On Metaverse Application Dependability Analysis

TL;DR

This paper analyzes how software aging and rejuvenation affect the dependability of Metaverse applications, proposing a hierarchical model to evaluate reliability and identify key parameters for improvement.

Contribution

It introduces a hierarchical Semi-Markov model to assess the impact of software aging and rejuvenation on Metaverse application dependability, considering both active and backup components.

Findings

The model accurately predicts dependability metrics.

Rejuvenation significantly improves system reliability.

Sensitivity analysis identifies critical parameters for dependability enhancement.

Abstract

Metaverse as-a-Service (MaaS) enables Metaverse tenants to execute their APPlications (MetaAPP) by allocating Metaverse resources in the form of Metaverse service functions (MSF). Usually, each MSF is deployed in a virtual machine (VM) for better resiliency and security. However, these MSFs along with VMs and virtual machine monitors (VMM) running them may encounter software aging after prolonged continuous operation. Then, there is a decrease in MetaAPP dependability, namely, the dependability of the MSF chain (MSFC), consisting of MSFs allocated to MetaAPP. This paper aims to investigate the impact of both software aging and rejuvenation techniques on MetaAPP dependability in the scenarios, where both active components (MSF, VM and VMM) and their backup components are subject to software aging. We develop a hierarchical model to capture behaviors of aging, failure, and recovery by applying Semi-Markov process and reliability block diagram. Numerical analysis and simulation experiments are conducted to evaluate the approximation accuracy of the proposed model and dependability metrics. We then identify the key parameters for improving the MetaAPP/MSFC dependability through sensitivity analysis. The investigation is also made about the influence of various parameters on MetaAPP/MSFC dependability.