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Metrics for Characterizing QoS in Multi-Channel, Multi-Radio,Multi-Rate Wireless Meshes

Metrics for Characterizing QoS in Multi-Channel, Multi-Radio,Multi-Rate Wireless Meshes. Ranjan Pal. Introduction. Basics of Wireless Mesh Networks Nodes may be fixed or mobile Interconnected with wireless links User devices act as both clients and routers

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Metrics for Characterizing QoS in Multi-Channel, Multi-Radio,Multi-Rate Wireless Meshes

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  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 Backbone AP AP WR WR WR User Cluster User Cluster

  5. Specialized Features of Wireless Mesh Networks • Nodesequipped 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 Computele<+such that expected multicommodity flow is l¢D

  10. Problem 2 Given a demand vector D and cost limit CL, find the expected multicommodity flow achieved i.e Compute le<+ 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?? • 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 • Developed a polynomial-time algorithm to • 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” Apply AcQoS(D) Apply AcQoS(D,CL) Prove algorithms in ‘P’ UseFV 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. 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 ???

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