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Symphony: Orchestrating Collisions in Enterprise Wireless Networks

Symphony: Orchestrating Collisions in Enterprise Wireless Networks. Tarun Bansal (Co-Primary Author), Bo Chen (Co-Primary Author), Prasun Sinha and Kannan Srinivasan Department of Computer Science and Engineering Ohio State University Columbus, Ohio. Enterprise Wireless LAN.

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Symphony: Orchestrating Collisions in Enterprise Wireless Networks

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  1. Symphony: Orchestrating Collisions in Enterprise Wireless Networks TarunBansal (Co-Primary Author), Bo Chen (Co-Primary Author), PrasunSinha and KannanSrinivasan Department of Computer Science and Engineering Ohio State University Columbus, Ohio

  2. Enterprise Wireless LAN Internet AP AP AP AP AP AP

  3. Do we Care for Uplink Cloud Computing Online Gaming Code Offloading VoIP, Video Chat Sensor Data Upload Uplink traffic is increasing at a rapid pace because:

  4. Uplink Traffic: Challenges • Traditionally, uplink traffic has received less attention in the design of algorithms/solutions for WLANs • Challenging to improve uplink throughput • Single antenna transmitters • Unlike downlink, no global information about which transmitters have packets to send

  5. An Example TDMA (Time Division Multiple Access) with global planning Switch Alice Bob AP 1 AP 2 How many slots does optimal TDMA take? 2 slots.

  6. Same Example with Symphony Switch Optimal TDMA: 2 slots Alice Bob AP 1 AP 2 AP1 decodes Alice’s packet Subtract Alice’s recreated samples - = Decodes remaining samples to obtain Bob’s packet • Two packets received in single slot: Better than optimal TDMA • Requires APs to only exchange decoded packets (and not samples) • Exchanging samples requires prohibitive bandwidth [Gollakotaet al. 2009] • The two APs act as two different interfaces to the same AP.

  7. Example with multiple transmitters Optimal TDMA: 4 slots Switch Bob (not in range of AP1) Alice AP 2 AP 1 Don Four packets received in two slots No global information about which transmitters have packets to send AP1 suppresses Alice = - AP2 suppresses Bob Carol (not in range of AP1) - = = - Symphony Time Slot 1 Symphony Time Slot 2: Only Carol and Don transmit What is the minimum number of time slots required by TDMA? Optimal TDMA takes 4 slots with one packet transmitted in each slot

  8. Challenge: Transmitter Identification Time Slot 1 at AP2 A • PN Sequence unique to the transmitter • [Magistrettiet al. 2012] B • SINR of all packets • is quite low C Identify as many transmitters as possible without knowing what they transmitted D Peak indicates presence of PN sequence [Magistrettiet al. 2012] Correlation Value Time

  9. Challenge: Computing Set of Transmitters to be Suppressed Linear combination of A and D Linear combination of A and D A • At the end of each slot, which AP suppresses which transmitter? • APs need to make a joint decision • One possible solution: AP1 suppresses A while AP2 suppresses D • APs need to decode A and D based on samples received in this slot • Requires APs to exchange samples: Not a valid solution B AP1 AP2 D C

  10. Dependence Graph One vertex for every link A→ AP1 A→ AP2 A B D→ AP1 B→ AP2 AP1 AP2 D C D→ AP2 C→ AP2

  11. Dependence Graph: Adding Edges Consider two pairs of links (A → AP1) and (B → AP2) AP2 cannot decode Buntil interference from A is cancelled Decoding of A at AP1 must happen before decoding of B at AP2 Draw a directed edge from (A → AP1) to (B → AP2) A→ AP1 A→ AP2 A B D→ AP1 B→ AP2 AP1 AP2 D C D→ AP2 C→ AP2 A B AP1 AP2

  12. Using dependence graph to determine suppressed transmitters • Phase 1: Weight (Pi→ APj) = Number of incoming edges X Number of outgoing edges • Weight function improves decoding probability in future • Weight function minimizes the overhead on the backbone

  13. Using dependence graph to determine suppressed transmitters D →AP2 D →AP2 • AP1 can decode A only after AP2 has decoded D. • AP2 can decode D only after AP1 has decoded A • Cyclic dependence prevents decoding A B A→AP1 A→AP1 AP1 AP2 D C

  14. Phase 2 of the algorithm AP1 suppresses A • Phase 2: Find the maximum weight induced acyclicsubgraph of the dependence graph using a greedy algorithm • Vertices of the acyclic subgraph indicate which APs should suppress which transmitter • Edges of the acyclic subgraph indicate how the APs should exchange packets on the backbone A→ AP1 AP1 decodes A and sends it to AP2 A B B→ AP2 AP1 AP2 AP2 suppresses B D C

  15. Other Challenges in Large Scale Deployment • Enterprise WLANs can consist of hundreds of APs • What happens when Symphony is deployed to a large scale EWLAN

  16. Challenge: No central server • No central server • How to compute the dependence graph and the acyclic subgraph? A B AP1 AP2 D C

  17. Challenge: Unreliable Backbone • Unreliable backbone with unpredictable latency • When exchanging information among APs, slower link may create bottleneck • Packets may get lost

  18. Challenge: Reliability C A B C AP1 A AP3 AP2 B C→ AP3 B→ AP2 A→ AP1 • Long chain of dependence • Decoding failure on one packet leads to failure at all dependent locations

  19. Challenge: Varying density of clients A1 A2 AP1 B AP3 AP2 C Ak-1 After one time slot… Ak One heavy loaded AP blocks the transmission for the entire network

  20. Experiment Setup • USRP Nodes (from Ettus Research): 1078 MHz; BPSK • Protocols studied : • Symphony: Distributed Implementation • Flex-Omniscient TDMA • Flex (like Symphony): Client can send data to any AP • Omniscient: With a central controller that has global queue information apriori • Backbone latency is zero • IEEE 802.11 (No RTS/CTS)

  21. Experiments Setup • Topology • Clients placed randomly in the three regions • Experiments done over multiple topologies

  22. Experiment Results 4.5x 1.6x 802.11 RTS OFF Flex-Omniscient TDMA Symphony

  23. Trace-driven Simulations • Setup • SNR data collected from a multiple client-AP testbed (Stanford) • Traces used from the experiments • Variation in PN sequence detection accuracy with SINR • Variation in cancellation accuracy with SINR • Variation in backbone delays with number of hops • Size of the packets and packet generation times • Generated using SIGCOMM [Schulman et al. 2008] dataset

  24. Simulation Results: Throughput 802.11 RTS OFF (15.1 Mbps) Flex-Omniscient TDMA (51.7 Mbps) Symphony (84 Mbps) On an average, total throughput in Symphony is 1.63x of TDMA 5.6x of 802.11

  25. Simulations: Fairness 802.11 RTS OFF (0.39) Flex-Omniscient TDMA (0.40) Symphony (0.68) Symphony has higher fairness since it allows all clients to transmit

  26. Related Work • Cooperative decoding of uplink packets: Bit-level combining [Miuet al.Mobicom 2005], Symbol level combining [Woo et al.Mobicom 2007], Coarse Symbol Representation [Gowdaet al. Infocom 2013] • Symphony decodes multiple packets received by multiple APs • Exchanging decoded packets over backbone: Interference Alignment [Gollakotaet al.Sigcomm 2009] • Requires multiple antennas at both APs and clients • MIMO:MegaMIMO for downlink [Rahul et al. Sigcomm 2012] • Symphony focuses on uplink

  27. Summary • Symphony leverages the unused wired backbone resources to improve the wireless throughput for single antenna systems • Symphony design takes into account the challenges that arise in practical large-scale deployments Thank you

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