Tradeoff Based Network Management for Wireless Networks
Huazhi GongNetMedia Lab@GIST
Date 2008/05/26
Ph. D Pre-Defence
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► 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
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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).