Background:
With the further development of 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 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 important factor affecting system performance. This article mainly addresses the
needs of eIoT in terminal network communication delay, connection failure, and resource shortage. And the new method
of resource scheduling and load balancing is proposed, The evaluation was performed and proved that our proposed
algorithm has better performance than the previous method, which brings new opportunities for the realization of eIoT.
Load Balancing Algorithm on the Immense Scale of Internet of Things in SDN for Smart Cities