Document Type : Original Research Paper
Authors
1 Department of Computer Science, Qom branch, Islamic Azad University, Qom, Iran.
2 Department of Computer Science, Arak Branch, Islamic Azad University, Arak, Iran.
3 Department of Computer Science, Faculty of Engineering, Qom branch, Islamic Azad University, Qom, Iran.
Abstract
Background and Objectives: With the great growth of applications sensitive to latency, and efforts to reduce latency and cost and to improve the quality of service on the Internet of Things ecosystem, cloud computing and communication between things and the cloud are costly and inefficient; Therefore, fog computing has been proposed to prevent sending large volumes of data generated by things to cloud centers and, if possible, to process some requests. Today's advances in 5G networks and the Internet of Things show the benefits of fog computing more than ever before, so that services can be delivered with very little delay as resources and features of fog nodes approach the end user.
Methods: Since the cloud-fog paradigm is a layered architecture, to reduce the overall delay, the fog layer is divided into two sub-layers in this paper, including super nodes and ordinary nodes in order to use the coverage of super peer networks to use the connections between fog nodes in addition to taking advantage of the features of that network and improving the performance of large-scale systems. It causes fog nodes to interact with each other in processing requests and fewer data will be sent to the cloud, resulting in a reduction in overall latency. To reduce the cost of bandwidth used among fog nodes, we have organized a sub-layer of super nodes in the form of a Perfect Difference Graph (PDG). The new platform proposed for aggregation of fog computing and Internet of Things (FOT) is called the P2P-based Fog supported Platform (PFP).
Results: We evaluate the utility of our proposed method by applying ifogsim simulator and the results achieved are as follows: (1) power consumption parameter in our proposed method 24% and 38% have improved compared to the structure three-layer fog computing architecture and without fog layer respectively; (2) network usage parameter in our proposed method 26% and 32% have improved compared to the structure three-layer fog computing architecture and without fog layer respectively; (3) average response time parameter in our proposed method 17% and 58% have improved compared to the structure three-layer fog computing architecture and without fog layer respectively; and (4) delay parameter in our proposed method 1% and 0.4% have improved compared to the structure three-layer fog computing architecture and without fog layer respectively.
Conclusion: Numerical results obtained from the simulation show that the delay and cost parameters are significantly improved compared to the structure without fog layer and three-layer fog computing architecture. Also, the results show that increasing number of things has the same effect in all cases.
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Open Access
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Publisher
Shahid Rajaee Teacher Training University
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