Abstract
Multi-access edge <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> omputing (MEC)-enabled Internet of Things (IoT) is considered as a promising paradigm to deliver computation-intensive and delay-sensitive services to users. IoT service requests can be served by multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> icro <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ervices (MSs) that form a chain, called a micro <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ervice <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> hain (MSC). However, the high complexity of MSs and security threats in MEC-enabled IoT pose new challenges to MSC dependability. Proactive rejuvenation techniques can mitigate the impact of resource degradation of MSs and host <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</i> perating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ystems (OSes) executing them. In this paper, we develop a multi-dimensional semi-Markov model to investigate the effectiveness of proactive rejuvenation techniques in improving the dependability (availability and reliability) of a dynamic and heterogeneous MSC. The results of numerical experiments firstly reveal how MSs can be effectively combined, in different deployment configurations, with host OSes to improve MSC dependability, secondly jointly optimize the rejuvenation trigger intervals of host OS and MSs running on it, and finally show the impact of time-varying parameters. We also identify the bottlenecks for MSC dependability improvement by sensitivity analysis, and give the ranges of important parameter values guaranteeing five-nines availability. In addition, the superiority of our model is demonstrated by comparison with the continuous-time Markov chain model.
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