A Resource Selection Model for Minimization of Disruptions in Public Fog Computing Environments

Fog computing providers have started providing services in closer proximity to the users by leasing the unused computational resources of users' devices. The leased devices are increasingly used for many time-sensitive and IoT applications. Despite the advantages of Fog, due to the highly mobile and dynamic nature of Fog devices, the resources or devices may fail at any time or may not always be available for the processing of the applications, which leads to degradation of service quality and an increase in application processing time. Hence, effective selection of the resources in the Fog computing environment by considering mobility, heterogeneity, and failure of devices is a complex task. Traditional resource selection techniques may not be applied directly in Fog computing environments due to their dynamic and unique resource characteristics. Therefore, this article proposes a Markov chain-based resource selection model to improve the quality of service by minimizing disruptions and managing device failures. The effectiveness of the proposed algorithm is evaluated using simulations, which take failure traces, current resource usage, and mobility as input from a dataset archive. Our results demonstrate the effectiveness of the proposed algorithm in terms of average disruption rates, average latency, and average overutilization. Our analysis shows significant improvements in average latency reduction of approximately 11.83%, and an average overutilization improvement by 16.17%.