Intelligent capacity expansion SDN gateway system for cloud computer

Abstract

The invention discloses an intelligent capacity expansion SDN gateway system for a cloud computer, belongs to the technical field of cloud computing, and aims to solve the technical problem of how to realize capacity expansion of an SDN gateway in a cloud computer environment so as to deal with network traffic congestion. Comprising an index acquisition monitoring module used for acquiring key performance indexes from an SDN gateway, the key performance indexes comprising a CPU usage rate, a memory usage rate, a disk read-write rate and a real-time network flow; the intelligent prediction module is used for training and optimizing the network traffic prediction model based on the historical key performance indexes and the user use habit data, and predicting the traffic trend of the SDN gateway in a future predetermined time period based on the trained and optimized network traffic prediction model; and the intelligent decision-making module is used for carrying out expansion decision-making based on the key performance index, the flow trend of the SDN gateway in the future predetermined time period and the resource utilization rate.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and in particular to an intelligent capacity expansion SDN gateway system for cloud computers. Background Art

[0002] In the cloud computing environment, the SDN gateway is a key node connecting the internal and external networks of the cloud. Its performance is directly related to the stability and service quality of the entire cloud platform. With the growth of business volume or the emergence of sudden traffic events, the SDN gateway may face resource bottlenecks, such as CPU overload, insufficient memory, disk I / 0 saturation, and network traffic congestion. Traditional expansion solutions often require manual intervention, which not only has a slow response speed, but may also cause service interruptions due to operational errors, affecting user experience and system reliability.

[0003] How to expand the capacity of SDN gateways in a cloud computing environment to cope with network traffic congestion is a technical problem that needs to be solved. Summary of the invention

[0004] The technical task of the present invention is to address the above shortcomings and provide an intelligent expansion SDN gateway system for cloud computers to solve the technical problem of how to achieve SDN gateway expansion in a cloud computer environment to cope with network traffic congestion.

[0005] The present invention discloses an intelligent expansion SDN gateway system for cloud computers, comprising an indicator collection and monitoring module, an intelligent prediction module and an intelligent decision-making module;

[0006] The indicator collection and monitoring module is used to collect key performance indicators from the SDN gateway, and the key performance indicators include CPU usage, memory usage, disk read and write rate, and real-time network traffic;

[0007] The intelligent prediction module is configured with a network traffic prediction model, which is used to take key performance indicators and user usage habit data as input, predict and output the traffic trend of the SDN gateway in the future predetermined time period, and the intelligent prediction module is used to train and optimize the network traffic prediction model based on historical key performance indicators and user usage habit data, and predict the traffic trend of the SDN gateway in the future predetermined time period based on the trained and optimized network traffic prediction model;

[0008] The intelligent decision-making module is used to make expansion decisions based on key performance indicators, traffic trends of the SDN gateway in the future scheduled time period, and resource utilization.

[0009] Preferably, the indicator collection and monitoring module is used to deploy an indicator collector inside the SDN gateway, and continuously collect key performance indicators of the SDN gateway through the indicator collector.

[0010] As a preferred option, the key performance indicators reported by the SDN gateway are continuously monitored based on Prometheus and syslog technologies.

[0011] Preferably, the intelligent prediction module is used to train and optimize the network traffic prediction model based on historical key performance indicators and user usage habit data through parameter tuning and LoRA.

[0012] Preferably, the intelligent prediction module is used to visualize the traffic trend of the SDN gateway in a future predetermined time period output by the prediction in the form of a chart.

[0013] Preferably, a key performance indicator threshold is configured in the intelligent decision-making module, and the intelligent decision-making module is used to compare the key performance indicators collected by the indicator collection module with the indicator threshold, and to make expansion decisions based on the comparison results, the traffic trend of the SDN gateway in a future scheduled time period, and the resource utilization rate.

[0014] As a best practice, expansion decisions follow the following rules:

[0015] No capacity expansion is required: if the current key performance indicator is lower than the preset indicator threshold, and the traffic trend of the SDN gateway in the future scheduled time period is less than the threshold, no capacity expansion is performed;

[0016] Temporary capacity expansion measures: If the current key performance indicators are lower than the preset indicator threshold and the traffic growth of the SDN gateway in the future scheduled time period is slow, temporary capacity expansion measures are implemented. The capacity expansion method includes online adjustment of the specifications of the SDN gateway to cope with possible traffic peaks;

[0017] Immediate capacity expansion: If the current key performance indicators are significantly higher than the preset thresholds and the traffic of the SDN gateway increases rapidly in the future scheduled time period, the capacity expansion process is started immediately. The capacity expansion methods include increasing the number of SDN gateway instances or upgrading the network bandwidth to ensure stable system operation and meet business needs;

[0018] Dynamic and automated capacity expansion: Based on the comparison results of key performance indicators with preset thresholds and the traffic growth of the SDN gateway in a predetermined time period in the future, the capacity expansion process is automatically triggered. The capacity expansion methods include adding virtual machine instances, expanding the specifications of the SDN gateway, or upgrading the network bandwidth to ensure that the SDN gateway can quickly adapt to traffic growth or changes in service resources.

[0019] Preferably, the intelligent decision-making module is used to return the capacity expansion decision to the intelligent prediction module, and the intelligent prediction module is used to optimize the network traffic prediction model based on the capacity expansion decision.

[0020] The intelligent expansion SDN gateway system for cloud computers of the present invention has the following advantages: it effectively solves the resource bottleneck problem of SDN gateways in cloud computer environments. It significantly improves the expansion response rate, avoids manual operation errors, and ensures the stability of cloud computers accessing the Internet; at the same time, the intelligent decision-making and prediction mechanism optimizes resource allocation, reduces resource waste, improves system integrity and user experience, and enhances system reliability and scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] The present invention is further described below in conjunction with the accompanying drawings.

[0023] Figure 1 This is a working principle block diagram of an intelligent expansion SDN gateway system for cloud computers in Example 1;

[0024] Figure 2 This is an implementation diagram of the capacity expansion of an intelligent capacity expansion SDN gateway system for cloud computers in Example 1. DETAILED DESCRIPTION

[0025] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments may be combined with each other.

[0026] The embodiment of the present invention provides an intelligent expansion SDN gateway system for cloud computing, which is used to solve the technical problem of how to expand the SDN gateway in a cloud computing environment to cope with network traffic congestion.

[0027] Embodiment 1:

[0028] The present invention discloses an intelligent expansion SDN gateway system for cloud computers, comprising an indicator collection and monitoring module, an intelligent prediction module and an intelligent decision-making module.

[0029] The indicator collection and monitoring module is used to collect key performance indicators from the SDN gateway, and the key performance indicators include CPU usage, memory usage, disk read and write rate, and real-time network traffic.

[0030] As a specific implementation of the indicator collection and monitoring module, this module is used to deploy an indicator collector inside the SDN gateway, continuously collect the key performance indicators of the SDN gateway through the indicator collector, and continuously monitor the key performance indicators reported by the SDN gateway based on Prometheus and syslog technology. Prometheus can collect system indicators such as CPU usage, memory usage, disk read and write rates, and real-time network traffic in real time, while supporting custom queries and alarm rules, and can efficiently send the collected data to the backend for storage and analysis. In addition, the SDN gateway sends log information (such as network traffic data, security event logs, etc.) to the log server through the syslog protocol to parse these logs, obtain key performance indicators, and provide data support for subsequent intelligent analysis and decision-making.

[0031] The intelligent prediction module is configured with a network traffic prediction model, which is used to take key performance indicators and user usage habit data as input, and predict and output the traffic trend of the SDN gateway in a future predetermined time period. The intelligent prediction module is used to train and optimize the network traffic prediction model based on historical key performance indicators and user usage habit data, and predict the traffic trend of the SDN gateway in a future predetermined time period based on the trained and optimized network traffic prediction model.

[0032] As a specific implementation of the intelligent prediction module, this module is used to train and optimize the network traffic prediction model based on historical key performance indicators and user usage habit data through parameter tuning and LoRA. The module is also used to visualize the traffic trend of the SDN gateway in the future scheduled time period in the form of a chart.

[0033] In this embodiment, the historical key performance indicators of the SDN gateway (such as CPU usage, memory usage, number of concurrent connections, network card receiving and sending rates, etc.), as well as user behavior logs, traffic patterns and other user usage habit data are used as the model input part, and parameter tuning and LoRA are used to deeply train and optimize the model. In terms of algorithm selection, a deep learning algorithm suitable for time series (such as LSTM) is selected to intelligently predict the future traffic trend of the SDN gateway. At the same time, the prediction results are visualized in the form of charts, large screens, etc., so that relevant personnel can intuitively understand the future traffic trend.

[0034] The intelligent decision-making module is used to make expansion decisions based on key performance indicators, traffic trends of the SDN gateway in the future scheduled time period, and resource utilization.

[0035] As a specific implementation of the intelligent decision-making module, this module is configured with key performance indicator thresholds, which are used to compare the key performance indicators collected by the indicator collection module with the indicator thresholds, and to make capacity expansion decisions based on the comparison results, the traffic trend of the SDN gateway in the future scheduled time period, and the resource utilization rate.

[0036] The expansion decision follows the following rules:

[0037] (1) No capacity expansion is required: the current key performance indicator is lower than the preset indicator threshold, and the traffic trend of the SDN gateway in the future scheduled time period is less than the threshold, so no capacity expansion is performed;

[0038] (2) Temporary capacity expansion measures: If the current key performance indicators are lower than the preset indicator threshold and the traffic growth of the SDN gateway in the future predetermined time period is slow, temporary capacity expansion measures are implemented. The capacity expansion method includes online adjustment of the specifications of the SDN gateway to cope with possible traffic peaks;

[0039] (3) Immediate capacity expansion: If the current key performance indicators are significantly higher than the preset thresholds and the traffic of the SDN gateway increases rapidly in the future scheduled time period, the capacity expansion process is immediately initiated. The capacity expansion methods include increasing the number of SDN gateway instances or upgrading the network bandwidth to ensure stable operation of the system and meet business needs;

[0040] (4) Dynamic and automated capacity expansion: Based on the comparison results of key performance indicators with preset thresholds and the traffic growth of the SDN gateway in a predetermined time period in the future, the capacity expansion process is automatically triggered. The capacity expansion methods include adding virtual machine instances, expanding the specifications of the SDN gateway, or upgrading the network bandwidth to ensure that the SDN gateway can quickly adapt to traffic growth or changes in service resources.

[0041] Taking the expansion of SDN gateway memory as an example, the implementation process is as follows: Figure 2 As shown, the following steps are included:

[0042] Step 1: Specify the expansion strategy and target parameter determination: Based on the intelligent decision-making results, formulate the SDN gateway expansion strategy, including determining the target memory size for expansion, the estimated expansion waiting time, and the rollback plan for expansion failure.

[0043] Step 2: Comprehensively evaluate the expansion needs and feasibility: Obtain the SDN gateway instance details from the database, such as the current memory usage, virtualization driver, maximum available memory size, and initial memory size, and comprehensively evaluate the expansion needs and feasibility to ensure the rationality and safety of the expansion action. If the current system does not meet the resources required for expansion, such as insufficient memory, it will be reported and fed back to the intelligent prediction and decision-making module.

[0044] Step 3: Execute the SDN gateway memory expansion operation: After confirming that the expansion is feasible, expand the SDN gateway instance memory by calling relevant scripts and API interfaces through the Memory Balloon technology.

[0045] Step 4: Monitor expansion results and emergency response: Wait for the expansion result in step 4. If the expansion operation is not completed within the expected time, trigger an alarm and perform manual intervention. If successful, update the SDN gateway instance memory information. If the expansion fails, start the pre-specified rollback plan to quickly restore to the state before the expansion to reduce the potential impact on the business.

[0046] The system of this embodiment effectively solves the resource bottleneck problem of SDN gateway in the cloud computer environment through intelligent monitoring, prediction and dynamic expansion. It significantly improves the expansion response rate, avoids manual operation errors, and ensures the stability of cloud computer access to the Internet. At the same time, the intelligent decision-making and prediction mechanism optimizes resource allocation, reduces resource waste, improves system integrity and user experience, and enhances system reliability and scalability.

[0047] The above is a detailed introduction to an intelligent expansion SDN gateway system for cloud computers provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An intelligent expansion SDN gateway system for cloud computers, characterized in that: It includes indicator collection and monitoring module, intelligent prediction module and intelligent decision-making module; The indicator collection and monitoring module is used to collect key performance indicators from the SDN gateway, and the key performance indicators include CPU usage, memory usage, disk read and write rate, and real-time network traffic; The intelligent prediction module is configured with a network traffic prediction model, which is used to take key performance indicators and user usage habit data as input, predict and output the traffic trend of the SDN gateway in the future predetermined time period, and the intelligent prediction module is used to train and optimize the network traffic prediction model based on historical key performance indicators and user usage habit data, and predict the traffic trend of the SDN gateway in the future predetermined time period based on the trained and optimized network traffic prediction model; The intelligent decision-making module is used to make expansion decisions based on key performance indicators, traffic trends of the SDN gateway in the future scheduled time period, and resource utilization.

2. The intelligent expansion SDN gateway system for cloud computers according to claim 1 is characterized in that: The indicator collection and monitoring module is used to deploy an indicator collector inside the SDN gateway, and continuously collect key performance indicators of the SDN gateway through the indicator collector.

3. The intelligent expansion SDN gateway system for cloud computers according to claim 2 is characterized in that: Based on Prometheus and syslog technology, the key performance indicators reported by the SDN gateway are continuously monitored.

4. The intelligent expansion SDN gateway system for cloud computers according to claim 1 is characterized in that: The intelligent prediction module is used to train and optimize the network traffic prediction model based on historical key performance indicators and user usage habit data through parameter tuning and LoRA.

5. The intelligent expansion SDN gateway system for cloud computers according to claim 1 is characterized in that: The intelligent prediction module is used to visualize the traffic trend of the SDN gateway in the future predetermined time period output by the prediction in the form of a chart.

6. The intelligent expansion SDN gateway system for cloud computers according to claim 1 is characterized in that: The intelligent decision-making module is configured with a key performance indicator threshold, and is used to compare the key performance indicators collected by the indicator collection module with the indicator threshold, and to make expansion decisions based on the comparison results, the traffic trend of the SDN gateway in a future scheduled time period, and the resource utilization rate.

7. The intelligent expansion SDN gateway system for cloud computers according to claim 6 is characterized in that: The expansion decision follows the following rules: No capacity expansion is required: if the current key performance indicator is lower than the preset indicator threshold, and the traffic trend of the SDN gateway in the future scheduled time period is less than the threshold, no capacity expansion is performed; Temporary capacity expansion measures: If the current key performance indicators are lower than the preset indicator threshold and the traffic growth of the SDN gateway in the future scheduled time period is slow, temporary capacity expansion measures are implemented. The capacity expansion method includes online adjustment of the specifications of the SDN gateway to cope with possible traffic peaks; Immediate capacity expansion: If the current key performance indicators are significantly higher than the preset thresholds and the traffic of the SDN gateway increases rapidly in the future scheduled time period, the capacity expansion process is started immediately. The capacity expansion methods include increasing the number of SDN gateway instances or upgrading the network bandwidth to ensure stable system operation and meet business needs; Dynamic and automated capacity expansion: Based on the comparison results of key performance indicators with preset thresholds and the traffic growth of the SDN gateway in a predetermined time period in the future, the capacity expansion process is automatically triggered. The capacity expansion methods include adding virtual machine instances, expanding the specifications of the SDN gateway, or upgrading the network bandwidth to ensure that the SDN gateway can quickly adapt to traffic growth or changes in service resources.

8. The intelligent expansion SDN gateway system for cloud computers according to claim 6 is characterized in that: The intelligent decision-making module is used to return the capacity expansion decision to the intelligent prediction module, and the intelligent prediction module is used to optimize the network traffic prediction model based on the capacity expansion decision.

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