load balancing in sdn networks using metaheuristic methods

GYTE - Bilgisayar Mühendisliği Bölümü

Bilgisayar Mühendisliği Bölümü


Load Balancing in SDN Networks

using Metaheuristic Methods

CSE624

Mahmud Rasih ÇELENLİOĞLU

December 2013


Outline

• Overview of Software Defined Networking

• Related Works

• LB-VNS

• LB-GA

• Conclusion

2


SDN Architecture

3

Control Plane:

• Logically centralized

• Smart

• Slow

Data Plane:

• Dummy

• Fast

Protocol for Controller – Switch Communication

(e.g. OpenFlow Protocol)


Software-Defined Networking

• Traffic Engineering

• Network Virtualization

• Virtual Private Networks

Scalability

• Signalling

• Computational Complexity

• Switch Configuration

Reliability

• Controller

4


Related Works – MPLS

5


Virtual Path Approach

6

• Create virtual links

• Capacity of VL is proportional to pyhsical link

capacity

• Local Solution


Objective Function

7

• Max load balancing with min path change

• Path Change Ratio (PCR): How many flows are

shifted to other paths

• Avg Distance Ratio (ADR): Avg distance btw current

loads and ideal loads for all paths


Details

8

• Distribute N flows to M paths

• Solution Representation

8 20 12 16 32 14 10 8 8 24

2 2 1 3 1 2 1 1 3 3

2 2 2 3 3 2 2 1 1 2


LB-VNS

9

Neighborhood Structures:

– Smart Insert: Randomly chosen flow is assigned to the path whose

distance is maximum

– Random Insert: Randomly chosen flow is assigned to a randomly

chosen path

– Smart Swap: Randomly chosen two flows are swapped between two

paths with max distances. Distance of one path exceeds the ideal load

and the another one does not

– Random Swap: Randomly chosen two flows are assigned to two

randomly chosen paths

– Fully Random Assignment: All flows are randomly assigned to paths

without any consideration

Local Search: Smart Insert, Smart Swap

Shaking: Fully Random Assignment


LB-VNS

10


GA

11

• Population Size: 20

• Tournament Selection:

– First Tournament:

1. Select F number of individuals

2. Pick best among them

– SecondTournament:

1. Discard individuals attended to first tournament

2. Select S number of individuals & Pick best among them

• F = Population Size/3 & S = (Population Size - F)/3path


GA

12

Crossover:

• Dominant Gene: Flows Size < Th-Min OR Flows Size > Th-Max

• Recessive Gene: Th-Min <= Flows Size <= Th-Max

• Dominant Gene: Stay in the same path with probability of 0.9 otherwise

inherited from one of parents

• Recessive Gene: Always inherit assignment of one of parents

Mutation:

• Shift a flow belongs to overloaded path to underloaded

• Perform after crossover


LB-GA

13


LB-GA Experiment

14

• 10 run for each path number [2,5]

• 1000 iteration


LB-GA Experiment

15


Conclusion

16

• LB-GA works better than LB-VNS

• LB-VNS is faster

• Performance of LB-VNS can be increased by changing local

search method


Questions?

Thank You

&

Questions?

17


Resources

18

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Resources

19

13. Mohammad Al-Fares, Sivasankar Radhakrishnan, Barath Raghavan, Nelson Huang, and Amin Vahdat. 2010. "Hedera:

dynamic flow scheduling for data center networks." In Proceedings of the 7th USENIX conference on Networked systems design

and implementation (NSDI'10). USENIX Association, Berkeley, CA, USA, 19-19.

14. Minlan Yu, Jennifer Rexford, Michael J. Freedman, and Jia Wang Scalable flow-based networking with DIFANE, ACM

SIGCOMM, New Delhi, India, August 2010.

15. Richard Wang, Dana Butnariu, and Jennifer Rexford OpenFlow-based server load balancing gone wild, Workshop on Hot

Topics in Management of Internet, Cloud, and Enterprise Networks and Services (Hot-ICE), Boston, MA, March 2011.

16. H.E. Egilmez, B. Gorkemli, A.M. Tekalp, S. Civanlar. Scalable Video Streaming Over OpenFlow Networks: An Optimization

Framework For QoS Routing, Proc. IEEE International Conference on Image Processing (ICIP 2011), Brussels, Belgium,

September 2011.

17. A. Tootoonchian and Y. Ganjali, "Hyperflow: A distributed control plane for openflow," in Proceedings of the 2010 internet

network management conference on Research on enterprise networking. USENIX Association, 2010, pp. 3–3.

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Software and Applications Conference (COMPSAC), 2013 IEEE 37th Annual , vol., no., pp.467,470, 22-26 July 2013.

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Retrieved 2012-04-17.

http://www.wired.com/wiredenterprise/2012/04/going-with-the-flow-google/all/1

load balancing in sdn networks using metaheuristic methods

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