A reliability-aware resource provisioning scheme for real-time industrial applications in a Fog-integrated smart factory

Timeliness and reliability are two major requirements of industrial applications. Hence, these two requirements should be carefully taken into account in design of a smart factory. Using a hybrid Cloud (combining a public Cloud with a private Cloud) is a well-known method to enable Cloud computing to provide reliability and timeliness. However, for strict time-sensitive applications using a hybrid Cloud is not enough to guarantee meeting their hard-deadlines. Supplying an intermediate computing layer-called Fog-between the factory and the Cloud is a promising solution to deal with reliability and latency requirements of strict time-sensitive applications. Integration of a Cloud data center, a private local Cloud, Fog nodes, and edge nodes constitute a very complex multilayer computing model in a smart factory. The goal of this paper is to provide a resource provisioning scheme for partitioning of a given workload among these multiple computing layers subject to reliability and real-time requirements. Partitioning of the workload can provide us prominent design decisions specifying how much computing resources are required to develop a local private Cloud in cooperating with Fog nodes like networking devices, how large should the minimum communication bandwidth be between the Fog and the public Cloud data center, and how many replicas for each application are required to satisfy the reliability requirement of the considered application. To evaluate the proposed method, we have conducted a set of experiments ranging from small scale to large scale scenarios. The results indicate that in 86 percent of the small scale experiments, the proposed algorithm exactly found the optimal solution achieved by a Branch and Bound (BB) based exhaustive search algorithm, while there is an improvement of around 85 percent on the execution time of our proposed method compared to the exhaustive search method. (C) 2019 Elsevier B.V. All rights reserved.