Towards UAV-Based MEC Service Chain Resilience Evaluation: A Quantitative Modeling Approach

Abstract

Unmanned aerial vehicle (UAV) and network function virtualization (NFV) facilitate the deployment of multiaccess edge computing (MEC). In the UAV-based MEC (UMEC) network, virtualized network function (VNF) can be implemented as a lightweight container running on UMEC host operating system (OS). However, UMEC network is vulnerable to attack, which can result in resource degradation and even UMEC service disruption. Rejuvenation techniques, such as failover technique and live container migration technique, can mitigate the impact of resource degradation but their effectiveness to improve the resilience of UMEC services should be evaluated. This paper presents a quantitative modeling approach based on semi-Markov process to investigate the resilience of a UMEC service chain consisting of any number of VNFs executed in any number of UMEC hosts in terms of availability and reliability. Unlike existing studies, the semi-Markov model constructed in this paper can capture the time-dependent behaviors between VNFs, between host OSes, and between VNFs and host OSes on the condition that the holding times of the recovery and failure events follow any kind of distribution. We perform the sensitivity analysis to identify potential resilience bottlenecks. The results highlight that migration time is the parameter significantly affecting the resilience, which shed the insight on designing the UMEC service chain with high-grade resilience requirements. In addition, we carry out the numerical experiments to reveal that: (i) the type of failure time distribution has a significant effect on the resilience; and (ii) the resilience increases with decreasing number of VNFs, while the availability increases with increasing number of UMEC hosts and the reliability decreases with increasing number of UMEC hosts, which can provide meaningful guidance for the UAV placement optimization in the UMEC network.

Research on Service Function Chain Embedding and Migration Algorithm for UAV IoT

“…Qin et al [3] described the application scenario of SFC for vehicle-assisted or UAVassisted edge computing, studied the SFC migration problem in dynamic networks with long-term cost budget constraints, and used the Lyapunov optimization method to solve it. In the same scenario, Bai et al [19] used a quantitative modeling method based on semi-Markov process to study the potential impact of VNF migration time and network elasticity in SFC. In order to maximize the acceptance rate of service function chain requests (SFCRs) and reduce VNF migration and energy consumption as much as possible, Hu et al [20] summarized the relevant factors such as node status, link status, energy consumption, migration node, and mapping success.…”

Online Joint Optimization of Virtual Network Function Deployment and Trajectory Planning for Virtualized Service Provision in Multiple-Unmanned-Aerial-Vehicle Mobile-Edge Networks

“…J. Bai et al [18] explored the service-chain resilience in UAV-enabled MEC networks in terms of availability and reliability, and they presented a quantitative modeling approach based on a semi-Markov process. They focused on deploying VNFs in UAV networks so that service effectiveness could be increased.…”