Virtual Network Provisioning and Fault-Management across Multiple Domains

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1 Virtual Network Provisioning and Fault-Management across Multiple Domains Distinguished Speaker Series Democritus University of Thrace, Greece Panagiotis Papadimitriou November 2010

2 Introduction The Internet architecture suffers from ossification: The technology in the Internet core has remained unchanged No incentives for ISPs to deploy new technology (although it is available) Current architecture hinders innovation Emerging network applications and services seek wide-area deployment: IPTV Online Gaming Video conferencing Many of them have different requirements, e.g. delay, loss, security

3 Network Virtualization: An Enabler Physical Infrastructure Virtualized Substrate Virtualization of Resources (partitioning of physical infrastructure into slices ) Provisioning of Virtual Networks (on-demand instantiation of virtual networks) Virtual Network Virtual Network Management of Virtual Networks

4 Network Virtualization for a Future Internet Desirable properties of network virtualization: Abstraction Resource sharing Isolation among VNets Challenges and implications with VN provisioning: Heterogeneity among ISPs Limited information disclosure from ISPs Fast on-demand provisioning of functional VNets at large scale

5 Outline VN Provisioning Dynamic VN Management VN Fault-Management Prototype Implementation Evaluation Conclusions

6 VN Provisioning

7 VN Provisioning: Roles and Actors Service Provider VN Provider Phys. Infrastructure Provider. Phys. Infrastructure Provider Infrastructure Provider: Owns and manages the physical infrastructure VNet Provider: Assembles resources from one or multiple Infr. Prov into a VN Service Provider: Deploys services on VNs

8 Substrate Node Clustering Dendrogram Substrate Network Substrate nodes that share similar properties are grouped into one cluster

9 VN Provisioning: Overview VN Request Discovery VN Provider Selection Allocation Infrastructure Provider Virtual Network

10 Resource Discovery and VN Splitting Find resource candidates based on similarity search methods (static resource attributes) Split VN graph among Infrastructure Providers Discovery VN Request VN Provider Selection Allocation Infrastructure Provider Virtual Network

11 Resource Selection Select among resource candidates using optimization algorithms (dynamic resource attributes) Discovery VN Request VN Provider Selection Allocation Infrastructure Provider Virtual Network

12 Centralized vs. Distributed Resource Selection Centralized resource selection: Central coordinator with global knowledge of the substrate Significant number of control/signaling messages Scalability limitation Distributed resource selection: Avoids single point of failure Processing load distribution Localized control for resource failures

13 Multi-agent based Approach Decentralize resource selection across substrate nodes: Substrate nodes should be autonomic on decision making Substrate nodes should communicate and collaborate to plan collective VN decisions Solution: multi-agent based approach Deploy an agent in each substrate node Ensure distributed negotiation and synchronization between the agents

14 Dynamic VN Management

15 Challenges in VN management: Elasticity Node / link failures, load changes Mobility, topology changes Maintenance Motivation Is a new embedding sufficient for achieving fault-tolerance and elasticity?

16 VN Re-Provisioning Case 1: VN extension / contraction VN topology changes Updated VN Request Case 2: (virtual) node/link failures load changes severe performance degradation maintenance VN Request Discovery VN Provider Discovery VN Provider Selection Allocation Virtual Network Infrastructure Provider Our approach: Adaptive VN Provisioning Selection Allocation Virtual Network Infrastructure Provider Updated Resources

17 Case 1: VN extension / contraction VN topology changes Adaptive VN Provisioning Case 2: (virtual) node/link failures load changes severe performance degradation maintenance

18 VN Fault-Management

19 Dynamic Host Discovery MSG(n v, dism) MSG(n v, dism) 4 MSG(n MSG(n dism) v, dism) 3 1. Notification of all agents in the same cluster 2. Dissimilarity score (dism) computation 3. Exchange of dism among agents in the cluster MSG(n v, dism) 9 X MSG(n v, dism) Each agent compares its dism with all received dism 5. The agent with the minimum dism is the new host

20 Dissimilarity Metric Attributes of virtual node: n v = ((att 1, x v1 ),, (att f, x vp )) Attributes of substrate node: n s = ((att 1, x s1 ),, (att f, x sp )) dism( i, j) p f 1 p w f 1 ( f ) ij w d ( f ) ij ( f ) ij where: p is the number of node attributes ( f ) d ij is the dissimilarity of nodes i, j d ( f ) ij 0, 1, if if x x if if x x jf jf ( f ) w ij is a priority coefficient for att f

21 Adaptive VN Embedding: 1. Dynamic host discovery 2. Virtual node migration or instantiation in the new host depending on the type of failure 3. Virtual link reassignment using a distributed shortest-path algorithm 4. Virtual link setup Adaptive VN Embedding

22 Prototype Implementation

23 Infrastructure and Software Heterogeneous Experimental Network (HEN), UCL > 110 computer nodes with multiple network interfaces Force10 E1200 switch with 500 high-speed network ports and VLAN support Software: Python Xen Click Modular Router (in Linux kernel)

24 Main Features XML-based resource description Resource discovery Resource assignment On-the-fly virtual node setup and configuration (multi-thread) On-the-fly virtual link setup (e.g., IP-in-IP) Real-time CPU load and bandwidth monitoring Management access to instantiated VNets

25 Prototype Overview 1. Resource Discovery 2. VN Splitting Resource Description VN Provider Substrate Node Management Node Management Interface Experimental Interface Management Network Experimental Network Resource Repository 1. Resource Assignment for Partial VN 2. VN Setup

26 Evaluation

27 Performance Evaluation: Experimental Scenarios Infrastructure: Heterogeneous Experimental Network (HEN), UCL, London Dell 2950, 2 Intel Xeon X5355 CPUs 8 Gb Memory Scenario: Adaptive VN Embedding and Binding Scalability Evaluation: Infrastructure: GRID 5000, France Scenario: Adaptive VN Embedding

28 Performance Evaluation (1) virtual network substrate single virtual node failure 1 VN, 10 substrate nodes, HEN

29 Performance Evaluation (2) 3 virtual networks multiple virtual node failures 3 VNs, 10 substrate nodes, HEN substrate

30 Scalability Evaluation single virtual node failure substrate nodes, full-mesh topologies, GRID 5000

31 Adaptive VN embedding: Conclusions is more efficient that a new embedding by utilizing information from the initial embedding (e.g. clustering) provides fault-tolerance for VNs Our prototype shows that adaptive VN embedding: can react very quickly to resource failures exhibits nice scalability properties

32 Acknowledgments Lancaster University L. Mathy UCL A. Greenhalgh M. Handley Deutsche Telecom A. Feldmann O. Maennel G. Schaffrath Karlsruhe University R. Bless C. Werle Telecom SudParis I. Houidi W. Louati D. Zeghlache

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