Routing and Traffic Engineering in Multi-hop Wireless Networks: An optimization based approach Vinay Kolar Ph.D. Candidate SUNY, Binghamton Advisor: Dr. Nael Abu-Ghazaleh

2 Multi-hop Wireless Networks (MHWNs) Wireless nodes co-operate to forward traffic. Minimal infrastructure demands Extensive applications:  Mesh Networks, Vehicular Networks, Sensor Networks, Ubiquitous computing, … Figure 1 Figure 2

3 New challenges Vagaries of wireless channel  Complex interference patterns, sparse bandwidth Self-configuration  Mobility, energy-constrained Delivering packets across multiple wireless hops – The Routing Problem Figure 3 Figure 4 ABC

4 High-level motivation Theory Systems Formal MHWN modelsHeuristic solutions Complex problem domain + Idealistic assumptions Incomplete characterization of parameter space GOAL Practical, theoretically grounded models

5 High-level motivation Deriving protocol behavior from mathematical models has been proven to be effective in wired networks (e.g. FAST-TCP) However, main challenge in MHWNs  Modeling interference At wireless channel – Physical layer At neighborhood – MAC layer Across end-hosts – Routing layer Substantially different from the wired network models

6 Goals of my work Develop practical interference-aware models for MHWNs  Efficient routes  Accounting for realistic effects (CSMA scheduling)  Low-complexity Applications:  Develop near-optimal distributed protocols  Performance analysis of existing protocols  Insightful for the analysis of MHWNs  QoS, Resource allocation, Provisioning,…  Some are long term…

7 Background Physical layer  How does the signal propagate? Signal fading with distance MAC layer  How to send packet to neighbors? Routing layer  How to route across multiple hops?

8 Background – MAC Protocol Carrier Sense Multiple Access/ Collision Avoidance (CSMA/CA) Primary Issues:  Hidden-terminals Packet collision Interference effect  Exposed terminals Conservative transmissions  Under-utilization of channel capacity A B C A B CD

9 Background – IEEE Prominent MAC protocol standard Handshake – Basic, RTS/CTS Rules  Wait for certain time before sending  Exponential backoff on packet collision In Summary:  All CSMA issues not prevented  Subtle changes in node positions can lead to drastically different results [Garetto05]

10 Background – Routing layer Transmit packet from source to destination  Possibly across multiple hops First generation routing protocols  Choose path with shortest number of hops  But … Shortest number of hops  longer hop length  Higher probability for errors

11 Background – Second generation Routing Link quality aware routing  Transmit packets across stronger links  Lesser packet collisions  Efficient use of channel  Greater performance Is this enough?  Do they estimate other parameters? Channel capacity, greedy forwarding

12 Overview Motivation and Related work Contributions  Interference Aware Routing Decomposition based Routing model  Scheduling Effects Interaction representations and Contention fairness Conclusions Future Work

13 Motivation Recall: MHWN model is …  Insightful for analyzing MHWNs  Applications of Traffic-engineering models  Development of near-optimal distributed protocols

14 Motivation – An example Routes may not be interference separated (even with link-quality aware protocols) Blue nodes suffer interference from 2 connections Can greedy approaches lead to optimal routes? Figure 10

15 Motivation – Practical Design Some layers are harder to modify than others  Physical (?), MAC (?) Approach: Reverse-engineering MAC and Physical  Capture the behavior Forward-engineering Higher Layer protocols  Optimize them

16 Related work – Routing models Interference separated routes  A network-flow based model [Jain03, Kodialam03] The focus is to calculate network capacity Shortcomings:  Optimal scheduler Scheduling effects due to IEEE  Split route  Interaction among multiple connections

17 Related work – Scheduling models Models to capture scheduling details [Garetto05]  Detailed stochastic models But…  Input is a set of active links  Cannot directly calculate routes Can we use it inside a routing model to evaluate candidate solutions?  Iterative in nature – Long run times

18 Overview Motivation and Related work Contributions  Interference Aware Routing  Scheduling Effects Conclusions Future Work

19 Contributions Interference-aware routing model  Maximize throughput, Minimize delay  Complexity An efficient decomposition based model  Interference at MAC/PHY layer Interaction graphs Scheduling-aware routing Accuracy  Contention fairness  Throughput under two link interaction Towards a unified framework for traffic-engineering…

20 Overview Motivation and Related work Contributions  Interference Aware Routing (IAR)  Scheduling Effects Conclusions Future Work

21 Interference Aware Routing (IAR) The problem:  Find interference separated routes in a given topology The approach: Model routing as Network-flow optimization problem Multi-commodity flow problem  ‘n’ sources and sinks

22 IAR model – Goals Interference separated routes  Maximize throughput, minimize delay for all connections Single path for each route  Model realistic single-path routing protocols No packet splitting, multi-path routing No path-inflation, connection coupling Single Linear objective

23 IAR – Shape of routes Figure 11

24 IAR – Results

25 From IAR to … Complexity  The model is Mixed Integer Linear Program An NP-hard problem Cannot analyze medium/large networks  Approximate polynomial time algorithm Ideal vs. CSMA Scheduling  Coarse estimates of busy time does not capture CSMA behavior under high loads Scheduling aware routing formulation

26 Overview Motivation and Related work Contributions  Interference Aware Routing (IAR) Decomposition based Routing model (d-IAR)  Scheduling Effects  Scheduling-aware routing model (SAR) Improving the accuracy  Contention fairness, throughput estimation Conclusions Future Work

27 Decomposition model The problem:  Approximate the NP-hard IAR routing to a polynomial time algorithm  Why decomposition? Enables routing in larger networks Effective distributed protocol development [Chiang07] Parallel implementations

28 Decomposition – Simulation study Performance comparable to IAR model Much better than the “best” DSR routes  Under smaller connections Orders of magnitude improvement in run time Figure 14

29 IAR  d-IAR  …? Complexity has been reduced But …  Ideal v/s CSMA Scheduling  The scheduling effects take a toll as the density of the traffic increases  Higher level abstractions like ‘Commitment Period’ not enough

30 Overview Motivation and Related work Contributions  Interference Aware Routing (IAR) Decomposition based Routing model (d-IAR)  Scheduling Effects  Scheduling-aware routing model (SAR) Improving the accuracy Conclusions Future Work

31 Scheduling Aware Routing (SAR) The problem:  A routing model that is aware of interference and scheduling effects The approach:  Run the d-IAR model  Capture detrimental scheduling interactions  Rate scheduling + exclude detrimental links  Re-Run d-IAR

32 Scheduling model Do we need a new scheduling model? Why cant existing accurate scheduling models be used? [Garetto06]  The scheduling model is evaluated for each candidate IAR route Iterative

33 Capturing scheduling - Interaction graphs Convert a network scenario to a “Conflict graph” [Vaidya02]  Each active link is a node  An edge indicates that these nodes can transmit concurrently Figure 16

34 Interaction graphs (IGs) Pairwise IGs – Insufficient to capture temporal interactions Concepts :  Compute “Maximal Independent Contention Set (MICS)” A problem of ‘Maximal independent set’ Red lines indicate hidden terminals Figure 17

35 Interaction graphs Figure 16

36 Finding scheduling effectiveness Construct the MICS for a given protocol and capture detrimental interactions IEEE with RTS/CTS mode  RTS timeouts and DATA packet collisions Find the probability of packet drops for each link Need to find out the probability that each MICS will occur  Detailed computation is rigorous Compute conflicting links and the overall link quality We refer to these link quality metrics as “Interaction Based Link Rating” (IBLR)

37 SAR – Results Figure 19

38 IAR, d-IAR, SAR - What next? Can we improve the scheduling model?  Low-complexity  Realistic physical models Important results that can be used in the routing model  Probability of MICS activation Unfairness and effect of minimum backoff window  Quantify the hidden-terminal effect Effect of backoff/unfairness

39 Overview Motivation and Related work Contributions  Interference Aware Routing (IAR)  Scheduling Effects Improving the accuracy  Contention fairness Conclusions Future Work

40 Scheduling – Contention Fairness The problem:  Even in the absence of Hidden Terminals, CSMA is far away from ideal scheduling Example: Flow in the middle (FIM)  Link B starves due to link A and/or link C  Link B gets only 2 % of the total throughput!!  Why? Figure 20 Figure 21

41 Scheduling – Contention fairness Example steps : Distribution of the “channel idle” times  Renewal-reward theory Expected rate of transition between MICS Get limiting probabilities of MICS  Continuous-time Markov Compute the throughput Figure 22 Time

42 Contention Fairness – Random Topology

43 Contention fairness - Results Can we dynamically alter backoff to avoid starvation?

44 Contention Fairness - Protocol Contention-aware Adaptive Backoff Communicate contention information Adapt backoff w.r.t. neighbors contention information

45 Overview Motivation and Related work Contributions  Interference Aware Routing (IAR)  Scheduling Effects Conclusions Future Work

46 Conclusions A routing model for capturing interference  MHWN operation as an optimization problem  Low-complexity Capturing scheduling effects  Low-complexity  Integrated Interference and Scheduling Aware routing Traffic-engineering under realistic schedulers  Improve the accuracy Interaction graphs Contention fairness Throughput computation for two-flows A design towards effective distributed solutions

47 Overview Motivation and Related work Contributions  Interference Aware Routing Decomposition based Routing model  Scheduling Effects Interaction representations and Contention fairness Conclusions Future Work

48 Future Work Long-term  Route-planning tool  Formally designing distributed protocols [Chiang07]. Short-term  Extending hidden-terminal behavior  Integrating Contention-fairness and Hidden terminals  Unsaturated traffic  Capturing the pipelining effect in routes

49 Thank you all Questions/Comments id:

50 References [Chiang07]  Chiang, M., Low, S. H., Calderbank, A. R., and Doyle, J. C. Layering as optimization decomposition: A mathematical theory of network architectures. In Proceedings of IEEE (2007). [Garetto05]  Garetto, M., Shi, J., and Knightly, E. W. Modeling media access in embedded twoflow topologies of multi-hop wireless networks. In MobiCom ’05 (2005). [Garetto06]  Garetto, M., Salonidis, T., and Knightly, E. W. Modeling per-flow throughput and capturing starvation in CSMA multi-hop wireless networks. IEEE INFOCOMM (2006). [Jain03]  Jain, K., Padhye, J., Padmanabhan, V. N., and Qiu, L. Impact of interference on multi-hop wireless network performance. In MobiCom (2003). [Kodialam03]  Kodialam, M., and Nandagopal, T. Characterizing achievable rates in multi-hop wireless networks: the joint routing and scheduling problem. In MobiCom (2003). [Vaidya02 ]  Yang, X., and Vaidya, N. H. Priority scheduling in wireless ad hoc networks. In MobiHoc ’02: Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing (New York, NY, USA, 2002), ACM Press, pp [Razak07]  S. Razak, V. Kolar and N. B. Abu-Ghazaleh, "Modeling and Analysis of Two-Flow Interactions in Wireless Networks", IEEE/IFIP WONS Figure 1: Figure 2: Figure 4: Figure 4a, 4b: Figure 4c: