ViTeNA: An SDN-Based Virtual Network Embedding Algorithm for Multi-Tenant Data Centers

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Data centers offer computational resources with various levels of guaranteed performance to the tenants, through differentiated Service Level Agreements (SLA). Typically, data center and cloud providers do not extend these guarantees to the networking layer. Since communication is carried over a network shared by all the tenants, the performance that a tenant application can achieve is unpredictable and depends on factors often beyond the tenant’s control.


We propose ViTeNA, a Software-Defined Networking-based virtual network embedding algorithm and approach that aims to solve these problems by using the abstraction of virtual networks. Virtual Tenant Networks (VTN) are isolated from each other, offering virtual networks to each of the tenants, with bandwidth guarantees. Deployed along with a scalable OpenFlow controller, ViTeNA allocates virtual tenant networks in a work-conservative system. Preliminary evaluations on data centers with tree and fat-tree topologies indicate that ViTeNA achieves both high consolidation on the allocation of virtual networks and high data center resource utilization.

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ViTeNA: An SDN-Based Virtual Network Embedding Algorithm for Multi-Tenant Data Centers

  1. 1. ViTeNA: An SDN-Based Virtual Network Embedding Algorithm for Multi-Tenant Data Centers Daniel Caixinha, Pradeeban Kathiravelu, Lu s Veigaıı Presented by: André Negrão INESC-ID Lisboa / Instituto Superior Técnico Universidade de Lisboa, Portugal The 15th IEEE International Symposium on Network Computing and Applications (NCA 2016) November 1st , 2016. Cambridge, MA.
  2. 2. 2 Introduction ● Differentiated SLAs for data center tenants. ● Lack of guarantees in bandwidth. ● Shared bandwidth → Unpredictable performance. ● Software-Defined Networking (SDN) offers unified and enhanced control to the network. – From higher levels.
  3. 3. 3 Motivation ● Virtual Network Embedding (VNE) aims to completely virtualize the network. – Performance isolation among tenants in the network level. – Major challenge in network virtualization. ● Can we leverage SDN for a better VNE approach?
  4. 4. 4 Contributions ● A practical solution for the virtual network embedding problem. ● High consolidation within the placement of virtual networks ● High utilization of physical resources – Servers and network.
  5. 5. 5 ViTeNA ● A Virtual Network Embedding Algorithm – For Multi-Tenant Data Centers – Leveraging SDN. ● Tenants’ bandwidth requirements – Enforced through virtual networks.
  6. 6. 6 Deployment Landscape ● Reduce number of hops
  7. 7. 7 Deployment Landscape ● Reduce number of hops – Increase locality. – Reduce communication delays.
  8. 8. 8 ViTeNA Architecture ● Tenant demands as an XML file. ● Allocation based on the network state.
  9. 9. 9 Implementation ● Floodlight 1.1 as the OpenFlow controller. ● Mininet 2.2.1 and Open vSwitch 2.3.1 to emulate the data center.
  10. 10. 10 Evaluation Deployment ● A computer with Intel ® Quad-Core i7 870 @ 2.93 GHz processor – 12 GB DDR3 @ 1333 MHz RAM – 450 GB Serial ATA @ 7200 rpm hard disk – Ubuntu 14.04.3 LTS (Linux Kernel 3.13.0). ● Stop an experiment when the controller returns false to an experiment. ● Experiments run 1000 times.
  11. 11. 11 Emulated System ● A tree topology (depth = 3; fanout = 5) – with 125 servers – 31 switches and 155 links ● A fat-tree topology – factor k = 32, i.e. switches consist of 32 ports – with 128 servers – 160 switches and 384 links
  12. 12. 12 Evaluation Approach ● Scalability ● High consolidation ● High resource utilization. ● Bandwidth guarantees in a work-conservative system
  13. 13. 13 Scalability to data center scale ● Allocation time with tree topology.
  14. 14. 14 Scalability to data center scale ● Allocation time with fat-tree topology.
  15. 15. 15 High Consolidation ● Allocate the VMs of a virtual network as close as possible. ● Tree topology ● Fat-Tree topology
  16. 16. 16 High Resource Utilization ● Server and network utilization (%) – For tree and fat-tree.
  17. 17. 17 Conclusion ● Conclusions – ViTeNA addresses the unpredictable performance of the applications. ● Using the abstraction of virtual networks. – Evaluations confirm ● low execution time ● high consolidation on the virtual network allocation. ● high data center resource utilization. ● Future Work – Reliability and isolation guarantees
  18. 18. 18 Thank you! ● Questions? pradeeban.kathiravelu@tecnico.ulisboa.pt

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