Tradeoff Based Network Management for Wireless Networks

Huazhi GongNetMedia Lab@GIST

20036075hankgong@gist.ac.kr

Date 2008/05/26

Ph. D Pre-Defence

2

► Ch. 1: Introduction► Ch. 2: Background and Related Work► Ch. 3: WLAN Planning Framework Based on Tabu

Search► Ch. 4: Association Management for Wireless Networks► Ch. 5: Network Monitoring Based on Network Coding► Ch. 6: Conclusion

Part II: Contents

Part III: Summary

Part I: Background

Ch1

Ch3

Ch2

Ch4Ch5

Ch6

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Current Wireless Networks Wireless Local Area Network (WLANs): widely deployed

IEEE 802.11a/b/g Wireless Mesh Network (WMNs): popular for research

IEEE 802.11s standard is still not finished INTEL and CISCO are active in this area

IEEE 802.11a/b/g

IEEE 802.11s

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General Network Management Architecture Normally centralized for wired network For wireless network, distributed or hybrid management is better

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Motivations More complexity at the network edges Distributed v.s. centralized Relatively high loss rates on links Fairness v.s. efficiency QoS demands on mobile clients

Scalable network planning Distributed association management Realtime link monitoring

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Network Management Architecture for Wireless Networks Wireless network: single-hop (WLANs), multi-hop (WMNs) Network management: WLAN planning, association management, and network

monitoring

WLAN Planning

Association ManagementNetwork monitoring

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Ch1

Ch3

Ch2

Ch4Ch5

Ch6

► Ch. 1: Introduction► Ch. 2: Background and Related Work► Ch. 3: WLAN Planning Framework Based on Tabu

Search► Ch. 4: Association Management for Wireless Networks► Ch. 5: Network Monitoring Based on Network Coding► Ch. 6: Conclusion

Part II: Contents

Part III: Summary

Part I: Background

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AP Placement and Channel Assignment Modeling the channel assignment and QoS satisfication Closed-form formulations: Minimizing Number of Required AP (MNRAP) and

Optimizing Tradeoff Objective (OTOBJ) Tabu Search based optimization framework to solve the formulation

Demand Points QoS demand investigation

MNRAP

OTOBJ

Chosen placement points and its channel assignement

Tabu search

Demand points

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Related Work Different objectives: previous work only consider one aspect or another

Finding the minimum number of APs to meet the specific QoS requirements of wireless users

In [Bejerano2002] and [Chandra2004], the objective is to find the minimum number of gateways to relay traffic between the wired backbone network and the multi-hop wireless networks

Placing the given number of APs to achieve a specific optimal performance This objective can be the sum of the signal strength levels on all mobile users

[Rodrigues2000] Minimizing the maximum loads on all APs [Lee2002] A tradeoff objective considering efficiency and fairness [Ling2005]

Solving method Most of heuristic algorithms are based on greedy strategy State-of-art optimization software: CPLEX

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Airtime Usage Model for Single Channel Case Interference model

Communication range, interference range Two communication pairs should not be in interference range of each other

Airtime usage (QoS demand/bit rate): ),(, AaUuv

q

ua

u Interference Matrix Association Matrix

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Closed Form for Multiple Channels The airtime occupied by the RPs

inside its interference range no matter which AP they are associated with

the airtime occupied by the APs inside a's interference range used to satisfy the QoS demands of the MUs associated with them

Part 1 and Part 2 share some DPs

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Define Two Optimization Problems Minimizing Number of Required AP (MNRAP)

Optimizing Tradeoff Objective (OTOBJ): minimizing F

Additional assumption: best-RSSI-based association

Both of them are NP-hardness

So we focus on using meta heuristic algorithm to find the solution

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Tabu Search Kind of meta heuristic algorithm like

Genetic Algorithm or Simulated Annealing

Give chance to loop out of local optima

OpenTS (open source tabu search) library is used for my implementation

The initial solutions are calculated by greedy-based heuristic algorithm

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Numerical Evaluation: Validity

For regular small topology, it takes 10 mins for optimization software to calculate the optimal solution, the proposed algorithm use 10 secs to get the same results

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Numerical Evaluation: Scalability

Relaxed formulation (ILP)solved by GLPK

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Ch1

Ch3

Ch2

Ch4Ch5

Ch6

► Ch. 1: Introduction► Ch. 2: Background and Related Work► Ch. 3: WLAN Planning Framework Based on Tabu

Search► Ch. 4: Association Management for Wireless Networks► Ch. 5: Network Monitoring Based on Network Coding► Ch. 6: Conclusion

Part II: Contents

Part III: Summary

Part I: Background

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Association Management in Wireless Networks Association Management also can be called as AP Selection Control Let each mobile user choose a suitable access point: mostly load

balancing issues Default association scheme in IEEE 802.11a/b/g

Best signal strength (RSSI) Performance anomaly problem for multi-rate WLANs [Huesse2003]

11Mbps

5.5Mbps

1Mbps

802.11 DCF designed to give the same chance to for all MNs

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Related Work Centralized schemes

Bejerano et al. formulate the AP selection for max-min fairness of MU throughput based on integer linear programming and solve it by relaxation and approximation [MobiCom2004]

Kumar et al. have studied AP selection for proportional fair sharing relying on optimization software [NCC2005]

Distributed schemes Fukuda et al. propose a distributed selection scheme that balances the load

according to the number of MUs associated with the APs without rate information [VTC2005]

Takeuchi et al. and Siris et al. propose distributed fair algorithms by incorporating the multi-rate information based on IEEE 802.11e protocol [WCNC2006]

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Two Tiers of Multiple Channel Multiple Interface WMN Backbone (backhaul) layer: wireless mesh AP (MAP), gateway AP is

called as mesh portal (MP) Local service layer: mobile nodes associate with MAP’s wireless interface

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Association Management Formulation for WMN Assuming the maximum uplink rate of each

MAP can be measured by itself Through of MAP can not be more than the

uplink rate

Each MN’s throughput is the simple average of AP’s throughput

Formulation of AP selection problem

Rm

Efficiency: maximizing all throughputs

Fairness: maximizing the lowest throughputs

λ∈[0,1]: tradeoff weighting factor

Maximizing

Nonlinear Integer Problem

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Performance Evaluation The solution can be found by some

advanced algorithm like genetic algorithm (GA) etc.

I run Lingo to calculate a medium size problem (upto 9 APs and 50 MNs) Configured with multiple random

start seed Run for 30 mins

Fairness is evaluated by

The position of MNs are randomly generated

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Evaluation Results

Good tradeoff

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Distributed Association Management For wireless networks, distributed association management is more preferable

Wireless link is not stable Centralized management need additional hardware deployment

APs...MUProbe Request

Probe Response

Min

Cha

nT

ime

Ma

xCh

an

Tim

e Prob

ing

Bea

conBeacon

Probe Request

Probe Response

Periodically probing

AP load calculation

Selection by script

Adding to beacon and probe response

Packet error esitmation

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Define the Metric of AP Load AP load: the aggregate period of

time that takes AP a to provide a unit of traffic volume to all its associated users

Periodical operation on APs

RTS CTS

DIF

S

SIF

S

Pre

amble

SIF

S

PLCPhe

ader

MAChea

der

Data

CRC ACK

SIF

SMPDU

SIFSCTSRTSACKSIFSDATAmBODIFSd uaua 2)(

.ua

macu r

HLreamblePDATA

.

aUu

uau d

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Distributed Association Scheme

MNAP

Probing

Reply with current load

)()()()(~uuaaa Aatdtyty

Estimate load if associated

Association

Stability

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Numerical Evaluation Realistic measurement trace

from Dartmouth University website

The MNs has human mobility

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Evaluation Results: Efficiency and Fairness

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NS2 Simulation Results

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Testbed Prototype Testbed prototype is based on

laptop installed with Madwifi-ng AP and MN are modified

differently

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Testbed Prototype: Measured Result

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Ch1

Ch3

Ch2

Ch4Ch5

Ch6

► Ch. 1: Introduction► Ch. 2: Background and Related Work► Ch. 3: WLAN Planning Framework Based on Tabu

Search► Ch. 4: Association Management for Wireless Networks► Ch. 5: Network Monitoring Based on Network Coding► Ch. 6: Conclusion

Part II: Contents

Part III: Summary

Part I: Background

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Introduction to Network Coding Generalization of traditional store &

forward on router Information can be operated on in

network, not just transported At beginning, it was proposed to

improve multicast traffic

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Network Monitoring by Network Coding End-to-end network monitoring

infers network characteristics by sending and collecting probe packets from the network edges, referred to as Network Tomography

Traditional tomography: multicast probing, unicast probing, and per-link monitoring

Network coding based approach More number of links can be

identified Saving network resources by

reducing the number of transmissions

By observing lots of probing results, maximum likelihood can be applied to estimate the loss rate

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Ch1

Ch3

Ch2

Ch4Ch5

Ch6

► Ch. 1: Introduction► Ch. 2: Background and Related Work► Ch. 3: WLAN Planning Framework Based on Tabu

Search► Ch. 4: Association Management for Wireless Networks► Ch. 5: Network Monitoring Based on Network Coding► Ch. 6: Conclusion

Part II: Contents

Part III: Summary

Part I: Background

35

Thesis Contributions: Chapter 3 Modeling the channel assignment and

QoS demand by airtime usage model A closed-form formulation for two

AP placement stages: Minimizing Number of Required AP (MNRAP) and Optimizing Tradeoff Objective (OTOBJ)

Proposing Tabu Search based optimization framework to solve the formulation

General technique to solve nonlinear optimization problem

Plan to use this technique to solve other planning problem, such as wireless sensor network

A Tabu Search Based Optimization Framework for IEEE 802.11 WLAN

Planning with QoS Guarantees, submitted to COMCOM, Elsevier

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Thesis Contributions: Chapter 4 Modeling tradeoff between efficiency and

fairness in WMN Analyze the tradeoff and evaluate for fixed and

random topologies

Distributed scheme Define AP load metric for multi-rate WLAN for

load balancing Prototype implementation

Basically clustering problem Plan to apply it for choosing super node in other

type of networks, such as P2P and DTN Distributed multi-hop extension

Huazhi Gong, Kitae Nahm and JongWon Kim, Distributed Fair Access Point Selection for Multi-Rate

IEEE 802.11 WLANs, IEICE Transactions on Information and Systems 2008, E91-D(4):1193-1196.

Huazhi Gong, Kitae Nahm and JongWon Kim, "Access point selection tradeoff for multi-channel

multi-interface wireless mesh network," in Proc. of CCNC2007

Huazhi Gong, Kitae Nahm and JongWon Kim, Distributed Fair Access Point

Selection forMulti-Rate IEEE 802.11 WLANs, in Proc. of CCNC2008.

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

submitted to IEEE Transcation of Consumer & Electronics

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Thesis Contributions (Intended): Chapter 5 Network tomography based on

network coding Monitoring the loss rate of wireless

links by sending probing packets Considering the random linear coding

feature of wireless networks Still under investigation

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Publication List Submitted Journals

Dynamic Load Balancing through Association Control of Mobile Users in WiFi Networks, submitted to IEEE Transcation of Consumer & Electronics.

A Tabu Search Based Optimization Framework for IEEE 802.11 WLAN Planning with QoS Guarantees, submitted to COMCOM, Elsevier.

International Journals Huazhi Gong, Kitae Nahm and JongWon Kim, Distributed Fair Access Point

Selection for Multi-Rate IEEE 802.11 WLANs, IEICE Transactions on Information and Systems 2008, E91-D(4):1193-1196.

International Conferences Huazhi Gong, Kitae Nahm and JongWon Kim, Distributed Fair Access Point

Selection forMulti-Rate IEEE 802.11 WLANs, in Proc. of CCNC2008. Huazhi Gong, Kitae Nahm and JongWon Kim, "Access point selection tradeoff for

multi-channel multi-interface wireless mesh network," in Proc. of CCNC2007. Huazhi Gong and JongWon Kim, "A multi-channel solution with a single network

interface for multi-hop WLAN coverage expansion", in Proc. of ITC-CSCC 2005, Vol. 3, pp815-816, Jun. 2005. (Also presented in Graduate Workshop in KAIST).