Cloud native dynamic capacity expansion and contraction method based on telecommunication service triggering

Abstract

The invention relates to a cloud native dynamic capacity expansion and contraction method based on telecommunication service triggering, and belongs to the technical field of cloud native networks. The system comprises a data acquisition module, a detection condition processing module, a capacity expansion and contraction strategy module, an execution module and a data recording module. By constructing a detection condition system, formulating a dynamic capacity expansion and contraction strategy adaptive to telecommunication service characteristics and establishing a comprehensive effect data recording mechanism, more accurate and efficient dynamic capacity expansion and contraction of telecommunication services in a cloud native network are realized, and a solid data support is provided for subsequent capacity expansion and contraction optimization.

Description

Technical Field

[0001] This invention relates to a cloud-native dynamic scaling method based on telecommunications services. Background Technology

[0002] With the continuous development of cloud-native technologies, their application in the telecommunications field is becoming increasingly widespread. Telecommunications services possess significant characteristics such as high reliability, high availability, and low latency, which places extremely high demands on the dynamic scaling capabilities of cloud-native networks.

[0003] However, the dynamic scaling process of existing cloud-native systems has many shortcomings: First, the scaling is not targeted enough. The existing system's inspection conditions are relatively simple and lack detection conditions for telecommunications services, making it difficult to meet the special needs of telecommunications services. Second, the scaling strategy has defects. The existing strategy does not dynamically adjust according to the characteristics of telecommunications services, resulting in poor efficiency and effectiveness of scaling. Third, the data recording of scaling effect is incomplete. After the scaling is completed, the relevant effect data of telecommunications services is not recorded, which cannot provide data support for subsequent scaling optimization and is not conducive to the continuous improvement of the system.

[0004] Therefore, developing a cloud-native network dynamic scaling method for telecommunications services that can solve the above problems has important practical significance and application value. Summary of the Invention

[0005] To address the problems existing in the prior art, the purpose of this invention is to provide a technical solution for a cloud-native dynamic scaling method based on telecommunications services.

[0006] The cloud-native dynamic scaling method based on telecommunications services is characterized by the following steps: Data Acquisition: Through the data acquisition module, operational data of telecommunications services in the cloud-native network are collected in real time using a distributed acquisition method; Detection condition processing: The detection condition processing module compares and analyzes the running data with the preset detection conditions and thresholds for different telecommunications services. When the running data reaches the expansion or contraction threshold, expansion or contraction detection results are generated. Scaling-down strategy formulation: The scaling-down strategy module receives the scaling-down detection results and, based on the type of the telecommunications service and the detection results, matches and formulates a corresponding dynamic scaling-down strategy. Scaling up and down execution: The execution module receives the dynamic scaling up and down strategy and calls the resource management interface of the cloud-native network to execute the scaling up and down operation; Data recording: The data recording module records the running data collected in the data acquisition step and the scaling execution record generated in the scaling execution step, forming a scaling effect dataset.

[0007] The cloud-native dynamic scaling method based on telecommunications services is characterized in that, in the data acquisition step, the running data includes, but is not limited to, one or more combinations of service traffic, latency, packet loss rate, and basic unit load.

[0008] The cloud-native dynamic scaling method based on telecommunications services is characterized in that, in the data collection step, different collection methods and collection frequencies are configured for different telecommunications services.

[0009] The cloud-native dynamic scaling method based on telecommunications services is characterized in that, in the detection condition processing step, the detection conditions and thresholds include a performance threshold set based on the type of telecommunications service and a dynamic traffic threshold set based on peak and valley periods of service traffic.

[0010] The cloud-native dynamic scaling method based on telecommunications services is characterized in that the performance thresholds include latency thresholds, packet loss rate thresholds, and basic unit load thresholds, and the dynamic traffic thresholds are configured according to a preset time period.

[0011] The cloud-native dynamic scaling method based on telecommunications services is characterized in that, in the scaling strategy formulation step, the factors considered by the dynamic scaling strategy include the timing of scaling execution, the type of cloud-native network resources targeted by scaling, the number of resources to be scaled, and the execution order of the scaling steps.

[0012] The cloud-native dynamic scaling method based on telecommunications services is characterized in that the scaling effect dataset recorded in the data recording step includes the running data before and after the scaling operation, the scaling operation time, and the amount of resources adjusted, which is used for subsequent optimization analysis of scaling strategies.

[0013] A cloud-native dynamic scaling system for implementing the method as described in any one of claims 1-7, characterized in that it comprises: The data acquisition module is configured to collect operational data of telecommunications services in the cloud-native network in real time using a distributed acquisition method; The detection condition processing module is communicatively connected to the data acquisition module and is configured to receive the running data and compare and analyze it with preset detection conditions and thresholds to generate expansion and contraction detection results. The scaling-up / scaling strategy module is communicatively connected to the detection condition processing module and is configured to receive the scaling-up / scaling detection results, and match and formulate corresponding dynamic scaling-up / scaling strategies based on the type of the telecommunications service and the detection results. The execution module is communicatively connected to the scaling strategy module and is configured to receive the dynamic scaling strategy and call the resource management interface of the cloud-native network to perform scaling operations. The data recording module is communicatively connected to both the data acquisition module and the execution module, and is configured to record the running data and scaling execution records to form a scaling effect dataset.

[0014] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the steps of the method as described in any one of claims 1 to 7.

[0015] A computer-readable storage medium having a computer program stored thereon, characterized in that, when executed by a processor, the program implements the steps of the method as described in any one of claims 1 to 7.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. Improve the targeting of scaling up and down: such as Figure 1 The data acquisition module can accurately collect various operational data of telecommunications services and transmit them to the detection condition processing module. The detection condition processing module then processes the data based on detection conditions set specifically for the characteristics of the telecommunications services, such as... Figure 2 As described in the "Detection Condition Processing S102" step, the collected data is compared and analyzed with the detection conditions, so that the dynamic expansion and contraction can more accurately respond to the needs of telecommunications services and meet their requirements for high reliability, high availability, and low latency.

[0017] 2. Optimize scaling strategy: From Figure 1 It can be seen that the scaling strategy module formulates a strategy after receiving the detection results from the detection condition processing module; combined with... Figure 2 The “S103” step of “Expansion and contraction strategy formulation” comprehensively considers factors such as changes in business traffic, basic unit load and peak and off-peak business periods to formulate an appropriate expansion and contraction strategy, which improves the efficiency and effectiveness of expansion and contraction and avoids resource waste or insufficiency.

[0018] 3. Improve the recording of expansion and contraction effect data: Figure 1 Both the data acquisition module and the execution module transmit information to the data recording module; in coordination Figure 2 The "Data Recording S105" step records detailed operational data before and after scaling up or down, establishing a comprehensive effect data recording mechanism to provide rich data support for subsequent scaling up or down optimization and help continuously improve the system's dynamic scaling up or down capability. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the system for implementing the present invention; Figure 2 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings: To address the issues of insufficient targeting, simplistic strategies, and lack of performance data recording in dynamic scaling of existing cloud-native systems supporting telecommunications services, this invention constructs a detection condition system, formulates dynamic scaling strategies adapted to the characteristics of telecommunications services, and establishes a comprehensive performance data recording mechanism. This enables more accurate and efficient dynamic scaling of telecommunications services in cloud-native networks and provides solid data support for subsequent scaling optimization.

[0021] This invention discloses a cloud-native dynamic scaling system, comprising: The data acquisition module is configured to collect operational data of telecommunications services in the cloud-native network in real time using a distributed acquisition method; The detection condition processing module is communicatively connected to the data acquisition module and is configured to receive the running data and compare and analyze it with preset detection conditions and thresholds to generate expansion and contraction detection results. The scaling-up / scaling strategy module is communicatively connected to the detection condition processing module and is configured to receive the scaling-up / scaling detection results, and match and formulate corresponding dynamic scaling-up / scaling strategies based on the type of the telecommunications service and the detection results. The execution module is communicatively connected to the scaling strategy module and is configured to receive the dynamic scaling strategy and call the resource management interface of the cloud-native network to perform scaling operations. The data recording module is communicatively connected to both the data acquisition module and the execution module, and is configured to record the running data and scaling execution records to form a scaling effect dataset.

[0022] As shown in Figure 1, the implementation system of the present invention includes a data acquisition module 0101, a detection condition processing module 0102, a scaling strategy module 0103, an execution module 0104, and a data recording module 0105.

[0023] Data acquisition module 0101 is connected to both the detection condition processing module 0102 and the data recording module 0105. Employing distributed acquisition technology, it selectively collects real-time operational data of telecommunications services within the cloud-native network, verifies data integrity, and confirms data validity. The collected valid data is then transmitted to both the detection condition processing module 0102 and the data recording module 0105. The acquisition module is pre-configured with different acquisition methods and frequencies for different telecommunications services. Specific acquisition methods and frequencies can be configured and selected according to the specific needs of the telecommunications services.

[0024] Detection Condition Processing Module 0102: Connected to the Expansion / Shrinkage Strategy Module 0103. The detection condition processing module 0102 presets detection conditions for different telecommunications services. These detection conditions can be configured according to the needs of the telecommunications service, and thresholds for expansion and shrinkage can be set. When the collected real-time operating data reaches the threshold, a detection result indicating the need for expansion or shrinkage is generated, and this result is notified to the expansion / shrinkage strategy module 0103.

[0025] Scaling-down strategy module 0103: Connected to execution module 0104. Scaling-down strategy module 0103 presets different scaling-down strategies for different telecommunications services and inspection results. Strategies include, but are not limited to, the timing of scaling-down execution, the type and quantity of cloud-native network resources targeted, and the execution order of scaling-down steps. After matching a specific scaling-down strategy based on the detection results, it notifies execution module 0104 to implement the specific scaling-down.

[0026] Execution Module 0104: Connected to Data Recording Module 0105. After receiving a specific scaling strategy, this module calls the relevant resources through the cloud-native network resource management interface to implement scaling. Simultaneously, it transmits the scaling execution record to Data Recording Module 0105.

[0027] Data recording module 0105: This module records real-time operational data of telecommunications services in the cloud-native network collected by data acquisition module 0101, and also records scaling-up / scaling-down execution records from execution module 0104. This forms a complete record of scaling-up / scaling-down effects.

[0028] The method of the present invention includes the following steps: Data Acquisition S101: The data acquisition module employs distributed acquisition technology to collect real-time operational data such as service traffic, latency, packet loss rate, and basic unit (smallest unit for scaling up and down) load of telecommunications services in the cloud-native network. The acquisition frequency is set according to the service type. For example, for voice call services, the number of concurrent calls, call latency, and packet loss rate data of the basic unit are collected every 500 milliseconds; for data transmission services, the service data throughput, number of transmission requests, transmission latency, and basic unit load data are collected every 1 second.

[0029] Detection Condition Processing S102: The detection condition processing module presets detection conditions for different telecommunications services. Based on the current service time period, it compares and analyzes the collected data with preset detection conditions specific to the telecommunications service time period (service traffic threshold, latency threshold, packet loss rate threshold, basic unit load threshold, etc.) to generate detection results. Service traffic is essentially the number of concurrent services; in audio and video services, this refers to the real-time concurrent audio and video services, and in data transmission services, it refers to the data transmission volume and data request volume. Taking high-definition video call service as an example, the basic unit concurrent video call expansion threshold is set to 300 channels, the reduction threshold to 100 channels, the latency expansion threshold to 200 milliseconds, the packet loss rate expansion threshold to 1%, and the basic unit load expansion threshold to 80% and the reduction threshold to 30%. Simultaneously, it sets weekday peak service periods as 8:00-10:00 AM and 7:00-9:00 PM, and weekday off-peak periods as 11:00 PM-6:00 AM. The detection condition processing module compares the collected data with preset expansion thresholds during peak business hours. If the number of concurrent video calls per basic unit at 8 PM is 330, or the latency is 220 milliseconds, or the packet loss rate is 1.2%, or the basic unit load is 85%, then a detection result indicating that expansion is required is generated. The detection condition processing module also compares the collected data with preset reduction thresholds. If the number of concurrent video calls per basic unit at midnight is 90, or the basic unit load is 25%, then a detection result indicating that reduction is required is generated.

[0030] S103 Expansion Strategy Formulation: The expansion / contraction strategy module formulates expansion strategies based on detection results. Strategies are developed based on detection results and the characteristics of telecommunications services (peak hours). Expansion strategies are generated according to different services and different detection trigger conditions (the number of resources added, expansion time requirements, and expansion order vary). For the aforementioned high-definition video call service requiring expansion, considering the current peak hours and increased traffic compared to normal times, and receiving detection results indicating the need for expansion, a strategy is formulated to add two basic units within the next 5 minutes to reduce latency, packet loss rate, and basic unit load.

[0031] S104 Expansion Execution: According to the established expansion strategy, increase the resource allocation in the cloud-native network (increase the number of basic units or resources). After receiving the expansion strategy, the execution module calls the relevant resources through the cloud platform's interface, and completes the deployment and configuration of 2 basic units within 5 minutes to achieve resource expansion.

[0032] Expansion Data Record S105: Records changes in telecommunications service operation data before and after expansion, expansion time, increased resource volume, etc., forming an expansion effect data record. The data recording module records the latency of the high-definition video call service before expansion as 220 milliseconds, packet loss rate as 1.2%, basic unit load as 85%, the number of basic units used as 10, the expansion time as 8 PM, and the number of basic units added as 2; it also records the latency of the service after expansion for 10 minutes as 180 milliseconds, packet loss rate as 0.8%, basic unit load as 50%, and the number of basic units used as 12, forming a complete expansion and contraction effect data record.

[0033] S1031: The scaling-down strategy module formulates a scaling-down strategy based on the detection results. This strategy is based on the detection results and the characteristics of telecommunications services (off-peak hours). Different services and different detection trigger conditions generate scaling-down strategies (different scaling-down resource quantities and scaling-down order). For the aforementioned high-definition video call service requiring scaling-down, considering that it is currently an off-peak period and service traffic is lower than usual, and a detection result indicating the need for scaling-down has been received, a scaling-down strategy is formulated to reduce two basic units within the next 5 minutes to reduce resource consumption.

[0034] Scaling down execution S1041: According to the defined scaling down strategy, reduce the resource allocation in the cloud-native network (reduce the number of basic units or resources). After receiving the scaling down strategy, the execution module calls the relevant resources through the cloud platform's interface and completes the release of 2 basic units within 5 minutes to achieve resource scaling down.

[0035] S1051, the data recording module, records changes in telecommunications service operation data before and after capacity reduction, including reduction time and resource reduction amount, forming a data record of the capacity reduction effect. Before capacity reduction, the module records that the basic unit concurrent video calls for the high-definition video call service were 90, the basic unit load was 25%, and the number of basic units used was 10. The capacity reduction occurred at midnight (24:00), and the number of basic units was reduced by 2. It also records that 10 minutes after capacity reduction, the basic unit load was 45%, and the number of basic units used was 8, forming a complete data record of the capacity reduction effect.

[0036] The present invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a cloud-native dynamic scaling method triggered by telecommunications services.

[0037] The present invention also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a cloud-native dynamic scaling method triggered by telecommunications services.

[0038] The core value of this invention lies in providing a dedicated and efficient solution for the dynamic scaling of telecommunications services in cloud-native networks. Existing cloud-native systems' dynamic scaling functions do not fully consider the specific characteristics of telecommunications services, while this invention fills this gap by constructing a targeted detection condition system, formulating adaptive strategies, and improving data recording.

[0039] From a practical application perspective, its core value is reflected in three aspects: First, it ensures the stable operation of telecommunications services by precisely scaling up and down to ensure that services maintain good performance under different load conditions, such as low latency and low packet loss rate. Second, improve resource utilization efficiency, avoid resource idleness or overuse due to improper expansion and contraction strategies, and reduce operating costs; Third, it provides data-driven support for the continuous optimization of cloud-native networks for telecommunications services. By accumulating data on scaling up and down effects, it continuously iterates and upgrades strategies, enabling the system to better adapt to the development and changes in telecommunications services, and promoting the deepening and improvement of cloud-native technology applications in the telecommunications field.

Claims

1. A cloud-native dynamic scaling method based on telecommunications services, characterized in that... Includes the following steps: Data Acquisition: Through the data acquisition module, operational data of telecommunications services in the cloud-native network are collected in real time using a distributed acquisition method; Detection condition processing: The detection condition processing module compares and analyzes the running data with the preset detection conditions and thresholds for different telecommunications services. When the running data reaches the expansion or contraction threshold, expansion or contraction detection results are generated. Scaling-down strategy formulation: The scaling-down strategy module receives the scaling-down detection results and, based on the type of the telecommunications service and the detection results, matches and formulates a corresponding dynamic scaling-down strategy. Scaling up and down execution: The execution module receives the dynamic scaling up and down strategy and calls the resource management interface of the cloud-native network to execute the scaling up and down operation; Data recording: The data recording module records the running data collected in the data acquisition step and the scaling execution record generated in the scaling execution step, forming a scaling effect dataset.

2. The cloud-native dynamic scaling method based on telecommunications service triggering according to claim 1, characterized in that... In the data acquisition step, the operational data includes, but is not limited to, one or more combinations of business traffic, latency, packet loss rate, and basic unit load.

3. A cloud-native dynamic scaling method based on telecommunications service triggering, as described in claim 1 or 2, characterized in that... In the data acquisition steps, different acquisition methods and frequencies are configured for different telecommunications services.

4. The cloud-native dynamic scaling method based on telecommunications service triggering according to claim 1, characterized in that... In the detection condition processing step, the detection conditions and thresholds include performance thresholds set based on telecommunications service types and dynamic traffic thresholds set based on peak and off-peak periods of service traffic.

5. A cloud-native dynamic scaling method based on telecommunications service triggering according to claim 4, characterized in that... The performance thresholds include latency thresholds, packet loss rate thresholds, and basic unit load thresholds, and the dynamic traffic thresholds are configured according to a preset time period.

6. The cloud-native dynamic scaling method based on telecommunications service triggering according to claim 1, characterized in that... In the scaling-up / scaling strategy formulation step, the factors considered by the dynamic scaling-up / scaling strategy include the timing of scaling-up / scaling, the type of cloud-native network resources targeted by scaling-up / scaling, the number of resources to be scaled-up / scaled, and the execution order of the scaling-up / scaling steps.

7. A cloud-native dynamic scaling method based on telecommunications service triggering according to claim 1, characterized in that... The scaling effect dataset recorded in the data recording step includes the running data before and after the scaling operation, the scaling operation time, and the amount of resources adjusted, which is used for subsequent optimization analysis of scaling strategies.

8. A cloud-native dynamic scaling system for implementing the method as described in any one of claims 1-7, characterized in that, include: The data acquisition module is configured to collect operational data of telecommunications services in the cloud-native network in real time using a distributed acquisition method; The detection condition processing module is communicatively connected to the data acquisition module and is configured to receive the running data and compare and analyze it with preset detection conditions and thresholds to generate expansion and contraction detection results. The scaling-up / scaling strategy module is communicatively connected to the detection condition processing module and is configured to receive the scaling-up / scaling detection results, and match and formulate corresponding dynamic scaling-up / scaling strategies based on the type of the telecommunications service and the detection results. The execution module is communicatively connected to the scaling strategy module and is configured to receive the dynamic scaling strategy and call the resource management interface of the cloud-native network to perform scaling operations. The data recording module is communicatively connected to both the data acquisition module and the execution module, and is configured to record the running data and scaling execution records to form a scaling effect dataset.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 7.

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