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Rohan Murty Harvard University Jitendra Padhye , Ranveer Chandra, Alec Wolman, and Brian Zill Microsoft Research. Designing High Performance Enterprise Wi-Fi Networks. Trends in Enterprise Wi-Fi Networks. Increased adoption and usage [ Forrester ]

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Rohan Murty

Harvard University

JitendraPadhye, Ranveer Chandra, Alec Wolman, and Brian Zill

Microsoft Research

Designing High Performance Enterprise Wi-Fi Networks


Trends in enterprise wi fi networks
Trends in Enterprise Wi-Fi Networks

  • Increased adoption and usage [Forrester]

  • Culture of mobility: Users tend to use Wi-Fi even when wired connections are available [Gartner, Forrester, Economist]

  • Move towards an all wireless office

Users want wire-like performance

from wireless networks


Capacity of conventional corporate wlans
Capacity of Conventional Corporate WLANs

  • Corporate WLAN Study:

    • 12 users

    • < 1 Mbps each


Characteristics of conventional corporate wlans
Characteristics of Conventional Corporate WLANs

  • Focus on coverage

    • Fewer APs than clients

    • Clients talk to APs far away; worsens rate anomaly

  • Clients pick APs to associate with

    • Use RSSI of beacon packets

    • Agnostic to channel load at APs

  • Lack adaptive behavior

    • No load balancing; fixed channel assignments

    • Congestion and hotspots worsen


Denseap
DenseAP

  • Focus on capacity

    • Lots of APs; densely deployed

    • Clients can talk to APs near by; mitigates rate anomaly

  • Infrastructure picks client-AP associations

    • Global view of network conditions (channel load, interference, etc.)

  • Adaptability

    • Load balance associations; Dynamic channel assignment

    • Redistributes load away from local hotspots


Denseap is practical
DenseAP is Practical

  • No client modifications

    • Works with legacy clients

    • Changes limited to the infrastructure

    • Easy to deploy

  • Self-managing


Denseap system architecture
DenseAP System Architecture

Interface with clients

Send summaries to DC

DenseAP Nodes (DAPs)

Summarized

Data

Commands

Wired Network

Commands to DenseAP nodes

Summarized Data

from DenseAP nodes

Associations

Channel Assignments

Load Balancing

DenseAP Central

Controller (DC)


Key challenges
Key Challenges

  • Controlling Associations

    • Mechanisms

    • Policy

  • Dynamic Channel Assignment

    • Mechanism

    • Policy

  • Load Balancing

    • Mechanism

    • Policy


Association control in denseap
Association Control in DenseAP

Probe Request

Probe Request

00:09:5B:5A:1F:4F

Probe Request


Association control in denseap1
Association Control in DenseAP

Probe Request

MAC = 00:09:5B:5A:1F:4F

RSSI = 30

Probe Request

MAC = 00:09:5B:5A:1F:4F

RSSI = 42

00:09:5B:5A:1F:4F

Probe Request

MAC = 00:09:5B:5A:1F:4F

RSSI = 40


Association control in denseap2
Association Control in DenseAP

Probe Response

Accept Client

00:09:5B:5A:1F:4F

Client only sees one DAP at any given time


Association policy
Association Policy

  • What is the quality of a connection between a client and a DAP? (rate)

  • How busyis the medium around each DAP?

Overall goal: Associate client with a DAP

that will yield good throughput


A metric for dap selection
A Metric for DAP Selection

Expected

Transmission-Rate

(Mbps)

Available

Capacity (AC)

(Mbps)

Free Air Time

(%)

=

X


RSSI = 10

Free air

time = 0.22

DAP2

Probe Request

Probe Request

Probe Response

Accept Client

RSSI = 30

DAP3

Probe Request

Free air

time = 0.45

DAP1

RSSI = 20

Free air time = 0.35


Ratemap estimating expected transmission rate
RateMap: Estimating Expected Transmission-Rate

  • Correlation between

    • RSSI of Probe Request packets

    • Avg. throughput between a DAP-client pair

  • Rough approximation - ordering of DAPs

  • Online profiling method that builds RSSI to data-rate estimates

Upload and RSSI correlation = 0.71

Download and RSSI correlation = 0.61


Estimating free air time
Estimating Free Air Time

  • Estimate how busy is the medium around at a DAP

  • Technique similar to ProbeGap*

    • Measure time taken to finish a packet transmission

  • Estimates match up closely with offered traffic load

*Lakshminarayan et al., 2004

*Vasudevan et al., 2005


Channel assignment
Channel Assignment

  • Integrated into the association process

  • DAPs not discovered by clients don’t need channels

  • A DAP is assigned a channel only when it goes from being passive (no clients)to active (services at least one client)

    • Central controller assigns channel with least load


Re evaluating associations
Re-evaluating Associations

  • So far, associations when a new client joins the network

  • No association is perfect

    • Client traffic demands change

    • Local hotspots created


Load balancing
Load Balancing

  • Central controller monitors load on every DAP

  • When channel load on a DAP crosses a certain threshold

    • Client causing most load is determined

    • Moved to less loaded DAP nearby

    • Ensure client continues to get at least as much available capacity at the new DAP

  • Load balancing achieved via handoffs

    • Use association control; manipulate ACLs on DAPs



Testbed
Testbed

1 Corp AP

24 DAPs

24 Clients

802.11 a/bg


Results roadmap
Results: Roadmap

  • Performance

    • Density

    • Channels

    • Intelligent Association

  • Load Balancing


Overall denseap performance 802 11a
Overall DenseAP Performance: 802.11a

  • Gains due to

  • More channels

  • DAP density

  • Intelligent associations

1250% gain

Why?


Exploring the impact of density
Exploring the impact of density

  • Put all DAPs on the same channel

  • Factors out

    • Channels

    • Intelligent Associations: same load on all DAPs

  • Single out impact of

    • Density


Impact of density using only 1 channel
Impact of Density: Using only 1 channel

Higher density provides better performance



Why does intelligent association matter
Why does intelligent association matter?

  • Client-Driven

    • Disable intelligent association control

    • Let clients pick DAP to associate with (conventional WLANs)

  • Compare with DenseAP

  • Factors out

    • Channels

    • Density

  • Single out impact of

    • Intelligent association


Necessity of the association policy
Necessity of the Association Policy

160% gain

Intelligent association policy is necessary



Load balancing2
Load Balancing

Client 1 improves

Clients 2 & 3 improve

Client 1 moved

Client 2 moved


Other details and results in the paper
Other Details and Results in the Paper

  • Load balancing algorithm and mechanism

  • Mobility

  • Performance

    • Fewer DAPs

    • Fewer channels

    • 802.11g

    • …..

  • Scalability


Related work
Related Work

  • Plenty of prior work on static channel assignment, power control and associations

    • Each studied each aspect in isolation

    • Require client modifications [Ramani and Savage, Infocom 2005]

  • SMARTA [Ahmed et al., CoNext 2006]

    • Examines channel and power control

    • Increase overall network capacity

    • Does not consider associations, load balancing

  • MDG [Broustis et al., MOBICOM 2007]

    • Identified tuning channel, power and associations

    • Studies the order in which these knobs must be tuned

    • Requires client modifications


Overall contributions
Overall Contributions

  • Practical system

  • How do density, intelligent association, and more channels affect capacity?

  • Adaptive system

  • Future directions

    • Impact of hidden terminals

    • Heterogeneous mix of client traffic patterns

    • Other backhauls: e.g. Wireless, powerline


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