Network Cooperation forClient-AP Association Optimization

Akash Baid, Ivan Seskar, Dipankar Raychaudhuri

WINLAB, Rutgers University

Introduction• Exponential rise in no. of planned WiFi deployments -

telecom, cable, service companies• Large WiFi networks leverage years of research on

enterprise WLAN management• However, less focus on how one managed network

interacts/interferes/coordinates with another

APs of two networks in Brooklyn area of New York City

This work: Study the effect of inter-network interference on the intra-network performance optimization

→Motivation

○ System Model

○ Problem Formulation

○ Optimal Solution

○ Simulation Results

○ Conclusion

Operational Cooperation Model

• Each network periodically shares the info about the location and operating channels of its APs with all other networks operating in the same area

• Clients belonging to one network cannot join other networks

• Advantages of operational coop. over full access coop:– Authentication functionality within each network– Extra capacity provisioning not required– A network can retain the control of sessions, policy, and billing

We show how each network can optimize client-AP associations to minimize the effects of inter-network interference.

→Motivation

○ System Model

○ Problem Formulation

○ Optimal Solution

○ Simulation Results

○ Conclusion

Motivating Example

ChosenAP

54 Mbps48 Mbps

36 Mbps

27 Mbps24 Mbps

ChosenAP

Default Selection: Connect to closest (AP1)

Intra-network optimization: Take AP load into account (AP2)

Inter-network optimization: Take effect of foreign APs into account (AP3)

Intra-network optimization of client-AP associations can lead to inefficient results in presence of foreign networks

→Motivation

○ System Model

○ Problem Formulation

○ Optimal Solution

○ Simulation Results

○ Conclusion

System Model

• independently operated WiFi networks: indexed by • Access Points; Clients in the th network

connection state between the th client and th AP of the th networkfraction of time provided by the AP to the client

effective bit rate

set of co-channel foreign APs within carrier sense range

set of co-channel foreign APs outside carrier sense but within interference range (potential hidden nodes)

Effective rate of th client of th network:

○ Motivation

→System Model

○ Problem Formulation

○ Optimal Solution

○ Simulation Results

○ Conclusion

System Model

• Assumptions:– No priority order between clients– Each AP enforces proportional fairness between

connected clients ⇒ equal time share [Liew’05]– Only downlink traffic (from APs to clients)– Full buffer (clients always have pending data requests

at the AP)

Parameter in the range (0,1) which captures the average effect of hidden

node interference per interferer

• Average channel time for a client:

○ Motivation

→System Model

○ Problem Formulation

○ Optimal Solution

○ Simulation Results

○ Conclusion

Intra-Network Optimization

• Each network aims to maximize the sum utility of all its clients by controlling the association

• However, in this case, a network does not know what foreign networks are doing ⇒ no , terms

• Non-linear integer program:

○ Motivation

○ System Model

→Problem Formulation

○ Optimal Solution

○ Simulation Results

○ Conclusion

Cooperative Optimization

• The optimization is now done cooperatively by all networks– For each AP of each network, the number of interferers is known

• Combined optimization problem:

○ Motivation

○ System Model

→Problem Formulation

○ Optimal Solution

○ Simulation Results

○ Conclusion

Solving the integer program

• For intra-network problem:– Essentially the same approach as [Yang’08]

– approximate solution in polynomial time

Non-linear integer program

Relaxed discretized

linear program

Shmoys & Tardos’

rounding process

• For cooperative problem:– Once the inter-network parameters are known,

problem decomposes into different problem– Each network can individually solve the problem

using the same approach as above

○ Motivation

○ System Model

○ Problem Formulation

→Optimal Solution

○ Simulation Results

○ Conclusion

Simulation

• Comparison between:– Least Distance: Each client connects to the closest AP of the

same network (benchmark case)– Intra-Network Optimization: Each network optimizes the

association pattern of its clients.– Cooperative Optimization: All networks share information for

optimizing the client association

• 2-6 overlapping networks, 15-35 APs/network, 50-250 clients/network

• Two types of deployments: – Uniform-random– Clustered

○ Motivation

○ System Model

○ Problem Formulation

○ Optimal Solution

→Simulation Results

○ Conclusion

Random Deployment

• APs and clients uniformly placed in a 500 x 500m area• Minimum separation of 50m between 2 APs of same

network; no minimum across networks• Frequency selection: each AP chooses one of the three

orthogonal channels in the 2.4 GHz range that minimizes the number of co-channel APs in its range

- Carrier Sense radius: 215m - Interference radius: 250m

- Physical data rates selected based on distance between the client and AP

○ Motivation

○ System Model

○ Problem Formulation

○ Optimal Solution

→Simulation Results

○ Conclusion

Simulation Results

• 2 Networks, 25 APs, 150 clients per network• 2x gains in low rate clients, slight gain in median

○ Motivation

○ System Model

○ Problem Formulation

○ Optimal Solution

→Simulation Results

○ Conclusion

Simulation Results

• Gains consistent as no. of overlapping networks increase, loss in mean rate reduces

N=2 N=3 N=40

0.2

0.4

0.6

Thr

ough

put (

Mbp

s)

10 percentile client throughput

N=2 N=3 N=40

0.5

1

1.5

2

Thr

ough

put (

Mbp

s)

Mean client throughput

Least Distance Intra-network Optim. Cooperative Optim.

○ Motivation

○ System Model

○ Problem Formulation

○ Optimal Solution

→Simulation Results

○ Conclusion

Clustered Deployment

• Aim is to study topology-specific interference patterns• Reflects realistic scenarios where some networks have

dense deployments in a popular spot

• Two network example:– APs of 1st network clustered in 3 rectangular regions of size

200x200 meters each– APs of 2nd network still uniformly random across the area– Client placement, other parameters still same as before

○ Motivation

○ System Model

○ Problem Formulation

○ Optimal Solution

→Simulation Results

○ Conclusion

Clustered Deployment

10-2

10-1

100

101

102

0

0.2

0.4

0.6

0.8

1

Client Rates (Mbps)

CD

F

Network 1

10-2

10-1

100

101

102

0

0.2

0.4

0.6

0.8

1

Client Rates (Mbps)

CD

F

Network 2

Least Distance

Intra-NetworkCooperative

Least Distance

Intra-Network

Cooperative

7x Gain in 10 %ile

throughput

Net 1 APs are clustered

Effect of Net 2 APs is less

Info from Net 2 not veryuseful

⇓⇓

Net 1 APs are clustered

Effect of the cluster ofNet 1 APs on Net 2 is high

Info from Net 1 helps a lot

⇓⇓

○ Motivation

○ System Model

○ Problem Formulation

○ Optimal Solution

→Simulation Results

○ Conclusion

Moving Forward○ Motivation

○ System Model

○ Problem Formulation

○ Optimal Solution

○ Simulation Results

→ Conclusion

• New initiative towards network collaboration for spectrum allocation

• Starting under the new NSF EARS program

Moving Forward○ Motivation

○ System Model

○ Problem Formulation

○ Optimal Solution

○ Simulation Results

→ Conclusion

• Software Defined Network (SDN) approach to implementing network collaboration

Thanks !Questions?

Extras

Motivating ExampleDefault Selection: Closest AP Intra-Network Optimization

Inter-Network Optimization

ChosenAP

ChosenAP

ChosenAP