Load balancing implementation method and system based on cluster

Abstract

The invention relates to the technical field of load balancing, in particular to a cluster-based load balancing implementation method, which comprises the following steps of: S1, defining load balancing requirements and targets; s2, selecting a load balancer; s3, deploying a load balancer; s4, configuring a server pool; s5, realizing a load balancing algorithm; s6, realizing a fault-tolerant processing mechanism; s7, a monitoring and alarming system; s8, recording and analyzing logs; s9, performing regular maintenance and optimization; and S10, continuously improving. The cluster internal network topology structure is optimized through the network optimization module, the data transmission efficiency and the response speed are improved, the system performance and the user experience are improved through the load balancer, the safety protection module, the prediction module and the data processing transmission module, and the stability and the performance of the whole system are improved. The higher disaster recovery capability is realized through the load balancing architecture module, the more accurate load distribution is realized through the algorithm processing module, the load balancing strategy is adjusted in advance through the load balancing adjustment module, and the intelligence is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of load balancing, and in particular to a cluster-based load balancing implementation method and system. Background Art

[0002] Load balancing is built on the existing network structure. It provides a cheap, effective and transparent method to expand the bandwidth of network devices and servers, increase throughput, enhance network data processing capabilities, and improve network flexibility and availability. Under normal circumstances, load balancing is implemented by a single processor controlling it separately, which is costly. Therefore, load balancing can be scheduled and directed based on a cluster.

[0003] A Chinese patent discloses a cluster load balancing implementation method and system (authorization announcement number CN116170443A). Through the present invention, the control cluster formed by at least one first computer instance replaces the hardware router to realize the load balancing between multiple working nodes in the service cluster under the BGP mode. However, the above cluster load balancing implementation method adopts the method of deploying software services to realize the load balancing function. The processing capacity of this method is relatively weak, the server is under great pressure, and the system has the risk of freezing, which affects the data transmission efficiency and response speed, is not conducive to the stability of data processing, and the data storage is imperfect, data is lost, which affects the judgment of data failure, and the learning ability is poor, which is not conducive to the intelligence of the load balancing method. Therefore, those skilled in the art provide a cluster-based load balancing implementation method and system to solve the problems raised in the above background technology. Summary of the invention

[0004] The purpose of the present invention is to provide a cluster-based load balancing implementation method and system to solve the problems raised in the above background technology.

[0005] To achieve the above purpose, the present invention provides the following technical solution: a cluster-based load balancing implementation method, the steps are as follows:

[0006] S1. Define load balancing requirements and goals: Clarify the requirements and goals of the load balancing system;

[0007] S2. Select load balancer: Select the required load balancer;

[0008] S3. Deploy load balancer: Deploy a multi-level load balancing architecture through the load balancing architecture module to distribute traffic to different data centers or geographical locations;

[0009] S4. Configure the server pool: Configure the server pool according to the number of servers, health checks and server weights. Optimize the internal network topology of the cluster through the network optimization module to reduce network latency and packet loss, and select the optimal path based on factors such as user location and network conditions.

[0010] S5. Implement the load balancing algorithm: select the algorithm through the algorithm processing module and perform appropriate tuning through the load balancing adjustment module;

[0011] S6. Implement fault-tolerant processing mechanism: Implement fault-tolerant mechanism through failover and service degradation;

[0012] S7, monitoring and alarm system: the fault monitoring module runs the inspection mechanism, realizes fast and seamless failover when a faulty node is found, monitors the running status of the load balancer and backend server, and the security protection module runs to enhance the security protection capability of the cluster;

[0013] S8, log recording and analysis: use the data processing and transmission module to collect and store the operation logs of the load balancer and server, use the backup module to back up the data, analyze the logs, optimize and adjust them, and use the prediction module to predict the trend of future traffic and server load conditions based on the log data, and adjust the load balancing strategy in advance to deal with emergencies;

[0014] S9. Regular maintenance and optimization: Regular system maintenance and optimization, including software updates, performance tuning, and security reinforcement, ensure that the system is always in the best condition and meets the ever-changing load requirements;

[0015] S10. Continuous improvement: Regularly evaluate the performance and stability of the system, collect user feedback and suggestions, and continuously optimize and improve the system.

[0016] As a further solution of the present invention: the requirements and goals in S1 include expected load type, load amount, fault tolerance requirements and performance indicators.

[0017] As a further solution of the present invention: the content in S4 also includes:

[0018] Number of servers: Determine the number of servers based on load demand and performance requirements;

[0019] Health check: Configure a health check mechanism to regularly check the health status of the server and promptly detect and remove faulty nodes;

[0020] Server weight: Set the weights of different servers according to server performance and load conditions to achieve dynamic scheduling of the load balancer.

[0021] As a further solution of the present invention: the S5 load balancing algorithm includes:

[0022] Polling algorithm: The polling algorithm distributes requests to each server in turn, in a cycle;

[0023] Weighted Round Robin: The weighted round robin algorithm introduces a weight parameter based on the round robin algorithm, allocating requests according to the processing capacity of the server to achieve load balancing;

[0024] Least Connection Algorithm: The least connection algorithm distributes requests to the server with the least current connections to ensure a more balanced load.

[0025] Hash algorithm: The hash algorithm calculates the hash value based on the characteristics of the request, and then distributes the request to the corresponding server to ensure that the same request is always sent to the same server;

[0026] Response time weighted algorithm: Dynamically adjust the weight according to the server's response time to achieve more intelligent load balancing.

[0027] As a further solution of the present invention: the content of the fault tolerance mechanism in S6 is as follows:

[0028] Failover: When the target server fails, the traffic is transferred to other servers;

[0029] Service degradation: Temporarily lower the priority of a service when the load is too high or a failure occurs.

[0030] A system for implementing a cluster-based load balancing method: comprising:

[0031] The server is used to form multiple servers into a cluster to jointly handle requests from clients;

[0032] The network optimization module is used to optimize the internal network topology of the cluster, reduce network delay and packet loss, and select the optimal path based on factors such as user location and network status;

[0033] The load balancer is used to distribute requests to each server in the cluster according to certain algorithms or rules to ensure that each server can get a balanced load;

[0034] The security protection module is used to enhance the security protection capability of the cluster and ensure the stable operation of the cluster;

[0035] The prediction module is used to predict future traffic trends and server load conditions, and adjust the load balancing strategy in advance to cope with emergencies;

[0036] The data processing and transmission module is used to achieve efficient data synchronization, ensure data consistency between the load balancer and the server, adopt a cache strategy, reduce unnecessary data transmission and processing, and improve the system's response speed and throughput;

[0037] The backup module is used to establish a disaster recovery backup mechanism, which can quickly restore services in the event of natural disasters, human errors and unforeseen events;

[0038] The fault monitoring module is used to design a robust health check mechanism to achieve fast and seamless failover when a faulty node is found to avoid affecting users.

[0039] As a further solution of the present invention: the load balancer includes:

[0040] The load balancing architecture module is used to deploy a multi-level load balancing architecture to distribute traffic to different data centers or geographical locations. The load balancing system architecture includes:

[0041] Single-tier architecture: Single-tier load balancing architecture deploys the load balancer and application server on the same tier;

[0042] Two-tier architecture: The two-tier load balancing architecture deploys multiple load balancers at the front end and then distributes traffic to the application servers at the back end;

[0043] Multi-layer architecture: Multi-layer load balancing architecture distributes traffic to different data centers or geographical locations by introducing multiple layers of load balancers;

[0044] The algorithm processing module is used for intelligent load balancing algorithms based on real-time load conditions and server performance;

[0045] The load balancing adjustment module is used to dynamically adjust the load conditions and prediction results, and to provide predictive load balancing and adaptive adjustments to cope with emergencies and load fluctuations.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The cluster-based load balancing implementation method of the present invention implements load balancing management based on the cluster, improves the system processing capability, reduces the load of a single server, optimizes the internal network topology of the cluster through the network optimization module, reduces network delay and data packet loss, and improves data transmission efficiency and response speed;

[0048] 2. Improve the system performance and user experience through load balancer, security protection module, prediction module, and data processing and transmission module to improve the stability and performance of the entire system;

[0049] 3. Achieve higher disaster recovery capabilities through the load balancing architecture module, achieve more accurate load distribution through the algorithm processing module, adjust the load balancing strategy in advance through the load balancing adjustment module, realize adaptive adjustment of the load balancing system, and improve intelligence. DETAILED DESCRIPTION

[0050] Example

[0051] The cluster-based load balancing implementation method is as follows:

[0052] S1. Define load balancing requirements and goals: Clarify the requirements and goals of the load balancing system, including the expected load type, load volume, fault tolerance requirements and performance indicators;

[0053] S2. Select load balancer: Select the required load balancer;

[0054] S3. Deploy load balancer: Deploy a multi-level load balancing architecture through the load balancing architecture module to distribute traffic to different data centers or geographical locations. The content also includes:

[0055] Number of servers: Determine the number of servers based on load demand and performance requirements;

[0056] Health check: Configure a health check mechanism to regularly check the health status of the server and promptly detect and remove faulty nodes;

[0057] Server weight: Set the weight of different servers according to server performance and load conditions to achieve dynamic scheduling of the load balancer;

[0058] S4. Configure the server pool: Configure the server pool according to the number of servers, health checks and server weights. Optimize the internal network topology of the cluster through the network optimization module to reduce network latency and packet loss, and select the optimal path based on factors such as user location and network conditions.

[0059] S5. Implement the load balancing algorithm: select the algorithm through the algorithm processing module and perform appropriate tuning through the load balancing adjustment module. The load balancing algorithm includes:

[0060] Polling algorithm: The polling algorithm distributes requests to each server in turn, in a cycle;

[0061] Weighted Round Robin: The weighted round robin algorithm introduces a weight parameter based on the round robin algorithm, allocating requests according to the processing capacity of the server to achieve load balancing;

[0062] Least Connection Algorithm: The least connection algorithm distributes requests to the server with the least current connections to ensure a more balanced load.

[0063] Hash algorithm: The hash algorithm calculates the hash value based on the characteristics of the request, and then distributes the request to the corresponding server to ensure that the same request is always sent to the same server;

[0064] Response time weighted algorithm: dynamically adjust the weight according to the server's response time to achieve more intelligent load balancing;

[0065] S6. Implement fault-tolerant processing mechanism: Implement fault-tolerant mechanism through failover and service degradation. The contents of the fault-tolerant mechanism are as follows:

[0066] Failover: When the target server fails, the traffic is transferred to other servers;

[0067] Service degradation: Temporarily lower the priority of a service when the load is too high or a failure occurs;

[0068] S7, monitoring and alarm system: the fault monitoring module runs the inspection mechanism, realizes fast and seamless failover when a faulty node is found, monitors the running status of the load balancer and backend server, and the security protection module runs to enhance the security protection capability of the cluster;

[0069] S8, log recording and analysis: use the data processing and transmission module to collect and store the operation logs of the load balancer and server, use the backup module to back up the data, analyze the logs, optimize and adjust them, and use the prediction module to predict the trend of future traffic and server load conditions based on the log data, and adjust the load balancing strategy in advance to deal with emergencies;

[0070] S9. Regular maintenance and optimization: Regular system maintenance and optimization, including software updates, performance tuning, and security reinforcement, ensure that the system is always in the best condition and meets the ever-changing load requirements;

[0071] S10. Continuous improvement: Regularly evaluate the performance and stability of the system, collect user feedback and suggestions, and continuously optimize and improve the system.

[0072] A system for implementing a cluster-based load balancing method, comprising:

[0073] The server is used to form multiple servers into a cluster to jointly handle requests from clients;

[0074] The network optimization module is used to optimize the internal network topology of the cluster, reduce network delay and packet loss, and select the optimal path based on factors such as user location and network status;

[0075] The load balancer is used to distribute requests to each server in the cluster according to certain algorithms or rules to ensure that each server can get a balanced load;

[0076] The security protection module is used to enhance the security protection capability of the cluster and ensure the stable operation of the cluster;

[0077] The prediction module is used to predict future traffic trends and server load conditions, and adjust the load balancing strategy in advance to cope with emergencies;

[0078] The data processing and transmission module is used to achieve efficient data synchronization, ensure data consistency between the load balancer and the server, adopt a cache strategy, reduce unnecessary data transmission and processing, and improve the system's response speed and throughput;

[0079] The backup module is used to establish a disaster recovery backup mechanism, which can quickly restore services in the event of natural disasters, human errors and unforeseen events;

[0080] The fault monitoring module is used to design a robust health check mechanism to achieve fast and seamless failover when a faulty node is found to avoid affecting users;

[0081] The load balancer includes:

[0082] The load balancing architecture module is used to deploy a multi-level load balancing architecture to distribute traffic to different data centers or geographical locations. The load balancing system architecture includes:

[0083] Single-tier architecture: Single-tier load balancing architecture deploys the load balancer and application server on the same tier;

[0084] Two-tier architecture: The two-tier load balancing architecture deploys multiple load balancers at the front end and then distributes traffic to the application servers at the back end;

[0085] Multi-layer architecture: Multi-layer load balancing architecture distributes traffic to different data centers or geographical locations by introducing multiple layers of load balancers;

[0086] The algorithm processing module is used for intelligent load balancing algorithms based on real-time load conditions and server performance;

[0087] The load balancing adjustment module is used to dynamically adjust the load conditions and prediction results, and to cope with emergencies and load fluctuations through predictive load balancing and adaptive adjustment. The load balancing adjustment module is based on historical data and machine learning algorithms to predict future load conditions, adjust the load balancing strategy in advance, and realize adaptive adjustment of the load balancing system.

[0088] What is described above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A cluster-based load balancing implementation method, characterized in that: Here are the steps: S1. Define load balancing requirements and goals: Clarify the requirements and goals of the load balancing system; S2. Select load balancer: Select the required load balancer; S3. Deploy load balancer: Deploy a multi-level load balancing architecture through the load balancing architecture module to distribute traffic to different data centers or geographical locations; S4. Configure the server pool: Configure the server pool according to the number of servers, health checks and server weights. Optimize the internal network topology of the cluster through the network optimization module to reduce network latency and packet loss, and select the optimal path based on factors such as user location and network conditions. S5. Implement the load balancing algorithm: select the algorithm through the algorithm processing module and perform appropriate tuning through the load balancing adjustment module; S6. Implement fault-tolerant processing mechanism: Implement fault-tolerant mechanism through failover and service degradation; S7, monitoring and alarm system: the fault monitoring module runs the inspection mechanism, realizes fast and seamless failover when a faulty node is found, monitors the running status of the load balancer and backend server, and the security protection module runs to enhance the security protection capability of the cluster; S8, log recording and analysis: use the data processing and transmission module to collect and store the operation logs of the load balancer and server, use the backup module to back up the data, analyze the logs, optimize and adjust them, and use the prediction module to predict the trend of future traffic and server load conditions based on the log data, and adjust the load balancing strategy in advance to deal with emergencies; S9. Regular maintenance and optimization: Regular system maintenance and optimization, including software updates, performance tuning, and security reinforcement, ensure that the system is always in the best condition and meets the ever-changing load requirements; S10. Continuous improvement: Regularly evaluate the performance and stability of the system, collect user feedback and suggestions, and continuously optimize and improve the system.

2. The cluster-based load balancing implementation method according to claim 1, characterized in that: The requirements and goals in S1 include expected load type, load volume, fault tolerance requirements and performance indicators.

3. The cluster-based load balancing implementation method according to claim 1, characterized in that: The content of S4 also includes: Number of servers: Determine the number of servers based on load demand and performance requirements; Health check: Configure a health check mechanism to regularly check the health status of the server and promptly detect and remove faulty nodes; Server weight: Set the weights of different servers according to server performance and load conditions to achieve dynamic scheduling of the load balancer.

4. The cluster-based load balancing implementation method according to claim 1, characterized in that: The S5 load balancing algorithm includes: Polling algorithm: The polling algorithm distributes requests to each server in turn, in a cycle; Weighted Round Robin: The weighted round robin algorithm introduces a weight parameter based on the round robin algorithm, allocating requests according to the processing capacity of the server to achieve load balancing; Least Connection Algorithm: The least connection algorithm distributes requests to the server with the least current connections to ensure a more balanced load. Hash algorithm: The hash algorithm calculates the hash value based on the characteristics of the request, and then distributes the request to the corresponding server to ensure that the same request is always sent to the same server; Response time weighted algorithm: Dynamically adjust the weight according to the server's response time to achieve more intelligent load balancing.

5. The cluster-based load balancing implementation method according to claim 1, characterized in that: The contents of the fault tolerance mechanism in S6 are as follows: Failover: When the target server fails, the traffic is transferred to other servers; Service degradation: Temporarily lower the priority of a service when the load is too high or a failure occurs.

6. A system for implementing the cluster-based load balancing implementation method according to any one of claims 1 to 5, characterized in that: include: The server is used to form multiple servers into a cluster to jointly handle requests from clients; The network optimization module is used to optimize the internal network topology of the cluster, reduce network delay and packet loss, and select the optimal path based on factors such as user location and network status; The load balancer is used to distribute requests to each server in the cluster according to certain algorithms or rules to ensure that each server can get a balanced load; The security protection module is used to enhance the security protection capability of the cluster and ensure the stable operation of the cluster; The prediction module is used to predict future traffic trends and server load conditions, and adjust the load balancing strategy in advance to cope with emergencies; The data processing and transmission module is used to achieve efficient data synchronization, ensure data consistency between the load balancer and the server, adopt a cache strategy, reduce unnecessary data transmission and processing, and improve the system's response speed and throughput; The backup module is used to establish a disaster recovery backup mechanism, which can quickly restore services in the event of natural disasters, human errors and unforeseen events; The fault monitoring module is used to design a robust health check mechanism to achieve fast and seamless failover when a faulty node is found to avoid affecting users.

7. The system for implementing a cluster-based load balancing method according to claim 6, characterized in that: The load balancer includes: The load balancing architecture module is used to deploy a multi-level load balancing architecture to distribute traffic to different data centers or geographical locations. The load balancing system architecture includes: Single-tier architecture: Single-tier load balancing architecture deploys the load balancer and application server on the same tier; Two-tier architecture: The two-tier load balancing architecture deploys multiple load balancers at the front end and then distributes traffic to the application servers at the back end; Multi-layer architecture: Multi-layer load balancing architecture distributes traffic to different data centers or geographical locations by introducing multiple layers of load balancers; The algorithm processing module is used for intelligent load balancing algorithms based on real-time load conditions and server performance; The load balancing adjustment module is used to dynamically adjust the load conditions and prediction results, and to provide predictive load balancing and adaptive adjustments to cope with emergencies and load fluctuations.