AdaptiveInverse Multiplexingfor Wide-Area Wireless Networks

Alex C. SnoerenMIT Laboratory for Computer Science

IEEE Globecom ’99Rio de Janeiro, December 5, 1999

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

inverse multiplexing

• Idea: simulate a “large” logical channel out of some number (called a bundle) of “smaller” ones

InverseMultiplexor

High Bandwidth Link

Low Bandwidth Links

High Bandwidth Link

InverseMultiplexor

goals

• High link utilization and low fragmentation Low bandwidth wireless links

• Tight reordering constraints TCP doesn’t handle reordered packets well

• Adaptive scheduling Throughput of shared wireless links is unstable

over many time scales

contributions

• Standard Inverse Multiplexing Commonly used in ISDN, fractional T1/T3, ATM Private links with no contention Stable & similar channel characteristics

• Link Quality Balancing Adapts to varying capacity shared access

channels Efficient bandwidth utilization TCP-friendly reordering bound

outline

• Scheduling techniques Link Quality Balancing with stable links

• Adaptation Measuring and reacting to channel variations

• Implementation results Constant Bit Rate (CBR) Traffic TCP flows

known scheduling methods

• Round Robin Does not assure optimum link usage Provides no bounds on delay, ordering

• Deficit Round Robin, Fair Queuing Provide efficient link usage, but... Require information about queue lengths

– In CDPD, queues are often buried inside the networks, hence information is unavailable

Don’t provide ordering guarantees

deficit round robin

InverseMultiplexor

12345678

InverseMultiplexor

2165374 8

fragmentation: an extreme

InverseMultiplexor

12345678

InverseMultiplexor

12345678

weighting

InverseMultiplexor

12345678

InverseMultiplexor

1 2345678

x2

x2

x1

x1

link quality balancing• Idea: Fragment traffic in proportion to

individual link throughputs For each link, compute a relative MTU

– For fastest link, use optimum MTU– On all other links, use a proportionately smaller one

Fragment 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

our approach: balancing

InverseMultiplexor

12345678

InverseMultiplexor

12345678

x2

x2

x1

x1

measurement• Problem: Individual link throughputs are

highly variable over many time scales• How do we measure current throughput?

Absolute 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 Synthetic probes are problematic

– Without priority queuing, introducing synthetic traffic may cause loss of actual traffic

link quality metric• Solution: Don’t! Relative metrics suffice

Simply maintain proportional estimates End-to-end bandwidth probing will do the rest

• But which metric? Packet arrival times

– Theoretically ideal, but far too noisy to be used in reality Short-term throughput

– Similarly difficult to measure Loss Rates

– With bounded queues, loss rates are a rough indicator of appropriate throughput, and easy to measure

feedback loop• Invariant: Always schedule traffic so that

quality metric will be identical across links As a corollary, any perceived deviation at the

receiver implies an improper estimate Use the receiver’s data to periodically update the

Multiplexor’s scheduling proportions End-to-end bandwidth probing should cause the

weakest link to fail first and/or more often

• Links are asymmetric; measure both ways

cbr traffic

0

2000

4000

6000

8000

10000

12000

14000

0 100 200 300 400 500

ChannelsLogical

Modem 1Actual 1

Modem 2Actual 2

Time(secs)

Thro

ughp

ut (b

its/s

ec)

tcp traffic

0

5000

10000

15000

20000

25000

30000

0 50 100 150 200 250 300 350 400

ChannelsLogical

Modem 1Actual 1

Modem 2Actual 2

Time(secs)

Thro

ughp

ut (b

its/s

ec)

evaluation & future work

• LQB handles shared wireless links well Fragmentation is minimal Reordering is tightly bounded Adapts well to varying channel characteristics

• But we’d like to find a better metric Loss rates are delayed and very coarse grained Perhaps filtering functions exist for inter-packet

arrival times