A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks

A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks

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While the effectiveness of fog computing in Internet of Things (IoT) applications has been widely investigated in various studies, there is still Baby Care a lack of techniques to efficiently utilize the computing resources in a fog platform to maximize Quality of Service (QoS) and Quality of Experience (QoE).This paper presents a resource management model for service placement of distributed multitasking applications in fog computing through mathematical modeling of such a platform.Our main design goal is to reduce communication between the candidate nodes hosting different task modules of an application by selecting a group of nodes near each other and as close to the source Fan Shop - Licensed Baseball - Clothing of the data as possible.

We propose a method based on a greedy principle that demonstrates a highly scalable and near-optimal performance for resource mapping problems for multitasking applications in fog computing networks.Compared with the commercial Gurobi optimizer, our proposed algorithm provides a mapping solution that obtains 93% of the performance, attributed to a higher communication cost, while outperforming the reference method in terms of the computing speed, cutting the mapping execution time to less than 1% of that of the Gurobi optimizer.