The Edge of Things

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This is because frequent CPU speed alteration is not recommended as it causes system unreliability and inefficiency. The comparison of the energy consumption of the system executing in OnDemand mode with the system that executes completely on fog and does not perform any kind of speed scaling is illustrated in the Fig. The results show a significant gain in the energy utilization as the OnDemand mode exhibits a drastic transformation in the graph when the speed scaling is performed.

However, the case where the speed scaling is set to the highest, i. Other than that, the network usage has also produced credible results for the OnDemand mode. The execution time of the OnDemand mode shows a promising result in achieving minimum time for processing the demands of IoT applications.

Why Edge Computing in an IoT Environment?

As shown in the Fig. Otherwise, even for higher configurations, the OnDemand gives a significantly better result. The inner loop delays of the IoT application that are greatly responsible for assessing the better service quality of fog computing over cloud computing is showing a drastic difference between the two comparison graphs Fig. The graph of without the OnDemand mode shows a linearity at about 8. However, again both the graphs converge when the CPU speed is set to the highest configurations and consume equal loop delays.

The paper has proposed a framework to extend the possibilities of advancement in EoT infrastructure with least energy consumption. The methodology has proven to be immensely liable for utilization of least energy while assuring maximum quality of service. The frequency scaling is controlled dynamically where the system monitors the workload through three counters and performs the scaling when a uniformity in the amount of workload is encountered which makes the system prone to resist frequent alteration of its configurations.

The authors have resisted the frequent speed scaling as it harms the system performance and hence the proposed methodology has proven to be the most appropriate solution to generate minimum consumption of energy even under higher processing or storage demands by an IoT application. Abdel-Basset, G. Manogaran, M. Mohamed, E. Rushdy, in Concurrency and Computation: Practice and Experience.

Internet of things in smart education environment: Supportive framework in the decision-making process Wiley Online Library, , p. Aslam, S. Khan, M. Ahmed, A.

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Liang, Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption.