April 30, 2020

Env2Vec: Testing Virtual Network Functions at Scale via a Single Deep Learning Model

  • Braun S.
  • Hilt V.
  • Lugones D.
  • Nicholson P.
  • Piao G.

Performance testing of virtual network functions (VNFs) is problematic for software testing engineers, given the wide variety of deployment environments for the VNFs. By environment, we refer to a combination of aspects that influence performance such as hardware, middleware, software version, build, enabled features, network topol- ogy, and test case, to name a few. One common approach to automate the testing process uses machine learning (ML) and consists of two steps: (1) learning a resource characterization model for each VNF as a function of traf- fic using historical data where no performance problem occurred, and; (2) using the model to predict the resource utilization with respect to current traffic metrics during a new test. A performance problem is reported if the pre- dicted value deviates significantly from the observed re- source utilization. Previous ML approaches propose train- ing and updating a model for each combination of appli- cation and environment, which is not feasible, due to the problem of combinatorial explosion. To address this problem, we propose a single charac- terization model called Env2Vec, short for environment to vector, that uses deep learning to map environment pa- rameters to vector embeddings. These vector embeddings not only provide insight into the similarites of the various environments, but allow the model to extrapolate predic- tions to unseen environments: an action not possible using previous approaches. A comprehensive evaluation over large, real-world data sets, shows that our proposed ap- proach can achieve state-of-the-art performance for char- acterizing CPU utilization of VNFs, as well as a false alarm rate about 23% lower than baseline approaches for the downstream task of detecting performance problems, despite using a single model for all environments.

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