Service quality prediction method and system based on deep neural network

A deep neural network and quality of service technology, applied in the field of service quality prediction method and system based on deep neural network, can solve the problems of limited context data, difficult expansion, and lack of modeling methods for QoS response sequence

CN110460463BActive Publication Date: 2021-12-14HAINAN UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Publication Date
2021-12-14

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Abstract

The invention discloses a service quality prediction method based on a deep neural network and a system thereof, and relates to the technical field of network services. The method includes the following steps: input request context variable information, and encode in the encoding module through entity expression matrix to obtain embedded request matrix; compress the encoded context variable information and perform feature extraction to obtain the described Request the embedded request vector according to the time sequence of the context variable information; according to the time sequence, input the embedded request vector to the LSTM network module to output the prediction data information of the deep neural network to the next input request context variable information; The predicted data information is sensed to obtain the decoded predicted data information, and the decoded predicted data information is restored to service quality data and output.
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Description

technical field

[0001] The invention relates to the technical field of network services, in particular to a service quality prediction method and system based on a deep neural network. Background technique

[0002] The number of Web Services in the Internet has increased dramatically in the past ten years. With the increase in the number of public Web services, the application of SOA (Service Oriented Architecture, service-oriented architecture) architecture has also become very extensive. However, the complex network environment of the Internet makes the status of services ever-changing, so effective service quality prediction is very important at this time.

[0003] Traditional service quality prediction methods are mostly based on CF model (Collaborative Filtering, collaborative filtering model) and MF model (Matrix factorization, matrix factorization model), which can use limited context data. At the same time, it is difficult for these two methods to be easily extended...

Examples

Embodiment Construction

[0029] It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0030] The present invention will be further described below in conjunction with the accompanying drawings.

[0031] A service quality prediction method based on deep neural network, such as figure 1 shown, including the following steps:

[0032] Input the request context variable information and encode it in the encoding module through the entity expression matrix to obtain the embedded request matrix.

[0033] In a specific embodiment, the request context variable information includes numeric and non-numeric features, which need to be preprocessed before being input to the encoding module. For continuous data, it needs to be sampled to convert it into discretized data, so as to fuse the service quality data information with the request context information. For non-numerical variables, the first six dimensions (u1...