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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
Sin-seok Seo, POSTECH PhD Thesis Defense 2/34
Outline
Introduction
Related Work
Dynamic Traffic Engineering
Validation
Conclusion
Sin-seok Seo, POSTECH PhD Thesis Defense 3/34
Sin-seok Seo, POSTECH PhD Thesis Defense 4/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 5/34
Research Background (2/3)
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)
Sin-seok Seo, POSTECH PhD Thesis Defense 6/34
Research Background (3/3)
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
Sin-seok Seo, POSTECH PhD Thesis Defense 7/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 8/34
Sin-seok Seo, POSTECH PhD Thesis Defense 9/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 10/34
Sin-seok Seo, POSTECH PhD Thesis Defense 11/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 12/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 13/34
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.
Sin-seok Seo, POSTECH PhD Thesis Defense 14/34
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
… … …
Sin-seok Seo, POSTECH PhD Thesis Defense 15/34
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
… … …
Sin-seok Seo, POSTECH PhD Thesis Defense 16/34
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
... … … …
Pod 0 Pod 1 Pod 2 Pod 3
InsufficientBW
Algorithm* Source: Network Traffic Characteristics of Data Centers in the Wild, IMC ’10 [8]
Sin-seok Seo, POSTECH PhD Thesis Defense 17/34
Optimal Topology Composition (2/4)
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)
Sin-seok Seo, POSTECH PhD Thesis Defense 18/34
Optimal Topology Composition (3/4)
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
Sin-seok Seo, POSTECH PhD Thesis Defense 19/34
Optimal Topology Composition (4/4)
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
Pod 0 Pod 1 Pod 2 Pod 3
Sin-seok Seo, POSTECH PhD Thesis Defense 20/34
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
Pod 0 Pod 1 Pod 2 Pod 3
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
Sin-seok Seo, POSTECH PhD Thesis Defense 21/34
Traffic Load Balancing (2/2)
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
Sin-seok Seo, POSTECH PhD Thesis Defense 22/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 23/34
Sin-seok Seo, POSTECH PhD Thesis Defense 24/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 25/34
Demonstration
Sin-seok Seo, POSTECH PhD Thesis Defense 26/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 27/34
Optimal Topology Composition (Heuristic)
* Link power cost: 1, Switch power cost: 150 [2]
* Set 2
* Set 3 * Set 4
* Set 1
Sin-seok Seo, POSTECH PhD Thesis Defense 28/34
Traffic Load Balancing (1/2)
On Entire Topology* Set 1 * Set 2
* Set 3 * Set 4
Average
67%
6%
Average LU
94%
27% 21%
Sin-seok Seo, POSTECH PhD Thesis Defense 29/34
Traffic Load Balancing (2/2)
On Optimal Topology (Heuristic)* Set 1 * Set 2
* Set 3 * Set 4
Average
6%4%
4%
24%
11%
6%
Sin-seok Seo, POSTECH PhD Thesis Defense 30/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 31/34
Sin-seok Seo, POSTECH PhD Thesis Defense 32/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 33/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 34/34
Dynamic Traffic Engineering for Improving
Energy Efficiency and Network Utilizationof Data Center Networks
PhD Thesis Defense
Sin-seok Seo
Dec. 20, 2013
Sin-seok Seo, POSTECH PhD Thesis Defense 35/34
References
[1] N. Wang, K. H. Ho, G. Pavlou, and M. Howarth, “An overview of routing optimization for Internet traffic engineering," IEEE Communications Surveys and Tutorials, vol. 10, no. 1, pp. 36–56, 2008.
[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.
[3] M. Al-Fares, S. Radhakrishnan, B. Raghavan, N. Huang, and A. Vahdat, “Hedera: Dynamic flow scheduling for data center networks,” in Proc. 7th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’10), San Jose, USA, Apr. 28–30, 2010, pp. 1–15.
[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.
Sin-seok Seo, POSTECH PhD Thesis Defense 36/34
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.
Sin-seok Seo, POSTECH PhD Thesis Defense 37/34
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)
Sin-seok Seo, POSTECH PhD Thesis Defense 38/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 39/34
Sin-seok Seo, POSTECH PhD Thesis Defense 40/34
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
...
Sin-seok Seo, POSTECH PhD Thesis Defense 41/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 42/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 43/34
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.
Sin-seok Seo, POSTECH PhD Thesis Defense 44/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 45/34
Optimal Topology Composition Heuristic
Back
Sin-seok Seo, POSTECH PhD Thesis Defense 46/34
Predicted Traffic Load Balancing Heuristic
Back
Sin-seok Seo, POSTECH PhD Thesis Defense 47/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 48/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 49/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 50/34
Fat-Tree (4/4)
Default Fat-Tree Flow Table Setup
* Adopted and modified original Fat-Tree Switching Algorithm [8]
Sin-seok Seo, POSTECH PhD Thesis Defense 51/34
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)
Sin-seok Seo, POSTECH PhD Thesis Defense 52/34
Equal-Cost Multi Paths Acquisition in Fat-Tree
k=4
Sin-seok Seo, POSTECH PhD Thesis Defense 53/34
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.
Sin-seok Seo, POSTECH PhD Thesis Defense 54/34
Unpredicted Traffic Load Balancing (2/3)
Heuristic
Sin-seok Seo, POSTECH PhD Thesis Defense 55/34
Unpredicted Traffic Load Balancing (3/3)
Performance Evaluation on Entire Topology* Set 2
* Set 3 * Set 4
* Set 1
Average
59%
12%88%
29% 17% Average LU
Sin-seok Seo, POSTECH PhD Thesis Defense 56/34
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)
Sin-seok Seo, POSTECH PhD Thesis Defense 57/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 58/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 59/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 60/34
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
Sin-seok Seo, POSTECH PhD Thesis Defense 61/34
Communication with Outside of DCN