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A Flow Aggregation Method Based on End-to-End Delay

in SDNTakuya Kosugiyama†, Kazuki Tanabe†, Hiroki Nakayama‡,

Tsunemasa Hayashi‡ and Katsunori Yamaoka†

† Tokyo Institute of Technology, Japan‡ Bosco Technologies Inc., Japan

IEEE ICC 2017 CQRM“A Flow Aggregation Method Based on End-to-End Delay in SDN”

T. Kosugiyama, K. Tanabe, H. Nakayama, T. Hayashi, K. YamaokaOverview

BackgroundGoalModelingAlgorithmResultSummary

2


T. Kosugiyama, K. Tanabe, H. Nakayama, T. Hayashi, K. YamaokaWhat is SDN?

Software-Defined Networking (SDN)• Controllable via API

3

Application Plane

Control Plane

Data Plane

Business Application

Northbound API

Southbound API

Network ServiceSDNController

Network Device Network Device

Network DeviceNetwork Device


T. Kosugiyama, K. Tanabe, H. Nakayama, T. Hayashi, K. YamaokaProblem

Routing Mechanism in OpenFlow (OF)

4

1. Proactive 2. ReactiveOF Controller

OF Switch

① Configure rule

1 2

dst IP: A → Port: 2

③ Forwarding

1 2To: A To: A

③ Configure rule

dst IP: A → Port: 2② Request rule

④ Forwarding

② Packet comes ① Packet comes

[1] S.H. Yeganeh, A. Tootoonchian, and Y. Ganjali, “On scalability of software-defined networking,” IEEE Communications Magazine,vol.51, no.2, pp.136–141, Feb. 2013.

OF Controller

OF Switch


T. Kosugiyama, K. Tanabe, H. Nakayama, T. Hayashi, K. YamaokaProblem

Routing Mechanism in OpenFlow (OF)

5

1. Proactive 2. ReactiveOF Controller

OF Switch

① Configure rule

1 2

dst IP: A → Port: 2

③ Forwarding

1 2To: A To: A

③ Configure rule

dst IP: A → Port: 2② Request rule

④ Forwarding

② Packet comes ① Packet comes

• Rule update rate (1000 rules/s)• Rule capacity (10〜50 K) [1] SDN scaling issues

[1] S.H. Yeganeh, A. Tootoonchian, and Y. Ganjali, “On scalability of software-defined networking,” IEEE Communications Magazine,vol.51, no.2, pp.136–141, Feb. 2013.

OF Controller

OF Switch


T. Kosugiyama, K. Tanabe, H. Nakayama, T. Hayashi, K. YamaokaGoal

How to manage massive flows in SDN• Aggregate flows and reduce number of flows

• Rule aggregation method based on bandwidth [1][2]• Rule division method to satisfy capacity [3][4]

There are no works to aggregate flows based on End-to-End allowable delay• Target: IoT / M2M flow

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[1] F. Giroire, J. Moulierac, and T. K. Phan. Optimizing rule placement in software-defined networks for energy-aware routing. In 2014 IEEE Global Communications Conference, pp. 2523–2529, Dec 2014. [2] Xuan Nam Nguyen, Damien Saucez, Chadi Barakat, and Thierry Turletti. OFFICER: A general optimization framework for OpenFlow rule allocation and endpoint policy enforcement. Proceedings - IEEE INFOCOM, Vol. 26, pp. 478–486, 2015. [3] Yossi Kanizo, David Hay, and Isaac Keslassy. Palette: Distributing tables in software-defined networks. Proceedings - IEEE INFOCOM, pp. 545–549, 2013. [4] Nanxi Kang, Zhenming Liu, Jennifer Rexford, and David Walker. Optimizing the ”One Big Switch” Abstraction in Software-Defined Networks. Conext’13, p. 17, 2013.

A flow aggregation method to minimize number of flowssatisfying allowable delay


T. Kosugiyama, K. Tanabe, H. Nakayama, T. Hayashi, K. YamaokaApproach

Basis of flow aggregation• Consider flows on same section as one flow

• Route by aggregated flow• ex) Full mesh flows (allowable delay = 80)

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f1

f2

f’1

f’2

f12

Tunnel and labeling

(a) Network (b) Maximum aggregationnot satisfying allowable delay

2020

2040

20+20+20+40= 100 > 80

• flow: 3 → 1

Link cost


T. Kosugiyama, K. Tanabe, H. Nakayama, T. Hayashi, K. YamaokaApproach

Basis of flow aggregation• Consider flows on same section as one flow

• Route by aggregated flow• ex) Full mesh flows (allowable delay = 80)

8

f1

f2

f’1

f’2

f12

Tunnel and labeling

(a) Network (b) Maximum aggregationnot satisfying allowable delay

2020

2040

Link cost

(c) Maximum aggregationsatisfying allowable delay


T. Kosugiyama, K. Tanabe, H. Nakayama, T. Hayashi, K. YamaokaModel setting (1)

SDN as a directed graph• Node:• Link:

• Link cost:

• Flow: • Allowable cost:

• Relation between flow and link

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Constraints to route flow

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There is only one flow from a source node and to a destination node




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There is no flow to a source node and from a destination node




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Number of incoming flows equals to number of outgoing flows on a node




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A node is visited only one time



Constraint of allowable cost

Aggregate flows• Flows on same link and same direction

• Objective: minimize number of flows

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T. Kosugiyama, K. Tanabe, H. Nakayama, T. Hayashi, K. Yamaoka

Deformation••

•

••

Model setting (4)15

Apply to

0,1-Knapsack Problem⇒ NP-hard

y and z are vectors (0,…1,…,0) which correspond to the index of h and g



Overview1. Ascending sort by (minimum cost - allowable cost) ⇒ Fs2. Select the first element of Fs⇒ f3. [Flow compotision]

• Compose route of f using existing flows4. [Flow aggregation]

• Aggregate flows in Fs into existing flows5. Return to 2. unless Fs is empty

Heuristics16

Flow composition Flow aggregationExisting flows

= flexibility of changing route


T. Kosugiyama, K. Tanabe, H. Nakayama, T. Hayashi, K. YamaokaFlow composition

Path composition by Best-First Search (BFS)• Evaluation value of node :

• : number of times existing rules are used• : minimum cost to reach from to

• Prioritize two nodes

• Time complexity:

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Flow composition by Best-First Search•

Flow composition: Ex.118

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3

2

2

4

4

2

35

3

2

2

4

4

2

Dashed arrows: existing flows

{0,0}

{0,3}

{0,5}

Evaluation value: {t, c}t : number of times existing rules are usedc : minimum cost to reach from source to current





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3

2

2

4

4

2

35

3

2

2

4

4

2


{0,0}

{0,3}

{0,5}

{1,5}






35

3

2

2

4

4

2

35

3

2

2

4

4

2


{0,0}

{0,3}

{1,5}

{2,9}


{1,8}





35

3

2

2

4

4

2

35

3

2

2

4

4

2


{0,0}

{0,3}

{1,5}

{2,9}

{2,13}≦

Evaluation value: {t, c}

Number of adding flows: 2

t : number of times existing rules are usedc : minimum cost to reach from source to current

{1,8}





35

3

2

2

4

4

2

35

3

2

2

4

4

2


{0,0}

{0,3}

{1,5}

{2,9}

{2,13} >

{1,8}





35

3

2

2

4

4

2

35

3

2

2

4

4

2


{0,0}

{0,3}

{1,5}

{2,9}{1,8}





35

3

2

2

4

4

2

35

3

2

2

4

4

2


{0,0}

{0,3}

{1,5}

{2,9}{1,8}

{1,10}≦

Number of adding flows: 3



1. Ascending sort by (minimum cost - allowable cost) ⇒ Fs2. Select the first element of Fs⇒ f3. [Flow compotision]

• Compose route of f using existing flows4. [Flow aggregation]

• Aggregate flows in Fs into existing flows5. Return to 2. unless Fs is empty

Overview

Heuristics25

Flow composition Flow aggregationExisting flow

f1 f2

f3 (f1, f2)


T. Kosugiyama, K. Tanabe, H. Nakayama, T. Hayashi, K. YamaokaFlow aggregation

Delete flows in Fs over existing flows• Calculate flows can be reached within allowable

delay by Dijkstra’s algorithm• Time complexity:

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f3 (f1’, f2’)

f1

f2 Flow aggregationf1’

f2’

f3 can be routed using part of f1 and f2



Simulation settings

Simulation• Topology

• Model: ER / BA• Size: |V| = 50, 500

• Parameters• Link cost: [5, 15], discrete uniform distribution• alpha: ratio of allowable cost of flows to the maximum

value of the minimum delay of flows• Ex) min cost = 10, alpha = 2 ⇒ Cf = 20

• Comparison• Minimum cost / Minimum hop routing with aggreagtion

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Table: topologies



Performance evaluation (1)

Effect of allowable cost• |F| = 1000

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Fig. 1 Effect of allowable cost of flows (Scalefree-small)

There is not much effect of alpha




Effect of allowable cost• |F| = 1000

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Fig. 1 Effect of allowable cost of flows (Scalefree-small)

Flow composition selects first route→ No more effect of allowable delay




Effect of number of flows• α = 5

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Fig. 2 Random-large Fig. 3 Scalefree-large

HIGH link density→ more effective


T. Kosugiyama, K. Tanabe, H. Nakayama, T. Hayashi, K. YamaokaSummary & Future work

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We propose a flow aggregation method to minimize number of flows satisfying End-to-End delaySimulation in several topologies• Higher performance than simple comparison method• Flexibility of changing route is importantFuture work• Expand our model more realistic• Bypassing on weak topology

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