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Revolutionizing Network Flexibility with Virtual Routers as a Service

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This presentation discusses the concept of Virtual Routers as a Service (VRaaS), emphasizing its role in enhancing network flexibility through virtualization. Key topics include an overview of virtualization principles, problem statements concerning network resource allocation, and algorithms for optimal virtual router location selection. It addresses practical use cases, including enterprise connectivity solutions and regional provider applications. The architecture of virtual routers, live migration capabilities, and future outlooks for refining path and node selection strategies are also covered.

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Revolutionizing Network Flexibility with Virtual Routers as a Service

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  1. Towards Virtual Routers as a Service 6th GI/ITG KuVS Workshop on “Future Internet”November 22, 2010Hannover Zdravko Bozakov

  2. Towards Virtual Routers as a Service • Talk outline • Virtualization overview • Use case: virtual routers as a service • Problem statement • Resource allocation algorithm • Virtual router location selection • In brief • Virtual router architecture • Live-migration • Summary and outlook

  3. Network Virtualization Overview • Virtualization aims to decouple logical and physical network resources and increase network flexibility • Variable mapping of physical and logical entities • Slice network hardware for multiple customers • Handle multiple network devices using a single control plane • Live-migration of logical routers • Load balancing (capacity, routing tables, CPU) • Scheduled router maintenance • Energy conservation

  4. Virtual Routers as a Service

  5. Virtual Routers as a Service • On-demand provision of connectivity over core network • Enterprise branch offices • Regional providers • University campuses • Single virtual router for edge interconnection • Reduction of customer management overhead • Consolidation of provider resources and transparent remapping • Port relay nodes (PRN) • Forward traffic to virtual router (root node)

  6. Problem Statement What we have: • Backbone network • Weighted graph Gwith weights W • Link utilization U and capacityC (u/c) • Customer requirements • Subset of edge nodes Γ • Capacity demand Dfor edge nodes γ What we need: • Optimal location of VR root node R • Optimal paths from R to Γ satisfying capacity constraints R W=1

  7. Path Selection Algorithm Trivial case: unlimited backbone capacity • For each γ calculate shortest path to R(e.g. Dijkstra) • Does not work for capacity constrained networks • Solution with constraints: flow network theory (successive shortest paths) w=0C=1 w=0 C=4 DST SRC

  8. Root Node Selection • Optimal location of root node R • Minimize the cost S of bandwidth consumed by the VR links V • Root selection using total enumeration • Nodes with insufficient resources are pruned (e.g. capacity, CPU, memory) • Example: optimal root node locations with total cost S=4

  9. Virtual Router Architecture • Root node: hardware accelerated virtual router • Control plane virtualization using standard VMson commodity servers • Programmable data plane using Openflow-enabled switches • Port relay nodes (PRN) • Forward packets based on L2 virtual router addresses along computed paths • Openflow implementation

  10. Live Router Migration • Virtual router architecture allows live router migration • Setup outbound PRN paths for new root node R* • Clone forwarding table from old root R and remap physical ports • Control plane continuously updates routing tables on R and R* • Asynchronously setup inbound paths for R* • Tear down old paths and root node Watch the demo during the break!

  11. Conclusion and Outlook • Conclusion • On-demand connectivity usingsingle logical router instance reduces management overhead • Presented approach allows optimal computation of paths to a router located within network core • Basic root node selection strategy • Architecture is capable of live-migration • Outlook • Refine path selection algorithm and analyze alternative approaches • Optimize root node selection method • Detailed evaluation of live-migration performance • Implement and evaluate fallback strategies

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