Dynamic Traffic Engineering for Improving Energy ...dpnm.postech.ac.kr/thesis/13/sesise/thesis_presentation_sesise.pdf · Dynamic Traffic Engineering for Improving Energy Efficiency

Sin-seok Seo, POSTECH PhD Thesis Defense 1/34

Dynamic Traffic Engineering for Improving Energy Efficiency and Network Utilization

of Data Center Networks

- PhD Thesis Defense -

Sin-seok Seo

Dept. of Computer Science and EngineeringPOSTECH, Korea

Email: [email protected]

Supervisor: Prof. James Won-Ki HongCo-supervisor: Prof. Jae-Hyoung Yoo

Dec. 20, 2013

mailto:[email protected]


Outline

Introduction

Related Work

Dynamic Traffic Engineering

Validation

Conclusion



Research Background (1/3)

Three Keywords Data Center Network (DCN)

Traffic Engineering (TE)

Software Defined Networking (SDN)

Data Center Network (DCN) Tens of thousands of servers

Virtualization

Hierarchical topology

North-South traffic East-West traffic



Traffic Engineering (TE) Definition

• Routing optimization for enhancing network service capability without causing network congestions [1]

Objective

• Load balancing

• Power saving

• Failure recovery

• Etc.

Approach

• Multi-Protocol Label Switching (MPLS)

• Internet Protocol (IP)

• Software Defined Networking (SDN)



Software Defined Networking (SDN) Separate the control plane from the data plane

OpenFlow Protocol

• Standard by Open Networking Foundation (ONF) for communications between SDN controllersand network devices

Flow Entry Match Field Counter Action

1 Dst IP: 10.0.1.2 22 Port 3

2 Dst TCP/UDP Port: 80 14 Drop

... ... ... ...

Control Plane

Data Plane

Mgmt.

Plane

Traditional Network SDN

Controller

OpenFlow Protocol

Flow Table

* OpenFlow 1.0 기준

IngressPort

SrcMAC

DstMAC

EtherType

VLANID

VLANPriority

SrcIP

DstIP

IPProto.

IPToS

SrcPort

DstPort

L1 L2 L3 L4

Suitable fordynamic TE


Research Motivation and Goal

Inefficient Power Consumption To reduce power consumption

Congestion due to Static Routing To minimize congestion

Lack of Efficient Method for Failure Recovery To rapidly restore from failures

Pod 0 Pod 1 Pod 2 Pod 3

CongestionsFailure

Failure

Idle

Idle Idle



Traffic Engineering for DCN

CategoryOur

ApproachElasticTree(NSDI ’10 [2])

Hedera(NSDI ’10 [3])

microTE(CoNEXT ’11 [4])

PEFT(TPDS ’13 [5])

DLB(ICC ’13 [6])

ObjectiveMin. MLU and Power

Cost

Min.PowerCost

Max.Bisection

BW

Min.MLU

Min.MLU

LoadBalancing

Failure Recovery

Yes No Yes No No No

Optimization Global Global Local Global Local Local

Approach SDN SDN SDN SDN IP SDN

TEGranularity

Flow FlowElephant

FlowFlow Packet Flow

ValidationTopology

Fat-Tree [7] Fat-Tree Fat-Tree TreeTree &Fat-Tree

Fat-Tree



System Architecture and Assumption

System Architecture Assumption Traffic Matrix (TM) can be

estimated

• TM is a set of (Flow, Demand) tuples

Switches and links can be turned on/off

Flow tables in a switch can be separately updated

Overheads of flow table modifications are negligible


Traffic Engineering Manager

Traffic Engineering Manager

Switch & Link On/Off Flow Table Update

Optimal Topology Composition

Traffic Matrix(hour)/

Failure Info.

TrafficLoad Balancing

Traffic Matrix(min or sec)/Failure Info.

Failure Recovery Failure Info./Link Statistics

Switch & Link On/Off Status

Available Link Capacity

Network Topology and Link Capacity

Long-term Cycle(~hours)

Short-term Cycle(~minutes)

Failure Occurrence

Optimal Topology Composition

Traffic Load Balancing

Failure Recovery


TE Algorithmic Approach

Linear Programming (LP) Mathematical model for finding an optimal solution

• Represented as linear relationships

Multi Commodity Flow (MCF) Problem

• Primitive LP-based approach for TE

• Allocate flows to each link

Path-based MCF (proposed)

• Simplified variation of the MCF

• Allocate flows to each path

Heuristic Approximation algorithm for solving a large scale problem

• Find a near-optimal solution in a short period of time

MCFComp.


Path-based MCF (1/2)

Input Topology: 𝐺(𝑉, 𝐸)

Traffic Matrix: 𝑇, where 𝑇𝑖 = (𝑠𝑖 , 𝑡𝑖 , 𝑑𝑖)

Link capacity: ∀ 𝑢, 𝑣 ∈ 𝐸, 𝑐(𝑢, 𝑣)

Set of considered paths: ∀𝑖, 𝑃𝑇𝑖 = {𝑝𝑖,0, … , 𝑝𝑖,𝑗 , … , 𝑝𝑖,𝑙}

Decision Variable Flows along each path: ∀𝑖, ∀𝑝 ∈ 𝑃𝑇𝑖 , 𝑓𝑖(𝑝)

Src IP (s) Dst IP (t) Demand (d)

10.0.0.2 10.1.1.3 10

10.3.0.3 10.3.0.2 5

… … …


Path-based MCF (2/2)

Constraint

Capacity limitation: ∀ 𝑢, 𝑣 ∈ 𝐸, 𝑖=1𝑘

𝑝∈𝑃𝑇𝑖𝑢,𝑣 𝑓𝑖 (𝑝) ≤ 𝑐(𝑢, 𝑣)

Demand satisfaction: ∀𝑖, 𝑝∈𝑃𝑇𝑖𝑓𝑖 (𝑝) = 𝑑𝑖

Capacity Limitation:≤ 𝑐(𝑢, 𝑣)

10 10DemandSatisfaction

5 5

Src IP (s) Dst IP (t) demand (d)

10.0.0.2 10.1.1.3 10

10.1.0.3 10.1.0.2 5

… … …


Optimal Topology Composition (1/4)

Heuristic For each flow

• Get equal-cost shortest paths

• Assign the flow to the leftmost path with sufficient available BW

• Decrease the available BWs of links comprising the selected path

Composite a subset topology with only used switches and links

i Src IP Dst IP Demand

1 10.0.0.2 10.1.1.3 10

2 10.1.0.3 10.1.0.2 5

... … … …


InsufficientBW

Algorithm* Source: Network Traffic Characteristics of Data Centers in the Wild, IMC ’10 [8]



Linear Programming (using Path-based MCF) Input

• Topology: 𝐺(𝑉, 𝐸)

• Traffic Matrix: 𝑇, where 𝑇𝑖 = (𝑠𝑖 , 𝑡𝑖 , 𝑑𝑖)

• Link capacity: ∀ 𝑢, 𝑣 ∈ 𝐸, 𝑐(𝑢, 𝑣)

• Set of considered paths: ∀𝑖, 𝑃𝑇𝑖 = {𝑝𝑖,0, … , 𝑝𝑖,𝑗 , … , 𝑝𝑖,𝑙}

• Power cost of links and switches: 𝑎 𝑢, 𝑣 and 𝑏(𝑢)

Decision Variable

• Flows along each path: ∀𝑖, ∀𝑝 ∈ 𝑃𝑇𝑖 , 𝑓𝑖(𝑝)

• Binary variable indicating power status of a link: 𝑋𝑢,𝑣

• Binary variable indicating power status of a switch: 𝑌𝑢

Objective

• Minimize (𝑢,𝑣)∈𝐸𝑋𝑢,𝑣 × 𝑎 𝑢, 𝑣 + 𝑢∈𝑆𝑌𝑢 × 𝑏(𝑢)



Linear Programming (cont’d) Constraint

• Capacity limitation: ∀ 𝑢, 𝑣 ∈ 𝐸, 𝑖=1𝑘

𝑝∈𝑃𝑇𝑖𝑢,𝑣 𝑓𝑖 (𝑝) ≤ 𝑋𝑢,𝑣 × 𝑐(𝑢, 𝑣)

• Demand satisfaction: ∀𝑖, 𝑝∈𝑃𝑇𝑖𝑓𝑖 (𝑝) = 𝑑𝑖

• Bidirectional link power: ∀ 𝑢, 𝑣 ∈ 𝐸, 𝑋𝑢,𝑣 = 𝑋𝑣,𝑢

• Switch-to-link correlation: ∀𝑢 ∈ 𝑆, 𝑋𝑢,𝑤 = 𝑋𝑤,𝑢 ≤ 𝑌𝑢

• Link-to-switch correlation: ∀𝑢 ∈ 𝑆, 𝑌𝑢 ≤ 𝑋𝑤,𝑢 = 𝑋𝑢,𝑤



Extra Switch and Link Addition High utilization of links in the optimal topology High probability of link congestions

Solution

• Adding extra switches and links to the optimal topology

• Post-processing: Traffic load balancing



Traffic Load Balancing (1/2)

Heuristic Sort TM according to demands

For each flow

• Get equal-cost shortest paths

• Assign the flow to the path with minimum MLU

• Decrease the available BWs of links comprising the selected path


i Src IP Dst IP Demand

1 10.0.0.2 10.1.1.3 10

2 10.1.0.3 10.1.0.2 5

... … … …

Algorithm



Linear Programming (using Path-based MCF) Input

• Topology: 𝐺(𝑉, 𝐸)

• Traffic Matrix: 𝑇, where 𝑇𝑖 = (𝑠𝑖 , 𝑡𝑖 , 𝑑𝑖)

• Link capacity: ∀ 𝑢, 𝑣 ∈ 𝐸, 𝑐(𝑢, 𝑣)

• Set of considered paths: ∀𝑖, 𝑃𝑇𝑖 = {𝑝𝑖,0, … , 𝑝𝑖,𝑗 , … , 𝑝𝑖,𝑙}

Decision Variable

• Maximum Link Utilization (MLU): 𝑚

• Flows along each path: ∀𝑖, ∀𝑝 ∈ 𝑃𝑇𝑖 , 𝑓𝑖(𝑝)

Objective

• Minimize 𝑚

Constraint

• Capacity limitation: ∀ 𝑢, 𝑣 ∈ 𝐸, 𝑖=1𝑘

𝑝∈𝑃𝑇𝑖𝑢,𝑣 𝑓𝑖 (𝑝) ≤ 𝑚 × 𝑐(𝑢, 𝑣)

• Demand satisfaction: ∀𝑖, 𝑝∈𝑃𝑇𝑖𝑓𝑖 (𝑝) = 𝑑𝑖


Failure Recovery

Recovery using Detour Path Minimize the number of switches to be modified: 3

Consider network status when selecting detour paths

Uplink flow recovery

Downlink flow recovery

Edge

Aggregation

Core. . .k = 8Fat-Tree

. . . . . .

Algorithm



Mininet (Network Emulator)

Implementation Detail

Floodlight(SDN Controller)

OpenFlow

Flow TableUpdate

DCN Topology,Traffic Matrix,Link Capacity

Traffic EngineeringManager

(Python, puLP, Gurobi)

Open vSwitch(OpenFlow)

iperf(traffic generation)

Web UI

Failed Links


Demonstration


Simulation Environment

Computing Hardware Intel Xeon X5690 CPUs @ 3.47 GHz

48 GB memory

Traffic Matrix Data Set

Category Set 1 Set 2 Set 3 Set 4

#Hosts(k of Fat-Tree)

16-11,664(4-36)

16-11,664(4-36)

8,192(32)

8,192(32)

#Flowsper Host

2 4 1-5 2

Intra-RackTraffic Ratio [%]

50 50 50 10-90

Traffic Demands 10-20% of a maximum link capacity


Optimal Topology Composition (Heuristic)

* Link power cost: 1, Switch power cost: 150 [2]

* Set 2

* Set 3 * Set 4

* Set 1



On Entire Topology* Set 1 * Set 2

* Set 3 * Set 4

Average

67%

6%

Average LU

94%

27% 21%



On Optimal Topology (Heuristic)* Set 1 * Set 2

* Set 3 * Set 4

Average

6%4%

4%

24%

11%

6%


Computation Time

* Computation time of traffic load balancing and failure recovery algorithms using data set 1

42,600 s(about 12 h)

0.07 s

432 11,664

29,000 s(about 8 h)

6.35 s

0.036 s

6.10 s

0.008 s



Summary

Problem of Current DCNs Inefficient power consumptions

Congestions due to static routing

Lack of efficient methods for failure recovery

Dynamic TE for DCNs Optimal topology composition

Traffic load balancing

Failure recovery using detour paths

Implementation and Performance Evaluation Reduced power consumptions about 41% on average

Reduced MLU about 66% on average

Found detour paths within 36 ms to recover from a link failure


Contribution and Future Work

Contribution Dynamic TE system architecture for DCNs

TE algorithms (LP and Heuristic)

• Optimal topology composition

• Traffic load balancing

• Failure recovery

SDN-based implementation

Future Work Monitoring traffic data and estimating Traffic Matrix

Measuring impacts on performances of flow table modifications

Deploying and testing on a real large-scale test-bed


Dynamic Traffic Engineering for Improving

Energy Efficiency and Network Utilizationof Data Center Networks

PhD Thesis Defense

Sin-seok Seo

Dec. 20, 2013


References

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[2] B. Heller, S. Seetharaman, P. Mahadevan, Y. Yiakoumis, P. Sharma, S. Banerjee, and N. McKeown, “Elastictree: Saving energy in data center networks,” in Proc. 7th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’10), San Jose, USA, Apr. 28–30, 2010, pp. 1–16.

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[4] T. Benson, A. Anand, A. Akella, and M. Zhang, “MicroTE: Fine grained traffic engineering for data centers,” in Proc. 7th International Conference on emerging Networking EXperiments and Technologies (CoNEXT ’11), Tokyo, Japan, Dec. 6–9, 2011, pp. 1–12.

[5] F. P. Tso and D. P. Pezaros, “Improving data center network utilization using near-optimal traffic engineering,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, pp. 1139–1148, June 2013.

[6] Y. Li and D. Pan, “OpenFlow based load balancing for Fat-Tree networks with multipath support,” in Proc. 12th IEEE International Conference on Communications (ICC ’13), Budapest, Hungary, June 9–13, 2013, pp. 1–5.

[7] M. Al-Fares, A. Loukissas, and A. Vahdat, “A scalable, commodity data center network architecture,” in Proc. ACM SIGCOMM ’08, Seattle, USA, Aug. 17–22, 2008, pp. 63–74.

[8] T. Benson, A. Akella, and D. A. Maltz, “Network traffic characteristics of data centers in the wild,” in Proc. ACM Internet Measurement Conference 2010 (IMC ’10), Melbourne, Australia, Nov. 1–3, 2010, pp. 267–280.

[9] R. N. Mysore, A. Pamboris, N. Farrington, N. Huang, P. Miri, S. Radhakrishnan, V. Subramanya, and A. Vahdat, “PortLand: A scalable fault-tolerant layer 2 data center network fabric,” in Proc. ACM SIGCOMM ’09, Barcelona, Spain, Aug. 17–21, 2009, pp. 39–50.

[10] A. Greenberg, J. R. Hamilton, N. Jain, S. Kandular, C. Kim, P. Lahiri, D. A. Maltz, P. Patel, and S. Sengupta, “VL2: A scalable and flexible data center network,” in Proc. ACM SIGCOMM ’09, Barcelona, Spain, Aug. 17–21, 2009, pp. 51–62.

[11] C. Guo, H. Wu, K. Tan, L. Shi, Y. Zhang, and S. Lu, “DCell: A scalable and fault-tolerant network structure for data centers,” in Proc. ACM SIGCOMM ’08, Seattle, USA, Aug. 17–22, 2008, pp. 75–86.

[12] C. Guo, G. Lu, D. Li, H. Wu, X. Zhang, Y. Shi, C. Tian, Y. Zhang, and S. Lu, “BCube: A high performance, server-centric network architecture for modular data centers,” in Proc. ACM SIGCOMM ’09, Barcelona, Spain, Aug. 17–21, 2009, pp. 63–74.

[13] A. Singla, C.-Y. Hong, L. Popa, and P. B. Godfrey, “Jellyfish: Networking data centers randomly,” in Proc. 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’12), San Jose, USA, Apr. 25– 27, 2012, pp. 1–14.


Publications (1/3)

International Journal (4) Sin-seok Seo, Joon-Myung Kang, Alberto Leon-Garcia, Yoonseon Han, and James Won-Ki Hong, “User-centric Context Data

Collection and Provision Harnessing Content-Centric Networking Paradigm,” International Journal of Network Management, Nov. 21, 2013 (published online first). (SCIE)

Sungsu Kim, Joon-Myung Kang, Sin-seok Seo, and JamesWon-Ki Hong, “A Cognitive Model based Approach for Autonomic Fault Management in OpenFlow Networks," International Journal of Network Management, vol. 23, no. 6, pp. 383-401, Nov./Dec. 2013. (SCIE)

Joon-Myung Kang, Sin-seok Seo, and James Won-Ki Hong, “Personalized Battery Lifetime Prediction for Mobile Devices based on Usage Patterns," Journal of Computing Science and Engineering, vol. 5, no. 4, pp. 338-345, Dec. 2011.

Joon-Myung Kang, John Strassner, Sin-seok Seo, and James Won-Ki Hong, “Autonomic Personalized Handover Decisions for Mobile Services in Heterogeneous Wireless Networks," Computer Networks, vol. 55, no. 7, pp. 1520-1532, May 16, 2011. (SCIE)

International Conference/Workshop (16) Sin-seok Seo, Joon-Myung Kang, Yoonseon Han, and James Won-Ki Hong, “Analysis and Performance Evaluation of Data

Transport Methods in Content-Centric Networking,” in Proc. 15th Asia-Pacific Network Operations and Management Symposium (APNOMS ’13), Hiroshima, Japan, Sep. 25–27, 2013.

Yoonseon Han, Joon-Myung Kang, Sin-seok Seo, Ahmed Mehaoua, and James Won-Ki Hong, “An Energy Efficient User Context Collection Method for Smartphones,” in Proc. 15th Asia-Pacific Network Operations and Management Symposium (APNOMS ’13), Hiroshima, Japan, Sep. 25–27, 2013.

Sin-seok Seo, Joon-Myung Kang, Alberto Leon-Garcia, Yoonseon Han, and James Won-Ki Hong, “Secure and Efficient Context Data Collection using Content-Centric Networking,” in Proc. 3rd IEEE International Workshop on Smart Communication Protocols and Algorithms (SCPA ’13), Budapest, Hungary, Jun. 9, 2013, pp. 1041–1045.

Sungsu Kim, Sin-seok Seo, Joon-Myung Kang, Guy Pujolle, and James Won-Ki Hong, “Autonomic Resource Allocation for Video Streaming Services in Content Delivery Networks,” in Proc. 4th Global Information In- frastructure and Networking Symposium (GIIS ’12), Choroni, Venezuela, Dec. 17–19, 2012.

Sin-seok Seo, Joon-Myung Kang, Yoonseon Han, and James Won-Ki Hong, “Context Management for User-centric Context-aware Services over Pervasive Networks,” in Proc. 14th Asia-Pacific Network Operations and Management Symposium (APNOMS ’12), Seoul, Korea, Sep. 25–27, 2012.

Sungsu Kim, Sin-seok Seo, Joon-Myung Kang, and James Won-Ki Hong, “Autonomic Fault Management based on Cognitive Control Loops,” in Proc. 4th IEEE/IFIP International Workshop on Management of the Future Internet (ManFI ’12), Maui, Hawaii, USA, April 20, 2012, pp. 1104–1110.

Arum Kwon, Jin Xiao, Sin-seok Seo, James Won-Ki Hong, and Raouf Boutaba, “The Effect of Network Performance on Perceived Video Quality and User Experience in H.264/AVC,” in Proc. 13th IEEE/IFIP Network Operations and Management Symposium (NOMS ’12), Maui, Hawaii, USA, April 16–20, 2012, pp. 1061–1067.


Publications (2/3)

International Conference/Workshop (16) (Cont’d) Sin-seok Seo, Young J. Won, and James Won-Ki Hong, “Witnessing Distributed Denial-of-Service Traffic from an Attacker’s

Network,” in Proc. 7th International Conference on Network and Service Management (CNSM ’11), Paris, France, Oct. 24–28, 2011.

Joon-Myung Kang, Sin-seok Seo, and James Won-Ki Hong, “Usage Pattern Analysis of Smartphones,” in Proc. 13th Asia-Pacific Network Operations and Management Symposium (APNOMS ’11), Taiwan, Taipei, Sep. 21–23, 2011.

Sin-seok Seo, Arum Kwon, Joon-Myung Kang, John Strassner, and James Won-Ki Hong, “PYP: Design and Implementation of a Context- Aware Configuration Manager for Smartphones,” in Proc. 1st International Workshop on Smart Mobile Applications (SmartApps ’11), San Francisco, USA, Jun. 12–15, 2011.

Joon-Myung Kang, Sin-seok Seo, John Strassner, and James Won-Ki Hong, “HMNToolSuite: Tool Support for Mobility Management of Mobile Devices in Heterogeneous Mobile Networks,” in Proc. 14th Communications and Networking Simulation Symposium (CNS ’11), Boston, MA, USA, April 4–7, 2011, pp. 95–102.

Sin-seok Seo, Arum Kwon, Joon-Myung Kang, and James Won-Ki Hong, “OSLAM: Towards Ontology-based SLA Management for IPTV Services,” in Proc. 3rd IFIP/IEEE International Workshop on Management of the Future Internet (ManFI ’11), Dublin, Ireland, May 27, 2011, pp. 1224– 1230.

Sin-seok Seo, Joon-Myung Kang, Nazim Agoulmine, John Strassner, and James Won-Ki Hong, “FAST: A Fuzzy-based Adaptive Scheduling Technique for IEEE 802.16 Networks,” in Proc. 12th IFIP/IEEE International Symposium on Integrated Network Management (IM ’11), Dublin, Ireland, May 23–27, 2011, pp. 201–208.

Arum Kwon, Joon-Myung Kang, Sin-seok Seo, Sung-Su Kim, Jae Yoon Chung, John Strassner, and James Won-Ki Hong, “The Design of a Quality of Experience Model for Providing High Quality Multimedia Services,” in Proc. 5th International Workshop on Modelling Autonomic Communication Environments (MACE ’10), ser. LNCS, vol. 6473, Niagara Falls, Canada, Oct. 28, 2010, pp. 24–36.

Sin-seok Seo, Sung-Su Kim, Nazim Agoulmine, and James Won-Ki Hong, “On Achieving Self-Organization in Mobile WiMAXNetwork,” in Proc. 5th IEEE/IFIP International Workshop on Broadband Convergence Networks (BcN ’10), Osaka, Japan, Apr. 19, 2010, pp. 43–50.

Joon-Myung Kang, Chang-Keun Park, Sin-seok Seo, Mi-Jung Choi, and James Won-Ki Hong, “User-Centric Prediction for Battery Lifetime of Mobile Devices,” in Proc. 11th Asia-Pacific Network Operations and Management Symposium (APNOMS ’08), ser. LNCS, vol. 5297, Beijing, China, October 2008, pp. 531–534.

Domestic Journal (4)

Domestic Conference (13)


Publications (3/3)

Invited Talk (1) Sin-seok Seo, “데이터 센터 네트워크 기술 동향 및 SDN을 활용한 트래픽 엔지니어링 기법,” KNOM Tutorial 2013, Seoul, Korea, Nov.

29, 2013.

International Patent (4) James Won-Ki Hong, Sin-seok Seo, Joon-Myung Kang, and Yoonseon Han, “Apparatus for Managing User-Centric Context and

Method Thereof,” Application No.: 2013-162423, China, 2013.08.26.

James Won-Ki Hong, Sin-seok Seo, Joon-Myung Kang, and Yoonseon Han, “Apparatus for Managing User-Centric Context and Method Thereof,” Application No.: 13971577, USA, 2013.08.20.

James Won-Ki Hong, Sin-seok Seo, Joon-Myung Kang, and Yoonseon Han, “Apparatus for Managing User-Centric Context and Method Thereof,” Application No.: 2013-162423, Japan, 2013.08.05.

James Won-Ki Hong, Sin-seok Seo, Joon-Myung Kang, and Yoonseon Han, “Apparatus for Managing User-Centric Context and Method Thereof,” Application No.: EP13178596.6, Europe, 2013.07.30.

Domestic Patent (4) 홍원기, 최태상, 김도연, 이재기, 보우타바 라우프, 이태호, 권아름, 샤오 진, 서신석, “비디오 품질 측정 장치 및 그 방법,” 등록번호: 10-

1327709, 2013.11.04. (Registered)

홍원기, 강준명, 서신석, “이동 통신 시스템에서 자동 응답 방법 및 이를 위한 장치,” 등록번호: 10-1199702, 2012.11.02. (Registered)

홍원기, 서신석, 강준명, 한윤션, “사용자 중심의 상황정보 관리 장치 및 그 방법,” 출원번호: 10-2012-0097624, 2012.09.04.

홍원기, 서신석, 강준명, “네트워크 트래픽 상황 정보를 활용한 동적인 패킷 스케률링 장치 및 방법,” 출원번호: 10-2011-0033251, 2011.04.11.

Software (5) BatteryLogger for Android, 2011-01-199-005295, 한국저작권위원회

Personalize Your Phone (PYP) for Android, 2011-01-199-005306, 한국저작권위원회

SmartAnswer for Android SmartPhones (안드로이드 기반 스마트폰을 위한 상황 정보 기반의 자동 응답 응용 표로그램), 2010-01-199-004192, 한국저작권위원회

퍼지 로직을 이용한 WiMAX 자가 구성 네트워크 시율레이터, 2010-01-241- 004191, 한국저작권위원회

HMNToolSuite (이종 이동 통신 네트워크를 위한 에율레이션 및 시율레이션 시스템), 2009-01-241-004298, 한국저작권위원회



Related Work: State-of-the-Art DCN Topology

Clos Network

VL2 [9]

Server-Centric Random Graph

DCell [10] (n=4, k=2)

BCube [11] (n=4, k=2)

Jellyfish [12]Fat-Tree [7],PortLand [8] (k=4)

And More

...


Related Work: SDN-based Failure Recovery

Path Restoration Redirect affected flows one by one

Long recovery time (>200ms)

Can handle multiple failures

Path Protection Set a backup path in advance

Fast recovery time (< 50ms)

Cannot handle a failure of a backup path

Fast Flow Setup (FFS) by DPNM

Extend the restoration approach

Implant path information to only the first flow setup message

• The alternative path information is delivered just by the switches

Faster recovery time (between restoration and protection)

Can handle multiple failures

Require OpenFlow protocol modifications


Multi Commodity Flow (MCF) Problem

Input Topology: 𝐺(𝑉, 𝐸)

Traffic Matrix: 𝑇, where 𝑇𝑖 = (𝑠𝑖 , 𝑡𝑖 , 𝑑𝑖)

Link capacity: ∀ 𝑢, 𝑣 ∈ 𝐸, 𝑐(𝑢, 𝑣)

Decision Variable Flows along each link: ∀𝑖, ∀ 𝑢, 𝑣 ∈ 𝐸, 𝑓𝑖(𝑢, 𝑣)

Constraints

Capacity limitation: ∀ 𝑢, 𝑣 ∈ 𝐸, 𝑖=1𝑘 𝑓𝑖 𝑢, 𝑣 ≤ 𝑐(𝑢, 𝑣)

Flow conservation:∀𝑖, ∀𝑣 ∈ 𝑉 − 𝑠𝑖 , 𝑡𝑖 , (𝑢,𝑣)∈𝐸 𝑓𝑖 𝑢, 𝑣 = (𝑣,𝑤)∈𝐸 𝑓𝑖 𝑣,𝑤

Demand satisfaction:∀𝑖, 𝑤∈𝑉 𝑓𝑖 𝑠𝑖 , 𝑤 = 𝑤∈𝑉 𝑓𝑖 𝑤, 𝑡𝑖 = 𝑑𝑖

Back


MCF and Path-based MCF Comparison

Complexity of MCF1

If flow splitting is not allowed NP-complete

If allowed Can be solved in polynomial time using LP

The Number of Decision Variables and Constraints

Back

Category MCF Example2 Path-based MCF Example2

#Decision Variable 𝑘 × |𝐸| 6,144,000 𝑘 × |𝑃| 64,000

Const.

#CapacityLimitation

|𝐸| 6,144 |𝐸| 6,144

#FlowConservation

𝑘 × ( 𝑉 − 2) 1,342,000 - 0

#DemandSatisfaction

2 × 𝑘 2,000 𝑘 1,000

Total 𝑘 × 𝐸 + 𝑉 + |𝐸| 1,350,144 𝑘 × 𝑃 + 1 + |𝐸| 7,144

2. Assumed a Fat-Tree topology with 1,024 hosts (k=1,000, |V|=1,344, |E|=6,144, |P|=64)

1. S. Even et al., “On the complexity of time table and multi-commodity flow problems,“ in Proc.16th Annual Symposium on Foundations of Computer Science, (USA), pp. 184-193, Oct. 13-15, 1975.


Flow Split Prevention

Basic MCF or Path-based MCF Find a solution that probably split flows

Incur packet reordering problem

Cannot directly apply to OpenFlow switches

Solution Add decision variables

• ∀𝑖, ∀ 𝑢, 𝑣 ∈ 𝐸, 𝑟𝑖(𝑢, 𝑣)

• Binary variables indicating whether flow 𝑖 uses link (𝑢, 𝑣)

Add flow split prevention constraints

• ∀𝑖, ∀ 𝑢, 𝑣 ∈ 𝐸, 𝑓𝑖 𝑢, 𝑣 = 𝑑𝑖 × 𝑟𝑖(𝑢, 𝑣)

• Ensure the traffic on link (𝑢, 𝑣) of flow 𝑖 isequal to either the full demand 𝑑𝑖 or zero


Optimal Topology Composition Heuristic

Back


Predicted Traffic Load Balancing Heuristic

Back


Baseline DCN Topology: Fat-Tree (1/4)

Characteristic Leverage k-port cheap commodity switches

Support k3/4 hosts with 5k2/4 switches

Advantage Low CAPEX for building large-scale DCNs

Good scalability

Multiple equal-cost paths

• 1 for intra-rack flow

• k/2 for intra-pod flow

• k2/4 for inter-pod flow

Easily implementableusing OpenFlow

k=4

k 4 8 16 32 64 128

#Hosts 16 128 1,024 8,192 65,536 524,288

#Switches 20 80 320 1,280 5,120 20,480


Fat-Tree (2/4)

Addressing Within the private 10.0.0.0/8 block

Pod switches: 10.pod.switch.1

Core switches: 10.k.j.i• j and i denote switch’s coordinates in the core switch grid

Hosts: 10.pod.switch.ID

Simplifies building process of routing tables

k=4


Fat-Tree (3/4)

Default Fat-Tree Static Routing

Suffix OP

0.0.0.2/8 2

0.0.0.3/8 3

Prefix OP

10.0.0.0/24 0

10.0.1.0/24 1

0.0.0.0/0

Prefix OP

10.2.0.0/24 0

10.2.1.0/24 1

0.0.0.0/0 Suffix OP

0.0.0.2/8 2

0.0.0.3/8 3

Prefix OP

10.0.1.2/32 0

10.0.1.3/32 1

0.0.0.0/0 Suffix OP

0.0.0.2/8 3

0.0.0.3/8 2

Prefix OP

10.2.0.2/32 0

10.2.0.3/32 1

0.0.0.0/0 Suffix OP

0.0.0.2/8 2

0.0.0.3/8 3

Prefix OP

10.0.0.0/16 0

10.1.0.0/16 1

10.2.0.0/16 2

10.3.0.0/16 3

0 1

2 30 1

2 3

0 1 2 3

0 1

2 3

0 1

2 3

* Dst IP : 10.2.0.3


Fat-Tree (4/4)

Default Fat-Tree Flow Table Setup

* Adopted and modified original Fat-Tree Switching Algorithm [8]


Application of TE Result

(’10.0.1.2’, ‘10.0.1.1’)(’10.0.1.1’, ‘10.0.2.1’)(’10.0.2.1’, ‘10.4.1.1’)(’10.4.1.1’, ‘10.2.2.1’)(’10.2.2.1’, ‘10.2.0.1’)(’10.2.0.1’, ‘10.2.0.3’)

TM: {0: (’10.0.1.2’, ‘10.2.0.3’, 5)}

Switch Src IP Dst IP Out Port Priority

10.2.2.1 * 10.0.0.0/24 0 40000(high)

10.2.2.1 * 10.0.1.0/24 1 40000

... … … … …

10.0.1.1 10.0.1.2/32 10.2.0.3/32 2 32768

10.0.2.1 10.0.1.2/32 10.2.0.3/32 3 32768

... … … … …

10.0.2.1 * 0.0.0.2/8 2 20000(low)

10.0.2.1 * 0.0.0.3/8 3 20000

match first (prefix)

match last (suffix)

match 2nd (TE)


Equal-Cost Multi Paths Acquisition in Fat-Tree

k=4


Unpredicted Traffic Load Balancing (1/3)

Handling Unpredicted Flows Predicted TLB Algorithms

• Allocate flows in an estimated TM

What about unpredicted flows not specified in the TM?

• Reactive approach

– Decide a path whenever a new unpredicted flow is generated

– Lack of scalability

• Proactive approach

– Decide a path in advance considering all the possible cases

Host-to-Host ToR-to-ToRvs.



Heuristic



Performance Evaluation on Entire Topology* Set 2

* Set 3 * Set 4

* Set 1

Average

59%

12%88%

29% 17% Average LU


Failure Recovery Time Analysis

1800 2000 22000

3

6

9

12

15

Num

ber

of P

ackets

Time (ms)

335ms

Path Calculation (10 ms)

+ Flow Setup (45 ms)

Failure Detection

* Calculated using a DCN topology containing 16 hosts (k=4)


TE with VLAN (1/4)

VLAN Support in OpenFlow Protocol

VLAN ID Out Port

10 0, 2

20 1, 20 1

2

0 1

43VLAN ID Out Port

10 0, 3, 4

20 1, 3, 4


TE with VLAN (2/4)

VLAN Configuration in Fat-Tree Topology Intra-Rack: VLAN 1

Intra-Pod: VLAN 2

Inter-Pod: VLAN 3

m11 m12 m13 m21 m22 m31 m32 m23 m24

VLAN3 VLAN3VLAN1VLAN2


TE with VLAN (3/4)

Flow Table Setup using OpenFlow Possible to configure VLAN using unicast instead of broadcast

m11 m12 m13 m21 m22 m31 m32 m23 m24

VLAN3 VLAN3VLAN1VLAN2

Dst IP, MAC VLAN ID Out Port Priority

m11 2 0 11

m12 2 1 11

m13 2 2 11

10.0.0.2 * 0 10

10.0.0.3 * 1 10

*.*.*.2 * 3 1

*.*.*.3 * 2 1

Dst IP, MAC VLAN ID Out Port Priority

m21, m22 3 0 11

m23, m24 3 2 11

10.1.0.* * 0 10

10.1.1.* * 1 10

*.*.*.2 * 3 1

*.*.*.3 * 2 1

Prefix

Suffix

VLAN


TE with VLAN (4/4)

Application of TE Result Configure unicast VLANs using OpenFlow

• VLAN traffic can be represented as TM too

– (Src MAC, Dst MAC, Demand) ≈ (Src IP, Dst IP, Demand)

• Improve network utilization thanks to unicast

Proposed TE algorithms can be applied

Src MAC Dst MAC VLAN ID Src IP Dst IP Out Port Priority

m11 m12 1 * * 1 45000

* * * * * … …

* * * ... ... ... 40000

* * * 10.0.1.2 10.2.0.3 2 32768

* * * … … … …

* * * ... ... ... 20000

VLAN

Prefix

TE

Suffix


Communication with Outside of DCN

Documents

Dynamic Traffic Engineering for Improving Energy ...dpnm.postech.ac.kr/thesis/13/sesise/thesis_presentation_sesise.pdf · Dynamic Traffic Engineering for Improving Energy Efficiency