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Horizon: Balancing TCP over multiple paths in wireless mesh networks

This paper explores the challenges and solutions for achieving efficient resource utilization, fair allocation, and good application performance in wireless mesh networks utilizing multiple paths for TCP traffic. The authors propose a backpressure-based algorithm and discuss the implementation challenges and experimental results.

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Horizon: Balancing TCP over multiple paths in wireless mesh networks

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  1. Horizon: Balancing TCP over multiple paths in wireless mesh networks Bozidar Radunovic, Christos Gkantsidis, Dinan Gunawardena, Peter Key Microsoft Research Cambridge, UK

  2. Wireless Mesh Networks

  3. Goals • Efficient use of resources • Use multiple paths • “Fair” allocation of resources • Many users, long-distance vs. short-distance flows • Good application performance • TCP in particular • Deployable on existing network • Minor modifications of the existing 802.11 stack.

  4. Outline • Controlling the network: Backpressure • Dealing with TCP • Experimental results

  5. Controlling the network: Backpressure Algorithms to achieve utilization, fairness, good experience? Protocol: Which node transmits? Which user/flow takes priority? Where to forward the packets? Our answer: Backpressure-based algorithms Packet Queue • Essence of backpressure: give priority to • Nodes that have smaller queues • Difficult to implement • Flows that have smaller number of packets • Provably optimal • Very long theoretical support Flow A Flow B

  6. Backpressure challenges • Our goal: • First to implement backpressure for wireless meshes • Lots of theory, no practice so far • Lots of challenges arise from practical constraints • Use multiple paths • Provide good performance for current applications Flow A Flow B Examples: Queuing by pressure triggers TCP congestion control Back-pressure increases delay Limited TCP window size penalizes path estimation Out-of-order packet arrivals ... And many others Complex Interactions

  7. Our Multi-Path Routing • Input: • queuei(f): packet from flow f queued at node i • Output: • Ci(f): cost from node i to destination of flow f • bestFlowi: the flow to select for transmission at i • bNHi(f): the best next hop at i for flow f Algorithm at node i: Select where to transmit a packet:bNH(f)i=bestNextHopi(f) = argminj(queuei(f) / rate(i,j) + Cj(f)) Select the flow from which to transmit a packet:bestFlowi = argmaxf(queuei(f) / rate(i,bNHi(f))) Update costs:Ci(f) = maxf(queuei(f)/ rate(i,bNHi(f))) + CbNHi(f) Propagate costs

  8. Simple Example Comparison: Our scheme : 4 packets Back-pressure: 13 packets Main advantages: • Minimal queuing: queue sizes do not grow with network • Estimates path quality with realistic TCP window size • Fast convergence C1(1) = 4 S1 100 100 C1(1) = 2 C1(1) = 2 C3(1)= 4 S2 C2(2) = 6 C2(1) = 6 2 4 D2 C3(1) = 2 C5(1) = 0 C4(1) = 0 C2(1) = 2 100 100 C6(1) = 0 C5(1) = 0 C4(1) = 0 D1

  9. Outline • Controlling the network: Backpressure • Dealing with TCP • Experimental results

  10. Challenges dealing with TCP (1) • Our system performs congestion control ... • ... so does TCP • ... need to make sure that they are compatible • Idea: signal congestion to TCP • ECN-like approach • in some cases we communicate congestion by generating duplicate ACKs

  11. Challenges dealing with TCP (2) • Recall: We use multiple paths ... • ... TCP gets confused (path delay estimation, out of order delivery, etc.) • Solution: Use reassemble queue: • Minimize packet reordering • Avoid time-outs at all costs TCP

  12. Outline • Controlling the network: Backpressure • Dealing with TCP • Experimental results

  13. Load balancing across two flows Performance decreased:need more channelsor better MAC Both total rate and fairness improved No significantdifference Flow S2-D2 stops • 2 disjoint areas • Only 6 nodes aredual-homed ~ double the rate for both flows

  14. Multi-homed networks • Different access points/base stations • Same or different radios Single flow 2 1.5 Relative improvement 1 0.5 0

  15. More flows • More flows → all resources used →cannot increase total rate • Instead, we improve fairness (e.g. smallest rate) 8 flows

  16. Goals • Efficient use of resources • Use multiple paths • “Fair” allocation of resources • Many users, long-distance vs. short-distance flows • Good application performance • TCP in particular • Deployable on existing network • No modifications of the existing 802.11 stack. How to select paths? Other types of traffic? How to deal with UDP? What can we do with small changes?

  17. Thank You

  18. Optimizing utility of the network • Assume set of flows F, with fF • Flow conservation constraint: For each node i and flow f: Total flow into i for f= Total flow out of ifor f • Wireless constraints: Feasible node transmission scheduling and rates • Goal: max U(f) f where U(f) is the utility function, e.g. U(f) = log(xf), xf is the rate of f

  19. Dual optimization problem Primal problem Dual problem Goal: Minimize cost Link costs (price) • Goal: Maximize rate • Link constraints i.e. rates & scheduling Solution of dual problem Backlog

  20. Challenges implementing dual problem Dual problem: • Goal: Minimize cost • Link prices • Difficulties: • Scheduling is NP-hard • Scheduling difficult to decentralize • Backpressure assumes queuing in the network Use 802.11 scheduling Schedule only flows ⇒Approximate Does NOT work well with TCP

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