Performance Modeling of Softwarized Network Services Based on Queuing Theory with Experimental Validation
Metadatos
Mostrar el registro completo del ítemAutor
Prados Garzón, Jonathan; Ameigeiras Gutiérrez, Pablo José; Ramos Muñoz, Juan José; Navarro Ortiz, Jorge; Andres-Maldonado, Pilar; López Soler, Juan ManuelEditorial
Institute of Electrical and Electronics Engineers (IEEE)
Materia
Network Softwarization NFV performance modeling queuing theory queuing model softwairzed network services resource dimensioning dynamic resource provisioning
Fecha
2019-12-25Referencia bibliográfica
J. Prados, P. Ameigeiras, J. J. Ramos-Munoz, J. Navarro-Ortiz, P. Andres-Maldonado and J. M. Lopez-Soler, "Performance Modeling of Softwarized Network Services Based on Queuing Theory with Experimental Validation," in IEEE Transactions on Mobile Computing.
Patrocinador
This work has been partially funded by the H2020 research and innovation project 5G-CLARITY (Grant No. 871428); National research project 5G-City: TEC2016-76795-C6-4-R; Spanish Ministry of Education, Culture and Sport (FPU Grant 13/04833). We would also like to thank the reviewers for their valuable feedback to enhance the quality and contribution of this workResumen
Network Functions Virtualization facilitates the automation of the scaling of softwarized network services (SNSs).
However, the realization of such a scenario requires a way to
determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is
known as resource dimensioning, and it can be efficiently tackled
by performance modeling. In this vein, this paper describes an
analytical model based on an open queuing network of G/G/m
queues to evaluate the response time of SNSs. We validate our
model experimentally for a virtualized Mobility Management
Entity (vMME) with a three-tiered architecture running on
a testbed that resembles a typical data center virtualization
environment. We detail the description of our experimental
setup and procedures. We solve our resulting queueing network
by using the Queueing Networks Analyzer (QNA), Jackson’s
networks, and Mean Value Analysis methodologies, and compare
them in terms of estimation error. Results show that, for medium
and high workloads, the QNA method achieves less than half of
error compared to the standard techniques. For low workloads,
the three methods produce an error lower than 10%. Finally,
we show the usefulness of the model for performing the dynamic
provisioning of the vMME experimentally.