Multi-objective optimization computing unloading method in mobile edge computing network

Abstract

The invention relates to a multi-objective optimization computing unloading method in a mobile edge computing network, and belongs to the field of Internet of Things and artificial intelligence. The method is based on a joint optimization model oriented to time delay and energy consumption, and power selection and proportional unloading of multiple users during task unloading are realized by designing an intelligent algorithm GPSO. Firstly, a network architecture of mobile edge computing is determined, modeling is conducted according to the network architecture, models comprise a system model,an application program model, a communication model and a computing model, then an objective function is solved according to the established models, and a problem is converted into a mixed nonlinearprogramming problem. And finally, a hierarchical calculation algorithm GPSO based on a particle swarm algorithm and a genetic algorithm is designed, thereby realizing user unloading proportion strategy and power selection under delay and energy consumption joint optimization. According to the method, optimal selection of a multi-user task unloading strategy in mobile edge computing is realized byutilizing related intelligent algorithms in the field of artificial intelligence.

Description

technical field [0001] The invention belongs to the field of Internet of Things and artificial intelligence, and in particular refers to a strategy of proportional unloading when multi-users in mobile edge computing perform task unloading. Background technique [0002] The rapid development of the Internet and the Internet of Things has promoted the continuous emergence of various new services, causing mobile communication traffic to experience explosive growth in the past few years. The development of mobile user devices such as smartphones or laptops has gone hand in hand with the development of new mobile applications. , meanwhile, computation-intensive and latency-sensitive applications, such as interactive games, image / video processing, augmented / virtual reality, and face recognition, are becoming increasingly popular on mobile devices. However, running computationally demanding applications on user devices is constrained by the limited battery capacity and energy consu...

AI Technical Summary

Problems solved by technology

Existing research on computing offload only considers the trade-off between cost and performance to determine whether an application should be executed locally or offloaded to an edge server

There are still many offloading studies that either consider the delay problem unilaterally, or consider the energy consumption unilaterally

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