1. Adaptive Inverse Multiplexing for Wide-Area Wireless Networks Alex C. Snoeren MIT Laboratory for Computer Science IEEE Globecom ’99 Rio de Janeiro, December 5, 1999

2. context Goal: Provide speech and graphical interfaces to wireless devices over wide- area networks Challenge: Construct a well-behaved high bandwidth channel out of low bandwidth shared access technologies

3. inverse multiplexing Idea: simulate a “large” logical channel out of some number (called a bundle) of “smaller” ones Inverse Multiplexor High Bandwidth Link Low Bandwidth Links High Bandwidth Link Inverse Multiplexor

4. goals High link utilization and low fragmentation 7Low bandwidth wireless links Tight reordering constraints 7TCP doesn’t handle reordered packets well Adaptive scheduling 7Throughput of shared wireless links is unstable over many time scales

5. contributions Standard Inverse Multiplexing 7Commonly used in ISDN, fractional T1/T3, ATM 7Private links with no contention 7Stable & similar channel characteristics Link Quality Balancing 4Adapts to varying capacity shared access channels 4Efficient bandwidth utilization 4TCP-friendly reordering bound

6. outline Scheduling techniques 7Link Quality Balancing with stable links Adaptation 7Measuring and reacting to channel variations Implementation results 7Constant Bit Rate (CBR) Traffic 7TCP flows

7. known scheduling methods Round Robin 7Does not assure optimum link usage 7Provides no bounds on delay, ordering Deficit Round Robin, Fair Queuing 7Provide efficient link usage, but... 7Require information about queue lengths –In CDPD, queues are often buried inside the networks, hence information is unavailable 7Don’t provide ordering guarantees

8. deficit round robin Inverse Multiplexor 1234 5678 Inverse Multiplexor 2165 3 7 48

9. fragmentation: an extreme Inverse Multiplexor 1234 5678 Inverse Multiplexor 1234 5678

10. weighting Inverse Multiplexor 1234 5678 Inverse Multiplexor 12 34 5678 x2 x1

11. link quality balancing Idea: Fragment traffic in proportion to individual link throughputs 7For each link, compute a relative MTU –For fastest link, use optimum MTU –On all other links, use a proportionately smaller one 7Fragment packets to fill MTU-sized buckets –Last fragment arrival times are the same on each link Guarantees no inter-round reordering; only possible reordering occurs in the same round –Requires no information on queue lengths –Work conserving; provides maximal link usage

12. our approach: balancing Inverse Multiplexor 1234 5678 Inverse Multiplexor 1 2 34 5678 x2 x1

13. measurement Problem: Individual link throughputs are highly variable over many time scales How do we measure current throughput? 7Absolute values are difficult and expensive to obtain –Without synthetic traffic, we are limited by the offered load; who knows if it actually is driving the links to full capacity 7Synthetic probes are problematic –Without priority queuing, introducing synthetic traffic may cause loss of actual traffic

14. link quality metric Solution: Don’t! Relative metrics suffice 7Simply maintain proportional estimates 7End-to-end bandwidth probing will do the rest But which metric? 7Packet arrival times –Theoretically ideal, but far too noisy to be used in reality 7Short-term throughput –Similarly difficult to measure 4Loss Rates –With bounded queues, loss rates are a rough indicator of appropriate throughput, and easy to measure

15. feedback loop Invariant: Always schedule traffic so that quality metric will be identical across links 7As a corollary, any perceived deviation at the receiver implies an improper estimate 7Use the receiver’s data to periodically update the Multiplexor’s scheduling proportions 7End-to-end bandwidth probing should cause the weakest link to fail first and/or more often Links are asymmetric; measure both ways

16. cbr traffic Time(secs) Throughput (bits/sec)

17. tcp traffic Time(secs) Throughput (bits/sec)

18. evaluation & future work LQB handles shared wireless links well 7Fragmentation is minimal 7Reordering is tightly bounded 7Adapts well to varying channel characteristics But we’d like to find a better metric 7Loss rates are delayed and very coarse grained 7Perhaps filtering functions exist for inter-packet arrival times