Farzaneh Pakzad, Marius Portmanny and Jared Hayward School of ITEE, The University of Queensland, Brisbane, Australia Email: farzaneh.pakzad@uq.net.au, marius@ieee.org, jared.hayward@uqconnect.edu.au 25th International Telecommunication Networks and Applications Conference November 18-20, 2015 UNSW, Sydney, Australia DOI: 10.1109/ATNAC.2015.7366814 SDN is a new approach to manage networks with a centralised, global view and control of the network, and a more fine grained and flexible approach to routing and forwarding of data packets. This has shown to achieve significantly increased network efficiency in a range of wired networks. SDN also has a great potential for wireless networks. One of the unique challenges of applying the SDN approach to wireless networks, in contrast to wired networks, is the dynamic nature of wireless links and the uncertainty about their capacity. In order to be able to do optimal routing and traffic engineering with SDN in a wireless network, it is critical to know the capacity of the available wireless links. This paper presents a link capacity estimation mechanism that can be implemented on any OpenFlow SDN controller. For this, we adapted the well-known technique of packet pair/train probing, and developed a method that allows the correction of estimation errors induced by cross traffic. We have implemented a prototype for the Ryu SDN controller, and our emulation-based experimental results show a promising accuracy of our proposed approach.

1. Link Capacity Estimation
in Wireless Software Defined Networks
Farzaneh Pakzad, Marius Portmann, and Jared Hayward
School of ITEE, The University of Queensland
Brisbane, Australia
Presented by Farzaneh Pakzad
25th International Telecommunication Networks and Applications Conference
November 18-20, 2015, UNSW, Sydney, Australia
1

2. Overview
• Background
• Motivation
• Bandwidth & Capacity Estimation in Traditional Networks
• Packet Pair/Train Probing
• Packet Pair/Train Probing in SDN
• Experiments
• Conclusion
2

3. 3
Background:
Logical View of Software Defined Networking (SDN) Architecture

4. 4
Background:
Logical View of Software Defined Networking (SDN) Architecture

5. The Promise of SDN
5

6. Background (contd.)
Wireless Mesh Networks (WMNs)
6
Wireless Routers
Gateways
Mesh Clients
Node Types
Wireless Mesh Networks Challenges and Opportunities, Mihail L. Sichitiu, Electrical and Computer Eng. Dept., NC State University, Raleigh, NC,
USA
Public Safety
Transportation
Mining
Enterprise Network
Emergency Response

7. Potential of using SDN for WMNs
7
Limitations with WMNs:
Network Topology Links Capacity

8. Available Bandwidth vs Link Capacity
The maximum possible bandwidth of a link
The maximum unused bandwidth
9

9. Bandwidth and Capacity Estimation in
Traditional Networks (active)
• Variable Packet Size probing (VPS) [1], [2]
capacity of individual hops
• Self-Loading Periodic Streams (SLoPS) and Trains
of Packet Pairs (TOPP) [4], [5]
end-to-end available bandwidth
• Packet Pair/Train Dispersion probing (PPTD)[3]
end-to-end capacity of the path
Estimates
Estimate
Estimates
10

10. Packet Pair/Train Probing(Basic Idea)
Sender Receiver
Packet1
Packet2
Packet1Packet2
Packet 2 Packet 1
Back-to-Back
Packet size:
Link Capacity:
Time Dispersion:

 LC


C
L
11

11. Packet Pair Probing in SDN
12
The implementation of Link capacity estimation mechanism on
any Standard OpenFlow SDN controller
SDN controller initiate sending of packet pair/train across a link

12. Packet Pair Probing in SDN
Port 2
Port 1
13
Port 1
Port2
P1P2
Probing Packets
P
P1
P2
H1 H2
12 tt  1t2t

 LC
d
Set the “EtherType”
of the Probe packet
to unused value

13. Implementation/Experiments
• Methodology
– Considered simple topology described
– Mininet: Linux based network emulator
– Ns3: Emulate wireless links
– Iperf: Measure wireless link capacity (as a reference)
14
• Ryu as our SDN controller platform

14. Link Capacity Estimation using Packet Pair Probing
15
Switch

15. Link Capacity Estimation using Packet Train Probing
40TLengthTrainPacket
16

16. Estimation Root mean Square Error(RMSE) and
Overhead as a Function of Train Length (T)
17

17. Impact of Cross Traffic
• Cross traffic cause underestimation of the link
capacity
• Two Type of Cross Traffic
– Forward Cross Traffic
Same Direction as the Probe Packets
– Reverse Cross Traffic
Reverse Direction of the Probe Packets
18

18. Impact of Cross Traffic
19
Distance d=0 Train Length T = 40
PPPPP
Forward Cross Traffic Packet Train
probe packets are interleaved with Reverse Cross Traffic

19. Compensate for the Impact of Reverse Cross
Traffic
• Controller query the port statistics from Switches , i.e. received
packet count at port 2 of switch S1
20
Port 2
)(
)1(
)(
T
LRT
TC



R = the number of interleaved reverse cross traffic
between the first and last packet of the train

20. Compensate for the Impact of Reverse Cross
Traffic
21
Distance d=0 Train Length T = 40

21. Conclusions
22
Implemented a Prototype using Ryu SDN controller
Good choice of Train length T
Compensate the impact of cross traffic

22. Future work
Evaluating this approach on a real wireless SDN test bed

23. References
[1] S. M. Bellovin, “A best-case network performance model,” 1992.
[2] V. Jacobson, “Pathchar: A tool to infer characteristics of internet paths,” 1997.
[3] V. Jacobson, M. J. Karels, “Congestion avoidance and control,” in ACM SIGCOMM
computer communication review, vol. 18, no. 4. ACM, 1988, pp. 314–329.
[4] M. Jain and C. Dovrolis, End-to-end available bandwidth: Measurement methodology,
dynamics, and relation with TCP throughput. ACM, 2002, vol. 32, no. 4.
[5] B. Melander, M. Bj¨orkman, and P. Gunningberg, “A new end-to-end probing and
analysis method for estimating bandwidth bottlenecks,” in Global Telecommunications
Conference, 2000. GLOBECOM’00. IEEE, vol. 1. IEEE, 2000, pp. 415–420.
[6] J. Guerin, M. Portmann, K. Bialkowski, W. L. Tan, and S. Glass, “Lowcost wireless link
capacity estimation,” in Wireless Pervasive Computing (ISWPC), 2010 5th IEEE International
Symposium on. IEEE, 2010, pp. 343–348.
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