Piao, Guangyuan and Nicholson, Patrick K. and Lugones, Diego (2020) Env2Vec: accelerating VNF testing with deep learning. EuroSys '20: Proceedings of the Fifteenth European Conference on Computer Systems, 41. pp. 1-16.

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Abstract

The adoption of fast-paced practices for developing virtual network functions (VNFs) allows for continuous software delivery and creates a market advantage for network operators. This adoption, however, is problematic for testing engineers that need to assure, in shorter development cycles, certain quality of highly-configurable product releases running on heterogeneous clouds. Machine learning (ML) can accelerate testing workflows by detecting performance issues in new software builds. However, the overhead of maintaining several models for all combinations of build types, network configurations, and other stack parameters, can quickly become prohibitive and make the application of ML infeasible. We propose Env2Vec, a deep learning architecture that combines contextual features with historical resource usage, and characterizes the various stack parameters that influence the test execution within an embedding space, which allows it to generalize model predictions to previously unseen environments. We integrate a single ML model in the testing workflow to automatically debug errors and pinpoint performance bottlenecks. Results obtained with real testing data show an accuracy between 86.2%-100%, while reducing the false alarm rate by 20.9%-38.1% when reporting performance issues compared to state-of-the-art approaches.

Item Type:	Article
Keywords:	Env2Vec; accelerating; VNF Testing; deep learning;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15634
Identification Number:	https://doi.org/10.1145/3342195.3387525
Depositing User:	Guangyuan Piao
Date Deposited:	08 Mar 2022 12:36
Journal or Publication Title:	EuroSys '20: Proceedings of the Fifteenth European Conference on Computer Systems
Publisher:	ACM Digital Library
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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