Dynamic Load Balancing through Association Control of Mobile

Users in WiFi Network

Presenter: Chia-Ming Lu

Huazhi Gong, Student Member, IEEE, Jong Won Kim, Senior Member, IEEE

IEEE Transactions on Consumer Electronics, 2008

Abstract

Association between mobile users (MUs) and access point (AP) is based on the signal strength information Extremely unfair bandwidth allocation among MUs

Propose a distributed association algorithm Achieve load balancing among the APs

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Outline

Introduction

IEEE 802.11 Basics

System Model and Problem Definition

Distributed Association Algorithm

Performance Evaluation

Conclusion

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Introduction

IEEE 802.11 MAC has an “performance anomaly” Data rate information is required to guide load balancing

schemes

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Introduction(cont.)

Default best-RSSI(receiving signal strength indicator)-based AP selection scheme Result in severe unfairness and even poor overall

performance

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Introduction(cont.)

AP selection algorithms can be divided into two categories: Centralized optimization

• NP-hard nature for performing the centralized computations

Distributed heuristic methods• Do not consider the multiple data rate information or

propose non-practical solutions

The proposed scheme Evaluated the performance by the numerical simulator

• Realistic scenario with mobility pattern Implemented a prototype on small-scale testbed

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IEEE 802.11 Basics

Distributed Coordination Function (DCF)

Association procedure for the roaming mobile user(MU)

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System Model and Problem Definition

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θ: MU throughtputU: MUsL: packet lengthya: AP load

d: time required to transmit one packet from MUr: physical data rate of MUm: number of retrials required to transmit one packet

aUuuaa dy

Distributed Association Algorithm

Association Algorithm for APs and MUs

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Performance Evaluation

Jain’s fairness index

Numerical Simulation for Realistic Scenario – large scale Packet Level Simulation – medium size Prototype Implementation – small scale

: total throughput

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Fig. 4. The snapshot of developed numerical simulator.

SimPy

Performance Evaluation(cont.)

Numerical Simulation for Realistic Scenario

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Fig. 5. A realistic scenario with measured mobility for numerical simulation. The red squares denote the APs and the blue circles denote the MUs at the beginning of simulation.

56 APs and 126 MUs1100x1000m2

Performance Evaluation(cont.)

Fig. 6. The throughput difference between RSSI-based scheme and proposed scheme

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Performance Evaluation(cont.)

Fig. 7. The Jain’s fairness value difference between RSSI-based scheme and proposed scheme

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Performance Evaluation(cont.)

Packet Level Simulation NS2 simulator 9 AP and 40 MUs 600×600m2

TABLE I COMPARISON FROM NS2 SIMULATIONS

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Performance Evaluation(cont.)

Prototype Implementation MadWifi-ng wireless driver Two Dell laptops as Mus Two Dell Desktops as APs

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Performance Evaluation(cont.)

16Fig. 10. The measurement results to compare the performance difference between the default RSSI-based scheme and the proposed scheme

Conclusion

The load balancing scheme Guarantee the throughput fairness among the MUs Gradually balances the AP loads

Feasibility of the proposed scheme Modifying open source wireless driver Achieve apparent load balancing

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