Global intelligent load balancing system

Abstract

The invention discloses a global intelligent load balancing system, which comprises a load monitoring layer, and is characterized in that the load monitoring layer collects load information of each storage node in CPU utilization rate, memory usage, disk I / O speed and network bandwidth in real time through a monitoring technology and means; the strategy decision-making layer serves as a core brain of the system and receives the data from the load monitoring layer; the method has the beneficial effects that by constructing a real-time and dynamic load monitoring system, the load information of each storage node in a cluster in a plurality of key dimensions can be accurately obtained; based on the real-time data, accurate and efficient load distribution is realized, and utilization of system resources is greatly optimized; with the help of advanced prediction and self-adaptive mechanisms, sudden access traffic changes can be responded rapidly; by intelligently adjusting a load distribution strategy, the continuity and stability of services in a high-concurrency scene are ensured, and the user experience is remarkably improved.

Description

Technical Field

[0001] The invention belongs to the technical field of load balancing, and in particular relates to a global intelligent load balancing system. Background Art

[0002] In today's era of rapid development of information technology, large-scale cluster storage products play a vital role in many key areas such as data centers and cloud computing. With the explosive growth of data volume and the increasing complexity of business needs, efficient and reliable storage systems have become the cornerstone of supporting various applications.

[0003] One of the core challenges of large-scale cluster storage systems is how to achieve balanced load distribution. This field involves many key technologies, and distributed storage architecture is an important component. By distributing data on multiple nodes, it not only enhances data reliability, but also significantly improves access performance. By dividing data into multiple fragments and creating replicas on different nodes, it effectively improves data availability and concurrent access capabilities. This data sharding and replica strategy provides a guarantee for the stable operation of the system.

[0004] In existing load balancing technologies, solutions based on static configuration or simple polling algorithms are more common. Taking request distribution based on pre-set node weights as an example, this method achieves preliminary load distribution to a certain extent. However, this method has obvious limitations.

[0005] First, the existing technology cannot perceive the load status of the node in real time and accurately, which means that the system cannot timely understand the actual situation of each node in terms of key indicators such as CPU utilization, memory usage, and disk I / O. As a result, some nodes may experience performance degradation due to excessive load, while other nodes are idle, seriously affecting the overall performance and resource utilization efficiency of the system; secondly, in the face of sudden large-scale access traffic, the existing load balancing mechanism lacks the ability to adjust quickly and effectively, which may cause significant delays in service responses, and even service interruptions in extreme cases, and cannot meet users' requirements for high availability and stability of the system; thirdly, static configuration and simple polling algorithms are essentially unable to fully consider the performance differences between nodes and the dynamic changes of storage resources, making the system inflexible and optimized in resource allocation, unable to fully tap the potential of each node, thereby limiting the overall performance improvement of the system. Summary of the invention

[0006] The purpose of the present invention is to provide a global intelligent load balancing system that can accurately obtain the load information of each storage node in the cluster in multiple key dimensions, ensure the continuity and stability of services in high concurrency scenarios, and significantly improve the user experience.

[0007] To achieve the above object, the present invention provides the following technical solution: a global intelligent load balancing system, comprising

[0008] The load monitoring layer collects the load information of each storage node in terms of CPU utilization, memory usage, disk I / O speed, and network bandwidth in real time through monitoring technologies and means;

[0009] The strategy decision layer, as the core brain of the system, receives data from the load monitoring layer and uses complex strategy models based on machine learning and intelligent optimization algorithms to perform in-depth analysis and calculations to develop the most optimized load distribution plan;

[0010] The load distribution layer distributes the received requests to the corresponding storage nodes accurately according to the precise solution generated by the policy decision layer to ensure that the requests are processed efficiently.

[0011] As a preferred technical solution of the present invention, the intelligent optimization algorithm includes a genetic algorithm and a particle swarm optimization algorithm.

[0012] As a preferred technical solution of the present invention, it also includes a monitoring process, which adopts a combination of periodic monitoring and real-time event triggering.

[0013] As a preferred technical solution of the present invention, the policy decision layer accurately calculates the best target node after comprehensively considering node performance, historical load data, current resource availability and preset policy rules.

[0014] As a preferred technical solution of the present invention, the load distribution layer responds to the instruction of the decision layer and forwards the request to the selected target node for processing.

[0015] As a preferred technical solution of the present invention, it also includes the setting of a monitoring period, and the monitoring period is adjusted between 1-10 seconds.

[0016] As a preferred technical solution of the present invention, the weight coefficient in the strategy decision layer is another important parameter, and the determination of the coefficient is based on an in-depth analysis of the actual system and training of a large amount of historical data.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] By building a real-time and dynamic load monitoring system, we can accurately obtain the load information of each storage node in the cluster in multiple key dimensions. Based on these real-time data, we can achieve accurate and efficient load distribution, greatly optimizing the utilization of system resources.

[0019] With advanced prediction and adaptive mechanisms, it can quickly respond to sudden changes in access traffic; by intelligently adjusting the load distribution strategy, it ensures the continuity and stability of services in high-concurrency scenarios, significantly improving user experience;

[0020] By fully considering the performance differences of nodes and the dynamic changes of storage resources, and using intelligent analysis and decision-making algorithms, the optimal allocation of resources is achieved, which not only improves the overall performance and efficiency of the system, but also provides strong technical support for meeting the ever-growing and changing business needs in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a system principle diagram of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Example 1

[0024] See also Figure 1 , which is the first embodiment of the present invention, and provides a global intelligent load balancing system, which builds a layered architecture, including a load monitoring layer, a policy decision layer, and a load distribution layer; this architecture design is intended to achieve clear division of functions and efficient collaboration;

[0025] The load monitoring layer plays a vital role. It uses advanced monitoring technologies and methods to collect real-time load information of each storage node in multiple dimensions, such as CPU utilization, memory usage, disk I / O speed, and network bandwidth. The accuracy and timeliness of this information are crucial for subsequent decision-making and allocation.

[0026] As the core brain of the system, the policy decision layer receives rich data from the load monitoring layer and uses complex policy models based on machine learning and intelligent optimization algorithms for in-depth analysis and calculation. These algorithms include but are not limited to genetic algorithms and particle swarm optimization algorithms, which can comprehensively consider the performance, load history and current resource status of the nodes, thereby formulating the most optimized load distribution plan.

[0027] The load distribution layer distributes the received requests to the corresponding storage nodes accurately according to the precise solutions generated by the policy decision layer, ensuring that the requests are processed efficiently.

[0028] Load monitoring mechanism:

[0029] In order to comprehensively and accurately evaluate the load status of the node, the present invention adopts a variety of monitoring indicators. In addition to the common CPU utilization, memory usage and disk I / O speed, key indicators such as network bandwidth are also introduced. Through these multi-dimensional indicators, a comprehensive and accurate node load portrait can be constructed;

[0030] The monitoring process adopts a combination of periodic monitoring and real-time event triggering. Periodic monitoring ensures regular checks on node load status, while real-time event triggering can respond quickly and update load information in a timely manner when node load changes significantly or abnormal conditions occur. This combination effectively ensures the timeliness and accuracy of load information, providing a solid data foundation for subsequent decision-making.

[0031] Strategy decision algorithm:

[0032] The present invention innovatively introduces algorithms based on machine learning and intelligent optimization, such as genetic algorithms and particle swarm optimization algorithms, which can automatically learn and adapt to the performance characteristics of nodes, load change trends, and the laws of dynamic resource adjustment in complex system environments;

[0033] Through training and analysis of large amounts of historical data, the algorithm can intelligently predict future load demands and develop a forward-looking and adaptive load distribution strategy based on current node status and resource distribution. This strategy can not only achieve optimized distribution in conventional business scenarios, but can also quickly adjust in sudden traffic and abnormal situations to ensure system stability and performance.

[0034] Workflow:

[0035] When a new request arrives at the system, the load monitoring layer starts immediately and quickly collects detailed load information of each storage node; this information includes but is not limited to CPU load, memory usage, disk I / O status, and network communication status;

[0036] The policy decision layer then receives these real-time load data and uses advanced algorithms and models to quickly calculate and analyze them. After comprehensively considering node performance, historical load data, current resource availability, and preset policy rules, it accurately calculates the best target node.

[0037] The load distribution layer responds quickly to the instructions of the decision-making layer and efficiently and accurately forwards the request to the selected target node for processing. The entire process achieves millisecond-level response and processing, ensuring the fast and stable operation of the system in high-concurrency scenarios.

[0038] Parameter selection:

[0039] The setting of the monitoring cycle is a key parameter selection. The monitoring cycle is usually flexibly adjusted between 1 and 10 seconds according to the system scale, number of nodes and business load characteristics. For large-scale systems with frequent load changes, a shorter monitoring cycle can provide more timely information. For relatively stable system environments, appropriately extending the monitoring cycle can reduce system overhead without affecting the effect of load balancing.

[0040] The weight coefficient in the strategy decision algorithm is another important parameter. The determination of these coefficients needs to be based on in-depth analysis of the actual system and training with a large amount of historical data. By continuously optimizing and adjusting these coefficients, the algorithm can better adapt to different system scenarios and business needs, thereby achieving a more accurate and efficient load distribution strategy.

[0041] The innovative design of the real-time load monitoring mechanism directly solves the core problem that the existing technology cannot timely and accurately perceive the load status; by providing comprehensive and real-time load information, it provides key data support for accurate load distribution, thereby avoiding waste of resources and degradation of system performance;

[0042] The application of intelligent optimization algorithms in policy decision-making effectively addresses the shortcomings of existing technologies in quickly responding to sudden traffic and adapting to dynamic changes in resources. By learning and adjusting policies in real time, it ensures that the system can maintain stable and efficient operation in various complex scenarios, significantly improving the system's service quality and availability.

[0043] The layered architecture design provides an effective solution to the system's scalability and maintainability issues; clear module division and independent function implementation enable the system to more easily cope with changing business needs and technical environments, reducing the cost and risk of system upgrades and maintenance.

[0044] Example 2

[0045] This is the second embodiment of the present invention, which is based on the previous embodiment, except that:

[0046] Replacement of load monitoring indicators: In addition to the core indicators mentioned above, such as CPU utilization, memory usage, disk I / O speed, and network bandwidth, you can also consider introducing auxiliary indicators such as node temperature and energy consumption to further improve the load evaluation system. For example, in a high-performance computing environment, a node's temperature is too high, which may indicate that its performance is about to be limited. Including temperature indicators in the monitoring range can provide early warning and take corresponding load adjustment measures. In addition, monitoring of energy consumption indicators is of great significance for scenarios such as data centers that focus on energy efficiency. By optimizing load distribution and reducing the overall energy consumption of the system, green computing can be achieved.

[0047] Alternatives to strategy decision algorithms: In addition to mainstream intelligent optimization algorithms such as genetic algorithms and particle swarm optimization algorithms, other advanced algorithms such as simulated annealing algorithms and ant colony algorithms can also be explored to formulate load distribution strategies; simulated annealing algorithms have good global search capabilities when dealing with complex optimization problems and can avoid falling into local optimal solutions; ant colony algorithms perform well in solving distributed optimization problems and are suitable for load balancing decisions in large-scale cluster storage systems.

[0048] Alternative system architecture: Consider adopting a distributed microservice architecture to deploy load monitoring, policy decision-making, and load distribution functions as independent microservices. Through service registration and discovery mechanisms, each microservice can communicate and collaborate dynamically. This architecture approach has higher flexibility and scalability, and can better adapt to the rapid deployment and elastic scaling requirements in a cloud-native environment. For example, when the system load suddenly increases, more load distribution microservice instances can be quickly started to improve the system's processing capabilities.

[0049] Although the embodiments of the present invention have been shown and described, as detailed above, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A global intelligent load balancing system, characterized in that: include The load monitoring layer collects the load information of each storage node in terms of CPU utilization, memory usage, disk I / O speed, and network bandwidth in real time through monitoring technologies and means; The strategy decision layer, as the core brain of the system, receives data from the load monitoring layer and uses complex strategy models based on machine learning and intelligent optimization algorithms to perform in-depth analysis and calculations to develop the most optimized load distribution plan; The load distribution layer distributes the received requests to the corresponding storage nodes accurately according to the precise solution generated by the policy decision layer to ensure that the requests are processed efficiently.

2. A global intelligent load balancing system according to claim 1, characterized in that: The intelligent optimization algorithm includes a genetic algorithm and a particle swarm optimization algorithm.

3. A global intelligent load balancing system according to claim 1, characterized in that: It also includes a monitoring process, which uses a combination of periodic monitoring and real-time event triggering.

4. A global intelligent load balancing system according to claim 1, characterized in that: The policy decision layer accurately calculates the best target node after comprehensively considering node performance, historical load data, current resource availability, and preset policy rules.

5. A global intelligent load balancing system according to claim 4, characterized in that: The load distribution layer responds to the instruction of the decision layer and forwards the request to the selected target node for processing.

6. A global intelligent load balancing system according to claim 1, characterized in that: It also includes the setting of the monitoring period, which can be adjusted between 1-10 seconds.

7. A global intelligent load balancing system according to claim 1, characterized in that: The weight coefficient in the strategy decision layer is another important parameter, and the determination of the coefficient is based on an in-depth analysis of the actual system and training with a large amount of historical data.

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