Large-scale networked software self-optimization device and method based on swarm intelligence

Abstract

The invention discloses a large-scale networked software self-optimization device and method based on swarm intelligence. The device comprises a swarm self-optimization control module, a strategy execution module, a feedback optimization module and a platform information base module. Wherein the group self-optimization control module is used for acquiring state data during operation of target software and optimization requirements of a user on software entities in the target software, and determining an optimization strategy of the target software from a plurality of preset strategies; the strategy execution module is used for optimizing the target software according to the optimization strategy; the feedback optimization module is used for evaluating and correcting the optimization strategy and storing the corrected optimization strategy to the platform information base module; and the platform information base module is used for storing a plurality of preset strategies. According to the method, it can be ensured that the target software is actively optimized before the function or performance problem occurs, meanwhile, the efficiency and accuracy of the self-optimization process can be improved through the feedback optimization mechanism, and online correction of the self-optimization mechanism is achieved.

Description

technical field [0001] The invention belongs to the field of computer technology, and in particular relates to a large-scale networked software self-optimization device and method based on swarm intelligence. Background technique [0002] In related technologies, patent 106775915A discloses a method for dynamic evolution optimization of software architecture. Specifically, in this method, first, the original software architecture is coded for dynamic evolution, and the original components that make up the original software architecture are defined as: Gi={ni,pi,fi,odi,idi,noi,Ii}; The original software architecture is initialized, and the initial components are randomly generated; the fitness function is constructed, and the fitness value of each initial component is calculated; then the initial component corresponding to the fitness value greater than or equal to the set threshold is selected as the target component, and the fitness value less than The initial component co...

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Problems solved by technology

However, this method can only be optimized for a preset optimization problem, and there is a problem that the optimization requirements are not comprehensive and cannot accurately reflect the optimization requirements.

[0004] To sum up, the existing software optimization methods mostly use reflective and lagging fault discovery and processing mechanisms, which are only processed after an abnormality occurs in the system, and most of them are pre-set optimization schemes. It can only be used in a global centralized architecture, and these methods cannot better meet the needs of subsequent software optimization work

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