1. Distributed Fair Scheduling in a Wireless LAN
Nitin Vaidya, Texas A&M University
Victor Bahl, Microsoft Research
Seema Gupta, now with Cisco
MobiCom 2000

2. Distributed Scheduling : What & Why

3. Wireless medium is a broadcast medium
Medium Access Control
Wireless medium is a broadcast medium
Transmissions by multiple nodes can interfere
Need medium access control (MAC)
Many proposals
Centralized
Distributed

4. Centralized Protocols
Base station coordinates access to the wireless channel
Node 1
Node 2
Base
Station
Node 3
Node n

5. Distributed Protocols
All nodes have identical responsibilities
Node 1
Node 2
Wireless LAN
Node 3
Node n

6. Disadvantages of Centralized Approach
If a node cannot talk to the base station, it cannot transmit to any other nodes
Base station needs to keep track of state of other nodes
Hard to use failure-prone nodes as coordinators in centralized protocols

7. Fairness

8. Fairness
Packets to be transmitted belong to several flows
Each flow is assigned a weight
Bandwidth assigned to each backlogged flow is proportional to its weight

9. Fairness Three flows with weights 2, 1, 1 Backlogged flows: Allocated
bandwidth
Backlogged
flows:

10. Many centralized fair queueing protocols exist
WFQ, WF2Q, SCFQ, SFQ, …
Scheduler needs to know state of all flows
Flow 1
Flow 2
Flow n
Output link

11. Distributed Fair Scheduling

12. Fully distributed fair scheduling protocol
Our Objectives
Fully distributed fair scheduling protocol
All nodes have identical responsibilities
Nodes do not need to be aware of each other’s state
Maintain compatibility / resemblance with an existing standard
specifically, IEEE Distributed Coordination Function

13. IEEE 802.11 Distributed Coordination Function (DCF)
Proposed Approach
Combination of
IEEE Distributed Coordination Function (DCF)
Carrier sense / collision avoidance
A centralized fair queueing protocol

14. Basic Carrier Sense Approach
A node wishing to transmit waits until channel is sensed as idle, and then transmits
If two nodes are waiting to transmit, they will collide
Collision avoidance mechanism needed to avoid this

15. IEEE 802.11 Distributed Coordination Function
Collision avoidance mechanism: When transmitting a packet, choose a backoff interval in the range [0,cw]
cw is contention window
Count down the backoff interval when medium is idle
When backoff interval reaches 0, transmit cw

16. 802.11 DCF Example B1 = 25 B2 = 20 B1 = 5 B2 = 15 data wait data wait
B1 and B2 are backoff intervals
at nodes 1 and 2
cw = 31

17. Self-Clocked Fair Queueing (SCFQ) [Golestani]
A centralized fair scheduling protocol
But more amenable for a distributed implementation than many others
The steps involved in deriving proposed distributed protocol starting from SCFQ are given in the paper
virtual time, start/finish tags
implementation does not need virtual time or tags

18. Distributed Fair Scheduling (DFS)
Node with smallest “length/weight” should transmit first
Caveat: This is a somewhat imprecise statement. DFS (implicitly) compares so-called virtual finish tags, which are a function of length/weight
See paper for details on the finish tags
Backoff intervals used as a way to distributedly determine whose “length/weight” is smaller

19. Distributed Fair Scheduling (DFS)
Choose backoff interval
= packet length / weight
packet length = 5
weight = 1/3
backoff interval = 5 / (1/3) = 15 slots

20. Distributed Fair Scheduling (DFS)
Collision !
data
wait
data
wait
Packet length = 15
Weight of node 1 = 1 ====> B1 = 15 / 1 = 15
Weight of node 2 = 3 ====> B2 = 15 / 3 = 5

21. Collisions occur when two nodes count down to 0 simultaneously
In centralized fair queueing, ties can be broken without causing “collisions”
To reduce the possibility of collisions:
Backoff interval =
Scaling_Factor * length / weight * random number with mean 1

22. Backoff Interval
Initial formula: Length / weight = 15 / 1 = 15
Scaling_factor * length / weight * random number
= * / * [0.9,1.1]
= [54,66]

23. Backoff Interval
802.11
Proposed DFS

24. Collisions Resolution
Collision occurs when two nodes count down to 0 simultaneously
Counting to 0 implies that it is a given node’s “turn” to transmit
To reduce “priority” reversals, a small backoff interval is chosen after the first collision
Backoff interval increased exponentially on further collisions

25. Impact of Small Weights
Backoff interval:
Scaling_factor * length / weight * random number
Backoff intervals can become large when weights are small
Large backoff intervals may degrade performance (time wasted in counting down)

26. Impact of Small Weights
Recall: Backoff intervals are being used to compare “length/weight”
Intuition: Any non-decreasing function of lenghth/weight may be used to obtain backoff intervals

27. Scaling_factor * length / weight * random number
Alternative Mappings
Chosen
backoff
interval
Linear mapping
SQRT
EXP
Scaling_factor * length / weight * random number

28. Alternative Mappings Advantage Disadvantage smaller backoff intervals
less time wasted in counting down when weights of all backlogged flows are small
Disadvantage
backoff intervals that are different on a linear scale may become identical on the compressed scale
possibility for greater number of collisions

29. Performance Evaluation
Using modified ns-2 simulator: 2 Mbps channel
Number of nodes = N
Number of flows = N/2
Odd-numbered nodes are destinations,
even-numbered nodes are sources
Unless otherwise specified:
flow weight = 1 / number of flows
backlogged flows with packet size 584 bytes (including UDP/IP headers)
Scaling_Factor = 0.02

30. Fairness measured as a function of
Fairness Index
Fairness measured as a function of
(throughput T / weight f) for each flow f over an interval of time
Unless specified, the interval is 6 seconds

31. Throughput / Weight Variation Across Flows (with 16 Flows)
802.11
Flatter
curve
is fairer
DFS
Throughput /
Weight
Flow destination identifier

32. Throughput - Fairness Trade-Off
802.11
Aggregate
throughput
(all flows
combined)
Number of flows

33. Throughput - Fairness Trade-Off
index
802.11
Number of flows

34. Scaled
Fairness of can be improved by using larger backoff intervals
Is DFS fairer simply because it uses large backoff intervals ?
Scaled = which uses backoff
interval range comparable with DFS

35. Number of packets transmitted by a flow
Short-Term Fairness
Narrow
distribution
is fairer
DFS is
fairer
DFS
Frequency
Scaled
802.11
Number of packets transmitted by a flow
(over 0.04 second windows)

36. Fairness versus Sampling Interval Size (24 flows)
DFS
Scaled
802.11
Fairness
index
802.11
Interval Size

37. Alternative Mappings for Backoff Intervals
See additional data in the paper
EXP and SQRT improve throughput compared to LINEAR mapping when all backlogged flows have low weights
but not too impressively
If at least one backlogged flow has a high weight, not much benefit

38. Conclusions (supporting arguments for some conclusions not presented in the talk: please see the paper)
DFS improves fairness compared to and Scaled
Alternative mappings somewhat beneficial
No distributed fair scheduling protocol may accurately emulate work-conserving centralized protocols (unless clocks are synchronized)

39. Possible to handle multiple flows per node
Conclusions
Possible to augment DFS with other techniques to improve fairness in presence of transmission errors
see Seema Gupta’s M.S. thesis
No performance cost even if weight assigned to a flow is changed on a per-packet basis
Execution complexity of centralized protocols would increase
Possible to handle multiple flows per node

40. Other Potential Applications of DFS
Wired LANs
Wireless multi-hop networks
see our 1999 Microsoft Research technical report for some initial ideas

41. Issues for Further Work
DFS is only the first step towards practical fairness:
How to choose parameters such as Scaling_Factor ?
Failure to choose reasonable values can degrade throughput or short-term fairness
How to choose flow weights ?
Let upper layer specify dynamically, or
Static assignment based on static criteria
Ad hoc network-related issues

42. Thank you!

43. Thank you!

44. Impact of Packet Size Flow throughput Three flows 802.11
with different
packet sizes
802.11
Packet size (bytes)

45. Impact of Scaling Factor (six flows with weights 1/2,1/4,1/8,1/16,1/32,1/32)
DFS
Fairness
index
Scaling Factor

46. Impact of Scaling Factor (six flows with weights 1/2,1/4,1/8,1/16,1/32,1/32)
Aggregate
throughput
DFS
Scaling factor

47. Related Past Work
Centralized fair queueing on wired links [Bennett,Demers,Parekh]
Centralized fair queueing in wireless environments, taking location-dependent errors into account [Bharghavan,Ramanathan,Zhang]
Distributed Real-time scheduling [Sobrinho]
Distributed Priority-based scheduling

48. How to choose these adaptively ?
Backoff Interval
Scaling factor
Small number : May result in more collisions
Large number: Larger overhead
Random number range
Small range will cause more collisions between synchronized nodes
How to choose these adaptively ?
This paper punts the issue
But heuristic solutions are easy to define
Heuristics yet to be evaluated