Gray release method, system and device, storage medium and program product

Abstract

The embodiment of the invention provides a gray release method, system and device, a storage medium and a program product. In the embodiment of the invention, gray release demand information of an application system can be acquired, and the gray release demand information comprises a specified user attribute and a gray traffic proportion; acquiring traffic feature information of a plurality of candidate users from a non-gray service node in the application system, wherein the candidate users are users with specified user attributes; selecting a gray level user from the plurality of candidate users according to the traffic characteristic information and the gray level traffic proportion of the plurality of candidate users; and routing the service request of the gray user to a gray service node in the application system to perform gray release on the application system. Through the mode, the percentage of the gray flow can be accurately controlled during gray release, and meanwhile, the routing of the gray flow is fixed, so that the user experience is improved.

Description

Technical Field

[0001] This application relates to the field of cloud computing technology, and in particular to a canary release method, system, device, storage medium, and program product. Background Technology

[0002] Microservices are a software architecture pattern that divides a complex application into a set of small, self-managed services. These services can be deployed independently and are loosely coupled. In practice, microservice systems can be upgraded to provide better service performance and security.

[0003] When upgrading microservices, canary releases can be used to minimize the impact on the entire microservice system. A canary release is a software deployment method that uses a gradual, incremental approach to test a new version (or gray version) in a production environment. Once tested and approved, it is released as the official version.

[0004] In order to minimize the impact of a canary release, the number of canary service nodes (i.e., service nodes running canary versions) is usually controlled, for example, not exceeding 20% ​​of the number of official service nodes. Then, user traffic is divided into canary traffic and official traffic, with canary traffic routed to canary service nodes and official traffic routed to official service nodes.

[0005] However, in a real production environment, if traffic is differentiated according to region or user identifier, it may be difficult to strictly control the size of gray-scale traffic, resulting in gray-scale service nodes being overloaded or underloaded. If a certain number of service requests are routed to gray-scale service nodes as gray-scale traffic according to a fixed percentage of gray-scale traffic, the routing of traffic for the same user will be inconsistent, reducing the user experience. Summary of the Invention

[0006] This application provides a canary release method, system, device, storage medium, and program product for accurately controlling the percentage of canary traffic during canary release, while fixing the routing of canary traffic to improve user experience.

[0007] This application provides a canary deployment method, comprising: obtaining canary deployment requirement information of an application system, the canary deployment requirement information including specified user attributes and canary traffic ratio; obtaining traffic characteristic information of multiple candidate users from non-canary service nodes in the application system, the candidate users being users with the specified user attributes; selecting canary users from the multiple candidate users according to the traffic characteristic information of the multiple candidate users and the canary traffic ratio; and routing the service requests of the canary users to the canary service nodes in the application system to perform canary deployment of the application system.

[0008] This application embodiment also provides a control device, including: a storage unit and a processing unit, wherein the storage unit is used to store a computer program, and the processing unit is coupled to the storage unit and is used to execute the computer program for: implementing the steps in the grayscale release method.

[0009] This application also provides a canary release system, including: a non-canary service node, a canary service node, and the control device.

[0010] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to implement the steps in the grayscale release method.

[0011] This application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, performs the steps in the grayscale release method.

[0012] In this embodiment, the canary release requirement information of the application system can be obtained. This canary release requirement information includes specified user attributes and canary traffic ratios. Traffic characteristic information of multiple candidate users (users with specified user attributes) is collected from non-canary service nodes in the application system. Canary users are selected from the multiple candidate users based on their traffic characteristic information and canary traffic ratios. Service requests from canary users are routed to the canary service nodes in the application system to perform canary releases. This method allows for more accurate control of the percentage of canary traffic during canary releases, while also fixing the routing of canary traffic, thus improving user experience. Attached Figure Description

[0013] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0014] Figures 1a-1b A schematic diagram of the architecture of a canary release system provided in an exemplary embodiment of this application;

[0015] Figure 2 A flowchart illustrating a grayscale release method provided for an exemplary embodiment of this application;

[0016] Figure 3a A schematic flowchart illustrating a grayscale user selection process provided for an exemplary embodiment of this application;

[0017] Figure 3b A schematic diagram of the process for reselecting grayscale users provided as an exemplary embodiment of this application;

[0018] Figure 4 A schematic diagram of the structure of a grayscale publishing device provided in an exemplary embodiment of this application;

[0019] Figure 5 A schematic diagram of the structure of a control device provided for an exemplary embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] It should be noted that, in the cases involving user information in the embodiments of this application, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse. In addition, the various models involved in this application (including but not limited to language models or large models) comply with relevant laws and standards.

[0022] When conducting canary releases, the number of canary service nodes is typically controlled, and user traffic is distinguished into canary traffic and official traffic. Canary traffic is routed to canary service nodes, and official traffic is routed to official service nodes. If traffic is distinguished according to region or user identifier, it may be difficult to strictly control the size of canary traffic, resulting in canary service nodes being overloaded or underloaded. If a fixed percentage of service requests are routed as canary traffic to canary service nodes, the routing of traffic from the same user will be inconsistent, reducing the user experience.

[0023] To address the aforementioned technical problems, this application provides a solution that allows specifying user attributes and gray-scale traffic ratios to collect traffic characteristic information for users with specified user attributes. Then, based on the preset gray-scale traffic ratio and traffic characteristic information, gray-scale users are selected from those users with specified user attributes, and their service requests are routed to gray-scale service nodes in the application system. On one hand, since the selection process for gray-scale users is constrained by a preset gray-scale traffic ratio, the selected gray-scale users meet this ratio, thus allowing for more accurate control of the percentage of gray-scale traffic. On the other hand, traffic characteristic information can be collected at the user level, and traffic with the same user attribute can be routed to fixed service nodes, such as gray-scale service nodes or non-gray-scale service nodes, thereby fixing the routing of gray-scale traffic and improving user experience.

[0024] Figures 1a-1b This is a schematic diagram of the architecture of a canary release system provided for an exemplary embodiment of this application. The canary release system includes an application system and a management and control device 11. The canary release process refers to the process of releasing a new version of a service process or thread in the application system. The canary release process can be executed by the management and control device 11, which can be any terminal device such as a mobile phone, tablet computer, or computer, or any type of server. This embodiment does not impose any restrictions.

[0025] like Figure 1a As shown, application system 12 can communicate with control device 11 and may include gray-scale service nodes and non-gray-scale service nodes. Gray-scale service nodes refer to service nodes in application system 12 responsible for running gray-scale versions of service programs or processes; non-gray-scale service nodes refer to service nodes in application system 12 responsible for running official versions of service programs or processes. Non-gray-scale service nodes can also be called official service nodes. Compared to the official version, gray-scale version service programs differ in functionality, performance, or security, such as increased, upgraded, or reduced functionality, and improved performance and security, but are not limited to these aspects. It should be noted that... Figure 1a as well as Figure 1b All of these are illustrative examples and do not constitute a limitation on the number of users, non-grayscale service nodes, and grayscale server nodes in the embodiments of this application.

[0026] Reference Figure 1a and Figure 1bAs shown in steps ①-②, the application administrator can send the application system's canary release request information to the management device 11. This canary release request information includes specified user attributes and canary traffic ratios. The management device 11 receives the canary release request information from the application administrator and parses out the specified user attributes and canary traffic ratios. It should be noted that the application administrator sending the canary release request information to the management device 11 is only one example; the management device 11 can also obtain canary release request information in other ways. For example, the management device 11 can read the canary release request information from a specified storage location, which can be local memory or local persistent storage media (such as a disk), or a remote database or cloud storage system. Specified user attributes describe the attribute information of users participating in the canary release, used for filtering and distinguishing user traffic. These attributes may include, but are not limited to, user ID, the user's region, or the user's terminal type. The canary traffic ratio describes the expected proportion of traffic participating in the canary release, which can be the proportion of traffic participating in the canary release to the total traffic.

[0027] After acquiring gray-scale release demand information and parsing out specified user attributes and gray-scale traffic ratios, the control device 11 can collect traffic characteristic information of multiple candidate users from non-gray-scale service nodes in the application system 12; wherein, candidate users refer to users with specified user attributes. In this embodiment, "traffic" refers to service requests initiated by users. Traffic characteristic information refers to various characteristic information reflecting the traffic situation of candidate users, such as including but not limited to: access frequency, response time, success rate, etc. In this embodiment, the method by which the control device 11 collects the traffic characteristic information of candidate users from non-gray-scale service nodes is not limited.

[0028] For example, the control device 11 can send a notification message to the non-grayscale service nodes to notify them to report the service requests they receive to the control device 11; the control device 11 selects service requests from candidate users from the service requests reported by the non-grayscale service nodes according to the specified user attributes.

[0029] For example, such as Figure 1a and Figure 1b As shown, a data collection component is deployed on the non-canary service nodes. This component can be used to collect traffic characteristic information of the user groups redirected to the non-canary service nodes. For example, it can be used to collect traffic characteristic information before the canary release. Figure 1a The diagram shows the collection of traffic characteristic information for user groups including users 1 through N. Optionally, a collection component can also be deployed on the canary service nodes to collect traffic characteristic information for user groups redirected to the canary service nodes after the canary release is completed. Based on this, the control node will distribute specified user attributes to each non-canary service node. (Refer to...) Figure 1a and Figure 1b As shown in step ③, the collection components on non-grayscale service nodes N1 to N4 can filter out candidate users that meet the specified user attributes from N users from user 1 to user N, such as user 1 to user 5, and collect the traffic characteristic information of each candidate user and report it to the control device 11.

[0030] See Figure 1a and Figure 1b In step ④, the control device 11 selects gray-scale users from users 1-5 based on the traffic characteristics of users 1-5 and the gray-scale traffic ratio issued by the application administrator. For example, the gray-scale users are users 4 and 5. Gray-scale users refer to users selected to participate in gray-scale deployment, that is, users selected to send service requests to gray-scale service nodes. Once gray-scale users are selected, the control device 11 routes the service requests from the gray-scale users to the gray-scale service nodes in the application system 12 to perform gray-scale deployment of service programs or processes in the application system 12. The method by which the control device 11 routes the service requests from gray-scale users to the gray-scale service nodes may vary depending on the implementation architecture of the application system 12.

[0031] like Figure 1a As shown, in this system architecture, each user's client node communicates directly with the service node. In this case, refer to... Figure 1a As shown in step ⑤, the control device 11 can send notification messages to grayscale users. (Refer to...) Figure 1a As shown in step ⑥ of the upper and lower diagrams, the notification message can be used to adjust the routing for users 4 and 5, enabling them to send service requests directly to the grayscale service node. This notification message may include the identification information of the grayscale service node, such as its IP address, name, or ID. Figure 1a In the diagram, solid lines represent links routed to non-grayscale service nodes, while dashed lines represent links routed to grayscale service nodes, in order to distinguish between the two.

[0032] like Figure 1b As shown, the application system 12 may also include a traffic ingress node K1, which has a traffic aggregation function and can be a load balancer or gateway node, etc. Service requests from various users are aggregated to the traffic ingress node K1, and then the traffic ingress node K1 distributes the service requests from each user to different service nodes. Based on this, refer to... Figure 1b As shown in step ⑤, the control device 11 can send information about grayscale users to the traffic ingress node K1. (Refer to...) Figure 1bAs shown in step ⑥ of the upper and lower diagrams, the traffic entry node K1 can perform routing adjustments, that is, based on the information of gray-scale users, service requests from gray-scale users are distributed to gray-scale service nodes, and service requests from non-gray-scale users are distributed to non-gray-scale service nodes, in order to perform gray-scale release of application system 12. Figure 1b In the diagram, solid lines represent links routed to non-grayscale service nodes, while dashed lines represent links routed to grayscale service nodes, in order to distinguish between the two.

[0033] The above combination Figures 1a-1b The system architecture shown illustrates the basic process of the embodiments of this application. The following will combine... Figure 2 The technical solutions provided in each embodiment of this application are described in detail.

[0034] Figure 2 This is a flowchart illustrating a canary release method provided for an exemplary embodiment of this application. The method is primarily described from the perspective of device management, such as... Figure 2 As shown, the method may include the following steps:

[0035] Step 21: Obtain the canary release requirement information of the application system. The canary release requirement information includes the specified user attributes and the canary traffic ratio.

[0036] Step 22: Obtain traffic characteristic information of multiple candidate users from the non-grayscale service nodes in the application system. Candidate users are users with specified user attributes.

[0037] Step 23: Select grayscale users from multiple candidate users based on the traffic characteristics and grayscale traffic ratio of multiple candidate users.

[0038] Step 24: Route the service requests of gray-scale users to the gray-scale service nodes in the application system to perform gray-scale deployment of the application system.

[0039] In this embodiment, the gray-scale release requirement information is used to describe the allocation rules of gray-scale traffic in the current gray-scale release task, and may include: specifying user attributes and gray-scale traffic ratio. Specifically, specifying user attributes is used to filter multiple users within a user group. This embodiment does not limit the specific implementation of the specified user attributes; the specified user attributes can be different under different dimensions. For example, each user may have their own UID (user identifier), and the specified user attribute can be a specified UID attribute, such as a UID starting with 0 or ending with 1, etc.; each user may be in different regions and use different mobile phone models, and the specified user attribute can be a specified region attribute or a specified mobile phone model attribute; this embodiment does not impose any restrictions. The gray-scale traffic ratio is used to describe the expected ratio of gray-scale traffic to formal traffic, and can be set to any value according to actual needs, such as 70%, 60%, or 50%, etc.; this embodiment does not impose any restrictions.

[0040] This application does not limit the specific method of obtaining the gray-scale release requirement information in its embodiments. In some optional embodiments, the control device may provide a gray-scale service interface to relevant personnel. The gray-scale service interface may include multiple configuration items for relevant personnel to submit gray-scale release requirement information. Relevant personnel can input information for at least one of the multiple configuration items, and the control device can respond to the input operation of relevant personnel and obtain the input gray-scale release requirement information. In other optional embodiments, relevant personnel can upload a requirement document to the control device through the gray-scale service interface provided by the control device, so that the control device can parse the requirement document to obtain the gray-scale release requirement information. In some optional embodiments, relevant personnel can also send the requirement document to the control device via email or other means, so that the control device can parse the requirement document to obtain the gray-scale release requirement information.

[0041] After obtaining the canary release requirement information, traffic characteristic information of multiple candidate users can be obtained from the non-canary service nodes in the application system. This traffic characteristic information of multiple candidate users can be collected by the non-canary service nodes in the application system, and thus, the traffic characteristic information of multiple candidate users can be obtained from the non-canary service nodes. Candidate users are users with specified user attributes, which can be selected from multiple users by the non-canary service nodes based on the specified user attributes. The application system can be a microservice system, but is not limited to this. For example, in some application scenarios, to facilitate disaster recovery or handle more user traffic, multiple servers can be deployed, each running the same server-side program. These server-side programs can be upgraded in a canary manner, and can be used for beautification, payment, video processing, etc. This implementation does not impose any restrictions.

[0042] The traffic characteristic information of any candidate user is used to describe the traffic characteristics exhibited by that candidate user when sending service requests to non-grayscale service nodes, including but not limited to at least one of the following: service request access frequency, queries per second (QPS), average response time, and request success rate. A non-grayscale service node may collect traffic characteristic information from one candidate user or multiple candidate users; this embodiment does not impose any limitation. It should be noted that this embodiment does not restrict the order in which grayscale release demand information is obtained and traffic characteristic information is collected. In other words, traffic characteristic information can be collected by the non-grayscale service node at any time before grayscale release, or it can be collected by the non-grayscale service node after obtaining the grayscale release demand information; this embodiment does not impose any limitation.

[0043] Based on the above steps, grayscale users can be selected from multiple candidate users according to their traffic characteristic information and grayscale traffic ratio. In other words, a subset of candidate users can be selected from multiple candidate users based on their traffic characteristic information, ensuring that the sum of the expected grayscale traffic generated by these candidate users matches the grayscale traffic ratio. Therefore, these candidate users can be used as grayscale users.

[0044] After selecting gray-scale users, their service requests can be routed to gray-scale service nodes in the application system for gray-scale deployment. Specifically, the routing address of gray-scale user service requests can be adjusted from the original non-gray-scale service node's routing address to the gray-scale service node's routing address, thus routing the gray-scale user service requests to the gray-scale service node.

[0045] In this embodiment, user attributes and gray-scale traffic ratios can be specified to collect traffic characteristic information for users with specified user attributes. Then, based on the preset gray-scale traffic ratio and traffic characteristic information, gray-scale users can be selected from the users with specified user attributes, and their service requests can be routed to gray-scale service nodes in the application system. On the one hand, since the selection process for gray-scale users is constrained by the preset gray-scale traffic ratio, the selected gray-scale users can meet this ratio, thus allowing for relatively accurate control of the percentage of gray-scale traffic. On the other hand, traffic characteristic information can be collected at the user level, and traffic with the same user attribute can be routed to fixed service nodes, such as gray-scale service nodes or non-gray-scale service nodes, thereby fixing the routing of gray-scale traffic and improving user experience.

[0046] This application does not limit the specific implementation method of obtaining traffic feature information of multiple candidate users from non-grayscale service nodes. In some optional embodiments, a collection component may be deployed on the non-grayscale service node. The collection component may be implemented as a functional module on the non-grayscale service node, or it may be implemented as an application running in the operating system of the non-grayscale service node. This embodiment does not limit the specific implementation method of the collection component.

[0047] Based on the collection component deployed on the non-grayscale service nodes, the management and control device can send specified user attributes to the collection component. The collection component can then collect feature information from the service requests received from the non-grayscale service nodes, summarizing the characteristics of service requests from candidate users and reporting this information to the management and control device. The management and control device can then use the feature information of service requests from candidate users as the traffic feature information of the candidate users.

[0048] The data collection component can select candidate users with specified user attributes from multiple users in a user group based on specified user attributes, and collect feature information of service requests from any candidate user from multiple user service requests received from non-grayscale service nodes. The feature information is used to describe the traffic characteristics when service requests are sent to non-grayscale service nodes, and may include: service request access frequency, average response time, and request success rate.

[0049] In this way, traffic characteristic information of multiple candidate users can be collected relatively accurately and efficiently from non-grayscale service nodes in the application system.

[0050] This application does not limit the specific implementation of selecting grayscale users from multiple candidate users. In some optional embodiments, it can be implemented based on the following steps 231-232:

[0051] Step 231: Determine the target traffic ratio for multiple candidate users based on their traffic characteristic information.

[0052] Step 232: Select gray users from multiple candidate users based on the target traffic ratio and gray traffic ratio of multiple candidate users.

[0053] This embodiment does not limit the specific implementation of step 231. In some optional embodiments, it can be implemented based on any of the following implementation methods. The calculation results of any of the following implementation methods are more accurate and have the advantages of being simple to implement and efficient:

[0054] Implementation Method 1: Based on the traffic characteristic information of multiple candidate users, determine the traffic value generated by multiple candidate users within a target time period. The target time period is the period for collecting traffic characteristic information, i.e., the period for collecting traffic characteristic information of multiple candidate users. This embodiment does not limit the specific implementation method for determining the traffic value generated by multiple candidate users within the target time period. Optionally, the traffic characteristic information may include at least one type of traffic sub-characteristic information, and different functional relationships may exist between different types of sub-traffic characteristic information and traffic. The functional relationship between each type of traffic characteristic sub-information and traffic can be learned in advance by performing regression analysis or data fitting on various types of historical traffic characteristic sub-information and their corresponding historical traffic. For example, traffic and average response time data can be collected in advance in various network environments, and the functional relationship between traffic and average response time can be learned using regression analysis or data fitting. Alternatively, query rate per second data at different times can be collected in advance in various network environments, and the functional relationship between query rate per second and time can be learned using regression analysis or data fitting. Furthermore, at least one initial traffic value for a candidate user can be calculated based on the functional relationship between at least one traffic characteristic sub-information of the candidate user and traffic. The calculation method for the initial traffic value will vary depending on the type of traffic characteristic sub-information. For example, when the traffic feature sub-information is the query rate per second, the query rate per second of the candidate user can be directly used as the initial traffic value of the candidate user; when the traffic feature sub-information is the average response time, the initial traffic value corresponding to the average response time of the candidate user can be determined based on the average response time of the candidate user and the functional relationship between the average response time and traffic; when the traffic feature sub-information is the request success rate, the initial traffic value corresponding to the request success rate of the candidate user can be determined based on the request success rate of the candidate user and the functional relationship between the request success rate and traffic. After calculating at least one initial traffic value, the traffic value generated by the candidate user within the target time period can be determined based on at least one initial traffic value. This embodiment does not limit the specific implementation method. In some optional embodiments, if the initial traffic value of the candidate user is one, the initial traffic value can be used as the traffic value generated by the candidate user in the target time period; various numerical calculations can also be performed based on the initial traffic value to obtain the traffic value generated by the candidate user in the target time period. For example, the initial traffic value can be corrected using a preset correction coefficient to obtain the traffic value generated by the candidate user in the target time period, or a preset correction value can be obtained, along with the weights of the correction value and the initial traffic value. Based on their respective weights and the preset correction value, the initial traffic value and the correction value can be weighted and summed to obtain the traffic value generated by the candidate user in the target time period.When multiple initial traffic values ​​are obtained, the weights of various types of traffic feature sub-information can be acquired, and the multiple initial traffic values ​​corresponding to the various types of traffic feature sub-information can be weighted and summed to obtain the traffic value generated by the candidate user in the target time period; alternatively, multiple initial traffic values ​​can be directly added together to obtain the traffic value; alternatively, the maximum value can be selected from multiple initial traffic values, and the maximum value can be corrected using a preset correction coefficient to obtain the traffic value generated by the candidate user in the target time period. This embodiment does not impose any restrictions.

[0055] After determining the traffic values ​​generated by multiple candidate users within the target time period based on the above process, the target traffic ratio of the candidate users can be calculated based on the traffic values ​​generated by each candidate user within the target time period and the sum of the traffic values ​​generated by multiple candidate users within the target time period. The target traffic ratio describes the proportion of traffic generated by the candidate user within the target time period. This embodiment does not limit the specific calculation method of the target traffic ratio. In some optional embodiments, the traffic value generated by each candidate user within the target time period can be divided by the sum of the traffic values ​​generated by multiple candidate users within the target time period to obtain the ratio, which can be used as the target traffic ratio of the candidate user. Alternatively, this ratio can be calculated first, and then corrected using a preset correction coefficient to obtain the target traffic ratio of the candidate user. The first correction coefficient can be set to any value according to actual design requirements, such as 0.9 or 0.8, etc., and this embodiment does not impose any restrictions. Alternatively, the traffic value generated by the candidate user in the target time period can be corrected by a preset second correction coefficient, and the corrected traffic value can be divided by the sum of the traffic values ​​generated by multiple candidate users in the target time period to obtain the target traffic ratio of the candidate user. The second correction coefficient can be set to any value according to actual design requirements, such as 0.9 or 0.8, etc., and this embodiment does not impose any restrictions.

[0056] Implementation method two, specifically based on steps R1-R4:

[0057] Step R1: Divide the target time period into multiple sub-time periods. Based on the traffic characteristic information of multiple candidate users, determine the traffic value generated by each candidate user in each sub-time period. This embodiment does not limit the method of dividing the target time period; it can be divided into any positive number of sub-time periods, such as 10, 20, 50, or 80, etc. The traffic characteristic information has a time attribute. Taking any candidate user as an example, based on the time attribute of the candidate user's traffic characteristic information, the traffic characteristic information can be divided into traffic characteristic information corresponding to each sub-time period, and the traffic value generated by the candidate user in each sub-time period can be calculated based on the traffic characteristic information corresponding to each sub-time period. For example, based on the time attribute of the candidate user's traffic characteristic information, the traffic characteristic information can be divided into traffic characteristic information for 10-11 o'clock, traffic characteristic information for 11-12 o'clock, and traffic characteristic information for 12-13 o'clock. The specific calculation method for calculating the traffic value generated by a candidate user in a sub-period based on the traffic characteristic information corresponding to any sub-period has been explained in detail in the relevant content of the aforementioned Implementation Method 1, "Determining the traffic value generated by multiple candidate users in a target period based on the traffic characteristic information of multiple candidate users," and will not be repeated here.

[0058] Step R2: Calculate the traffic ratio of each candidate user within a sub-time period based on the traffic values ​​generated by multiple candidate users within each sub-time period and the sum of their traffic values. This embodiment does not limit the specific calculation method. In some optional embodiments, the traffic values ​​generated by multiple candidate users within a sub-time period can be divided by the sum of their traffic values ​​to obtain the ratio, which can be used as the traffic ratio of the candidate user within the sub-time period. Alternatively, the traffic values ​​generated by multiple candidate users within a sub-time period can be divided by the sum of their traffic values ​​to obtain the ratio, and then corrected using a preset third correction coefficient to obtain the traffic ratio of the candidate user within the sub-time period. The third correction coefficient can be set to any value according to actual design requirements, such as 0.9 or 0.8, etc., and this embodiment does not impose any restrictions.

[0059] Step R3: Determine the functional relationship between the traffic proportion of candidate users and time based on the traffic proportion of candidate users across multiple sub-time periods. This can be achieved by performing regression analysis or data fitting on the traffic proportion of candidate users across multiple sub-time periods and the corresponding time intervals, thereby learning the functional relationship between the traffic proportion of candidate users and time. This embodiment does not limit the specific implementation of the regression analysis or data fitting. In some exemplary embodiments, local weighted regression or a piecewise function can be used to perform regression analysis to obtain an initial function image, and spline interpolation can be used to further increase the fitting degree of each point on the initial function image to obtain a target function image. This target function image can characterize the functional relationship between the traffic proportion of candidate users and time.

[0060] Step R4: Based on the functional relationship between the traffic ratio of the candidate user and time, calculate the traffic ratio of the candidate user at the time of gray-scale release as its target traffic ratio. In this functional relationship, each time point can be mapped to a traffic ratio. Based on this, the traffic ratio corresponding to the gray-scale release time can be determined as the target traffic ratio, according to the mapping relationship between the traffic ratio of the candidate user and time, and the gray-scale release time.

[0061] Based on the above implementation methods one and two, the target traffic ratio of multiple candidate users can be generated relatively accurately and efficiently according to the traffic characteristic information of multiple candidate users.

[0062] Following the previous embodiments, this embodiment does not limit the specific implementation of step 232, "selecting gray users from multiple candidate users based on the target traffic ratio and gray traffic ratio of multiple candidate users." In some optional embodiments, it can be implemented in the following ways:

[0063] The target traffic proportions of multiple candidate users are divided into multiple unit sets. That is, the target traffic proportion of a candidate user can only appear in one unit set. Each unit set corresponds to a base traffic proportion, and a base traffic proportion can correspond to one or more unit sets; this embodiment does not impose any restrictions. The base traffic proportion can be set to any value according to actual needs, such as 1%, 5%, 10%, or 30%, etc., this embodiment does not impose any restrictions. The error between the sum of the target traffic proportions in a unit set and the base traffic proportion corresponding to that unit set is within a first preset range. This first preset range can be set to any range according to actual needs, such as 0-1%, 0-6%, or 1%-4%, etc., this embodiment does not impose any restrictions. In other words, the sum of the target traffic proportions in a unit set can be the same as or close to the base traffic proportion corresponding to that unit set; ideally, they should be the same.

[0064] For example, there are n unit sets U1-Un with percentages of 1%, 1%, 5%, 5%, 10%, ..., 50%, respectively, and a first set range of 0-1%. Based on this, the target traffic percentage of candidate user H1 (-0.5%), the target traffic percentage of candidate user H2 (0.1%), and the target traffic percentage of candidate user H3 (0.4%) can be assigned to unit set U1 (corresponding to a base traffic percentage of 1%), and 0.5% + 0.1% + 0.4% is the same as that of unit set U1 (corresponding to a base traffic percentage of 1%). Similarly, the target traffic percentages of candidate user H10 (2.5%), candidate user H11 (3%), and candidate user H12 (4%) can be assigned to unit set U5 (corresponding to a base traffic percentage of 10%), and the error between 2.5% + 3% + 4% and that of unit set U5 (corresponding to a base traffic percentage of 10%) is within the first set range of 0-1%.

[0065] In this embodiment, the specific implementation method for dividing the target traffic ratios of multiple candidate users into multiple unit sets is not limited. In an exemplary embodiment, the number of basic traffic ratios and the specific value of each basic traffic ratio can be preset, thereby dividing the target traffic ratios of multiple candidate users into multiple unit sets corresponding to the basic traffic ratios based on the number of basic traffic ratios and the specific value of each basic traffic ratio. In an exemplary embodiment, the target number of basic traffic ratios, the initial value of the basic traffic ratios, and the step size can also be preset. The step size and the initial value can be set to any value according to actual design requirements, and this embodiment does not impose any restrictions. Furthermore, the step size can be continuously added based on the initial value of the basic traffic ratios. Each addition of a step size yields a basic traffic ratio, until the number of basic traffic ratios reaches the target number and the addition stops. In this way, multiple basic traffic ratios can be obtained, and then the target traffic ratios of multiple candidate users can be divided into multiple unit sets corresponding to the basic traffic ratios.

[0066] In some optional embodiments, at least one basic traffic ratio and a threshold number of unit sets corresponding to each basic traffic ratio can be determined first. The threshold number of unit sets corresponding to any basic traffic ratio refers to the maximum number of unit sets corresponding to that basic traffic ratio. For example, if the threshold number of unit sets corresponding to basic traffic ratio G1 is 2 and the threshold number of unit sets corresponding to basic traffic ratio G2 is 3, this means that basic traffic ratio G1 can correspond to at most two unit sets, and basic traffic ratio G2 can correspond to at most three unit sets. In other words, this embodiment only limits the specific value of the basic traffic ratio and the threshold number of unit sets corresponding to the basic traffic ratio, and is not limited by the total number of basic traffic ratios.

[0067] Then, the target traffic ratios of multiple candidate users can be accumulated step by step, following the order of traffic ratio from smallest to largest and the requirement that the target traffic ratio of the same candidate user participates in only one accumulation operation. In other words, the target traffic ratios of multiple candidate users can be sorted in ascending order of traffic ratio, and each candidate user's target traffic ratio can only participate in one accumulation operation.

[0068] Based on this, if the error between the sum of the target traffic proportions participating in the accumulation and any basic traffic proportion is within a first set range, and the number of unit sets corresponding to any basic traffic proportion has not yet reached the quantity threshold, the target traffic proportions participating in the accumulation can be divided into a unit set corresponding to any basic traffic proportion.

[0069] For example, suppose the threshold number of unit sets corresponding to the basic traffic ratio G1 (1%) is 2, and the threshold number of unit sets corresponding to the basic traffic ratio G2 (2%) is 3; the first set range is 0-1%; the target traffic ratios of multiple candidate users, from smallest to largest, are 0.4%, 0.5%, 0.6%, 2%, 2.8%, 4%, 8%, 9%, ..., 20% and 22%. Based on this, starting from 0.4%, 0.4%, 0.5%, and 0.6% can be added together to obtain 1.5%. The error between this and the basic traffic ratio G1 (1%) is within the first set range of 0-1%, and the number of unit sets corresponding to the basic traffic ratio G1 (1%) is currently only 1, which does not exceed the threshold number 2. Therefore, the 0.4% and 0.5% involved in the accumulation can be divided into one unit set corresponding to the basic traffic ratio G1 (1%). Subsequently, since the error between 0.6% and the basic flow ratio G1 (1%) is within the first set range of 0-1%, it is possible to consider classifying 0.6% into a separate unit set corresponding to the basic flow ratio G1 (1%). However, this would cause the number of unit sets corresponding to the basic flow ratio G1 to exceed the quantity threshold. Therefore, 0.6% and 2% can be classified into a single unit set corresponding to the basic flow ratio G2 (2%). The subsequent overlay operations will not be elaborated upon here.

[0070] By statistically analyzing the unit sets corresponding to each basic traffic ratio, and selecting unit sets according to the specified gray-scale traffic ratio and the basic traffic ratio of each unit set, the purpose of selecting gray-scale users can be achieved. This selection method is simpler and more accurate. In addition, it can support dynamic changes in the gray-scale traffic ratio. When the gray-scale traffic ratio changes, the unit sets can be selected again according to the basic traffic ratio of each unit set without repeating the aforementioned calculation process, which helps to save resources and improve the selection efficiency of gray-scale users.

[0071] Optionally, when the number of currently divided unit sets reaches a first number, and there are still target traffic ratios of candidate users not yet included in the accumulation, the sum of the basic traffic ratios corresponding to each currently divided unit set is calculated as the first traffic ratio. The first number can be set to any value, such as 7, 8, or 9; this embodiment does not impose any restrictions. For example, if the first number is 7, and there are currently 7 divided unit sets, the basic traffic ratios corresponding to these seven unit sets can be summed to obtain the first traffic ratio. The traffic ratio threshold can be flexibly set according to application requirements, for example, it can be 50%, 60%, etc. If the first traffic ratio is greater than the set traffic ratio threshold, the target traffic ratios of candidate users not yet included in the accumulation can continue to be accumulated, so that the target traffic ratios of candidate users not yet included in the accumulation are divided into no more than a second number of unit sets. The traffic ratio threshold and the second number can both be set to any value according to actual design requirements; this embodiment does not impose any restrictions. For example, the target traffic proportions of candidate users not yet included in the accumulation are 18%, 20%, 22%, and 25%, respectively. The second quantity is 2. The threshold for the number of unit sets corresponding to the basic traffic proportions G6-40% and G7-50% is 1. Therefore, 19.5% and 20% can be assigned to a new unit set corresponding to the basic traffic proportion G6-40%, and 24% and 25% can be assigned to a new unit set corresponding to the basic traffic proportion G7-50%. This optional approach can effectively avoid an excessive number of unit sets in the final partitioning, achieving rapid convergence in the unit set partitioning process.

[0072] Based on the above, after accurately dividing the target traffic ratios of multiple candidate users into multiple unit sets, at least one target unit set can be selected from the multiple unit sets according to the grayscale traffic ratio, and the candidate users corresponding to the target traffic ratios in at least one target unit set can be regarded as grayscale users. The error between the sum of the basic traffic ratios corresponding to at least one target unit set and the grayscale traffic ratio is within a second preset range. This second preset range can be set to any range according to actual design requirements, such as 0-1.5%, 0-6.5%, or 0.5%-4%, etc., and this embodiment does not impose any limitations. For example, see... Figure 3aThe gray-scale traffic ratio is 30%, and the second setting range is 0-3%. The basic traffic ratios of unit sets U1 to U8 are 1%, 1%, 5%, 5%, 10%, 10%, 30%, and 50%, respectively. Therefore, unit sets U1 (corresponding to a basic traffic ratio of 1%), U2 (corresponding to a basic traffic ratio of 1%), U3 (corresponding to a basic traffic ratio of 5%), U4 (corresponding to a basic traffic ratio of 5%), U5 (corresponding to a basic traffic ratio of 10%), and U6 (corresponding to a basic traffic ratio of 10%) can be selected from the basic traffic ratios of unit sets U1 to U8 as target unit sets, and the candidate users corresponding to each target unit set are used as gray-scale users. See [link / reference] Figure 3a In each target unit set: unit set U1 corresponds to candidate users 1-3, unit set U2 corresponds to candidate users 4-5, unit set U3 corresponds to candidate users 6-10, unit set U4 corresponds to candidate users 11-18, unit set U5 corresponds to candidate users 19-28, and unit set U6 corresponds to candidate users 29-39. These candidate users are the grayscale users.

[0073] In this way, gray-scale users can be selected more accurately from multiple candidate users based on the target traffic ratio and gray-scale traffic ratio of multiple candidate users.

[0074] Following the above embodiments, after routing the service requests of grayscale users to the grayscale service nodes in the application system, the unit set can be updated or grayscale users can be reselected based on the following implementation methods. Implementation methods three and four below can be executed individually or in combination; this embodiment does not impose any restrictions.

[0075] Implementation Method 3: Collect traffic characteristic information of non-grayscale users from non-grayscale service nodes; update multiple unit sets based on the traffic characteristic information of non-grayscale users and the grayscale traffic ratio to update grayscale users. In other words, after routing service requests from grayscale users to grayscale service nodes in the application system, traffic characteristic information of non-grayscale users can continue to be collected. This allows for dynamic updates to the previous unit sets based on the traffic characteristic information of non-grayscale users and the grayscale traffic ratio, ensuring the accuracy of the unit sets and enabling more accurate grayscale user updates.

[0076] Implementation Method 4: Collect traffic characteristic information of multiple candidate users from both gray-scale and non-gray-scale service nodes; update multiple unit sets based on the traffic characteristic information of multiple candidate users and the gray-scale traffic ratio to update gray-scale users. In other words, after routing service requests from gray-scale users to gray-scale service nodes in the application system, full collection of traffic characteristic information can be performed on both gray-scale and non-gray-scale users. This allows for dynamic updates to the previous unit sets based on the traffic characteristic information of both gray-scale and non-gray-scale users and the gray-scale traffic ratio, ensuring the accuracy of the unit sets and enabling more accurate gray-scale user updates.

[0077] Implementation Method 5, see below Figure 3b Step ⑦ in the process can collect traffic characteristic information of grayscale users from the grayscale service nodes; see [link to relevant documentation]. Figure 3b Step ⑧ involves reselecting gray users from multiple unit sets if the traffic fluctuation amplitude of a gray user exceeds a set fluctuation amplitude threshold based on the gray user's traffic characteristic information. The set fluctuation amplitude threshold can be any value; this embodiment does not impose any restrictions. See also... Figure 3b In step 9, a notification message can be sent to the newly selected grayscale users, such as sending a notification message to candidate user 2 and candidate user 3.

[0078] Specifically, the traffic fluctuation range of gray-scale users can be determined based on their traffic characteristic information and their traffic characteristic information when they are candidate users. This embodiment does not limit the specific implementation method for determining the traffic fluctuation range of gray-scale users. In one exemplary embodiment, a first traffic value can be calculated based on the traffic characteristic information of gray-scale users, and then a second traffic value can be calculated based on their traffic characteristic information when they are candidate users. Alternatively, the previously calculated result can be directly used as the second traffic value. Then, the difference between the first and second traffic values ​​can be calculated as the traffic fluctuation range of the gray-scale users. The specific implementation method for calculating the traffic value based on traffic characteristic information has been described in detail above and will not be repeated here. In another exemplary embodiment, the information weights corresponding to the traffic characteristic information of gray-scale users and their traffic characteristic information when they are candidate users can be used to perform a weighted calculation on the first and second traffic values ​​to obtain the traffic fluctuation range of the gray-scale users. Then, if the traffic fluctuation range exceeds a set fluctuation range threshold, gray-scale users can be reselected from multiple unit sets.

[0079] It should be noted that, due to the real-time and dynamic nature of traffic, the traffic of gray-scale users may fluctuate significantly over a period of time, such as a sharp increase or decrease, which may cause the traffic destined for the gray-scale nodes to gradually deviate from the expected gray-scale traffic ratio. However, through the above implementation method, after routing the service requests of gray-scale users to the gray-scale service nodes in the application system, the gray-scale user list can be dynamically updated, thereby effectively ensuring that the traffic destined for the gray-scale nodes meets the expected gray-scale traffic ratio.

[0080] In some optional embodiments, service requests from gray-scale users are routed to gray-scale service nodes in the application system for gray-scale deployment of the application system. This may include the following two scenarios:

[0081] Reference Figure 1a As shown, in the absence of a traffic ingress node in the application system, a notification message is sent to grayscale users. This notification message includes the identification information of the grayscale service node, enabling grayscale users to directly send service requests to the grayscale service node. The traffic ingress node is used for traffic control and can be a gateway, load balancer, etc., without limitation in this embodiment. The load balancer node refers to the device in the application system responsible for load balancing processing and forwarding load balancing-related requests, such as a physical server, virtual server, or load balancer.

[0082] Reference Figure 1b As shown, when the application system includes a traffic entry node for distributing service requests to non-grayscale nodes and grayscale nodes, the information of grayscale users is sent to the traffic entry node so that the traffic entry node can distribute the service requests from grayscale users to grayscale service nodes to perform grayscale release of the application system.

[0083] It should be noted that the execution subject of each step of the method provided in the above embodiments can be the same device, or the method can be executed by different devices. For example, the execution subject of steps 21 to 24 can be device A; or the execution subject of steps 21 and 22 can be device A, and the execution subject of steps 23-24 can be device B; and so on.

[0084] Furthermore, in some of the processes described in the above embodiments and accompanying drawings, multiple operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or they may be executed in parallel. The operation numbers, such as 22, 23, etc., are merely used to distinguish different operations and do not represent any execution order. Additionally, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0085] Figure 4 This is a schematic diagram of a grayscale publishing device provided as another exemplary embodiment of this application. (See diagram below.) Figure 4 As shown, the device includes: a demand information acquisition module 401, used to: acquire gray-scale release demand information of an application system, the gray-scale release demand information including specified user attributes and gray-scale traffic ratio; a feature information acquisition module 402, used to: acquire traffic feature information of multiple candidate users from non-gray-scale service nodes in the application system, the candidate users being users with the specified user attributes; a selection module 403, used to: select gray-scale users from the multiple candidate users according to the traffic feature information of the multiple candidate users and the gray-scale traffic ratio; and a routing module 404, used to: route the service requests of the gray-scale users to the gray-scale service nodes in the application system to perform gray-scale release of the application system.

[0086] Optionally, a collection component is deployed on the non-grayscale service node. The feature information acquisition module 402, when acquiring traffic feature information of multiple candidate users from the non-grayscale service node in the application system, is specifically used to: send the specified user attributes to the collection component so that the collection component can count the feature information of service requests from candidate users from the service requests received from the non-grayscale service node; and receive the feature information of service requests from candidate users that the collection component has counted, as the traffic feature information of the candidate users.

[0087] Optionally, when the selection module 403 selects a grayscale user from the multiple candidate users based on the traffic characteristic information of the multiple candidate users and the grayscale traffic ratio, it is specifically used to: determine the target traffic ratio of the multiple candidate users based on the traffic characteristic information of the multiple candidate users; and select the grayscale user from the multiple candidate users based on the target traffic ratio of the multiple candidate users and the grayscale traffic ratio.

[0088] Optionally, when the selection module 403 determines the target traffic ratio of the multiple candidate users based on the traffic characteristic information of the multiple candidate users, it is specifically used to: determine the traffic value generated by the multiple candidate users in a target time period based on the traffic characteristic information of the multiple candidate users, wherein the target time period is the time when the traffic characteristic information of the multiple candidate users is collected; and calculate the target traffic ratio of the candidate users based on the traffic value generated by the candidate users in the target time period and the sum of the traffic values ​​generated by the multiple candidate users in the target time period.

[0089] Optionally, the traffic feature information includes at least one traffic sub-feature information, and different types of traffic sub-feature information have different functional relationships with traffic; when the selection module 403 determines the traffic value generated by the multiple candidate users in the target time period based on the traffic feature information of the multiple candidate users, it is specifically used to: calculate at least one initial traffic value of the candidate user based on the functional relationship between at least one traffic feature sub-feature information of the candidate user and traffic; and determine the traffic value generated by the candidate user in the target time period based on the at least one initial traffic value of the candidate user.

[0090] Optionally, when the selection module 403 determines the target traffic ratio of the multiple candidate users based on their traffic characteristic information, it is specifically used to: divide the target time period into multiple sub-time periods; determine the traffic value generated by the multiple candidate users in each sub-time period based on their traffic characteristic information; the target time period is the time period for collecting the traffic characteristic information of the multiple candidate users; calculate the traffic ratio of any candidate user in the sub-time period based on the traffic value generated by the multiple candidate users in the sub-time period and the sum of the traffic values ​​generated by the multiple candidate users in the sub-time period; determine the relationship between the traffic ratio of the candidate user and time based on the traffic ratio of the candidate user in the multiple sub-time periods; and calculate the traffic ratio of the candidate user at the grayscale release time as its target traffic ratio based on the relationship between the traffic ratio of the candidate user and time.

[0091] Optionally, when the selection module 403 selects the grayscale user from the multiple candidate users based on the target traffic ratio of the multiple candidate users and the grayscale traffic ratio, it is specifically configured to: divide the target traffic ratio of the multiple candidate users into multiple unit sets; wherein, one unit set corresponds to one basic traffic ratio, one basic traffic ratio corresponds to one or more unit sets, and the error between the sum of the target traffic ratios in a unit set and the basic traffic ratio corresponding to that unit set is within a first preset range; select at least one target unit set from the multiple unit sets based on the grayscale traffic ratio, and take the candidate users corresponding to the target traffic ratios in the at least one target unit set as the grayscale user; wherein, the error between the sum of the basic traffic ratios corresponding to the at least one target unit set and the grayscale traffic ratio is within a second preset range.

[0092] Optionally, when the selection module 403 divides the target traffic ratios of the multiple candidate users into multiple unit sets, it is specifically used to: determine at least one basic traffic ratio and a threshold number of unit sets corresponding to each basic traffic ratio; gradually accumulate the target traffic ratios of the multiple candidate users according to the order of traffic ratios from smallest to largest and the requirement that the target traffic ratio of the same candidate user participates in one accumulation operation; when the error between the sum of the accumulated target traffic ratios and any basic traffic ratio is within the first set range, and the number of unit sets corresponding to any basic traffic ratio has not yet reached the threshold number, divide the accumulated target traffic ratios into one unit set corresponding to any basic traffic ratio.

[0093] Optionally, the selection module 403 is further configured to: when the number of currently divided unit sets reaches a first number, and there are still target traffic ratios of candidate users that have not participated in the accumulation, calculate the sum of the basic traffic ratios corresponding to each currently divided unit set as the first traffic ratio; when the first traffic ratio is greater than a set traffic ratio threshold, continue to accumulate the target traffic ratios of candidate users that have not yet participated in the accumulation, so as to divide the target traffic ratios of the candidate users that have not yet participated in the accumulation into no more than a second number of unit sets.

[0094] Optionally, after routing the service request of the grayscale user to the grayscale service node in the application system, the routing module 404 is further configured to: collect traffic characteristic information of non-grayscale users from the non-grayscale service node; update the plurality of unit sets according to the traffic characteristic information of the non-grayscale users and the grayscale traffic ratio, so as to update the grayscale users; and / or, collect traffic characteristic information of the plurality of candidate users from the grayscale service node and the non-grayscale service node; update the plurality of unit sets according to the traffic characteristic information of the plurality of candidate users and the grayscale traffic ratio, so as to update the grayscale users; and / or, collect traffic characteristic information of the grayscale user from the grayscale service node; and, if it is determined from the traffic characteristic information of the grayscale user that the traffic fluctuation amplitude of the grayscale user is greater than a set fluctuation amplitude threshold, reselect the grayscale user from the plurality of unit sets.

[0095] Optionally, when the routing module 404 routes the service requests of the grayscale users to grayscale service nodes in the application system for grayscale deployment of the application system, it is specifically used for: when the application system includes a traffic ingress node for distributing service requests to the non-grayscale nodes and the grayscale nodes, sending the information of the grayscale users to the traffic ingress node, so that the traffic ingress node can distribute the service requests from the grayscale users to the grayscale service nodes for grayscale deployment of the application system; when the application system does not include the traffic ingress node, sending a notification message to the grayscale users, the notification message including the identification information of the grayscale service nodes, so that the grayscale users can directly send service requests to the grayscale service nodes.

[0096] The above describes the internal functions and structure of the grayscale release device, such as... Figure 5 As shown, in practice, the grayscale publishing device can be implemented as a control device, including: a memory 501, a processor 502, and a communication component 503.

[0097] Memory 501 is used to store computer programs and can be configured to store various other data to support operation on the computing platform. Examples of this data include instructions for any application or method operating on the computing platform, contact data, phone book data, messages, pictures, videos, etc.

[0098] The memory 501 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0099] Processor 502, coupled to memory 501, is used to execute a computer program in memory 501 for: acquiring gray-scale release requirement information of an application system, the gray-scale release requirement information including specified user attributes and gray-scale traffic ratio; acquiring traffic characteristic information of multiple candidate users from non-gray-scale service nodes in the application system, the candidate users being users with the specified user attributes; selecting gray-scale users from the multiple candidate users according to the traffic characteristic information of the multiple candidate users and the gray-scale traffic ratio; and routing the service requests of the gray-scale users to the gray-scale service nodes in the application system to perform gray-scale release of the application system.

[0100] Optionally, the non-grayscale service node is equipped with a collection component, processor 502, which, when collecting traffic feature information of multiple candidate users from the non-grayscale service node in the application system, specifically performs the following: sending the specified user attributes to the collection component so that the collection component can statistically analyze the feature information of service requests from candidate users from the service requests received from the non-grayscale service node; and receiving the feature information of service requests from candidate users statistically analyzed by the collection component as the traffic feature information of the candidate users.

[0101] Optionally, when the processor 502 selects a grayscale user from the plurality of candidate users based on the traffic characteristic information of the plurality of candidate users and the grayscale traffic ratio, it is specifically configured to: determine the target traffic ratio of the plurality of candidate users based on the traffic characteristic information of the plurality of candidate users; and select the grayscale user from the plurality of candidate users based on the target traffic ratio of the plurality of candidate users and the grayscale traffic ratio.

[0102] Optionally, when the processor 502 determines the target traffic ratio of the multiple candidate users based on the traffic characteristic information of the multiple candidate users, it is specifically used to: determine the traffic value generated by the multiple candidate users within a target time period based on the traffic characteristic information of the multiple candidate users, wherein the target time period is the time when the traffic characteristic information of the multiple candidate users is collected; and calculate the target traffic ratio of the candidate users based on the traffic value generated by the candidate users within the target time period and the sum of the traffic values ​​generated by the multiple candidate users within the target time period.

[0103] Optionally, the traffic feature information includes at least one traffic sub-feature information, and different types of traffic sub-feature information have different functional relationships with traffic; when the processor 502 determines the traffic value generated by the multiple candidate users in the target time period based on the traffic feature information of the multiple candidate users, it is specifically used to: calculate at least one initial traffic value of the candidate user based on the functional relationship between at least one traffic feature sub-feature information of the candidate user and traffic; and determine the traffic value generated by the candidate user in the target time period based on the at least one initial traffic value of the candidate user.

[0104] Optionally, when the processor 502 determines the target traffic ratio of the multiple candidate users based on their traffic characteristic information, it specifically performs the following steps: dividing the target time period into multiple sub-time periods; determining the traffic values ​​generated by the multiple candidate users in each sub-time period based on their traffic characteristic information; the target time period being the period for collecting the traffic characteristic information of the multiple candidate users; calculating the traffic ratio of the candidate users in each sub-time period based on the traffic values ​​generated by the multiple candidate users in each sub-time period and the sum of the traffic values ​​generated by the multiple candidate users in each sub-time period; determining the relationship between the traffic ratio of the candidate users and time based on their traffic ratio in each sub-time period; and calculating the traffic ratio of the candidate users at the grayscale release time as their target traffic ratio based on the relationship between the traffic ratio of the candidate users and time.

[0105] Optionally, when the processor 502 selects the grayscale user from the plurality of candidate users based on the target traffic ratio of the plurality of candidate users and the grayscale traffic ratio, it specifically performs the following steps: dividing the target traffic ratio of the plurality of candidate users into a plurality of unit sets; wherein, one unit set corresponds to one base traffic ratio, one base traffic ratio corresponds to one or more unit sets, and the error between the sum of the target traffic ratios in a unit set and the base traffic ratio corresponding to that unit set is within a first predetermined range; selecting at least one target unit set from the plurality of unit sets based on the grayscale traffic ratio, and taking the candidate users corresponding to the target traffic ratios in the at least one target unit set as the grayscale user; wherein, the error between the sum of the base traffic ratios corresponding to the at least one target unit set and the grayscale traffic ratio is within a second predetermined range.

[0106] Optionally, when the processor 502 divides the target traffic ratios of the multiple candidate users into multiple unit sets, it is specifically used to: determine at least one basic traffic ratio and a threshold number of unit sets corresponding to each basic traffic ratio; gradually accumulate the target traffic ratios of the multiple candidate users according to the order of traffic ratios from smallest to largest and the requirement that the target traffic ratio of the same candidate user participates in one accumulation operation; when the error between the sum of the accumulated target traffic ratios and any basic traffic ratio is within the first set range, and the number of unit sets corresponding to any basic traffic ratio has not yet reached the threshold number, divide the accumulated target traffic ratios into a unit set corresponding to any basic traffic ratio.

[0107] Optionally, the processor 502 is further configured to: when the number of currently divided unit sets reaches a first number and there are still target traffic ratios of candidate users that have not participated in the accumulation, calculate the sum of the basic traffic ratios corresponding to each currently divided unit set as a first traffic ratio; when the first traffic ratio is greater than a set traffic ratio threshold, continue to accumulate the target traffic ratios of candidate users that have not yet participated in the accumulation, so as to divide the target traffic ratios of the candidate users that have not yet participated in the accumulation into no more than a second number of unit sets.

[0108] Optionally, after routing the service request of the grayscale user to the grayscale service node in the application system, the processor 502 is further configured to: collect traffic characteristic information of non-grayscale users from the non-grayscale service node; update the plurality of unit sets according to the traffic characteristic information of the non-grayscale users and the grayscale traffic ratio, so as to update the grayscale users; and / or, collect traffic characteristic information of the plurality of candidate users from the grayscale service node and the non-grayscale service node; update the plurality of unit sets according to the traffic characteristic information of the plurality of candidate users and the grayscale traffic ratio, so as to update the grayscale users; and / or, collect traffic characteristic information of the grayscale user from the grayscale service node; and, if it is determined from the traffic characteristic information of the grayscale user that the traffic fluctuation amplitude of the grayscale user is greater than a set fluctuation amplitude threshold, reselect the grayscale user from the plurality of unit sets.

[0109] Optionally, when the processor 502 routes the service requests of the grayscale users to grayscale service nodes in the application system for grayscale deployment of the application system, it specifically performs the following: when the application system includes a traffic ingress node for distributing service requests to the non-grayscale nodes and the grayscale nodes, it sends the information of the grayscale users to the traffic ingress node, so that the traffic ingress node can distribute the service requests from the grayscale users to the grayscale service nodes for grayscale deployment of the application system; when the application system does not include the traffic ingress node, it sends a notification message to the grayscale users, the notification message including the identification information of the grayscale service nodes, so that the grayscale users can directly send service requests to the grayscale service nodes.

[0110] Furthermore, such as Figure 5 As shown, the control device also includes other components such as a display 504, a power supply component 505, and an audio component 506. Figure 5 The diagram only shows some components and does not mean that the control equipment includes only these components. Figure 5 The components shown. Additionally... Figure 5 The components within the dashed box are optional, not mandatory, and their specific requirements depend on the product form of the work node. In this embodiment, the work node can be a terminal device such as a desktop computer, laptop computer, smartphone, or IoT device, or a server-side device such as a conventional server, cloud server, or server array. If the work node in this embodiment is implemented as a terminal device such as a desktop computer, laptop computer, or smartphone, it may include... Figure 5 The components within the dashed box; if the working node in this embodiment is implemented as a server-side device such as a conventional server, cloud server, or server array, it may be omitted. Figure 5 The component within the dashed box.

[0111] In this embodiment, canary release requirement information can be obtained, including specified user attributes and canary traffic ratios. Traffic characteristic information of multiple candidate users (users with specified user attributes) is obtained from non-canary service nodes in the application system. Canary users are selected from the multiple candidate users based on their traffic characteristic information and canary traffic ratios. Service requests from canary users are routed to the canary service nodes in the application system for canary release. This method allows for more accurate control of the percentage of canary traffic during canary releases, while also fixing the routing of canary traffic, thus improving user experience.

[0112] This application also provides a canary deployment system, including: non-canary service nodes, canary service nodes, and the control device. For a detailed description of the architecture and working principle of the canary deployment system, please refer to the foregoing embodiments, which will not be repeated here.

[0113] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to implement the steps in the grayscale release method.

[0114] This application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, performs the steps in the grayscale release method.

[0115] The aforementioned memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0116] The aforementioned communication components are configured to facilitate wired or wireless communication between the device containing the communication components and other devices. The device containing the communication components can access wireless networks based on communication standards, such as WiFi, 2G, 3G, 4G / LTE, 5G, or combinations thereof. In one exemplary embodiment, the communication components receive broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication components also include a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wide Band (UWB), Bluetooth (BT), and other technologies.

[0117] The aforementioned display includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a Touch Panel, the screen can be implemented as a touchscreen to receive input signals from the user. The Touch Panel includes one or more touch sensors to sense touches, swipes, and gestures on the Touch Panel. The touch sensors can sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation.

[0118] The aforementioned power supply components provide power to various components within the device in which they reside. These power supply components may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device in which they reside.

[0119] The aforementioned audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC) configured to receive external audio signals when the device containing the audio component is in an operating mode, such as call mode, recording mode, or voice recognition mode. The received audio signals can be further stored in memory or transmitted via a communication component. In some embodiments, the audio component also includes a speaker for outputting audio signals.

[0120] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, compact disc read-only memory (CD-ROM), optical storage, etc.) containing computer-usable program code.

[0121] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0122] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0123] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0124] In a typical configuration, a computing device includes one or more processors (Central Processing Unit, CPU), input / output interfaces, network interfaces, and memory.

[0125] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0126] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, Digital Video Disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0127] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0128] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method of gray release, characterized by, The method comprises: obtaining gray release requirement information of an application system, the gray release requirement information comprising a specified user attribute and a gray traffic proportion; obtaining traffic characteristic information of a plurality of candidate users from non-gray service nodes in the application system, the candidate users being users having the specified user attribute; selecting a gray user from the plurality of candidate users according to the traffic characteristic information of the plurality of candidate users and the gray traffic proportion; routing a service request of the gray user to a gray service node in the application system to perform gray release of the application system.

2. The method of claim 1, wherein, The non-gray service nodes are deployed with a collection component, and the method of obtaining traffic characteristic information of a plurality of candidate users from non-gray service nodes in the application system comprises: downloading the specified user attribute to the collection component to enable the collection component to count characteristic information of service requests from the candidate users from service requests received by the non-gray service nodes; and receiving the characteristic information of service requests from the candidate users counted by the collection component and reported by the collection component as the traffic characteristic information of the candidate users.

3. The method of claim 1, wherein, The method of selecting a gray user from the plurality of candidate users according to the traffic characteristic information of the plurality of candidate users and the gray traffic proportion comprises: determining a target traffic proportion of the plurality of candidate users according to the traffic characteristic information of the plurality of candidate users; and selecting the gray user from the plurality of candidate users according to the target traffic proportion of the plurality of candidate users and the gray traffic proportion.

4. The method of claim 3, wherein, The method of determining a target traffic proportion of the plurality of candidate users according to the traffic characteristic information of the plurality of candidate users comprises: determining a traffic value generated by the plurality of candidate users in a target period according to the traffic characteristic information of the plurality of candidate users, the target period being a period during which the traffic characteristic information of the plurality of candidate users is collected; and calculating a target traffic proportion of the candidate user according to the traffic value generated by the candidate user in the target period and a sum of the traffic values generated by the plurality of candidate users in the target period.

5. The method of claim 4, wherein, The traffic characteristic information comprises at least one type of traffic sub-characteristic information, and different types of traffic sub-characteristic information have different functional relationships with traffic. The method of determining a traffic value generated by the plurality of candidate users in a target period according to the traffic characteristic information of the plurality of candidate users comprises: calculating at least one initial traffic value of the candidate user according to a functional relationship between at least one traffic sub-characteristic information of the candidate user and traffic; and determining the traffic value generated by the candidate user in the target period according to the at least one initial traffic value of the candidate user.

6. The method of claim 3, wherein, The method of determining a target traffic proportion of the plurality of candidate users according to the traffic characteristic information of the plurality of candidate users comprises: dividing a target period into a plurality of sub-periods, determining a traffic value generated by the plurality of candidate users in each sub-period according to the traffic characteristic information of the plurality of candidate users, and the target period being a period during which the traffic characteristic information of the plurality of candidate users is collected; and calculating a traffic proportion of the candidate user in the sub-period according to the traffic value generated by the candidate user in the sub-period and a sum of the traffic values generated by the plurality of candidate users in the sub-period; determining a function relationship between the traffic proportion and time corresponding to the candidate user according to the traffic proportions of the candidate user in the plurality of sub-periods; calculating a traffic proportion of the candidate user at the gray release time as a target traffic proportion of the candidate user according to the function relationship between the traffic proportion and time corresponding to the candidate user.

7. The method according to any one of claims 3-6, characterized in that, selecting the gray user from the plurality of candidate users according to the target traffic proportions of the plurality of candidate users and the gray traffic proportion, comprising: dividing the target traffic proportions of the plurality of candidate users into a plurality of unit sets; wherein one unit set corresponds to one basic traffic proportion, one basic traffic proportion corresponds to one or more unit sets, and a sum of the target traffic proportions in one unit set has an error within a first set range with respect to the basic traffic proportion corresponding to the unit set; selecting at least one target unit set from the plurality of unit sets according to the gray traffic proportion, and taking the candidate user corresponding to the target traffic proportion in the at least one target unit set as the gray user; wherein a sum of the basic traffic proportions corresponding to the at least one target unit set has an error within a second set range with respect to the gray traffic proportion.

8. The method of claim 7, wherein, dividing the target traffic proportions of the plurality of candidate users into a plurality of unit sets, comprising: determining at least one basic traffic proportion and a quantity threshold of unit sets corresponding to each basic traffic proportion; gradually accumulating the target traffic proportions of the plurality of candidate users in order from small to large, and according to the requirement that the target traffic proportion of the same candidate user participates in one accumulation operation; in a case where a sum of the target traffic proportions participating in accumulation has an error within the first set range with respect to any basic traffic proportion, and the quantity of the unit sets corresponding to the any basic traffic proportion has not reached the quantity threshold, dividing the target traffic proportions participating in accumulation into one unit set corresponding to the any basic traffic proportion.

9. The method of claim 8, wherein, further comprising: in a case where the quantity of the unit sets that have been divided out reaches a first quantity, and there still exists the target traffic proportion of the candidate user that has not participated in accumulation, calculating a sum of the basic traffic proportions corresponding to each unit set that has been divided out as a first traffic proportion; in a case where the first traffic proportion is greater than a set traffic proportion threshold, then continuing to accumulate the target traffic proportions of the candidate users that have not participated in accumulation, so as to divide the target traffic proportions of the candidate users that have not participated in accumulation into no more than a second quantity of unit sets.

10. The method of claim 7, wherein, after routing the service request of the gray user to the gray service node in the application system, further comprising: collecting traffic feature information of non-gray users from the non-gray service nodes; and updating the plurality of unit sets according to the traffic feature information of the non-gray users and the gray traffic proportion, so as to update the gray user; and / or, collecting traffic feature information of the plurality of candidate users from the gray service node and the non-gray service node; updating the plurality of unit sets according to the traffic feature information of the plurality of candidate users and the gray traffic proportion, so as to update the gray users; and / or collecting traffic feature information of the gray users from the gray service node; and in a case where it is determined according to the traffic feature information of the gray users that a traffic fluctuation amplitude of the gray users is greater than a set fluctuation amplitude threshold, reselecting gray users from the plurality of unit sets.

11. A management device characterized by comprising: comprising: a storage unit and a processing unit, the storage unit being configured to store a computer program, and the processing unit being coupled to the storage unit and configured to execute the computer program to implement the steps in the method of any one of claims 1-10.

12. A gray release system characterized by, comprising: a non-gray service node, a gray service node, and the management device of claim 11.

13. A computer readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the processor is caused to implement the steps in the method of any one of claims 1-10.

14. A computer program product, characterised in that, comprising computer programs / instructions, which, when executed by a processor, perform the steps in the method of any one of claims 1-10.

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