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1 Implementing Utility-Optimal CSMA Mung Chiang Princeton University Joint work with Jinsung Lee, Junhee Lee, Yung Yi, Song Chong (KAIST, Korea) Alexandre Proutiere (Microsoft Research UK)

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2 Q: Can simple yet optimal (in theory) distributed scheduling be deployed? A: Brainstorming and initial answers

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3 Background and Related Work Theory Practice Theory-Practice Gap

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4 Scheduling in Wireless Networks When and How to Activate Links for “Good” Performance? Interference: Restriction on Simultaneous Link Activation Single vs. multi hop traffic, saturated vs. unsaturated Performance: Stability/Delay, Utility/Fairness Design Freedoms Centralized vs. Distributed With Collision vs. Without Collision

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5 Literature Taxonomy

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6 Related Work: R.A. w. Message Passing Wang Kar 2005 Lee Calderbank Chiang 2006 Mohsenian Huang Chiang Wong 2008 …

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7 Simplicity Driven Design Message passing undesirable Not fully distributed Security breach Synchronization and coordination Reduction of effective performance Questions Q1. How much can we achieve without passing any message? Q2. Can we implement it over legacy standard?

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8 Related Work: Adaptive CSMA Jiang–Walrand Allerton 08 Rajagopalan–Shah CISS 08 Liu-Yi-Proutiere-Chiang-Poor Microsoft TR 08 Ni-Srikant ITA 09 Other related work Jiang-Liew 08 Marbach-Eryilmaz 08

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9 Related Work: Implement Backpressure DiffQ 2009 Warrier Janakiranman Ha Rhee A theory motivated heuristic solution Backpressure-based congestion control 802.11e based MAC prioritization Implemented in the Linux kernel Horizon 2008 Radunovic Gkantsidis Gunawardena Key Backpressure-based multi-path routing Heuristic solution compatible with 802.11 and TCP Implemented between data link and network layer

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10 Background and Related Work Theory Practice Theory-Practice Gap

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11 CSMA No message passing Content Sense (and CA or CD) Hold the channel Random back-off 123 Interference graph 123 123 Example

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12 System Model Saturated single-hop sessions Symmetric Interference Objective: Utility Maximization Alternative: Rate Stability for unsaturated input λ1λ1 λ2λ2 Throughput-region Continuous time model first: Access the channel with Poisson rate Back-off counter: exponential distribution Hold the channel with mean duration Channel holding time: exponential distribution

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13 Utility-Optimal CSMA (UO-CSMA) Each link l does the following at slot t: 1. 2. 3. Parameters: (1) V >0 (determines “how optimal”), (2) b(t) (decreasing step size)

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14 Performance in Continuous Time Model Theorem. With UO-CSMA and decreasing step sizes,

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15 Stochastic Approximation Having controlled Markov noise Proof: Key Ideas Small for large t Slow time-scale Fast time-scale averaged, and stationary regime Lemma. O.D.E System with schedules with stationary regime Solving dual of “tweaked” problem V. Borkar, “Stochastic approximation with controlled Markov noise”, Systems and Control Letters, 2006

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16 Proof: Key Lemma Lemma. Two systems are asymptotically equivalent, i.e., ODE system Averaging effect Original system (continuous interpolation of )

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17 Pictorial Description of Key Lemma ODE system Original system Continuous interpolation t t t equal for large t, i.e., for large t Trajectories with the service rate by CSMA being stationary (i.e., averaged)

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18 Extension: General Weight Function Extension for strictly increasing, continuously differentiable W(.)

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19 Slotted Time Model Continuous Model No collisions Asymptotically arbitrarily close to optimal However, in practice Discrete back-off counters Collisions are unavoidable Two Questions Q1: What is the impact of collisions on efficiency? Q2: Tradeoff between short-term fairness and efficiency?

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20 Analysis Approach We can build a sequence of systems that converge to the continuous system, e.g., as ² decreases Gap between discrete and continuous case for a fixed Use the gap between discrete Bernoulli proc. and Poisson proc. Why tradeoff between short-term fairness and efficiency? Contention probability Gap btwn. discrete and continuous Channel holding time Efficiency Short-term fairness

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21 Efficiency and Short-Term Fairness Average duration during which link l do not transmit successfully Short-term fairness is inversely proportional to channel holding time For a given efficiency gap, Channel holding time grows with the order of Exponential price of short-term fairness for efficiency Short-term fairness index (also useful for TCP)

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22 Background and Related Work Theory Practice Theory-Practice Gap

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23 Main Goals Goal 1 Implement and deploy theory-driven scheduling algorithm UO CSMA on top of conventional hardware Goal 2 Discover, quantify, and bridge the gap between theory and practice for wireless distributed scheduling in general

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24 WiMesh Testbed@KAIST Open Research Testbed (See Song Chong) Campus-scale wireless mesh testbed with 55 nodes Easy programmable platform PC platform, Linux, FOSS Common Code [Mesh Router]

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25 Common Code Highway from Theory to Experimentation Reuse GloMoSim simulator for the real mesh router Experimentation without modification of simulation code All protocols over link layer –Overlay MAC layer (over IEEE 802.11) –Even cross-layer protocols Co-verification: co-simulation and experimentation SimulatorTestbed (Reuse without modification) Theory First step verification Second step verification

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26 Framework KAIST WiMesh Testbed with Common Code Song Chong and Yung Yi Common Code Applications TCP, UDP, … IP, AODV, DSR, … Overlay user MAC L7 L4 L3 L2.5 GloMoSim Codes SimulationImplementation 802.11, CSMA, … Two ray, free space, … Hardware Adaptor WLAN NIC Real physical worldSimulation world L2 L1 L2,L1 Easy and fast verification through simulation and experimentation using Common Code

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27 Simulation Simulation environment Two ray path loss model Slotted operation 1 timeslot = 1.6ms 1000byte packet size Link capacity 5Mbps SNR based packet reception model No ACK operation If collision occurs, it lasts for holding time Backoff counter can be chosen in [0, CW] randomly

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28 Implementation Features User space implementation Overlay + 802.11 MAC UO-CSMA Per-link queue structure Virtual queue update MAC parameter update MAC Adaptor Set 802.11e QoS parameters CW, TxOp and AIFS 802.11 MAC FIFO queue per interface Transmit actual packets Network Layer UO-CSMA (Overlay) 802.11 (Substrate) srcQ linkQ To dest a To dest b FIFO Queue Wireless (Radio) MAC Adaptor

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29 Indoor deployment: part of WiMesh 10 sender-receiver pairs located in 40mx20m Experimental Space 1 2 3 4 5 6 7 8 9 10

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30 Setup WLAN device Atheros 5212 chipset Utility and W function U(x)=log(x), W(x)=x or loglog(x) PHY 802.11a mode 5.745GHz band 6Mbps rate Misc. V = 20,100,500 Constant and diminishing step size FlowSingle-hop session Performance metrics Total throughput (or total utility), Throughput deviation Short-term fairness TrafficSaturated

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31 Experiment Results 3 link experiment 2-9-10 flows are used 9 interferes with 2 & 10 Total utility and throughput deviation 2 9 10

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32 Key Observations Basic Conclusions: UO CSMA works almost perfectly in simulation UO CSMA works well in physical reality and on top of legacy 802.11 drivers UO CSMA (in implementation) recovers 80% of the difference between DCF and UO CSMA (in simulation)

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33 Holding Time 2 9 10 Changing holding time Similar throughput Holding time imperfect 802.11 collision avoidance Degrades short-term fairness Same throughput behvior

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34 Queues for 0.01 and diminished by 0.9 every 10s Both cases have similar performance due to equal queue buildup Stepsizes Flow 9 Flow 10 Decreasing step size Flow 9 Flow 10 Fixed step size

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35 Parameter V Changing V parameter Throughput Can achieve up to nearly optimum as V increases Instantaneous Backlog Qmin = 0.1, Qmax = 2.3

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36 Function W Changing weight function with fixed step size W(x)=xW(x)=loglog(x) Converges in 80sec Converges in 40sec Small backlog Large backlog

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37 Background and Related Work Theory Practice Theory-Practice Gap

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38 Where Do the Gaps Come From? Sensing (sensing range and sensitivity) Holding (enforcing holding and backing off) Receiving (SIR based and capture effect) Asymmetry of interference Asynchronization of clock 802.11 protocol overhead Common Code architecture overhead TheorySimulationExperimentation Over 802.11 Gap

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39 Theory-Practice Gap 1 Theory-Simulation Gap Nonetheless, simulation follows theory very well GapTheorySimulation BackoffData-slot basedMini-slot based CollisionNo Yes, and last for holding time

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40 Theory-Practice Gap 2 Simulation-Implementation Gap Overhead from Common Code, but small Indirect physical information to the overlay MAC Imperfect queue nullification under the overlay MAC Packet-by-packet WLAN NIC configuration GapSimulationImplementation Carrier sensing Deterministic, CS range= tx range Probabilistic, CS range <= tx range Interference Symmetric, on-off relation Asymmetric, Probabilistic relation Collision Yes, and last for holding time Yes, with retransmission OverheadNo Common Code overhead

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41 Theory-Practice Gap 3 Clean slate – over 802.11 gap GapClean slateOver 802.11 HoldingPerfectNot perfect Contention controlBy access prob. By discrete backoff, but only 2^n -1 CW available Transmission typeUser definedUnicast with ACK Synchronization Physically synchronized Asynchronous Overhead Hardware dependent WLAN chipset dependent

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42 Samples of Workaround Solutions Ensuring correct holding time Reducing high prioritized beaconing MAC prioritization by AIFS and CWmin,max NAV option using overhearing of wireless Still, there is some hole Making CW=0 feasible CW only can have a form of 2^n-1 Similar transmission chances with CW=1 Still not perfect holding time execution Packet-by-packet parameter control Parameter setting in device driver

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43 Next Steps on Implementation This is an ‘interim report’ Many next steps: Large-scale network with multi-hop sessions Scaling up the deployment Routing needs to be considered Comparison with congestion controlled 802.11 802.11n chipset Ath9k driver is released Software upgrade Ath9k device driver support much more freedom Latest Linux kernel with mac80211 features Will make the ‘next report’

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44 Implementation-Inspired Theory Qs Many things theory assumed away Overhead Asymmetry Control granularity Many things theory modeled simplistically Imperfect holding and sensing SIR collision model with capture Many things theory analyzed loosely Convergence speed Transient behavior like queue buildup Parameter choice

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45 Theory-Practice From Dichotomy to Union: Theory Practice Theory Practice The Princeton EDGE Lab

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