Dynamic Load Balancing through Association Control of Mobile Users in WiFi Network

1. 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

2. 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

3. Outline Introduction IEEE 802.11 Basics System Model and Problem Definition Distributed Association Algorithm Performance Evaluation Conclusion

4. Introduction • IEEE 802.11 MAC has an “performance anomaly” • Data rate information is required to guide load balancing schemes

5. Introduction(cont.) • Default best-RSSI(receiving signal strength indicator)-based AP selection scheme • Result in severe unfairness and even poor overall performance

6. 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

7. IEEE 802.11 Basics Distributed Coordination Function (DCF) Association procedure for the roaming mobile user(MU)

8. System Model and Problem Definition θ: MU throughtput U: MUs L: packet length ya: AP load d: time required to transmit one packet from MU r: physical data rate of MU m: number of retrials required to transmit one packet

9. Distributed Association Algorithm Association Algorithm for APs and MUs

10. Performance Evaluation : total throughput Jain’s fairness index Numerical Simulation for Realistic Scenario – large scale Packet Level Simulation – medium size Prototype Implementation – small scale

11. Performance Evaluation(cont.) SimPy 56 APs and 126 MUs 1100x1000m2 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. Fig. 4. The snapshot of developed numerical simulator. Numerical Simulation for Realistic Scenario

12. Performance Evaluation(cont.) Fig. 6. The throughput difference between RSSI-based scheme and proposed scheme

13. Performance Evaluation(cont.) Fig. 7. The Jain’s fairness value difference between RSSI-based scheme and proposed scheme

14. Performance Evaluation(cont.) TABLE I COMPARISON FROM NS2 SIMULATIONS • Packet Level Simulation • NS2 simulator • 9 AP and 40 MUs • 600×600m2

15. Performance Evaluation(cont.) • Prototype Implementation • MadWifi-ng wireless driver • Two Dell laptops as Mus • Two Dell Desktops as APs

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

17. 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