WAN implementation? This work –Assesses effects of distributed network state information –Models some current proposals MP-lambda-S ASON

6. LIDS MIT Methodology Design distributed approaches –Combined routing, wavelength assignment –Connection setup Baseline flow switching architecture –Requested flows from user to user –Durations on order of seconds –All-optical Simulate approaches on WAN topology –End-to-end latency (“time of flight” only) –Approaches: Ideal, Tell-and-Go, Reverse Reservation Assess performance versus idealized approach –Blocking probability

7. LIDS MIT Ideal Approach Illustration ACB D -Changers ACB D Bidirectional Multi-fiber Link Network Infrastructure “Tell” cntl packet LLR Routing, Connection Setup Optical Flow Assume: Flow Requested from A->B

8. LIDS MIT Tell-and-Go Approach Illustration ACB D Link-state Updates Available : 1,2,3Available : 1,2 Available : 2,3Available : 2,3,4 Link-State Protocol ACB D Optical Flow Connection Setup “Tell” Packet - Single wavelength Assume: Flow Requested from A->B

9. LIDS MIT Reverse Reservation Approach Illustration ACB D Information Packets ACB D Route Discovery Route Chosen by B Reservation Packet Assume: Flow Requested from A->B Route, Wavelength Reservation

10. LIDS MIT Simulation Description Results shown as Blocking Probability vs. Traffic Intensity –Uniform, Poisson flow traffic per node Fixed WAN topology Parameters: –F = Number of fibers/link –L = Number of channels/link –K = Number of routes considered for routing decisions –U = Update interval (seconds) –  = Average service rate for flows (flows/second) – = Average arrival rate of flows (flows/second) –  = Traffic intensity. Equal to /  not utilization factor

11. LIDS MIT Simulation Topology

12. LIDS MIT Latency-free Control Network Results (1sec flows) RR: F=1, L=16, K=10TG: F=1, L=16, K=10

13. LIDS MIT Control Network With Latency Results (1sec flows) TG, RR: U=0.1, F=1, L=16, K=10

14. LIDS MIT Interesting Phenomenon Why is TG performance better than RR? –1 sec flows and large rho => small inter-arrival times Smaller than round trip time –Thus, with high probability, successive flows will see same state (at least locally) –Increases chance of collision Effect of distribution (latency) Why is Rand better than FF? –This is exactly opposite of analytical papers’ claim –Combination of reasons Nodes have imperfect information FF makes them compete for same wavelengths (false advertisement) –Not seen in analysis because distribution was ignored

15. LIDS MIT Scheduled OFS in ONRAMP Inaccurate information hurts performance –In this case: Simple speed of light –Biggest problem: Core network resources wasted Our proposal: Use of timing information to schedule flows –Deliver network information on time to make decisions –Exchange flow-based information –Maximize utilization of core network –Possible small delay for user Issues –Can timing be implemented cheaply, scaled? –Can schedules be implemented? –Must make use of current/future optical devices –Low cost ONRAMP OFS –Demonstration of scheduled OFS in access-area network –One example of an implementation

16. LIDS MIT Fixed Xponder Tunable Xponder Access Node #2 OXC Router GE IPFLOW IP Control Xmitter (X) Fixed Xponder Tunable Xponder Access Node #1 Router OXC GE IPFLOW IP Control Intermediate Node OXC Router Receiver (R ) Fixed Xponder Tunable Xponder Access Node #2 OXC GE IPFLOW IP Control ) Fixed Xponder Tunable Xponder Access Node #1 Router OXC GE IPFLOW IP Control Intermediate Node OXC Router X-  R-  OXC Sched Scheduling in ONRAMP

17. LIDS MIT Uses timeslotting and schedules for lightpaths X => i busy on output of node i at corresponding slot OXC Schedule Slot 1…..Slot 2Slot 3     XX X X ONRAMP Connection Setup

18. LIDS MIT -Overheads includes all timing uncertainty -Efficiency of any scheduled algorithm related to timing uncertainty, and switching/electronic overheads -Rough efficiency = Flow duration / Flow duration + Overhead Slot 1 Overhead - Dependent on timing uncertainty TIME Scheduling OH Cannot go in next timeslot Scheduling OH Can go in next timeslot Slot 3Slot 2 Algorithm Timeline

19. LIDS MIT Utilizing Link Capacity Sending GigE over transparent optical channel –Clock rate 1.244 Ghz –Rate 8/10 coding results in raw bit rate of 995.2 Mb/s Payload capacity for UDP –Send MTU-sized packets 9000 bytes Avoid fragmentation –Headers Ethernet (26 bytes) + IP (20 bytes) + UDP (8 bytes) = 54 bytes Result: 8946 bytes of payload/packet –Link payload limit 989.2288 Mb/s Rate-limited UDP –Input: desired rate –Timed sends of UDP packets achieve desired rates –Demonstrates transparency of OFS channel

20. LIDS MIT Experimental Setup OFS implemented in lab One second timeslots –Timing overhead negligible Routing/wavelength selection –All available wavelengths (currently 14) –Both directions around ring Gigabit Ethernet link layer –Flows achieve theoretical maximum link rate ~989 Mb/s Rate limited UDP –Unidirectional flows –No packet loss (100s of flows) –Variable rate –Demonstrates transparent use of optical connection

21. LIDS MIT OFS Performance

22. LIDS MIT Current Performance Limitations

23. LIDS MIT Current overhead is 0.10 seconds –Efficiency for one-second flows is therefore 90% –Analysis of overhead reveals possible overhead of Gigabit Ethernet frame sync Still under investigation –Switching overhead and timing uncertainty negligible –I.e. scheduling viable, efficient Current Performance Limitations(cont.) Flow Request time Begin Slot Scheduling Command GBE Sync? ReceiverLaser Switching Algorithm Overhead Timeline Flow begins………… 10ms150ms100ms