A micro-service architecture method based on cloud computing

Abstract

The invention discloses a micro-service architecture method based on cloud computing, and the method comprises the steps: carrying out the semantic processing of a service of a service registration center through a service registration module; the data analysis module adopts a deep learning model based on a convolutional neural network to provide application data presentation support for the micro-service, rapidly detects data meeting the service, and can present the data to a terminal user; the micro-service gateway scheduling module constructs a gateway negative balanced loading method basedon a genetic algorithm; the splitting service combination module evaluates the quality of splitting service combination by using quality of service (QoS) and guarantees the effectiveness and usability of service splitting. The invention discloses an applet generation and migration deployment module based on a differential power system. According to the method, service splitting, boundary clearness and context connectability can be realized conveniently, valuable data and more excellent usability can be acquired more accurately, splitting service combination is ensured to be more efficient, aservice combination chain construction process is ensured to be more stable, and a generated application applet is enabled to have a steady state.

Description

technical field [0001] The invention relates to the fields of cloud computing and software engineering, in particular to a microservice framework method based on cloud computing. Background technique [0002] Cloud computing has become one of the important basic technical supports for the development of a new generation of information technology. It has been successfully applied to the informatization of various industries, which has greatly promoted the development of the information industry and the generation of huge academic value, and has concentrated resources. It promotes the maximization of resource utilization, solves the prominent problems of distributed computing scalability and lack of elasticity, and also changes the traditional software computing and architecture model, comprehensively improves the efficiency of informatization development and maintenance, and realizes on-demand and volume-based terminals. Users provide resources to improve resource utilization...

AI Technical Summary

Problems solved by technology

However, different resources and services are distributed and deployed in nodes of different data centers, and the service description methods are more or less important, and the dependencies between services are complicated, resulting in the Internet software generated according to potential requirements. Deployment and migration are affected by service dependencies; and availability and ease of use are affected by the transparency of the network and resources where the service resides, resulting in weak looseness and low coupling of the generated software

Therefore, in recent years, with the development of big data technology and new artificial intelligence, the transformation of software engineering based on cloud computing has been promoted, and a software architecture model and generation method driven by data and its value have been gradually formed, further enriching the service-oriented architecture. (SOA) method, and effectively decoupled from service dependencies, using containers (such as Docker) to implement hosting and migration, and further promoted the evolution and evolution of software engineering, forming a new type of software based on MicroService (MicroService) as the framework. The architectural model has been effectively applied in many cloud computing environments, and can generate different microservice-based small programs and small applications according to different applications, effectively solving the problems of overly heavy service descriptions and migration and deployment difficulties faced by Internet software. Problems have formed a new software architecture and development model of microservices + deployment migration Docker + DevOps, but how to solve problems such as service dependencies, service splitting and fuses, and gateways between data and services are still facing huge challenges

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