A Method of Task Joint Execution Based on Genetic Algorithm

Abstract

A task joint execution method based on genetic algorithm, the execution system used in the execution method includes a user module, a service mode selection module and a genetic algorithm optimization module, the execution method includes the following steps: the working process of the user module; service mode selection The working process of the module; the working process of the genetic algorithm optimization module; at first, the present invention proposes a new time series matching method to obtain the user request history records consistent with the user request and with the smallest time interval, so as to provide a basis for the future service model. Selection provides guidance; secondly, the present invention proposes a mapping-level service mode and a cloud-level service mode to realize seamless switching of services and ensure service reliability; finally, a genetic algorithm is used to find the optimal task joint execution strategy. The invention can effectively realize the goals of low energy consumption and high reliability, avoids the inherent defect of mobile terminal resource limitation, and provides users with lower energy consumption, continuous, high-quality and reliable services.

Description

technical field [0001] The invention belongs to the field of mobile micro-learning, and more specifically relates to a method for jointly executing tasks based on a genetic algorithm. Background technique [0002] The green and reliable task joint execution strategy means that in the process of service provision, the individual needs of mobile users and the resources of mobile terminals are comprehensively considered, and the solution with the least energy consumption and the highest reliability is sought under the premise of satisfying user performance. . Therefore, based on the collaborative service provision process of local cloud and public cloud, research on low energy consumption and high reliability in the service provision process is the key to the current development of mobile micro-learning. [0003] Mobile micro-learning is a new type of learning model produced with the continuous development and integration of cloud computing and mobile Internet. Or information...

AI Technical Summary

Problems solved by technology

The provision and completion of mobile micro-learning requires continuous support between energy, storage and computing resources, but the mobility of mobile terminals largely limits its size and weight, resulting in its processing power, memory capacity, network connection and The problem of battery capacity and other aspects is becoming more and more prominent

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