dynamic load balancing through association control of mobile users in wifi network presenter:...
TRANSCRIPT
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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