Upload
jason-hoover
View
212
Download
0
Embed Size (px)
Citation preview
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 EngineeringOhio State University
Columbus, Ohio
Enterprise Wireless LAN
AP AP AP
AP AP AP
Internet
2
Do we Care for Uplink• Uplink traffic is increasing at a rapid pace because:
3
Cloud Computing
Online Gaming
Sensor Data Upload
Code Offloading VoIP,
Video Chat
Uplink Traffic: Challenges
4
• 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
AP 1 AP 2Alice Bob
2 slots.
Switch
How many slots does optimal TDMA take?
Same Example with Symphony
6
AP 1 AP 2Alice Bob
Switch
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 [Gollakota et al. 2009]
The two APs act as two different interfaces to the same AP.
Optimal TDMA: 2 slots
Example with multiple transmitters
7
AP 1 AP 2
Alice
Bob (not in range of AP1)
Switch
AP1 suppresses AliceCarol (not in range of AP1)
Don
Symphony Time Slot 1Symphony Time Slot 2: Only Carol and Don transmit
AP2 suppresses Bob
Optimal TDMA takes 4 slots with one packet transmitted in each slotWhat is the minimum number of time slots required by TDMA?
Optimal TDMA: 4 slots
- =
- = - =
Four packets received in two slots
No global information about which transmitters have packets to send
8
Challenge: Transmitter Identification
C
Time Slot 1 at AP2
A
B
D
SINR of all packets is quite low
Time
Corr
elati
on V
alue
Peak indicates presence of PN sequence [Magistretti et al. 2012]
PN Sequence unique to the transmitter
[Magistretti et al. 2012]
Identify as many transmitters as possible without knowing what they transmitted
9
Challenge: Computing Set of Transmitters to be Suppressed
• 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
A B
AP1 AP2
CD
Linear combination of A and D
Linear combination of A and D
Dependence Graph
10
A→ AP1
One vertex for every link
A B
AP1 AP2 CD
A→ AP2
B→ AP2
C→ AP2D→ AP2
D→ AP1
Dependence Graph: Adding Edges
11
Consider two pairs of links(A → AP1) and (B → AP2)
AP2 cannot decode B until interferencefrom 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 B
AP1 AP2 CD
A→ AP1 A→ AP2
B→ AP2
C→ AP2D→ AP2
D→ AP1
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
A → AP1
• 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
AP1 AP2 CD
D →AP2
A → AP1
14
Phase 2 of the algorithm
• Phase 2: Find the maximum weight induced acyclic subgraph 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 B
AP1 AP2 CD
A→ AP1
B→ AP2
AP1 suppresses A
AP1 decodes A and sends it
to AP2
AP2
suppresses B
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 CD
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
AP1AP2AP3 ABC
B→ AP2 A→ AP1C→ AP3
– Long chain of dependence– Decoding failure on one packet leads to failure at all
dependent locations
ABC
19
Challenge: Varying density of clients
AP1AP2AP3
A1
B
One heavy loaded AP blocks the transmission for the entire network
C
A2
Ak-1
Ak
After one time slot…
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
0.3566666667
3.2422222222
5.8777777778
11.8155555556
12.131646048
13.3907310012
13.921193134315.58
16.244435
18.0685684086
22.8315104339
25.12482194420
0.25
0.5
0.75
1
Client Throughput (Kbps)
CDF
802.11 RTS OFFFlex-Omniscient TDMASymphony
4.5x 1.6x
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
On an average, total throughput in Symphony is 1.63x of TDMA 5.6x of 802.11
5 6 7 8 10 12 15 18 22 27 330
20
40
60
80
100
120
140
Number of APs
Tota
l Thr
ough
put (
in M
bps) 802.11 RTS OFF (15.1 Mbps)
Flex-Omniscient TDMA (51.7 Mbps)Symphony (84 Mbps)
25
Simulations: Fairness
Symphony has higher fairness since it allows all clients to transmit
5 6 7 8 10 12 15 18 22 27 330
0.2
0.4
0.6
0.8
1
Number of APs
Jain
Fai
rnes
s In
dex
802.11 RTS OFF (0.39)Flex-Omniscient TDMA (0.40)Symphony (0.68)
26
Related Work
• Cooperative decoding of uplink packets: Bit-level combining [Miu et al. Mobicom 2005], Symbol level combining [Woo et al. Mobicom 2007], Coarse Symbol Representation [Gowda et al. Infocom 2013]– Symphony decodes multiple packets received by multiple APs
• Exchanging decoded packets over backbone: Interference Alignment [Gollakota et 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