fairness and load balancing in wireless lans using association control

Fairness and Load Balancing in Wireless LANs Using

Association ControlYigal Bejerano, Member, IEEE, Seung-Jae Han, Member, IEEE, and Li (Erran) Li, Member, IEEE

IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 15, NO. 3, JUNE 2007IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 15, NO. 3, JUNE 2007

Presented by 范姜竣韋范姜竣韋 (C.W. Fan-Chiang)(C.W. Fan-Chiang) 許宴毅許宴毅 (Y.Y. Hsu)(Y.Y. Hsu)

Contents

INTRODUCTION１

SIMULATION RESULTS６

FAIRNESS AND LOAD BALANCING３

ASSOCIATION CONTROL ALGORITHMS

４

ONLINE INTEGRAL-ASSOCIATION５

SYSTEM DESCRIPTION２

CONCLUSION７

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Contents

INTRODUCTION１




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CONCLUSION７

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Introduction

Load imbalance Each user associate itself with the AP that

has the strongest RSSI(received signal strength indicator), while ignoring its load condition

Some APs may idleSolution

Association control ：• Balance the load via intelligently selecting the

user-AP association

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Introduction(cont’d)

Association control can be used to achieve different objectives. To maximize the overall system throughput by

shifting not from the fairness viewpoint More desirable goal ：

provide fair bandwidth allocation, while maximizing the minimal fair share of each user

In this paper, we present efficient algorithms Ensure max-min fair bandwidth allocation This goal obtained by balancing the load on the APs

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Contents

INTRODUCTION１




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CONCLUSION７

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The Network Model

Assume that adjacent APs use noninterfering channels

Consider long-term fairness Greedy users that always have traffic consume all the allocated bandwidth

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System Discription

1. System requires relevant information on each user, such as and

2. It needs an algorithm to determine the user-AP association

3. It need a mechanism to enforce these association decisions

the collected information is reported to a network operation center(NOC)

• Periodically recalculates the optimal user association by using the offline algorithms

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Periodic Offline Optimization

Motivation ： By showing the weakness of the existing heuristic

load balancing mechanisms Example ：(b) ： Least-loaded-first(LLF)

Bandwidth ： {4/3, 1, 4/3}

b/4+b/2=1b=4/3

(c) 、 (d) ： strongest-signal-first(SSF)

Case1Bandwidth ： {8/7, 8/7, 8/7}

Case2Bandwidth ： {8/3, 8/3, 2}

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Wireless and Wired Bottlenecks

Wireless link is generally considered as the bottleneck this assumption is not always valid

Case I ： fair user association only from

the wireless perspective wireless: 0.5Mb/s to each user T1 line:

AP a: 0.5 Mb/s to user 5, 6 Wireless link is the bottleneck AP b: 3/8 to its associated user Wired link is the bottleneck

Case II ： A fair user association 0.5 Mb/s to each user over the wired and wireless channels

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Contents

INTRODUCTION１




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CONCLUSION７

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FAIRNESS AND LOAD BALANCING

Two association models1. Single-association model

=integral-association

2. Multiple-association model=fractional-association

Its bandwidth allocation is the aggregated bandwidth

Denote by all the users that associated with AP a A denotes the set of APs that u U is associated with.

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A. Max-Min Fairness

A bandwidth allocation is a matrix ： aggregated bandwidth to user Normalized bandwidth(NB) ： NBV ： sorted in increasing order AP a is required to serve user u a period of

over the wireless channel and over the infrastructure link

If a bandwidth allocation is feasible if and

Fair service ： all users have the same allocated bandwidth the degree of fairness may cause reduction of the throughputprovide max-min fairness

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A. Max-Min Fairness(cont’d)

max-min fairness No way to increase the bandwidth of a user without

decreasing the bandwidth of another user with the same or less normalized bandwidth

a user association is termed max-min fair if its corresponding bandwidth allocation is max-min fair

B1={1,1,B1={1,1,22,2,3},2,3}B2={1,1,B2={1,1,11,1,2},1,2}

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Example

(b): a feasible fair association ‧

‧every user receive b=1

(c): NBV‧ ={1,1,1,2,2}

(d): NBV‧ ={1,4/3,4/3,4/3,4/3}

NBV of a fractional max-minfairness allocation alwaysNBV of the integral max-minfairness allocation

The users can be divided into fairness groups consists of all users that experience the same NB allocation

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＞＝＞＝

B. Min-Max Load Balancing

The notion of load is not well defined Neither # of users associated with an AP nor

its throughput reflect the AP’s load

the load of an AP needs to reflect its inability to satisfy the requirements of its associated users and as such it should be inversely proportional to the average bandwidth that they experience

We are able to extend existing load balancing techniques to balance the AP loads and obtain a fair service

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B. Min-Max Load Balancing (cont’d)

A fractional association is a matrixfor each u U, holds.

Each specifies the fractional association of user u with AP a. Reflects the fraction of user u’s total flow that it expects to get from AP a

A fractional association is feasible if User produces a load of on the wireless

channel of AP and a load of on its backhaul link

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We define the load induced by user on AP to be the time that is required of AP to provide user a traffic volume of size

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Define the load vector of an association matrix sorted in decreasing order

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Example

(c) ： (integral)• = {1,1,1/2}

(d) ： (fractional)• = {1,3/4,3/4}

APs can be partitioned into load groups contains all the APs with the same load assigned in decreasing order.

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111111

2222

11¼+¼=1/2¼+¼=1/2

½+½=1½+½=1

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4/34/34/34/3

4/34/3

11¼+¼+¼=3/4¼+¼+¼=3/4

¼+½=3/4¼+½=3/4

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=1

11

normalizenormalize

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In the following we refer to the load group of the most loaded APs and the corresponding fairness group as the bottleneck groups

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Unfortunately, Theorem 5 is not satisfied in the case of a single association

Example: ={1,1,1/2}

={1,1,1,2,2} ={1,1,1,1,2}

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Contents

INTRODUCTION１




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CONCLUSION７


A extension of the scheduling unrelated parallel machines problem For any ε< ½, there’s NO polynomial-time ( 1+ε )

approximation algorithm exists, unless P = NP To seek for a complete min-max load

balanced association.

A 2-approximation algorithm for unweighted users,

A 3-approximation algorithm for weighted users

An optimal solution for fair time allocation.

A. ρ*-Approximation With Threshold

Intuitively, we would like to guarantee to each user a bandwidth of at least 1/ ρ of the bandwidth that it receives in the optimal integral solution, for a constant ρ≧ 1.

However, there is neither upper nor lower constant bounds for the ratio .

A. ρ*-Approximation With Threshold (cont’d)


Our practical goal is to reduce the load of highly loaded APs, there is no need to balance the load of APs with load below a certain threshold T, where T is the maximal load that a user may generate on an AP as formulated in

Recall that T is indeed a very small value and in practical 802.11 networks T ≦ 1 s/Mb.

B. Scheme Overview

Integral Load Balancing Algorithm.

1) Fractional Load Balancing Algorithm

B. Scheme Overview (cont’d)

we utilize a linear program, denoted as LP1 which calculates a feasible association and also minimizes the maximal load on all the APs over both their wireless and wired channels


Bottleneck-group Detection Routine

2) The Rounding Method

2) The Rounding Method

1 1 X X

2 X 1 X

3 X 1 X

4 X ½ ½

5 X X 1

aa 　　　　　　 bb 　　　　　　 cc

Ua

a { 1 }

Ub

b { 4,3,2 }

Uc

c { 4,5 }

QQa,1a,1={1}={1}

QQb,1b,1={4,3}={4,3}

QQb,2b,2={3,2}={3,2}

QQb,3b,3={2}={2}

QQc,1c,1={5,4}={5,4}

QQc,2c,2={5}={5}

1/21/2

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1/21/21/21/2

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C. Analysis of the Unweighted Case

C. Analysis of the Unweighted Case (cont’d)

D. Weighted Users

D. Weighted Users (cont’d)

E. Time Fairness

E. Time Fairness (cont’d)

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Contents

INTRODUCTION１




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CONCLUSION７

V. ONLINE INTEGRAL-ASSOCIATION

Contents

INTRODUCTION１




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CONCLUSION７

VI. SIMULATION RESULTS

Assume

Maximum transmission range: 150m 20 APs are located on 5 by 4 grid Number of users is either 100 or 250 Users are randomly positioned in a circle-

shaped hot spot with 150m near the center of the simulation network

Results are obtained from averaging 100 runs

Distance 50m 80m 120m 150m

bit rate 11Mb/s 5.5Mb/s 2Mb/s 1Mb/s

VI. SIMULATION RESULTS (cont’d)

VI. SIMULATION RESULTS (cont’d)

Each time slot replace 20% of usersEach time slot replace 20% of usersThe offline algorithm is invoked Every 15The offline algorithm is invoked Every 15 time time slots or when bottleneck difference exceeds slots or when bottleneck difference exceeds 25%25%

Contents

INTRODUCTION１




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CONCLUSION７

VII. CONCLUSION

The problem of providing fair service to users and balancing the load among APs. This goal is achieved by intelligently determining the user-AP association.

Our simulations confirm that the proposed methods, indeed, achieve close to optimal load balancing and max-min fair bandwidth allocation, and significantly outperform popular heuristics.

Moreover, we show that in some cases, by balancing the load on the APs the overall network throughput is increased. In the future, we intend to develop a practical management system based on the theoretical foundation presented in this study.

fairness and load balancing in wireless lans using association control

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