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Graph model-based QoS prediction method applied to cloud service recommendation

A prediction method, a technique of graphical models, used in character and pattern recognition, instruments, computer parts, etc.

Pending Publication Date: 2020-08-18
BEIJING JIAOTONG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, such an approach often ignores the call record generated by the user calling the service, and this call record is the most important link between the user and the service (such as figure 1 shown by the dotted line in the

Method used

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  • Graph model-based QoS prediction method applied to cloud service recommendation
  • Graph model-based QoS prediction method applied to cloud service recommendation
  • Graph model-based QoS prediction method applied to cloud service recommendation

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Embodiment 2

[0167] In order to verify the effectiveness of the algorithm, this embodiment uses the WSD REAM data set released by the Chinese University of Hong Kong. This data set collects the actual QoS measurement values ​​obtained from 339 users on 5,825 Web services. The specific values ​​are as follows Table 1 shows:

[0168] Table 1 Dataset description

[0169]

[0170] 1. Processing data

[0171] 1.1 Calculate the similarity between users:

[0172] 1.1.1 Collect the latitude and longitude information of the user;

[0173] 1.1.2 Convert user latitude and longitude information into similarity information between users;

[0174] 1.1.3 Collect user autonomous system information and calculate its similarity;

[0175] 1.1.4 Integrate latitude and longitude information and autonomous system information to obtain similarity information between end users.

[0176] 1.2 Calculate the similarity between services

[0177] 1.2.1 Collect WSDL document information;

[0178] 1.2.2 Destructu...

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Abstract

The invention provides a graph model-based QoS prediction method applied to cloud service recommendation. The method comprises the steps that a full-graph model containing multi-source information isconstructed, and the full-graph model comprises nodes representing users and services and edges using the similarity between the users, the similarity between the services and the similarity between the users and the services as weights; the full-graph model is segmented into a plurality of sub-graph models; a probability matrix decomposition algorithm for respectively optimizing the full-graph model and the sub-graph model is used for obtaining QoS of global and local prediction; and adaptive fusion processing is carried out on the QoS of global and local prediction to obtain the QoS of finalprediction. According to the method provided by the invention, the influence of multi-source information on QoS is fully considered, and local and global features are adaptively fused, so that the QoS prediction precision is improved. According to the method, the missing QoS value can be accurately predicted, then the sparse matrix is filled, the density of the matrix is improved, and the problemof QoS sparsity in the field of cloud service recommendation is solved to a certain extent.

Description

technical field [0001] The present invention relates to the field of computer application technology, in particular to a graph model-based QoS prediction method applied in cloud service recommendation. Background technique [0002] SOA (Service-Oriented Architecture, service-oriented architecture) is widely used in distributed computing environments, especially in the field of cloud computing. As the core part of SOA, services are a popular Way. In this case, many redundant services with similar functions but different QoS (Quality of Service, quality of service) appear. Faced with these massive services, users have encountered great problems in how to choose the appropriate cloud service. The emergence of the cloud service recommendation system is precisely to solve the problem that there are a large number of cloud services and information overload makes it impossible for users to quickly choose the appropriate service. In the case of the same function, the key to how t...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/22
Inventor 丁丁畅振华李浥东夏有昊
Owner BEIJING JIAOTONG UNIV
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