A dynamic service node management method based on user connection regularity analysis

Abstract

The application discloses a dynamic service node management method based on user connection rule analysis, which comprises the following steps: S1, determining a connection peak period; S2, dividing node resources; S3, route selection; S4, user end connection time analysis; S5, dynamic node expansion; S6, dynamic node reduction; S7, regular monitoring and adjustment. The application divides the node resources of a server into a persistent service node cluster and a temporary expansion node cluster, the persistent service node cluster is used for user connection with long-time connection, the temporary expansion node is used for user connection with high probability of connection only in the peak period, the node resources can fully meet the demands of different time periods, and the experience of users is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote connection, in particular to a dynamic service node management method based on user connection regularity analysis. BACKGROUND

[0002] With the popularity of remote office, online education and cloud services, the demand for remote connection technology has increased significantly. In particular, the connection peak period in a fixed time period (such as 9-11 am) is increasingly common.

[0003] Server resources are usually expensive and limited. During the connection peak period, it is necessary to increase the server nodes to meet a large number of connection requests, and during the non-peak period, it is necessary to reduce the nodes to save resources and reduce operating costs. The traditional load balancing method is inefficient in handling dynamic load changes, especially after the peak period, which cannot release resources in time, resulting in waste of resources.

[0004] Therefore, the prior art has defects and needs to be improved. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a dynamic service node management method based on user connection regularity analysis, which realizes dynamic management of service nodes by analyzing user connection usage regularity, and ensures efficient and stable operation of the server during the connection peak period and the non-peak period while saving server node resources.

[0006] The technical solution of the present application is as follows: a dynamic service node management method based on user connection regularity analysis is provided, comprising the following steps.

[0007] S1: determining the connection peak period; the time range of the peak period is determined by monitoring and statistical analysis of the number and time of connection requests, and then written into the script of the server node management to achieve dynamic adjustment of the peak period.

[0008] S2: division of node resources; the node resources of the server are divided into a persistent service node cluster and a temporary expansion node cluster, the persistent service node cluster is used for user connection with long-time connection, and the temporary expansion node is used for user connection with high probability of connection only in the peak period.

[0009] S3: Route selection; when a user's user terminal establishes a connection with the server, analyze the user's past connection usage, i.e. the time period range of the connection, if the user terminal connection is mostly used during peak hours, route the user's connection to the temporary nodes of the temporary expansion node cluster, otherwise route to the persistent nodes of the persistent service node cluster. The probability of the user terminal connecting during peak hours is calculated as follows: the number of times the user terminal has connected during peak hours in the past divided by the total number of user terminal connections. The probability that the user terminal connection is mostly used during peak hours is 70% or more.

[0010] S4: User terminal connection time analysis; when the user terminal initiates a connection request, record the user terminal connection time, calculate the user connection time duration after the user ends the connection, the size of the connection time in the peak period, etc. Collect user connection information and update it to the server user connection data storage to dynamically adjust the routing nodes for the next connection.

[0011] S5: Dynamic node expansion; during the connection peak period, if the number of connections exceeds the capacity of the persistent service nodes, trigger the creation of temporary expansion service nodes; the temporary expansion service nodes will be added to the service node cluster to meet the connection demand.

[0012] S6: Dynamic node reduction: after the connection peak period, if the number of connections on the temporary expansion service nodes is less than a certain threshold, trigger node reduction; call the server node management script to automatically remove the temporary expansion service nodes from the service node cluster to save server resources. In this step, the threshold is preferably 50-150.

[0013] S7: Regular monitoring and adjustment: regularly monitor the connection request situation and the load situation of the service nodes, adjust the number and capacity of the nodes according to the actual demand, and ensure efficient operation at different time periods.

[0014] The development of virtualization and cloud computing technology makes it possible to dynamically expand and reduce computing resources. Through virtualization technology, the number and configuration of server nodes can be quickly adjusted to meet different load demands. By analyzing user connection time and behavior patterns, server resources can be more intelligently allocated and managed, improving system efficiency and user experience. By dividing server node resources into a persistent service node cluster and a temporary expansion node cluster, the persistent service node cluster is used for long-term user connections, and the temporary expansion node is used for user connections that are mostly used during peak hours, allowing node resources to fully meet the needs of different time periods and improve user experience. The more past data the user terminal has connected to the server, the more accurate the analysis and prediction of connecting to the persistent service node cluster or the temporary expansion node cluster.

[0015] Further, the server dynamically expands the temporary node according to the start time of the write peak.

[0016] Further, the user end establishes a network connection with the server, and the connection request is first sent to the gateway service, and the gateway service obtains user historical connection analysis data from a data buffer (such as redis); and the user connection data is routed to the persistent node or the temporary node.

[0017] Further, the user end updates the connection analysis data after the connection is disconnected.

[0018] With the above scheme, the application provides a dynamic service node management method based on user connection regularity analysis, which has the following technical effects:

[0019] 1. Efficiently cope with connection peak; during the connection peak period, the application can quickly expand the service node to meet the connection demand of a large number of users, and avoid connection failure or performance degradation due to insufficient resources.

[0020] 2. Significantly save server resources; after the connection peak period, the application can timely reduce unnecessary service nodes, avoid resource waste, and reduce operating costs.

[0021] 3. Improve system flexibility and response speed; by analyzing user connection time, long-time connection users are allocated to persistent service nodes, and short-time connection users are allocated to temporary expanded service nodes, ensuring that the system can flexibly cope with different types of connection demands and improve response speed.

[0022] 4. Ensure the continuity and stability of the service; ensure that there are enough server resources to support existing connections at any time, avoid reducing nodes when the connection is not completely released, and thus ensure the continuity and stability of user experience.

[0023] 5. Intelligent resource management; by analyzing user connection time, the application intelligently allocates and manages nodes, optimizes resource allocation strategy, and improves overall system efficiency.

[0024] 6. Reduce operating costs; dynamically adjust the number of service nodes, reasonably expand nodes during the peak period, and timely reduce nodes during the non-peak period, thereby greatly reducing the use cost of server resources.

[0025] 7. Improve user experience; provide sufficient resources to ensure connection stability during the connection peak period, release resources during the non-peak period, ensure system performance stability, and improve user experience. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 The flowchart of the application. DETAILED DESCRIPTION

[0027] The application will be described in detail below in conjunction with the drawings and specific embodiments.

[0028] Please refer to Figure 1 The application provides a dynamic service node management method based on user connection regularity analysis, comprising the following steps.

[0029] S1: determining the connection peak period; the time range of the peak period is determined by monitoring and statistical analysis of the number and time of connection requests, and then written into the script of server node management to achieve dynamic adjustment of the peak period.

[0030] S2: division of node resources; the node resources of the server are divided into a persistent service node cluster and a temporary expansion node cluster, the persistent service node cluster is used for user connections with long-time connection, and the temporary expansion node is used for user connections that are only connected in the peak period.

[0031] S3: routing selection; when the user terminal of a user establishes a connection with the server, the use condition of the user terminal in the past is analyzed, i.e. the time period of the connection, and if the user terminal is only used in the peak period with a high probability, the connection of the user terminal is routed to a temporary node in the temporary expansion node cluster, otherwise it is routed to a persistent node in the persistent service node cluster. The probability of the user terminal being connected in the peak period is calculated by dividing the number of times the user terminal is connected in the peak period by the total number of times the user terminal is connected. The probability that the user terminal is only used in the peak period is 70% or more.

[0032] S4: analysis of user terminal connection time; when the user terminal initiates a connection request, the user terminal connection time is recorded, the time duration of the user connection is calculated after the user ends the connection, the size of the connection time in the peak period, etc. After collecting the user connection information, update it to the server user connection data storage, so as to dynamically adjust the routing node when the user connects next time.

[0033] S5: dynamic node expansion; if the number of connections exceeds the capacity of the persistent service node in the connection peak period, the creation of the temporary expansion service node is triggered; the temporary expansion service node will be added to the service node cluster to meet the connection demand.

[0034] S6: dynamic node reduction; after the connection peak period, if the number of connections on the temporary expansion service node is less than a certain threshold, the node reduction is triggered; the script of the server node management will be called to automatically remove the temporary expansion service node from the service node cluster to save server resources. In this step, the threshold is preferably 50-150.

[0035] S7: Regular monitoring and adjustment: Regularly monitor the situation of connection requests and the load of service nodes, and adjust the number and capacity of nodes according to actual needs to ensure efficient operation at different time periods.

[0036] The development of virtualization and cloud computing technology makes it possible to dynamically expand and reduce computing resources. Through virtualization technology, the number and configuration of server nodes can be quickly adjusted to meet different load requirements. By analyzing user connection time and behavior patterns, server resources can be allocated and managed more intelligently, improving system efficiency and user experience. By dividing server node resources into persistent service node clusters and temporary expansion node clusters, persistent service node clusters are used for long-term user connections, and temporary expansion nodes are used for users who are likely to connect only during peak periods, allowing node resources to fully meet the needs of different time periods and improve user experience. The more data the user has connected to the server in the past, the more accurate the analysis and prediction of connecting to persistent service node clusters or temporary expansion node clusters.

[0037] In this embodiment, the server dynamically expands temporary nodes according to the start time of the peak period of writing.

[0038] In this embodiment, the user end establishes a network connection with the server, and the connection request first goes to the gateway service, which obtains user past connection analysis data from the data cache (such as redis); according to the obtained user connection data, it is decided whether to route to the persistent node or the temporary node.

[0039] In this embodiment, the user end updates the connection analysis data after disconnecting.

[0040] In summary, the present application provides a dynamic service node management method based on user connection regularity analysis, which has the following technical effects:

[0041] 1. Efficiently handle connection peaks; during the connection peak period, the present application can quickly expand service nodes to meet the connection needs of a large number of users, avoiding connection failures or performance degradation due to insufficient resources.

[0042] 2. Significantly save server resources; after the connection peak period, the present application can timely reduce unnecessary service nodes, avoid resource waste, and reduce operating costs.

[0043] 3. Improve system flexibility and response speed; by analyzing user connection time, long-term connection users are allocated to persistent service nodes, and short-term connection users are allocated to temporary expansion nodes, ensuring that the system can flexibly respond to different types of connection needs and improve response speed.

[0044] 4. Continuity and stability of service; ensure that there are enough server resources to support existing connections at any time period, avoid reducing nodes when connections are not completely released, and thus guarantee the continuity and stability of user experience.

[0045] 5. Intelligent resource management; through algorithm analysis of user connection time, intelligent node allocation and management, optimization of resource allocation strategy, and improvement of overall system efficiency.

[0046] 6. Reduce operating costs; dynamically adjust the number of service nodes, reasonably expand nodes during peak periods, and timely reduce nodes during non-peak periods, thereby greatly reducing the use cost of server resources.

[0047] 7. Improve user experience; provide sufficient resources to ensure connection stability during connection peak period, release resources during non-peak period, ensure system performance stability, and improve user experience.

[0048] The above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement, and improvement made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A dynamic service node management method based on user connection regularity analysis, characterized in that, The method comprises the following steps: S1: determining the connection peak period; determining the peak period time range through monitoring and statistical analysis of the number and time of connection requests, and then writing into the script managed by the server node to achieve dynamic adjustment of the peak period; S2: division of node resources; dividing the node resources of the server into a persistent service node cluster and a temporary expansion node cluster, the persistent service node cluster being used for user connections of long-time connection, and the temporary expansion node being used for user connections of users who are likely to only connect in the peak period; S3: routing selection; when a user terminal establishes a connection with the server, the use condition of the user terminal in the past is analyzed, i.e., the time period range of the connection, and if the user terminal is likely to only use in the peak period, the connection of the user terminal is routed to a temporary node of the temporary expansion node cluster, otherwise, the connection is routed to a persistent node of the persistent service node cluster; S4: analysis of the connection time of the user terminal; when the user terminal initiates a connection request, the connection time of the user terminal is recorded, and the time length of the user connection is calculated after the user terminal ends the connection, the size of the connection time in the peak period is calculated, and the user connection information is collected and updated to the server user connection data storage, so as to dynamically adjust the routing node when the user connects next time; S5: dynamic node expansion; if the number of connections exceeds the capacity of the persistent service node in the connection peak period, the creation of the temporary expansion service node is triggered; the temporary expansion service node will be added to the service node cluster to meet the connection demand; S6: dynamic node reduction; after the connection peak period, if the number of connections on the temporary expansion service node is less than a certain threshold, the node reduction is triggered; the script managed by the server node is called to automatically remove the temporary expansion service node from the service node cluster to save server resources; S7: regular monitoring and adjustment; the connection request condition and the load condition of the service node are regularly monitored, and the number and capacity of the nodes are adjusted according to the actual demand to ensure efficient operation in different time periods; The calculation method of the probability of the user terminal connecting in the peak period is: the number of times that the user terminal connects in the peak period in the past divided by the total number of times that the user terminal connects; The probability that the user terminal is likely to only use in the peak period is 70% or above.

2. The dynamic service node management method based on user connection regularity analysis according to claim 1, characterized in that, The server dynamically expands the temporary node according to the start time of the written peak period.

3. The method of claim 1, wherein the method further comprises: The user terminal establishes a network connection with the server, the connection request first goes to the gateway service, the gateway service obtains the user connection analysis data in the past from the data buffer, and decides whether to route to the persistent node or the temporary node according to the obtained user connection data.

4. The dynamic service node management method based on user connection regularity analysis according to claim 1, characterized in that, The connection analysis data is updated after the user terminal disconnects.

5. The method of claim 1, wherein the method further comprises: In step S6, the threshold is 50-150.

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