Predictive Analysis in Network Function Virtualization
Zhijing Li
Zihui Ge
Ajay Mahimkar
Jia Wang
Ben Y. Zhao
Haitao Zheng
Joanne Emmons
Laura Ogden
Proceedings of 18th ACM SIGCOMM Internet Measurement Conference (IMC 2018)
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Paper Abstract
Recent deployments of Network Function Virtualization (NFV) architectures
have gained tremendous traction. While virtualization introduces benefits
such as lower costs and easier deployment of network functions, it adds
additional layers that reduce transparency into faults at lower layers. To
improve fault analysis and prediction for virtualized network functions
(VNF), we envision a runtime predictive analysis system that runs in
parallel with existing reactive monitoring systems to provide network
operators timely warnings against faulty conditions. In this paper, we
propose a deep learning based approach to reliably identify anomaly events
from NFV system logs, and perform an empirical study using 18 consecutive
months in 2016-2018 of real-world deployment data on virtualized provider
edge routers. Our deep learning models, combined with customization and
adaptation mechanisms, can successfully identify anomalous conditions
that correlate with network trouble tickets. Analyzing these anomalies can
help operators to optimize trouble ticket generation and processing rules
in order to enable fast, or even proactive actions against faulty
conditions.