LAUSR

By bringing the processing and storage capabilities of the cloud closer to the end devices, fog computing (FC) enhances the Quality of Service (QoS) for latency-critical Internet of Things (IoT) applications, such as autonomous driving, haptics, and augmented reality (AR). To facilitate the processing and storage of data packets, the fog nodes in the underlying FC-enabled IoT network (FC-IoTN) are to be provisioned with storage and processing resources. Existing resource provisioning solutions focus mainly on latency sensitivity and cost efficiency. They also operate under the assumption that these fog nodes are completely reliable and energy efficient. In reality, this is not true. The fog nodes are not 100% reliable. Neither are they energy efficient. In this study, we propose a novel resource provisioning framework for the fog nodes that considers reliability and energy efficiency, in addition to latency sensitivity and cost efficiency. We first give an analytical framework to model the failures and recoveries in a fog node and use this modeling to provision resources in the fog nodes such that the resultant resource provisioning is optimal in terms of cost and energy consumption. Further, to understand the effect of latency, reliability, cost, and energy on resource provisioning, we analyze and decode the interplay between these factors during resource provisioning in fog nodes. We finally show the efficacy of our approach over the scenario that does not consider reliability and energy efficiency while provisioning resources. Without affecting the latency sensitivity and reliability of the system, our framework achieves an enhancement of 35%, and 37% in terms of cost and energy consumption, respectively, over a nonoptimized framework.