Network Services Anomalies in NFV: Survey, Taxonomy, and Verification Methods

TL;DR

This paper surveys network service anomalies in NFV environments, providing a taxonomy, analyzing their impacts, comparing verification methods, and highlighting future research directions to enhance security and trust.

Contribution

It introduces a comprehensive taxonomy of NFV network service anomalies, analyzes their effects, and reviews existing verification mechanisms, identifying gaps and future research needs.

Findings

Taxonomy of network service anomalies in NFV

Comparison of existing anomaly verification methods

Identification of research gaps and future directions

Abstract

Network Function Virtualization (NFV) has emerged as a disruptive networking architecture whose galloping evolution is prompting enterprises to outsource network functions to the cloud and ultimately harvest the fruits of cloud computing, including elasticity, pay-as-you-go billing model, and on-demand services provisioning. However, many reluctant enterprises oppose the benefits of this outsourcing to their critical and pressing concerns about security, trust, and compliance. The latter anticipate possible security and QoS policy violations stemming from dishonest behaviors by cloud providers, attacks by co-resident competitors, misconfiguration by cloud administrators, or implementations flaws by NFV developers. As a result, migrating sensitive workloads to the cloud requires enterprises to first assess risks by gaining knowledge of possible network services' anomalies and second, to build trust in the cloud by designing effective mechanisms to detect such anomalies. This survey provides scrutiny of network services anomalies that may occur in the NFV environments. We first present a taxonomy of network service anomalies and analyze their negative impacts on critical service attributes, including security and performance. Second, we compare and classify the existing anomalies' verification mechanisms from the literature. Finally, we point out the literature gap and identify future research directions for anomalies verification in NFV.