Towards Semi-Markov Model-based Dependability Evaluation of VM-based Multi-Domain Service Function Chain

TL;DR

This paper develops a semi-Markov model to evaluate the dependability of VM-based multi-domain service function chains, considering software aging and proactive rejuvenation, providing insights into system reliability and availability.

Contribution

It introduces a semi-Markov model for dependability analysis of VM-based MSFCs, incorporating software aging, rejuvenation, and general failure/recovery distributions.

Findings

Derived formulas for steady-state availability and reliability.

Identified dependability bottlenecks through sensitivity analysis.

Quantified impact of proactive rejuvenation on system dependability.

Abstract

In NFV networks, service functions (SFs) can be deployed on virtual machines (VMs) across multiple domains and then form a service function chain (MSFC) for end-to-end network service provision. However, any software component in a VM-based MSFC must experience software aging issue after a long period of operation. This paper quantitatively investigates the capability of proactive rejuvenation techniques in reducing the damage of software aging on a VM-based MSFC. We develop a semi-Markov model to capture the behaviors of SFs, VMs and virtual machine monitors (VMMs) from software aging to recovery under the condition that failure times and recovery times follow general distributions. We derive the formulas for calculating the steady-state availability and reliability of the VM-based MSFC composed of multiple SFs running on VMs hosted by VMMs. Sensitivity analysis is also conducted to identify potential dependability bottlenecks.