Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Network and Systems Management Année : 2020

Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions

Résumé

Modern network services make increasing use of virtualized compute and network resources. This is enabled by the growing availability of softwarized network functions, which take on major roles in the total traffic flow (such as caching, routing or as firewall). To ensure reliable operation of its services, the service provider needs a good understanding of the performance of the deployed softwarized network functions. Ideally, the service performance should be predictable, given a certain input workload and a set of allocated (virtualized) resources (such as vCPUs and bandwidth). This helps to estimate more accurately how much resources are needed to operate the service within its performance specifications. To predict its performance, the function should be profiled in the whole range of possible input workloads and resource configurations. However, this input can span a large space of multiple parameters and many combinations to test, resulting in an expensive and overextended measurement period. To mitigate this, we present a profiling framework and a sampling heuristic to help select both workload and resource configurations to test. Additionally, we compare several machine-learning based methods for the best prediction accuracy, in combination with the sampling heuristic. As a result, we obtain a reduced dataset which can still model the performance of the network functions with adequate accuracy, while requiring less profiling time. Compared to uniform sampling, our tests show that the heuristic achieves the same modeling accuracy with up to five times less samples.
Fichier principal
Vignette du fichier
optimized_sampling_strategies_conditional_accept.pdf (1.05 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02354401 , version 1 (07-11-2019)
hal-02354401 , version 2 (19-01-2020)
hal-02354401 , version 3 (21-06-2020)

Identifiants

  • HAL Id : hal-02354401 , version 3

Citer

Steven van Rossem, Wouter Tavernier, Didier Colle, Mario Pickavet, Piet Demeester. Optimized Sampling Strategies to Model the Performance of Virtualized Network Functions. Journal of Network and Systems Management, In press. ⟨hal-02354401v3⟩
104 Consultations
214 Téléchargements

Partager

Gmail Facebook X LinkedIn More