1. 考慮端對端延遲與流量公平性之無線網狀網路最佳化建置
Deployment Optimization of Wireless Mesh Networks Considering End-to-End Delay and Throughput Fairness
考慮端對端延遲與流量公平性之無線網狀網路最佳化建置
蔡永斌
Tsai, Yung-Pin
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

2. Agenda Introduction Problem Formulation Background Motivation
Literature Survey
Problem Formulation
Problem Description
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

3. Background Taxonomy of Wireless Networks
Taxonomy of Wireless Mesh Networks
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

4. Taxonomy of Wireless Networks
Networking
Multi-hop
Infrastructure-less
Single-hop
Wireless Wide
Area Networks
(WWANs)
Mesh Networks
(WMNs)
Infrastructure-based
Wireless Local
(WLANs)
Metropolitan
(WMANs)
Personal Area
Networks
(WPANs)
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

5. Infrastructure-based
Taxonomy of WMNs
WMNs
Infrastructure-based
Infrastructure-less
Hybrid
Infrastructure/
backbone WMNs
Client WMNs
Hybrid WMNs
分類方法參考自: I. F. Akyildiz, X. Wang and W. Wang, “Wireless Mesh Networks: a Survey,” Computer Networks, Vol. 47, Issue 4, Pages , March 2005.
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

6. Infrastructure/backbone WMNs
圖擷取自: I. F. Akyildiz, X. Wang and W. Wang, “Wireless Mesh Networks: a Survey,” Computer Networks, Vol. 47, Issue 4, Pages , March 2005.
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

7. Client WMNs
圖擷取自: I. F. Akyildiz, X. Wang and W. Wang, “Wireless Mesh Networks: a Survey,” Computer Networks, Vol. 47, Issue 4, Pages , March 2005.
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

8. Hybrid WMNs
圖擷取自: I. F. Akyildiz, X. Wang and W. Wang, “Wireless Mesh Networks: a Survey,” Computer Networks, Vol. 47, Issue 4, Pages , March 2005.
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

9. Agenda Introduction Problem Formulation Background Motivation
Literature Survey
Problem Formulation
Problem Description
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

10. Motivation Performance limitation of multi-hop wireless networks:
Channel bandwidth is shared by a number of transmissions.
Benefit gained through building relay nodes decreases when network grows. [1]
Longer hop paths must bear significantly lower throughput compare with short paths. [2][3][4][5]
The performance of the network can improve is limited, because the total network capacity has been constrained by network architecture.
[1] P. Gupta and P. R. Kumar, “The Capacity of Wireless Networks,” IEEE Transactions on information theory, Vol. 46, No. 2, Pages , 2000.
[2] I. F. Akyildiz, X. Wang and W. Wang, “Wireless Mesh Networks: a Survey,” Computer Networks, Vol. 47, Issue 4, Pages , March 2005.
[3] B. Li, “End-to-End Fair Bandwidth Allocation in Multi-Hop Wireless Ad Hoc Networks,” Proc. IEEE ICDCS’05.
[4] V. Gambiroza, B. Sadeghi and E. W. Knightly, “End-to-End Performance and Fairness in Multihop Wireless Backhaul Networks,” Proc. ACM MobiCom, Pages , Sept.-Oct
[5] Y. F. Wen and Frank Y. S. Lin, “The Top Load Balancing Forest Routing in Mesh Networks”, Proc. IEEE CCNC, pages , Jan
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

11. Motivation
Even through during the operation stage we can deploy additional equipments or upgrade equipments to increase link capacity, throughput is not always raised[1].
The greatest motivation of our work:
We consider end-to-end throughput and delay fairness at network planning stage simultaneously.
Optimization issues like channel assignment, routing and flow control are jointly considered in the WMNs deployment problem we addressed.
With the objective to maximize the minimal end-to-end throughput, in other word, to maximize the total network capacity.
[1] A. Tang, J. Wang and S. H. Low, “Counter-Intuitive Throughput Behaviors ini Networks Under End-to-End Control,” IEEE Transactions on Networking, Vol. 14, No. 2, Pages , April 2006.
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

12. Agenda Introduction Problem Formulation Background Motivation
Literature Survey
Problem Formulation
Problem Description
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

13. Literature Survey Capacity of WMNs End-to-End Fairness of WMNs
IEEE s/D3
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

14. Capacity of WMNs
The capacity of WMNs is influenced by many factors including :
Network architecture
Network topology
Traffic pattern
Network node density
Number of channels used for each node
Transmission power level
I. F. Akyildiz, X. Wang and W. Wang, “Wireless Mesh Networks: a Survey,” Computer Networks, Vol. 47, Issue 4, Pages , March 2005.
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

15. Capacity of WMNs Capacity analysis of multi-hop wireless networks [1]
P. Gupta and P. R. Kumar
Optimal placed and random placed multi-hop wireless networks
Interference models: physical model and protocol model
Transmission power control
Tradeoff between spatial reuse efficiency and end-to-end delay. [2]
When a node has six neighboring nodes, there will be optimum transmission power level. [3,4]
Flexible transmission power control capability can significantly increase network performance.
[1] I. F. Akyildiz, X. Wang and W. Wang, “Wireless Mesh Networks: a Survey,” Computer Networks, Vol. 47, Issue 4, Pages , March 2005.
[2] L. Kleinrock and J. Silvester, “Optimum Transmission Radii for Packet Radio Networks or Why Six Is A Magic Number,” Proc. IEEE National Telecommunications Conference, Pages , December 1978.
[3] E. M. Royer, P. M. M. Smith and L. E. Moser, “An Analysis of the Optimum Node Density for Ad hoc Mobile Networks,” Proc. IEEE ICC, Vol. 3, Pages , June 2001.
[4] J. Gomez and A. T. Campbell, “Variable-Range Transmission Power Control in Wireless Ad Hoc Networks,” IEEE Transaction on Mobile Computing, Vol. 6, No. 1, Pages 87-99, January 2007.
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

16. Capacity of WMNs To increase the network capacity: End-to-end fairness
Multi-hop multi-channel multi-radio wireless networks [1, 2, 3, 4, 5]
Joint channel assignment, link scheduling, routing algorithms [1, 2, 4, 5]
End-to-end fairness
The tradeoff between fair quality of service (QoS) and network performance [6]
[1] H. Yu, P. Mohapatra and X. Lin, “Channel Assignment and Link Scheduling in Multi-Radio Multi-Channel Wireless Mesh Networks,” Mobile Networks and Applications, Vol. 13, Issue 1-2, Pages , April 2008.
[2] M. Kodialam and T. Nandagopal, “Characterizing the Capacity Region in Multi-Radio Multi-Channel Wireless Mesh Networks,” Proc. MobiCom’05.
[3] W. Wang and X. Liu, “A Framework for Maximum Capacity in Multi-channel Multi-radio Wireless Networks,” Proc. IEEE CCNC’06.
[4] X. Y. Li, A. Nusairat, Y. Wu, Y. Qi, J. Zhao, X. Chu and Y. Liu, “Joint Throughput Optimization for Wireless Mesh Networks,” IEEE Transactions on Mobile Computing, Vol. 8, Issue 7, Pages , July 2009.
[5] J. Tang, G. L. Xue and W. Y. Zhang, “Cross-Layer Optimization For End-To-End Rate Allocation in Multi-Radio Wireless Mesh Networks,” Wireless Networks, Vol. 15, No. 1, January 2009.
[6] B. Li, “End-to-End Fair Bandwidth Allocation in Multi-Hop Wireless Ad Hoc Networks,” Proc. IEEE ICDCS’05.
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

17. End-to-End Fairness of WMNs
In WMNs, competitions for resources include inter-flow contention and intra-flow contention [1].
Max-min fairness model
The fairness issue studied in our work includes:
End-to-end throughput fairness
End-to-end delay fairness
[1] B. Li, “End-to-End Fair Bandwidth Allocation in Multi-Hop Wireless Ad Hoc Networks,” Proc. IEEE ICDCS’05.
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

18. Inter-Flow Contention
In a single-hop wireless network:
w2 : w1 = 1 : 2 F1 F2
2/3B
1/3B
參考自: B. Li, “End-to-End Fair Bandwidth Allocation in Multi-Hop Wireless Ad Hoc Networks,” Proc. IEEE ICDCS’05.
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

19. Intra-Flow Contention
In a multi-hop wireless mesh network F1
F2.1
F2.2
F2.3
2/3B
1/9B
r2 : r1 = 1 : 6
unfair
1/9B
1/3B
1/9B
參考自: B. Li, “End-to-End Fair Bandwidth Allocation in Multi-Hop Wireless Ad Hoc Networks,” Proc. IEEE ICDCS’05.
2/5B
1/5B
r2 : r1 = 1 : 2
FAIR
1/5B
3/5B
1/5B
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

20. End-to-End Fairness of WMNs
To achieve end-to-end throughput fairness:
Bandwidth allocation [1]
Spatial reuse maximization
The method extends the per-hop link bandwidth allocation to multi-hop flow fairness.
This kind of approaches can be achieved through flow control
To achieve end-to-end delay fairness:
Load-balanced routing [2]
End-to-end aggregated mean delay and delay jitter have been used as the routing metric
Bandwidth allocation and load-balanced routing [2]
[1] B. Li, “End-to-End Fair Bandwidth Allocation in Multi-Hop Wireless Ad Hoc Networks,” Proc. IEEE ICDCS’05.
[2] Y.F. Wen, F.Y.S. Lin, Y.C. Tzeng and C.T. Lee, "Backhaul Assignment and Routing Algorithms with End-to-End QoS Constraints for Wireless Mesh Networks", Springer Wireless Personal Communications, Feb
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

21. End-to-End Fairness of WMNs
Cross-layer optimization approach [1]
Rate allocation, routing, scheduling, transmission power control and channel assignment problems
Max-min fairness (end-to-end throughput fairness)
Multi-hop wireless network deployment [2]
Backhaul and routing assignment problem which guarantees end-to-end delay fairness.
In our research:
Mesh router and backhaul assignment, power control, channel assignment, routing and flow control
Both end-to-end throughput and end-to-end delay fairness
[1] J. Tang, G. L. Xue and W. Y. Zhang, “Cross-Layer Optimization For End-To-End Rate Allocation in Multi-Radio Wireless Mesh Networks,” Wireless Networks, Vol. 15, No. 1, January 2009.
[2] Y.F. Wen, F.Y.S. Lin, Y.C. Tzeng and C.T. Lee, "Backhaul Assignment and Routing Algorithms with End-to-End QoS Constraints for Wireless Mesh Networks", Springer Wireless Personal Communications, Feb
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

22. IEEE 802.11s IEEE 802.11s/D3 Internet
An enhancement of the original standard to support WLAN implementations for more flexible interoperable wireless connectivity.
Internet
Portal 1
Portal 2 AP
MC1
MC2
MC3
MC4
MC5
MBSS
infrastructure BSS
Mesh STA 3
Mesh STA 7
Mesh STA 2
Mesh STA 6
Mesh STA 1
Mesh STA 8
Mesh STA 5
Mesh STA 4
圖文參考自: IEEE Standard, “Draft STANDARD for Information Technology- Telecommunications and information exchange- between systems- Local and metropolitan area networks- Specific requirements- Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications,” March 2009.
infrastructure BSS
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

23. Agenda Introduction Problem Formulation Background Motivation
Literature Survey
Problem Formulation
Problem Description
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

24. Problem Description
Objective in this paper is to establish an infrastructure WMN and make the network total network capacity as large as we can with end-to-end fairness requirements.
A dominated node represents Internet
Mesh STAs
Portals
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

25. System Model Decisions Given Dominated node Mesh STAs with
portal functionalities
Decisions
Mesh STAs with
AP functionalities
Given
Mobile clients
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

26. Protocol Interference Model
Suppose node transmits to a node Then this transmission is successfully received by node if
for every other node simultaneously transmitting over the same channel k
參考自: P. Gupta and P. R. Kumar, “The Capacity of Wireless Networks,” IEEE Transactions on information theory, Vol. 46, No. 2, Pages , 2000. i j
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

27. Contentions Between Flows
F1 and F2 are two different end-to-end flows.
Dash lines represented as intra-flow contention happens between F1 and F2
Inter-Flow Contention F1
F2.1
F2.2
F2.3
F1 and F2 are two different end-to-end flows.
Intra-flow contentions between sub-flows F2.1, F2.2 and F2.3
Intra-Flow Contention
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

28. Problem Description Routing Uplink Unicasting Single path
Dominated node
Mesh STAs with
portal functionalities
Mesh STAs with
AP functionalities
Mobile clients
2018/12/4

29. Problem Formulation Assumptions Each mesh STA is stationary.
Each mesh STA can be equipped with multiple radios each of which operates on a particular and non-overlapping channel.
Radios of each mesh STA are omni-directional.
The sources are greedy and the flow from each source is backlogged.
Packets can be buffered at mesh STAs while awaiting transmission.
The routing path of each OD-pair is single path.
Each mesh STA has AP functionalities and can be upgrade to have portal functionalities.
Each MC must home to a mesh STA and the selection is through system.
Each MC and mesh STA has the capability of perfect power control.
The channel MCs use is selected by system.
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

30. Problem Formulation Given CLs for mesh STAs
Weighting factors for each MC
Cost function for building a mesh STA and additional cost to have portal functionalities
Distances between nodes
Maximum transmission range of each radio
Limited number of available channels of each radio
Budget constraint
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

31. Problem Formulation Objective: Subject to:
To maximize the minimal weighted end-to-end throughput
Subject to:
Budget constraint
Mesh STA and portal assignment constraints
Routing constraints
Link assignment constraints
Link constraints
Capacity constraints
QoS constraints
Integer constraints
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

32. Given Parameters
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Network Optimization Laboratory, Information Management Department, National Taiwan University

33. 2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

34. Decision Variables
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

35. Problem Formulation Objective function: Budget constraint: LP1
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

36. Problem Formulation Mesh and portal assignment constraints:
Routing constraints
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

37. Problem Formulation Link Assignment Constraints
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

38. Problem Formulation Link constraints
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

39. Problem Formulation Capacity constraints
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

40. Problem Formulation QoS constraints
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

41. Problem Formulation Integer constraints
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University

42. Thanks for your listening
2018/12/4
Network Optimization Laboratory, Information Management Department, National Taiwan University