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

Dynamic Load Balancing through Association Control of Mobile

Users in WiFi Networks

Dynamic Load Balancing through Association Control of Mobile

Users in WiFi Networks

2013 YU-ANTL Seminal

November 9, 2013

Hyun dong HwangAdvanced Networking Technology Lab. (YU-ANTL)

Dept. of Information & Comm. Eng, Graduate School, Yeungnam University, KOREA

(Tel : +82-53-810-3940; Fax : +82-53-810-4742http://antl.yu.ac.kr/; E-mail : [email protected])

http://antl.yu.ac.kr/

Advanced Networking Tech. Lab.Yeungnam University (YU-ANTL)

YU-ANTL Lab SeminalHyun dong Hwang2

Outline

INTRODUCTION RELATED WORK AND IEEE 802.11 BASICS

Related Work IEEE 802.11 Protocols

SYSTEM MODEL AND PROBLEM DEFINITION DISTRIBUTED ASSOCIATION ALGORITHM PERFORMANCE EVALUATION CONCLUSION Reference



INTRODUCTION

Wireless LAN shortcoming Comparing with wired line, the wireless channel is notorious for

its instability owing to fading and losses. To be adaptive to the dynamic nature of wireless medium, data

rate adaptation mechanisms such as Auto Rate Fallback (ARF) or Receiver Based Auto Rate (RBAR) are widely deployed for current WiFi products.

802.11 MAC has an “anomaly” that the throughput of high data rate MUs in good channel condition is down-equalized to that of the lowest data rate peer in the network.

In this way, the data rate information is required to guide load balancing schemes.



INTRODUCTION

Example scenario

Default best-RSSI(receiving signal strength indicator)-based AP selection scheme : Does not provide any fair sharing functional-ity will lead to unbalanced traffic load distribution.

Bad MU-AP associations result in severe unfairness and even poor overall performance



INTRODUCTION

Centralized optimization Pros.

Collects information from the entire network, and then derive an op-timal configuration based on complex computation.

Con. Such an approach is not scalable due to the NP-hard nature of the

problem Requires a separate processing infrastructure for performing the cen-

tralized computations.

Distributed heuristic methods Pros.

More flexible and they do not require the management center. Cons.

Some of the distributed AP selection schemes do not consider the multiple data rate information

Propose non-practical solutions



INTRODUCTION

New distributed heuristic algorithm Objective

Achieve load balancing by incorporating the multi-rate information

Method Add one additional field in the AP beacon and probing packets. Some low complexity operations should be inserted in the Mus.



RELATED WORK AND IEEE 802.11 BA-SICS

Centralized optimization techniques Most of the fine-grained for association control are based on

centralized optimization techniques. Before applying the complex computation, researchers formu-

late the original problem for different fairness criteria Eg.) Max-min fairness ,Proportional fairness

Centralized schemes usually assume that all infor-mation about the network is already known before-hand to provide more accurate results.

However, it is not practical to let one control center collect all the information inside the network and distribute the association commands to the MUs.




Distributed schemes Propose a distributed selection scheme that balances the load

according to the number of MUs associated with the APs. -> did not incorporate the multi-rate information.

Recently propose distributed fair algorithms by incorporating the multi-rate information - > dependent on the specific fea-tures of not yet- deployed IEEE 802.11e.




IEEE 802.11 Protocols Two access methods

Distributed Coordination Function(DCF) : control by station Point Coordination Function(PCF) : control by AP

DCF Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) protocol Provides two access schemes, the basic scheme and the request to

send/clear to send (RTS/CTS) scheme. Channel is sensed to be idle for a time interval equal to the DCF inter-

frame space (DIFS), the MU simply transmits the packet.



SYSTEM MODEL AND PROBLEM DEFINI-TION

Condition Assume that the neighboring APs are configured with different

non-overlapping channels Total 11 non-overlapping channels in IEEE 802.11a WLANs, the

network manager can achieve this by careful frequency plan-ning

Assume that the traffic is saturated unidirectional UDP (user datagram protocol) packets with the same packet length throughout the network.

Denote Ap : A MU(Mobile User) : U Physical data rate of MU : r Packet length : L AP a load : Ya



SYSTEM MODEL AND PROBLEM DEFINI-TION

AP a is defined as the aggregate period of time AP a to provide a unit of traffic volume to all associated users u

∈ Ua. MU throughput θu for all the MUs associated with AP

a is

Time required to transmit one packet from MU u ∈ Ua

DATAu is the amount of time to transmit one data packet



DISTRIBUTED ASSOCIATION ALGORITHM

Association Algorithm for APs and Mus

Legacy IEEE 802.11 standard, the management packets from the AP do not contain any field indicating the AP load informa-tion.

Add one additional field to the beacon and probing packets



DISTRIBUTED ASSOCIATION ALGORITHM

APs should keep updating the AP load by iterative moving average

If a MU is not associated with any AP in the network, it immediately scans all channels by sending probe request messages and receives response packets from the available Aps

Proposed AP selection strategy Least estimated load by supposing that it will be associated

with all available AP



PERFORMANCE EVALUATION

Numerical evaluation based on the developed simu-lation program.

56 APs and 126 MUs with their mobility placed in a rectangle topology of size 1100×1000m2




Numerical Simulation for Realistic Scenario Total throughput achieved by the proposed scheme




Value of fairness metric




Packet Level Simulation Simulation for a scenario (9 APs and 40 MUs) with 10 MUs sud-

denly roaming around AP1




UDP or TCP traffic is applied separately for this sce-nario.

Configure UDP traffic with 2Mbps constant bit rate.




Prototype Implementation




The respective throughputs and total throughput were measured



CONCLUSION

Proposed a distributed and selfstabilized association scheme for the MUs in the multi-rate WLANs.

Proposed scheme gradually balances the AP loads in a distributed manner



Reference

[1] Huazhi Gong, JongWon Kim, “Dynamic Load Balancing through Association Control of Mobile Users in WiFi Networks”, IEEE Transactions on Consumer Electronics, May 2008

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

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