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Research on Fog Resource Scheduling based on Cloud-fog Collaboration Technology in the Electric Internet of Things

[ Vol. 14 , Issue. 3 ]

Author(s):

Youchan Zhu, Yingzi Wang* and Weixuan Liang   Pages 347 - 359 ( 13 )

Abstract:


Background: With the further development of the electric Internet of Things (eIoT), IoT devices in the distributed network generate data with different frequencies and types.

Objective: Fog platform is located between the smart collected terminal and cloud platform, and the resources of fog computing are limited, which affects the delay of service processing time and response time.

Methods: In this paper, an algorithm of fog resource scheduling and load balancing is proposed. First, the fog devices divide the tasks into high or low priority. Then, the fog management nodes cluster the fog nodes through the K-mean+ algorithm and implement the earliest deadline first dynamic (EDFD) task scheduling algorithm and De-REF neural network load balancing algorithm.

Results: We use tools to simulate the environment, and the results show that this method has strong advantages in -30% response time, -50% scheduling time, delay, -50% load balancing rate, and energy consumption, which provides a better guarantee for eIoT.

Conclusion: Resource scheduling is an important factor affecting system performance. This article mainly addresses the needs of eIoT in terminal network communication delay, connection failure, and resource shortage. A new method of resource scheduling and load balancing is proposed. The evaluation was performed, and it proved that our proposed algorithm has better performance than the previous method, which brings new opportunities for the realization of eIoT.

Keywords:

Power grid Internet of things, fog computing, data management, data cleaning, date integration, data storage.

Affiliation:

School of Control and Computer Engineering, North China Electric Power University, Baoding, School of Control and Computer Engineering, North China Electric Power University, Baoding, School of Control and Computer Engineering, North China Electric Power University, Baoding

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