# Logically Isolated, Actually Unpredictable? Measuring Hypervisor   Performance in Multi-Tenant SDNs

**Authors:** Arsany Basta, Andreas Blenk, Wolfgang Kellerer, Stefan Schmid

arXiv: 1704.08958 · 2017-05-01

## TL;DR

This paper investigates the performance overheads and unpredictability introduced by hypervisors in multi-tenant SDN environments, using a new benchmarking tool to measure various factors affecting OpenFlow-based virtual networks.

## Contribution

It provides the first detailed measurement and analysis of hypervisor impact on performance in multi-tenant SDNs, highlighting factors influencing overheads and unpredictability.

## Key findings

- Hypervisor technology significantly affects performance overheads.
- Number of tenants and message types influence network performance.
- Benchmarking tool enables precise measurement of OpenFlow message rates.

## Abstract

Ideally, by enabling multi-tenancy, network virtualization allows to improve resource utilization, while providing performance isolation: although the underlying resources are shared, the virtual network appears as a dedicated network to the tenant. However, providing such an illusion is challenging in practice, and over the last years, many expedient approaches have been proposed to provide performance isolation in virtual networks, by enforcing bandwidth reservations. We in this paper study another source for overheads and unpredictable performance in virtual networks: the hypervisor.   The hypervisor is a critical component in multi-tenant environments, but its overhead and influence on performance are hardly understood today. In particular, we focus on OpenFlow-based virtualized Software Defined Networks (vSDNs). Network virtualization is considered a killer application for SDNs: a vSDN allows each tenant to flexibly manage its network from a logically centralized perspective, via a simple API. For the purpose of our study, we developed a new benchmarking tool for OpenFlow control and data planes, enabling high and consistent OpenFlow message rates. Using our tool, we identify and measure controllable and uncontrollable effects on performance and overhead, including the hypervisor technology, the number of tenants as well as the tenant type, as well as the type of OpenFlow messages.

## Full text

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## Figures

13 figures with captions in the complete paper: https://tomesphere.com/paper/1704.08958/full.md

## References

28 references — full list in the complete paper: https://tomesphere.com/paper/1704.08958/full.md

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Source: https://tomesphere.com/paper/1704.08958