Android computing migration online decision-making method based on code analysis

Abstract

The invention relates to an Android computing migration online decision-making method based on code analysis. The method comprises the following steps of firstly, modeling the classes, methods, objects and calling relations of the applications based on a code analysis technology; secondly, on the basis of dynamic analysis, modeling an application model of the method execution time and the data transmission of the applications; and finally, based on the application model, proposing a context model help decision, and carrying out decision scheme evaluation and optimal scheme selection through afitness function. According to the method, the response time of each migration decision scheme is calculated based on an object call graph, the method execution time and the data transmission quantityobtained by the code analysis technology, and the optimal deployment scheme is obtained, so that the execution time and the energy consumption can be reduced.

Description

technical field [0001] The invention relates to an online decision-making method for Android computing migration based on code analysis. Background technique [0002] With the rise of artificial intelligence and big data, the computing and data processing of mobile applications are becoming more and more intensive. At the same time, with the rapid development of computing and communication technology, the computing platform of mobile applications has expanded from smartphones, tablets to wearables. Equipment, vehicles, drones, etc. Two key limitations of computing platforms for mobile applications have become increasingly prominent as user experience demands have increased memory computing complexity for applications. [0003] On the one hand, the hardware configurations of different computing platforms are highly heterogeneous, resulting in large differences in the performance of the same application on different configuration platforms, so even the same mobile application...

AI Technical Summary

Problems solved by technology

[0003] On the one hand, the hardware configurations of different computing platforms are highly heterogeneous, resulting in large differences in the performance of the same application on different configuration platforms, so even the same mobile application has a very different user experience on different mobile devices.

On the other hand, there are limitations on the battery power of mobile devices

Most computing platforms are powered by batteries. With the increasing complexity and calculation load of mobile applications, the power consumption of mobile devices is also increasing. The battery capacity of existing mobile devices can no longer meet the needs of complex mobile applications In view of the above two limitations faced by mobile devices, computing migration is proposed as an effective solution

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