AUTO-3P: An autonomous VNF performance prediction & placement framework based on machine learning

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)

Abstract

We propose Auto-3P, an Autonomous module for Virtual Network Functions Performance Prediction and Placement at network cloud and edge facilities based on Machine Learning (ML). Auto-3P augments the autonomous placement capabilities of MANagement and Orchestration frameworks (MANOs) by considering both resource availability at hosting nodes and the implied impact of a VNF node placement decisions on the whole service level end-to-end performance. Unlike that, most existing placement methods take a rather myopic approach after manual rule-based decisions, and/or based exclusively on a host-centric view that focuses merely on node-local resource availability and network metrics. We evaluate and validate Auto-3P with real-field trials in the context of a well-defined Smart City Safety use case using a real end-to-end application over a real city-based testbed. We meticulously conduct repeated tests to assess (i) the accuracy of our adopted prediction models; and (ii) their placement performance against three other existing MANO approaches, namely, a “Traditional”, a “Latency-aware” and a “Random” one, as well as against a collection of well-known Time Series Forecasting (TSF) methods. Our results show that the accuracy of our ML models outperforms the one by TSF models, with the most prominent accuracy performances being exhibited by models such as K-Nearest Neighbors Regression (K-NNR), Decision Tree (DT), and Support Vector Regression (SVR). What is more, the resulted end-to-end service level performance of our approach outperforms “Traditional”, “Latency-aware”, and Random MANO placement. Last, Auto-3P achieves load balancing at selected VNF hosts without degrading end-to-end service level delay, and without a need for a (fixed) overload threshold check, unlike what is suggested by other works in the literature for coping with heavy system-wide load conditions.

Original languageEnglish
Article number107433
Number of pages15
JournalComputer Networks
Volume181
Early online date19 Jul 2020
DOIs
Publication statusPublished - 9 Nov 2020

Keywords

  • Cloud and edge computing
  • End-to-End communication
  • Machine learning
  • Network function virtualization
  • Zero-Touch management

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