# Dynamic and Application-Aware Provisioning of Chained Virtual Security Network Functions

## TL;DR

This paper introduces PESS, a novel method for deploying virtual security network functions that considers application-specific requirements, reducing resource use and latency compared to existing models.

## Contribution

The paper presents PESS, a new mathematical model and heuristic for application-aware VSNF provisioning that improves resource efficiency and latency performance.

## Key findings

- Reduces resource utilization by up to 50%.
- Achieves up to 40% reduction in end-to-end latency.
- Supports higher number of security services.

## Abstract

A promising area of application for Network Function Virtualization is in network security, where chains of Virtual Security Network Functions (VSNFs), i.e., security-specific virtual functions such as firewalls or Intrusion Prevention Systems, can be dynamically created and configured to inspect, filter or monitor the network traffic. However, the traffic handled by VSNFs could be sensitive to specific network requirements, such as minimum bandwidth or maximum end-to-end latency. Therefore, the decision on which VSNFs should apply for a given application, where to place them and how to connect them, should take such requirements into consideration. Otherwise, security services could affect the quality of service experienced by customers. In this paper we propose PESS (Progressive Embedding of Security Services), a solution to efficiently deploy chains of virtualised security functions based on the security requirements of individual applications and operators' policies, while optimizing resource utilization. We provide the PESS mathematical model and heuristic solution. Simulation results show that, compared to state-of-the-art application-agnostic VSNF provisioning models, PESS reduces computational resource utilization by up to 50%, in different network scenarios. This result ultimately leads to a higher number of provisioned security services and to up to a 40% reduction in end-to-end latency of application traffic.