Predictable Performance Optimization for Wireless Networks

1. Predictable Performance Optimization for Wireless Networks Lili Qiu University of Texas at Austin lili@cs.utexas.edu Joint work with Yi Li, Yin Zhang, Ratul Mahajan, and Eric Rozner ACM SIGCOMM 2008 August 21, 2008

2. Motivation • Wireless networks are becoming ubiquitous • Managing wireless networks is hard • Our goal: develop systematic techniques to optimize the performance of wireless networks Wireline Wireless

3. Unpredictability of wireless networks 50% 100% bad-good D R S 100% 50% good-bad D S S R S Need predictable wireless performance optimization.

4. Model-driven optimization framework Network model Network measurement Constraints Given network Optimization Optimizedflow rates Traffic demands Routing Performance objectives: - Maximize fairness, total throughput, …

5. Existing models are insufficient • Models of asymptotic performance bounds • Cannot model any specific networks [GP00,LB+01,GT01,GV02] • Conflict graph based model • Assume perfect scheduling and over-estimate 802.11 performance [JPPQ03] • 802.11 DCF models • Restricted topologies or traffic demands [Bianchi00,KA+05,GLC06,GSK05 QZWH+07,KDG07] • They aim to estimate performance and cannot be easily incorporated into optimization procedure Need a better 802.11 network model for optimization.

6. Our network model • Provide a compact characterization of feasible solution space to facilitate optimization • Simple yet flexible and accurate • Handle asymmetric link loss rate • Handle asymmetric interference • Handle hidden terminals • Handle heterogeneous, multihop traffic demands Throughput constraints Loss rate constraints Sending rate constraints Network model Network measurement

7. Throughput constraints • Divide time into variable-length slot (VLS) • 3 types of slots: idle slot, transmission slot, deferral slot Success probability Probability of starting tx in a slot Expected payload transmission time Expected duration of a variable-length slot

8. Loss rate constraints • Inherent and collision loss are independent • Inherent loss • Based on one-sender broadcast measurement • 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

9. Sending rate feasibility constraints • 802.11 unicast • Random backoff interval uniformly chosen [0,CW] • CW doubles after a failed transmission until CWmax, and restores to CWmin after a successful transmission or when max retry count is reached • CW(pi): the expected contention window size under packet loss rate pi [Bianchi00] • Sending rate feasibility constraints SIFS ACKTransmission DIFS Random Backoff Data Transmission

10. Extensions to the basic model • RTS/CTS • Add RTS and CTS delay to VLS duration • Add RTS and CTS related loss to loss rate constraints • Multihop 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

11. Model-driven optimization framework Network model Network measurement Constraints Given network Optimization Optimizedflow rates Traffic demands Routing Performance objectives: - Maximize fairness, total throughput, …

12. Flow throughput feasibility testing • Test if given flow throughput are achievable • Challenge: strong interdependency • Our approach: iterative procedure Input: throughput Output:feasible/infeasible Initializeτ= 0 and p = pinherent Estimate τ from throughput and p no yes Converged? Check feasibility constraints Estimate p from throughput andτ Estimate throughput from p andτ

13. Fair rate allocation Initialization: add all demands to unsatSet Scale up all demands in unsatSet until some demand is saturated or scale1 yes if (scale 1) no Move saturated demands from unsatSet to X yes if (unsatSet≠) no Output X

14. Total throughput maximization • Formulate a non-linear optimization problem (NLP) • Solve NLP using iterative linear programming Maximize total throughput Link load is bounded bythroughput constraints Sending rate is feasible E2e throughput is bounded by demand

15. Evaluation methodology • Testbed experiment • Capture real-world complexities • 19 mesh nodes at UTCS building; up to 7 hops • Qualnet simulation • Controlled environment for a broad range of evaluation • Rate optimization schemes • No optimization • Conflict graph (CG) model: assume perfect scheduling • Our scheme • Traffic • TCP and UDP; saturated and random demands • Routing • Use hop count, ETX, MIC, and CG-based routing

16. Model validation: UDP traffic y=x y=0.8x 1) Most estimated rates are achievable within 20%.2) Rates scaled up by just 10% become unachievable.

17. Model validation: TCP traffic y=x y=0.8x Our model is accurate for TCP traffic.

18. Model validation: conflict graph model TCP UDP y=x y=x y=0.8x y=0.8x CG model significantly over-estimates sending rates.

19. Maximizing fairness UDP TCP Fairness index is close to 1 under our scheme, while it degrades quickly in other schemes.

20. Maximizing total throughput TCP UDP Our scheme significantly increases total throughput.

21. Impact on different routing schemes UDP TCP Our scheme helps all routing schemes considered.

22. Conclusions • Main contributions • Predictable wireless performance optimization • A simple yet accurate wireless network model • Effective model-driven optimization algorithms • Demonstrate their effectiveness through testbed experiments and simulation • Future work • Handle dynamic traffic and topologies • Use passive measurement to seed our model

23. Thank you!

24. TCP Pathologies under no rate control D1 S1 R D2 S2 TCP cannot set the rate that maximizes throughput.

25. Sensitivity of wireless network throughput to bottleneck location (I) good bad R D S good bad R D S S S sim testbed Performance degrade severely without rate limiting.

27. How to determine safe sending rates under wireless interference?

28. Throughput & VLS duration constraints • Divide time into variable length slots • Idle slot, transmission slot, deferral slot • Throughput constraint: • VLS duration constraint • EP(i): expected payload transmission time at link i • : probability of starting a transmission in a slot • : loss rate of link i • µ(i): expected VLS duration

29. Flow throughput feasibility testing Goal: if a given set of link throughput is achievable Capture interdependence • τdepends on link throughput and loss rate • Loss rate depends on link active probability • A link active probability depends on active probabilities of other links Initialize τ=0 and p = 0 Compute throughput No Check feasibility constraints Converged? Yes Estimate τ from throughput and p Estimate p from throughput andτ

30. Related Work • Interference modeling • Asymptotic performance bounds • Conflict graph based model • 802.11 DCF models • Simple but restrictive • All nodes are within communication range of each other • Restricted traffic demands • General but expensive • Both aim to predict performance and cannot facilitate optimization • Rate control and scheduling • Joint optimization of rate control and scheduling • IFRC: fair rate control for sensor networks and specific to tree topology and workload • Routing • Least cost path model [HopCount,ETX,WCETT,MIC]

31. Motivation (Cont.) • Vision: Bring wireless network management in par with wireline network management • This work provides answers to basic management questions: • What traffic demands can be supported in a network? • What is the impact of routing news and addition of new flows? • What is safe sending rates for a given set of flows?

32. Throughput constraints • EP(i): expected payload transmission time at link i • : probability of starting a transmission in a slot • : loss rate of link i • Variable length slots: • Idle slot • Transmission slot • Deferral slot

33. Lessons learned • Rate limiting is necessary • Proper rate limiting has to take into account of interference • Q: How to systematically estimate the safe sending rates that a network can support?

34. Throughput constraints Probability of starting tx in a slot Success probability Expected payload transmission time • Expected variable slot duration • Idle slot duration • Transmission slot duration • Deferral slot duration