1. Identifying High Throughput Paths in 802.11 Mesh Networks : A Model-based Approach Theodoros Salonidis (Thomson) Michele Garetto (University of Torino) Amit Saha (Tropos) Edward Knightly (Rice University)

2. 2 “Hot-spot” wireless networks Internet – Cellular-like high-speed wireless data networks – Use 802.11 for user access and wired Internet for backbone 802.11

3. 3 Internet – Aim: Low-cost / high-speed wireless access – Use 802.11 for both user access and backbone – Scale: Neighborhood to city-wide, US/Europe/Asia 802.11 wireless links 802.11 Multi-hop wireless “mesh” networks

4. 4 Internet – Fact: 802.11 CSMA MAC protocol is used for both user access and backbone – Problem: Severe throughput imbalances and starvation 802.11 wireless links 802.11

5. 5 Our contributions Analytical model – Predict per-flow throughput in arbitrary topologies employing 802.11 MAC protocol. – Explain the origin of starvation in CSMA-based multi-hop wireless networks Solution – High-throughput mesh routing

6. 6 Roadmap Overview of multi-hop 802.11 model Technique for available bandwidth computation Comparison of existing loss-based routing metrics with new routing metric that directly computes high- throughput paths

7. 7 The “channel view” of a node: … … Node’s transmission is successful idle slot Node’s transmission collides t channel busy due to activity of other nodes Modeled as a renewal-reward process Throughput (pkt/s) = P [event Ts occurs] Average duration of an event (s) Analytical model

8. 8 … … t Define: = probability that the node sends a packet = conditional collision probability = conditional busy channel probability Success IdleCollision Busy channel Event probabilities Analytical model

9. 9 Throughput formula (saturated link) General throughput formula Input rate Packet loss probability Fraction of busy time Analytical model

10. 10 Available bandwidth estimation Inter-flow step at each node – Use measured values of f B and p on adjacent links – Compute additional input rate needed to saturate each link Intra-flow step – Clique-based formulation to capture bandwidth sharing among links within the path 1 2 4 3 50 pkt/sec 100 pkt/sec 25 pkt/sec 20 pkt/sec Path BW = min () = 10 pkts/sec,

11. 11 Model validation Topology – Chaska.net – 196 APs / 14 GWs Simulation setup – 802.11b, single channel – Download/Upload traffic – Load gateways: 2Mbps

12. 12 Model validation Chaska download scenarioChaska upload scenario Good match between model available BW and achieved throughput

13. 13 Loss-based (LB) routing metrics ETX (MIT) ETT (Microsoft) IRU (UIUC) LB metrics are load-sensitive and depend only on packet loss probability p

14. 14 Single link performance Large deviation for high busy time! LB metrics Tput – Linear on p Model Tput – Non-linear on p – Linear on f B

15. 15 LB metrics can pick suboptimal paths A G1 B C G2 ? Load C->G1 Achievable G1 Achievable unused G2 LB metrics Tput loss

16. 16 AVAIL vs. LB metrics AVAIL: model-based routing metric Aim – Compare AVAIL with LB metrics (ETX, ETT and IRU) Routing protocol – LQSR: link state, source routing – Each node periodically broadcasts measured f B, p – Each node uses modified Dijkstra to compute AVAIL Simulation setup – 100 initial UDP upload flows (pick min-hop gateways) – One incoming UDP flow (50 random samples) Rate limiting – For all metrics, incoming flow rate-limited based on model

17. 17 Chaska comparison Max gateway load = 2Mbps LB metrics = AVAIL Tput on average

18. 18 Manhattan topology Topology – 14x14 / 4-neighbor – 196 APs / 10 GWs Simulation setup – 802.11b, single channel – Upload traffic – Load gateways: (30%- 100%) x maxload

19. 19 Manhattan comparison Max gateway load = 3Mbps AVAIL metric achieves x1.5 gain on average

20. 20 Manhattan comparison Max gateway load: 4Mbps AVAIL metric achieves x2.4 gain on average LB metrics starve!

21. 21 Analytical model accurately predicts available bandwidth Busy time crucial for high throughput routing LB metrics can pick suboptimal/starving paths Topologies that allow spatial reuse and longer paths yield highest gains Conclusions