%0 Generic %1 deric2018hypervisor %A Deric, Nemanja %A Varasteh, Amir %A Basta, Arsany %A Blenk, Andreas %A Kellerer, Wolfgang %B 2018 14th International Conference on Network and Service Management (CNSM) %D 2018 %I IEEE %K myown sendate sendate-planets %T SDN Hypervisors: How Much Does Topology Abstraction Matter? %U https://ieeexplore.ieee.org/document/8584963 %X SDN network hypervisors realize the virtualization of software-defined networks. They intercept the control path between tenant controllers and their respective virtual Software-Defined Networks (SDN). Over-utilizing SDN hypervisor resources (i.e., CPU) can degrade the control plane performance of the tenants. Although many hypervisor proposals exists, a detailed performance modeling of SDN hypervisors is missing in literature. A precise modeling of the required SDN hypervisor resources, however, is crucial for predictable and reliable operation of virtual software-defined networks. In this paper, we measure and evaluate how topology abstraction can affect the SDN hypervisor CPU utilization. We consider two topology abstraction cases: the (1) transparent and (2) big-switch abstraction. Our measurements taken from a real testbed indicate that the big-switch abstraction can reduce the SDN hypervisor CPU utilization up to ∼4×. Further, we evaluate different functions to model the SDN hypervisor CPU utilization based on our measurement results. Our evaluations show that a polynomial function provides the lowest fitting error. Motivated by our measurements, we conduct a first-step investigation of the impacts of topology abstraction on the Virtual Network Embedding (VNE) problem. Our initial simulation-based evaluations indicate that different topology abstraction procedures impact the results of the VNE problem.

@conference{deric2018hypervisor, abstract = {SDN network hypervisors realize the virtualization of software-defined networks. They intercept the control path between tenant controllers and their respective virtual Software-Defined Networks (SDN). Over-utilizing SDN hypervisor resources (i.e., CPU) can degrade the control plane performance of the tenants. Although many hypervisor proposals exists, a detailed performance modeling of SDN hypervisors is missing in literature. A precise modeling of the required SDN hypervisor resources, however, is crucial for predictable and reliable operation of virtual software-defined networks. In this paper, we measure and evaluate how topology abstraction can affect the SDN hypervisor CPU utilization. We consider two topology abstraction cases: the (1) transparent and (2) big-switch abstraction. Our measurements taken from a real testbed indicate that the big-switch abstraction can reduce the SDN hypervisor CPU utilization up to ∼4×. Further, we evaluate different functions to model the SDN hypervisor CPU utilization based on our measurement results. Our evaluations show that a polynomial function provides the lowest fitting error. Motivated by our measurements, we conduct a first-step investigation of the impacts of topology abstraction on the Virtual Network Embedding (VNE) problem. Our initial simulation-based evaluations indicate that different topology abstraction procedures impact the results of the VNE problem.}, added-at = {2019-01-28T16:29:51.000+0100}, author = {Deric, Nemanja and Varasteh, Amir and Basta, Arsany and Blenk, Andreas and Kellerer, Wolfgang}, biburl = {https://www.bibsonomy.org/bibtex/2002e26c9dee4289562bb043d52dc4825/lkn}, booktitle = {2018 14th International Conference on Network and Service Management (CNSM)}, interhash = {fdf8e6a6566069295c28fe8356adb74e}, intrahash = {002e26c9dee4289562bb043d52dc4825}, keywords = {myown sendate sendate-planets}, publisher = {IEEE}, timestamp = {2019-01-28T16:45:56.000+0100}, title = {SDN Hypervisors: How Much Does Topology Abstraction Matter?}, url = {https://ieeexplore.ieee.org/document/8584963}, year = 2018 }