1. Metrics for Characterizing QoS in Multi-Channel, Multi-Radio,Multi-Rate Wireless Meshes
Ranjan Pal

2. Introduction

3. Basics of Wireless Mesh Networks
Nodes may be fixed or mobile
Interconnected with wireless links
User devices act as both clients and routers
Targeted towards civilian applications
Example: MIT Roofnet

4. Wired Internet BackboneAP AP WR WR WR
User Cluster
User Cluster

5. Specialized Features of Wireless Mesh Networks
Nodes equipped with multiple radios
Can transmit on orthogonal multiple channels
Quality of channels vary over time (SNR)
- Shadowing & Fading
Radios adjust to transmit at multiple rates

6. Network Model

7. dda
cbd D B
daf A F C E
Flow Split
dcb

8. Problem Definitions

9. Problem 1
Given a demand vector D, find the expected multicommodity flow achieved
i.e
Compute l e <+ such that expected multicommodity flow is l¢D

10. Compute l e <+ such that expected multicommodity flow is l¢D
Problem 2
Given a demand vector D and cost limit CL, find the expected multicommodity flow achieved
i.e
Compute l e <+ such that expected multicommodity flow is l¢D

11. Problem 3
For a given multicommodity flow demand D, rank the links of the network in order of importance in achieving the demand
i.e
The link whose variation in capacity by a small amount, affects the achievability of D the maximum, is ranked 1 and so on…

12. Motivation

13. Why QoS metrics?? flows in MR2-MC WMN’s is an open issue
Evaluating QoS(analytically) of multicommodity
flows in MR2-MC WMN’s is an open issue
[Kodialam et.al ’05], [Mishra et.al ’05]
Judging suitability of applications for a n/w
Budgeting analysis for a service company
Performance tuning for bettering QoS support

14. My Contributions

15. Developed a polynomial-time algorithm to
evaluate l for a non cost constrained n/w
evaluate l for a cost constrained n/w
Used Fussell-Vesely* and Birnbaum* methods
to characterize link importances and have
shown thru simulations that using FV is better

16. Outline of Solution Methodologies

17. Remove “Curse of Dimensionality” Prove algorithms in ‘P’
Apply
AcQoS(D)
Apply
AcQoS(D,CL)
Prove algorithms in ‘P’
Use FV and Birnbaum methods to find link importances

18. Some facts about the algorithms
Stochastic flow based (obey Ford-Fulkerson)
Requires the values of pi’ for each link
Basic idea is to find a set of minimal
capacity vectors under which the demand is
satisfied and the probability that any system
capacity vector is greater than the minimals
Algorithms shown to be polynomial (in P) using
combinatorial analysis

19. Link Importance Measures

20. Birnbaum Measures
Gives the link that most significantly affects the achievability of a given D
Two types – SAD and MAD
SAD (Average Sum of Absolute Deviations)
- Considers the possible state levels of a link
MAD (Mean/Expected Absolute Deviation)
- Considers possible state levels & prob. of
a link being in that state

21. Fussell-Vesely Measures
Relative measures that account for the average change in achieved QoS when link states “negatively” contribute to achieved QoS
Two Types – GFVM and MFVM
GFVM (General Fussell-Vesely Measure)
- Considers the possible state levels of a link
MFVM (Mean Fussell-Vesely Measure)
- Considers possible state levels & prob. of
a link being in that state

22. Simulation Results

23. Topology

24. Performance With Three Channels

25. 6 5 4 2 1 3

26. General Birnbaum and Fussell-Vesely

27. Mean Birnbaum and Fussell-Vesely

28. Observations on Link Importances
Variances are very low (very robust)
Ranks for general Birnbaum and FV differ
Ranks for Mean Birnbaum and FV are same
Mean measures more consistent and robust
Prefer Mean FV as its variance is the lowest

29. Developed ways to characterize QoS in multi-radio
Conclusions
Developed ways to characterize QoS in multi-radio
multi-channel, multi-rate wireless meshes
Used Birnbaum and Fussell-Vesely measures to
find link importances w.r.t to a flow demand
Future Work
Compute bounds of achieved QoS in MR2-MC
wireless mesh networks
Tackle statistical link dependencies

30. Questions ???