1. Measurement-based models enable predictable wireless behavior Ratul Mahajan Microsoft Research Collaborators: Yi Li, Lili Qiu, Charles Reis, Maya Rodrig, Eric Rozner, David Wetherall, John Zahorjan, Yin Zhang,

2. Wireless Mesh Networks Can enable ubiquitous and cheap broadband access Witnessing significant research and deployment But early performance reports are disappointing ratul | kaist | june '092

3. Wireless performance is unpredictable Even basic questions are hard to answer Arguably the most frustrating aspect of wireless Mysteriously inconsistent performance Makes it almost impossible to plan and manage ratul | kaist | june '093 WirelessWired How much traffic can be supported? What if a node fails? Optimize for a given objective

4. An example of performance weirdness ratul | kaist | june '094 SourceRelaySink GoodBad SourceRelaySink Bad Good UDP throughput (Kbps) Loss rate on the bad link good-bad bad-good Testbed Loss rate on the bad link UDP throughput (Kbps) good-bad bad-good 2x Simulation bad-good good-bad Source rate (Kbps) UDP throughput (Kbps)

5. Predictable performance optimization Given a (multi-hop) wireless network: 1.Can its performance for a given traffic pattern be predicted? 2.Can it be systematically optimized per a desired objective such as fairness or throughput? Yes, and Yes, at least in the context of WiFi ratul | kaist | june '095

6. Predictability needs models To predict if specific nodes interfere and what happens when a set of nodes send together Without models, we must measure each possibility separately ratul | kaist | june '096 S1S2 R1R2 Success of failure?

7. Traditional wireless models Typical assumptions Transmission range is circular Interference range is twice the transmission range Then predict the result of various sending configurations ratul | kaist | june '097 S1S2

8. Shortcomings of traditional models RF propagation is a very complex, esp. indoors The assumptions almost always do not hold in practice Great for asymptotic behavior characterization E.g., expected max throughput as a function of number of nodes Pretty much useless for predicting behavior in a specific wireless network ratul | kaist | june '098

9. A move towards experimentation Instead of relying on models, test performance of new protocols on testbeds Hard to say if results generalize The lack of predictability remains Unless all possible configurations are tested ratul | kaist | june '099

10. Measurement-based models Can offer the best of traditional modeling and experimentation worlds ratul | kaist | june '0910 Capture the “RF profile” of the network by measuring simple configurations Use modeling to predict the behavior under more complex configurations

11. Lessons learned Simple measurements on off-the-shelf hardware can provide usable RF profile [SIGCOMM2006] It is possible to model interference, MAC, and traffic in a way that balances fidelity and tractability [MobiCom2007] Holistically controlling source rates is key to achieving desired outcomes [HotNets2007, SIGCOMM2008] ratul | kaist | june '0911

12. Measurement-based modeling and optimization ratul | kaist | june '0912 Measure the RF profile of the network Constraints on sending rate and loss rate of each link Find compliant source rates that meet the objective

13. Measurements One or two nodes broadcast at a time – O(n 2 ) measurements Other nodes listen and log received packets Yields information on loss and carrier sense probabilities ratul | kaist | june '0913 Measure the RF profile of the network Constraints on sending rate and loss rate of each link Find compliant source rates that meet the objective S1 S2 R

14. Modeling ratul | kaist | june '0914 Constraints on sending rate and loss rate of each link Find compliant source rates that meet the objective Makes no assumptions about topology, traffic, or MAC Lightweight yet realistic O(# active links) constraints capture the feasible operating region 1.Throughput constraints 2.Loss rate constraints 3.Sending rate constraints Measure the RF profile of the network

15. 15 Throughput constraints Divide time into variable-length slot (VLS) 3 types of slots: idle, transmission, deferral Expected payload transmission time Probability of starting transmission in a slot Success probability Expected slot duration ratul | kaist | june '09

16. Loss rate constraints Inherent and collision loss are independent Inherent loss is directly measured Collision loss Synchronous loss Two senders can carrier sense each other Occur when two transmissions start at the same time Asynchronous loss At least one sender cannot carrier sense the other Occur when two transmissions overlap 16ratul | kaist | june '09

17. Sending rate feasibility constraints 17 802.11 unicast – Random backoff interval uniformly chosen [0,CW] – CW doubles after a failed transmission until CW max, and restores to CW min after a successful transmission DIFS Data Transmission Random Backoff ACK Transmission SIFS Expected contention window size under loss rate p i ratul | kaist | june '09

18. Extensions to the basic model 18 RTS/CTS – Add RTS and CTS delay to VLS duration – Add RTS and CTS related loss to loss rate constraints Multi-hop traffic demands – Link load   routing matrix   e2e demand – Routing matrix gives the fraction of each e2e demand that traverses each link TCP traffic – Update the routing matrix: where  reflects the size & frequency of TCP ACKs ratul | kaist | june '09

19. Optimization ratul | kaist | june '0919 Constraints on sending rate and loss rate of each link Find compliant source rates that meet the objective Inputs: Traffic matrix Routing matrix Optimization objective – Total throughput, fairness, … Output: Per-flow source rate Predictable: output rates are actually achievable Measure the RF profile of the network

20. 20 Flow throughput feasibility testing Building block for optimization Uses an iterative procedure Initialize τ= 0 and p = p inherent Check feasibility constraints Converged? no yes Estimate τ from throughput and p Estimate p from throughput andτ Output: feasible/ infeasible Input: throughput ratul | kaist | june '09

21. Fair rate allocation Initialization: add all demands to unsatSet Scale up all demands in unsatSet until some demand is saturated or scale  1 Output X Move saturated demands from unsatSet to X If unsatSet ≠  if ( scale  1) yes no yes no ratul | kaist | june '0921

22. Total throughput maximization 22 Formulate a non-linear optimization problem (NLP) Solve NLP using iterative linear programming Sending rate is feasible E2e throughput is bounded by demand Link load is bounded by throughput constraints Maximize total txput ratul | kaist | june '09

23. The network is capable of achieving its model-predicted throughput ratul | kaist | june '0923 UDPTCP Results for a 19-node testbed

24. The network cannot achieve higher than model-predicted throughput ratul | kaist | june '0924 UDPTCP

25. Measurement-based models enable fair throughput distribution (predictably) ratul | kaist | june '0925 UDPTCP

26. Measurement-based models boost network throughput (predictably) ratul | kaist | june '0926 UDP TCP

27. Future work: Making it real Online measurement of RF profile Decentralized computation of source rates Joint optimization of routing and source rates ratul | kaist | june '0927

28. Conclusions Wireless behavior is unpredictable Complex RF propagation Interactions between MAC, traffic, and interference Measurement-based models: a new approach to obtain predictable behavior Measure the RF profile and model the rest Promising results in our experiments on real test beds Enables predictable optimization ratul | kaist | june '0928