Abstract
As a new computing model, how to use edge computing to forecast import and export trade has become an issue of concern. This research mainly discusses the prediction algorithm of international import and export trade based on heterogeneous dynamic edge computing system. The dynamic task migration system studied in this paper mainly includes four parts: edge computing environment simulator, task generator, resource predictor, and migration decision maker. These four parts are not independent modules in the working process; they will interact with each other in the edge computing environment. In the data processing offloading strategy, the customs business personnel transfer the trade data that need to be predicted to the edge device cluster through the mobile terminal. After receiving the data transmitted by the business personnel, the edge device cluster uses data processing technology to process the data. After the data processing operation is completed, the processed data is directly used for prediction work. After the prediction work is completed, the data and results are uploaded to the central server. Finally, after the prediction work is completed, the edge device will feed back the prediction result to the mobile terminal and display the result on the user interface through the mobile terminal so that business personnel can understand the trade risk status. From August 2018 data application period, the monthly data of the import and export trade volume for the subsequent time span of ten years were regularly forecasted, and the correlation coefficient was still over 83%, and the RMSE also dropped significantly. The system designed in this study can effectively predict the annual estimated value of various economic indicators of international import and export trade.
Highlights
Heterogeneous Dynamic Edge ComputingIf the user sets the value of λto 0.5, it means that all predicted requests will be handed over to the edge device cluster for processing, and the central server only performs resource storage and model training
In the current situation of increasingly severe import and export trade forms, studying the elasticity of national import and export commodities will help us to propose corresponding policies through price and income elasticity to effectively adjust and optimize the current import and export structure of commodities [2], so as to achieve a moderate maintenance of the number of imports and exports of some products which will achieve the goal of promoting economic growth and development on the one hand and alleviating the trade friction between countries on the other and reducing the impact of external economies on a country’s economy
Jiang et al proposed an improved multiobjective gray wolf optimizer algorithm (IMOGWO) to solve this problem. ey use an elite learning strategy based on Gaussian perturbation to avoid local optima. e algorithm he proposed was tested on twelve multiobjective benchmark problems selected from the CEC2009 test cases and compared with two popular heuristic optimization algorithms
Summary
If the user sets the value of λto 0.5, it means that all predicted requests will be handed over to the edge device cluster for processing, and the central server only performs resource storage and model training. At this time, the total time overhead: di 2Ce. e data processing task offloading framework in the edge cluster process is shown in Figure 1 [20, 21]. E main realization idea is to predict the future-period resource usage of server nodes whose current resource
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
