Task combined execution method based on genetic algorithm

Abstract

The invention provides a task combined execution method based on a genetic algorithm. An executing system used in the execution method comprises a user module, a service mode selecting module and a genetic algorithm optimizing module. The execution method comprises the following steps of performing a working process of the user module; performing a working process of the service mode selecting module; and performing the working process of the genetic algorithm optimizing module. Firstly, the invention provides a novel time sequence matching method, thereby obtaining a user request historical record which is same with the user request and has least time interval, thereby guiding selection of a future service mode. Secondly, the invention provides a mapping-grade service mode and a cloud-grade service mode, thereby realizing seamless service switching and ensuring high service reliability. Finally, the genetic algorithm is used for searching an optimal task combined execution strategy. The task combined execution method can effectively realize targets of low energy consumption and high reliability and prevents inherent defects of limited resource of a mobile terminal. Furthermore thecontinuous, high-quality and reliable service with lower consumption is supplied to the user.

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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