Implementation method for automatically adjusting network load balancing architecture

Abstract

The invention discloses an implementation method for automatically adjusting a network load balancing architecture. The implementation method comprises the following steps: receiving an incoming network request and distributing the request to different servers according to a defined algorithm; monitoring the network state and the performance index of the application server in real time; and analyzing the collected data and making a decision to adjust a load balancing strategy. Load distribution can be dynamically adjusted according to real-time data so as to adapt to continuously changing network conditions. The fast response mechanism ensures continuity and stability of network services, and efficient operation can be kept even under the condition that network traffic is increased or decreased suddenly.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to an implementation method for automatically adjusting a network load balancing architecture. Background Art

[0002] With the rapid development of Internet technology, the scale and complexity of network applications are increasing, making the management and optimization of network traffic an important issue. Network load balancing technology, as one of the key means to solve this problem, improves the availability and reliability of the network by reasonably allocating network traffic to multiple servers or resources.

[0003] However, existing network load balancing technologies have some limitations. Many load balancing solutions rely on static configurations and cannot be dynamically adjusted based on real-time network conditions and traffic patterns. Fixed load distribution strategies may cause some servers to be overloaded while other server resources are idle, thereby reducing resource utilization. In the case of burst traffic or network failures, existing systems may take a long time to redistribute the load, which may cause service interruptions or performance degradation. As the size of the network increases, some load balancing solutions are difficult to scale to adapt to the growing traffic demand.

[0004] Disadvantages of Hardware Load Balancing Strategy

[0005] Cost is a significant barrier, including high initial purchase and maintenance fees, which is particularly heavy for small organizations or startups with limited budgets. Compared with software solutions, hardware load balancing devices are less flexible in configuration and updating, and require professional intervention, making the maintenance and upgrade process slow.

[0006] The development and update cycle of hardware devices is usually long and may not keep up with the fast-developing software technology, causing hardware load balancing solutions to lag behind software solutions in some aspects. Hardware load balancing devices usually focus on traffic distribution and may not include advanced application-level optimization and security features, or these features may not be as comprehensive as software solutions.

[0007] Although hardware load balancing devices are designed to be highly reliable, they may still experience hardware failures, which may cause service interruptions, especially without effective redundancy. Hardware load balancing devices may not be as efficient in resource utilization as software solutions because they may not be able to dynamically adjust resource allocation based on actual traffic demand.

[0008] Disadvantages of software load balancing strategy

[0009] Performance limitation is a key issue because software load balancing relies on the server's CPU and memory resources, which may become a performance bottleneck when processing a large number of concurrent requests, affecting the overall network processing speed. In addition, resource competition is also a challenge. Since software load balancers share resources with other applications on the server, resource competition may occur between them, especially in a resource-constrained environment, which may reduce the overall performance of the system.

[0010] Management complexity is also an issue. The configuration of software load balancers can be relatively complex, especially in large and distributed environments, and requires expertise to properly configure and optimize. Reliability issues should not be ignored either. Software solutions may be affected by the stability and security of the operating system. Once a system crash or security vulnerability occurs, the availability of the load balancing service may be affected. Scalability challenges, update and compatibility issues, technical support limitations, and security risks are all issues that need to be seriously considered. Summary of the invention

[0011] In view of the above problems, the present invention is proposed to provide an implementation method of automatically adjusting a network load balancing architecture that overcomes the above problems or at least partially solves the above problems.

[0012] According to one aspect of the present invention, there is provided an implementation method for automatically adjusting a network load balancing architecture, the implementation method comprising:

[0013] Receives incoming network requests and distributes them to different servers based on a defined algorithm;

[0014] Real-time monitoring of network status and application server performance indicators;

[0015] Analyze the collected data and make decisions to adjust the load balancing strategy.

[0016] Optionally, the performance indicators specifically include: response time, throughput, error rate and resource utilization.

[0017] Optionally, receiving the incoming network request and distributing the request to different servers according to a defined algorithm specifically includes:

[0018] The core function of a load balancer is to ensure that network requests are efficiently and evenly distributed to backend servers;

[0019] When processing a request, the load balancer first receives the connection request from the client and performs the necessary network protocol handshake;

[0020] Perform health checks and status monitoring to ensure that only active and available servers participate in request distribution;

[0021] After determining the health status of the server, the load balancer selects a server to handle the request based on the configured algorithm;

[0022] Select a target server, and the request will be forwarded to the target server for processing;

[0023] The response packet is returned directly to the client or through a load balancer.

[0024] Optionally, the configured algorithm specifically includes: polling, weighted polling, least connection or source address hash.

[0025] Optionally, the real-time monitoring of network status and performance indicators of application servers specifically includes:

[0026] Collect performance metrics;

[0027] Clean and process the collected performance indicators. The processed data will be used to generate reports and warnings.

[0028] Performance data needs to be stored for long-term analysis and auditing, and provide users with a way to view performance data and alerts;

[0029] The processing of a performance monitoring system includes several key steps: data collection, processing, analysis, visualization, warning and notification, automated processing, data storage and user interface.

[0030] Optionally, the performance indicators include response time, throughput, error rate and resource utilization.

[0031] Optionally, the method for collecting performance indicators includes: network sniffing, log analysis, system commands and application performance management APM tools.

[0032] Optionally, the cleaning and processing of the collected performance indicators specifically include: removing noise, smoothing data, and calculating average values ​​or percentages.

[0033] Optionally, analyzing the collected data and making a decision to adjust the load balancing strategy specifically includes:

[0034] Monitor and analyze traffic patterns and server status in real time, and make intelligent decisions based on this data to dynamically adjust load balancing strategies;

[0035] Real-time monitoring and traffic analysis enable the engine to quickly respond to traffic fluctuations and server status changes. By analyzing traffic patterns and performance benchmarks, the engine can identify peak hours, uneven traffic distribution, or potential traffic anomalies;

[0036] Capabilities such as adaptive adjustment, predictive scaling, and automated response enable analytics and decision engines to intelligently optimize network traffic management.

[0037] The present invention provides an implementation method of an automatic adjustment network load balancing architecture, the implementation method comprising: receiving incoming network requests and distributing the requests to different servers according to a defined algorithm; monitoring the network status and performance indicators of application servers in real time; analyzing the collected data and making decisions to adjust the load balancing strategy. The load distribution can be dynamically adjusted according to real-time data to adapt to changing network conditions. This rapid response mechanism ensures the continuity and stability of network services, and can maintain efficient operation even in the case of a sharp increase or drop in network traffic.

[0038] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0040] Figure 1 A flow chart of a load balancer component provided for an embodiment of the present invention;

[0041] Figure 2 A flow chart of a performance detection system component provided by an embodiment of the present invention;

[0042] Figure 3 A flow chart of the analysis and decision engine components provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0044] The terms "comprises" and "having" and any variations thereof in the description embodiments, claims and drawings of the present invention are intended to cover non-exclusive inclusions, for example, including a series of steps or units.

[0045] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0046] The present invention provides an implementation method for automatically adjusting a network load balancing architecture, which includes three components: a load balancer component, a performance detection system component, and an analysis and decision engine component.

[0047] First load balancer component: responsible for receiving incoming network requests and distributing the requests to different servers according to the defined algorithm.

[0048] The second performance monitoring system component: real-time monitoring of network status and application server performance indicators, such as response time, throughput, error rate and resource utilization.

[0049] The third analysis and decision engine component: analyzes the collected data and makes decisions to adjust the load balancing strategy.

[0050] like Figure 1 As shown, the load balancer components:

[0051] The core function of a load balancer is to ensure that network requests are efficiently and evenly distributed to backend servers. When processing a request, the load balancer first receives the connection request from the client and performs the necessary network protocol handshake. It also performs health checks and status monitoring to ensure that only active and available servers participate in request distribution.

[0052] After determining the health status of the server, the load balancer selects a server to handle the request based on the configured algorithm, such as polling, weighted polling, least connection or source address hash. It can adapt to different application needs, optimize resource usage, and ensure the reasonable distribution of requests.

[0053] Once the target server is selected, the request will be forwarded to that server for processing. The response packet will be returned directly to the client or through the load balancer, depending on the configuration. The load balancer may have advanced features such as SSL termination, application firewall, DDoS protection, etc. to enhance security and performance. The whole process not only improves the high availability and scalability of the application, but also optimizes the response time and ensures the continuity and stability of the service.

[0054] like Figure 2 As shown, the performance monitoring system components are:

[0055] Data collection is the cornerstone of a performance monitoring system. The system needs to collect various performance indicators, including response time, throughput, error rate, and resource utilization. This data is collected in a variety of ways, such as network sniffing, log analysis, system commands, and application performance management (APM) tools. It provides the necessary input for subsequent steps.

[0056] Data processing involves cleaning and processing the collected raw data to facilitate analysis. This may include removing noise, smoothing data, calculating averages or percentages, etc. Data analysis is the core of a performance monitoring system. The processed data will be used to generate reports and alerts. This may include identifying trends, predicting future performance, detecting anomalies, etc. This step usually requires the use of statistical analysis, machine learning, or other data analysis techniques.

[0057] Data visualization makes it easier for users to understand performance data. This may include generating charts, dashboards, maps, etc. At the same time, if the performance indicator exceeds the predetermined threshold, the system should automatically send a warning or notification to help the operation and maintenance team find and solve the problem in time. In addition, the system can also take automated processing to improve performance, such as adjusting server configurations, launching new server instances, etc. Finally, performance data needs to be stored for long-term analysis and auditing, and provide users with a way to view performance data and warnings.

[0058] The process of performance monitoring system involves multiple key steps such as data collection, processing, analysis, visualization, warning and notification, automated processing, data storage and user interface. These steps are interrelated and together constitute an efficient and reliable performance monitoring system.

[0059] like Figure 3 As shown, the analysis and decision engine components:

[0060] The analysis and decision engine is a key component in the load balancing system. It is responsible for real-time monitoring and analysis of traffic patterns and server status, and makes intelligent decisions based on these data to dynamically adjust the load balancing strategy. Data collection is its basic function. The engine collects key data from the load balancer and backend servers, such as traffic statistics, response time, server load (including CPU, memory, disk I / O), and number of connections. These data provide necessary information for subsequent traffic analysis and performance evaluation.

[0061] Real-time monitoring and traffic analysis enable the engine to quickly respond to traffic fluctuations and server status changes. By analyzing traffic patterns and performance benchmarks, the engine can identify peak hours, uneven traffic distribution, or potential traffic anomalies. At the same time, health check analysis and decision making ensure that the system can adjust the load balancing strategy according to real-time conditions, such as adjusting server weights, switching algorithms, or redistributing traffic.

[0062] Features such as adaptive adjustment, predictive expansion, and automated response enable the analysis and decision engine to intelligently optimize network traffic management. Integrated optimization algorithms are designed to improve resource utilization, reduce response time, and enhance user experience. In addition, features such as security analysis, reporting and visualization, and configuration management further enhance the reliability and resilience of the system. In some advanced systems, the engine even has learning capabilities that can learn from historical data and optimize its decision-making process.

[0063] The present invention provides a system that can monitor network status in real time, predict traffic patterns, and automatically adjust load distribution strategies. It will use advanced data analysis technology and real-time feedback mechanisms to achieve adaptability, optimize resource usage, improve throughput, reduce latency, and enhance availability.

[0064] Adaptability can dynamically adjust load distribution based on real-time data to cope with changing network conditions. The system can quickly respond to changes in network traffic to ensure the continuity and stability of network services. Through intelligent allocation, the system can ensure that the resources of all servers are fully utilized to avoid resource waste. Improve resource utilization efficiency and reduce costs.

[0065] In terms of improving throughput, the system improves the overall processing capacity of the network by optimizing traffic distribution to meet high traffic demands. At the same time, through the rapid response mechanism, the system can reduce service delays caused by load redistribution, thereby reducing latency. These optimization measures will significantly improve network performance, allowing users to enjoy faster and more stable network services.

[0066] Beneficial effects:

[0067] 1. Adaptability: The present invention has excellent adaptability and can dynamically adjust load distribution according to real-time data to adapt to changing network conditions. The rapid response mechanism ensures the continuity and stability of network services, and can maintain efficient operation even when network traffic increases or decreases suddenly.

[0068] 2. Optimize resource usage: The present invention can ensure that the resources of all servers are fully utilized and avoid resource waste. It not only improves resource utilization efficiency, but also helps to reduce operating costs and maximize cost-effectiveness.

[0069] 3. Enhanced availability: The present invention adopts redundancy and failover strategies to ensure high availability of network services. Even if some servers fail, the system can quickly switch to backup servers to ensure service continuity. This design greatly reduces the risk of service interruption.

[0070] The above specific implementation methods further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for implementing an automatic adjustment network load balancing architecture, characterized in that: The implementation method includes: Receives incoming network requests and distributes them to different servers based on a defined algorithm; Real-time monitoring of network status and application server performance indicators; Analyze the collected data and make decisions to adjust the load balancing strategy.

2. The method for implementing an automatic adjustment network load balancing architecture according to claim 1, characterized in that: The performance indicators specifically include: response time, throughput, error rate and resource utilization.

3. The method for implementing an automatic adjustment network load balancing architecture according to claim 1, characterized in that: The receiving of incoming network requests and distributing the requests to different servers according to a defined algorithm specifically includes: The core function of a load balancer is to ensure that network requests are efficiently and evenly distributed to backend servers; When processing a request, the load balancer first receives the connection request from the client and performs the necessary network protocol handshake; Perform health checks and status monitoring to ensure that only active and available servers participate in request distribution; After determining the health status of the server, the load balancer selects a server to handle the request based on the configured algorithm; Select a target server, and the request will be forwarded to the target server for processing; The response packet is returned directly to the client or through a load balancer.

4. The method for implementing an automatic adjustment network load balancing architecture according to claim 3, characterized in that: The configured algorithms specifically include: polling, weighted polling, least connections or source address hashing.

5. The method for implementing an automatic adjustment network load balancing architecture according to claim 1, characterized in that: The real-time monitoring of network status and performance indicators of application servers specifically includes: Collect performance metrics; Clean and process the collected performance indicators. The processed data will be used to generate reports and warnings. Performance data needs to be stored for long-term analysis and auditing, and provide users with a way to view performance data and alerts; The processing of a performance monitoring system includes several key steps: data collection, processing, analysis, visualization, warning and notification, automated processing, data storage and user interface.

6. The method for implementing an automatic adjustment network load balancing architecture according to claim 1, characterized in that: The performance indicators include response time, throughput, error rate and resource utilization.

7. The method for implementing an automatic adjustment network load balancing architecture according to claim 1, characterized in that: The method for collecting performance indicators includes: network sniffing, log analysis, system commands and application performance management (APM) tools.

8. The method for implementing an automatic adjustment network load balancing architecture according to claim 1, characterized in that: The cleaning and processing of the collected performance indicators specifically include: removing noise, smoothing data, and calculating average values ​​or percentages.

9. The method for implementing an automatic adjustment network load balancing architecture according to claim 1, characterized in that: The analysis of the collected data and making decisions to adjust the load balancing strategy specifically includes: Monitor and analyze traffic patterns and server status in real time, and make intelligent decisions based on this data to dynamically adjust load balancing strategies; Real-time monitoring and traffic analysis enable the engine to quickly respond to traffic fluctuations and server status changes. By analyzing traffic patterns and performance benchmarks, the engine can identify peak hours, uneven traffic distribution, or potential traffic anomalies; Capabilities such as adaptive adjustment, predictive scaling, and automated response enable analytics and decision engines to intelligently optimize network traffic management.

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