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.
%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
}