Dynamic load balancing system and method for multi-cluster intelligent capacity expansion

Abstract

The invention provides a dynamic load balancing system and method for multi-cluster intelligent capacity expansion, and relates to the technical field of dynamic load balancing. The load saturation prediction module is used for performing load saturation prediction to obtain a plurality of predicted load saturations; the capacity expansion instruction analysis module is used for inputting a cluster capacity expansion judging device to obtain a cluster capacity expansion instruction; the global capacity expansion module is used for activating a global capacity expansion analysis channel to carry out global capacity expansion when a global capacity expansion instruction is given, so that a first multi-cluster optimization architecture is obtained; the local capacity expansion module is used for activating a local capacity expansion decision-making model to carry out local capacity expansion when a local capacity expansion instruction is given, so that a second multi-cluster optimization framework is obtained; and the load balancing management and control module is used for carrying out load balancing management and control. The technical problems that in the prior art, adaptive capacity expansion cannot be carried out on multiple clusters according to the load state, so that load distribution is unbalanced, and service response is delayed are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic load balancing, and in particular to a dynamic load balancing system and method for multi-cluster intelligent expansion. Background Art

[0002] In a multi-cluster environment, load distribution is often unbalanced. Some clusters may be overloaded, while other cluster resources are idle. This will lead to resource waste and response delays for some services, which will reduce the overall performance of the system and affect user experience. However, dynamic load balancing and capacity expansion decisions involve multiple factors, including current load, future load forecasts, resource constraints, etc. How to accurately evaluate these factors and make quick decisions is a major challenge; in addition, business needs may change very quickly, and load changes need to be perceived and responded to in a short period of time. Traditional load balancing methods often lack sufficient real-time performance and cannot respond to sudden traffic in a timely manner, resulting in a poor user experience. Summary of the invention

[0003] The present application provides a dynamic load balancing system and method for intelligent expansion of multiple clusters, aiming to solve the technical problem in the prior art that multiple clusters cannot be adaptively expanded according to load status, resulting in unbalanced load distribution and delayed service response.

[0004] The first aspect disclosed in the present application provides a dynamic load balancing system for multi-cluster intelligent expansion, the system comprising: a load feature collection module, the load feature collection module is used to collect load features according to a multi-cluster architecture to obtain a load feature data set, wherein the multi-cluster architecture includes multiple clusters, each cluster includes a number of computing nodes; a load saturation prediction module, the load saturation prediction module is used to predict the load saturation of the multiple clusters based on the load feature data set based on a predetermined future time zone window, and obtain multiple predicted load saturations; an expansion instruction parsing module, the expansion instruction parsing module is used to input the multiple predicted load saturations into a cluster expansion determiner to obtain a cluster expansion instruction, wherein the The cluster expansion instruction is a global expansion instruction or a local expansion instruction; a global expansion module, wherein the global expansion module is used to activate a global expansion resolution channel when the cluster expansion instruction is a global expansion instruction, and globally expand the multi-cluster architecture according to the global expansion resolution channel to obtain a multi-cluster optimized first architecture; a local expansion module, wherein the local expansion module is used to activate a local expansion decision model when the cluster expansion instruction is a local expansion instruction, and locally expand the multi-cluster architecture according to the local expansion decision model to obtain a multi-cluster optimized second architecture; a load balancing control module, wherein the load balancing control module is used to perform load balancing control according to the multi-cluster optimized first architecture or the multi-cluster optimized second architecture.

[0005] In a second aspect disclosed in the present application, a dynamic load balancing method for multi-cluster intelligent expansion is provided, the method is implemented by the above-mentioned dynamic load balancing system for multi-cluster intelligent expansion, the method comprising: collecting load characteristics according to a multi-cluster architecture to obtain a load characteristic data set, wherein the multi-cluster architecture comprises a plurality of clusters, each cluster comprises a plurality of computing nodes; based on a predetermined future time zone window, predicting load saturation of the plurality of clusters according to the load characteristic data set to obtain a plurality of predicted load saturations; inputting the plurality of predicted load saturations into a cluster expansion determiner to obtain a cluster expansion determiner; capacity expansion instruction, wherein the cluster capacity expansion instruction is a global capacity expansion instruction or a local capacity expansion instruction; when the cluster capacity expansion instruction is a global capacity expansion instruction, a global capacity expansion resolution channel is activated, and the multi-cluster architecture is globally expanded according to the global capacity expansion resolution channel to obtain a multi-cluster optimized first architecture; when the cluster capacity expansion instruction is a local capacity expansion instruction, a local capacity expansion decision model is activated, and the multi-cluster architecture is locally expanded according to the local capacity expansion decision model to obtain a multi-cluster optimized second architecture; load balancing management and control is performed according to the multi-cluster optimized first architecture or the multi-cluster optimized second architecture.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] By collecting the load characteristics of the multi-cluster architecture in real time, the current load status of each cluster and computing node can be mastered. This real-time monitoring can ensure timely response to load changes, and lay a data foundation for subsequent load prediction and expansion decisions, improving the accuracy of load management; based on the load characteristic data set, the load saturation of each cluster in the predetermined future time zone window is predicted, and multiple predicted load saturations are obtained, which improves the responsiveness of the system and enables timely expansion decisions under high load conditions to avoid performance degradation or service interruption caused by sudden load increases; based on the predicted results of load saturation, the cluster expansion determiner is used to decide whether to perform global expansion Whether it is local expansion or global expansion, such flexibility allows the system to make appropriate resource adjustments based on actual load requirements; during global expansion, the resource capacity of multiple clusters is comprehensively improved to form a multi-cluster optimization first architecture; during local expansion, the focus is on solving the load problem of a specific cluster to form a multi-cluster optimization second architecture. This dual expansion mechanism improves the adaptability of the system; after the expansion is completed, load balancing management and control are performed based on the new optimization architecture to ensure that the load of all clusters is reasonably distributed to avoid overload or idleness of a cluster. Through dynamic load balancing, the overall performance and resource utilization can be improved to ensure that each cluster can maintain a stable service quality during peak load periods.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of the structure of a dynamic load balancing system with multi-cluster intelligent expansion provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of the flow of a dynamic load balancing method for multi-cluster intelligent expansion provided in an embodiment of the present application.

[0011] Explanation of the accompanying drawings: load feature collection module 10, load saturation prediction module 20, capacity expansion instruction parsing module 30, global capacity expansion module 40, local capacity expansion module 50, load balancing management and control module 60. DETAILED DESCRIPTION

[0012] The embodiments of the present application provide a dynamic load balancing system and method for intelligent expansion of multiple clusters, thereby solving the technical problem in the prior art that multiple clusters cannot be adaptively expanded according to load status, resulting in unbalanced load distribution and delayed service response.

[0013] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a dynamic load balancing system for multi-cluster intelligent expansion, and the system includes:

[0015] A load feature acquisition module 10, the load feature acquisition module 10 is used to collect load features according to a multi-cluster architecture to obtain a load feature data set, wherein the multi-cluster architecture includes multiple clusters, each cluster includes a number of computing nodes; a load saturation prediction module 20, the load saturation prediction module 20 is used to predict the load saturation of the multiple clusters according to the load feature data set based on a predetermined future time zone window to obtain multiple predicted load saturations; an expansion instruction parsing module 30, the expansion instruction parsing module 30 is used to input the multiple predicted load saturations into a cluster expansion determiner to obtain a cluster expansion instruction, wherein the cluster expansion instruction is a global expansion instruction or a local expansion instruction. Command; a global expansion module 40, the global expansion module 40 is used to activate the global expansion resolution channel when the cluster expansion instruction is a global expansion instruction, and globally expand the multi-cluster architecture according to the global expansion resolution channel to obtain a multi-cluster optimized first architecture; a local expansion module 50, the local expansion module 50 is used to activate the local expansion decision model when the cluster expansion instruction is a local expansion instruction, and locally expand the multi-cluster architecture according to the local expansion decision model to obtain a multi-cluster optimized second architecture; a load balancing control module 60, the load balancing control module 60 is used to perform load balancing control according to the multi-cluster optimized first architecture or the multi-cluster optimized second architecture.

[0016] Furthermore, the load saturation prediction module is used to perform load saturation prediction on the multiple clusters based on the load characteristic data set based on the predetermined future time zone window to obtain multiple predicted load saturations, including:

[0017] Based on the load feature data set, feature identification is performed to obtain multiple current loads and multiple constrained loads corresponding to the multiple clusters; based on the predetermined future time zone window, load change trends of the multiple clusters are predicted according to the multiple current loads to obtain multiple predicted loads; based on the multiple predicted loads and the multiple constrained loads, the multiple predicted load saturations are calculated.

[0018] Furthermore, the capacity expansion instruction parsing module is used to input the multiple predicted load saturations into the cluster capacity expansion determiner to obtain a cluster capacity expansion instruction, wherein the cluster capacity expansion instruction is a global capacity expansion instruction or a local capacity expansion instruction, including:

[0019] Activate the local capacity expansion determiner within the cluster capacity expansion determiner; input the multiple predicted load saturations into the local capacity expansion determiner, wherein the local capacity expansion determiner includes a load saturation threshold; determine whether the multiple predicted load saturations are greater than the load saturation threshold; if any one of the multiple predicted load saturations is greater than the load saturation threshold, the local capacity expansion determiner outputs the local capacity expansion instruction.

[0020] Furthermore, the local capacity expansion determiner outputs the local capacity expansion instruction, including:

[0021] The predicted load saturation greater than the load saturation threshold is recorded as abnormal load saturation; the abnormal load saturation and the multiple predicted load saturations are calculated to obtain the abnormal saturation quantity ratio; it is determined whether the abnormal saturation quantity ratio is greater than a predetermined proportion threshold; if the abnormal saturation quantity ratio is greater than the predetermined proportion threshold, the global expansion determiner in the cluster expansion determiner is activated; the centralized value of the multiple predicted load saturations is calculated to obtain the global load saturation, and the global load saturation is input into the global expansion determiner, wherein the global expansion determiner includes a global load saturation threshold; it is determined whether the global load saturation is greater than the global load saturation threshold; if the global load saturation is greater than the global load saturation threshold, the global expansion determiner outputs the global expansion instruction and prohibits the local expansion instruction.

[0022] Furthermore, the global expansion module is used to activate a global expansion resolution channel when the cluster expansion instruction is a global expansion instruction, and perform global expansion on the multi-cluster architecture according to the global expansion resolution channel to obtain a multi-cluster optimized first architecture, including:

[0023] A global expansion feature analysis is performed according to the multi-cluster architecture to obtain a global expansion capacity; the global expansion analysis channel includes a global expansion decision model and a global expansion fitness analysis model; based on the global expansion capacity, multiple global expansion decisions are obtained according to the global expansion decision model; based on the global expansion fitness analysis model, the multiple global expansion decisions are iteratively optimized to obtain a global expansion strategy; the multi-cluster architecture is globally expanded according to the global expansion strategy to obtain the multi-cluster optimized first architecture.

[0024] Furthermore, the global expansion fitness analytical model includes a global expansion fitness analytical function, and the global expansion fitness analytical function is:

[0025]

[0026] Among them, GSA represents the global expansion fitness, exp represents the exponential function with the natural constant e as the base, csw and ccw represent the predetermined fitness analysis weight conditions, scx represents the predicted global load balance, sxo represents the expected global load balance, scc represents the predicted global expansion cost, and sco represents the expected global expansion cost.

[0027] Furthermore, the local expansion module is used to activate the local expansion decision model when the cluster expansion instruction is a local expansion instruction, and perform local expansion on the multi-cluster architecture according to the local expansion decision model to obtain a multi-cluster optimized second architecture, including:

[0028] Perform local expansion feature analysis according to the multi-cluster architecture to determine the local expansion cluster and the local expansion capacity; collect configuration parameters of the local expansion cluster to obtain cluster configuration feature data; input the cluster configuration feature data and the local expansion capacity into the local expansion decision model to obtain a local expansion decision; based on the multi-cluster architecture, expand the local expansion cluster according to the local expansion decision to obtain the multi-cluster optimized second architecture.

[0029] Through the detailed description of the dynamic load balancing method for multi-cluster intelligent expansion in the following description, those skilled in the art can clearly understand the dynamic load balancing system for multi-cluster intelligent expansion in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0030] Embodiment 2 is based on the same inventive concept as the multi-cluster intelligent expansion dynamic load balancing system in the previous embodiment. Figure 2 As shown, the embodiment of the present application provides a dynamic load balancing method for multi-cluster intelligent expansion, the method comprising:

[0031] Load characteristics are collected according to a multi-cluster architecture to obtain a load characteristic data set, wherein the multi-cluster architecture includes a plurality of clusters, and each cluster includes a plurality of computing nodes.

[0032] The multi-cluster architecture includes multiple clusters, each of which is composed of several computing nodes. These clusters can be distributed in different physical or virtual environments and collaborate with each other to handle the overall load of the application. Different clusters have different hardware configurations, resource limitations, and load characteristics. The computing nodes in each cluster can be physical servers, virtual machines, containers, etc. They perform specific computing tasks, and the computing nodes communicate and coordinate through the network.

[0033] Load characteristics are key performance indicators of the system during operation, including but not limited to CPU usage, memory usage, disk I / O, network bandwidth, process queue length, etc. Each cluster and node will have different load characteristics at different time points. The monitoring system automatically collects cluster and node load data. The specific sampling frequency should be determined according to the actual needs and real-time requirements of the system. For multi-cluster architectures, the load data between different clusters is aggregated and integrated to obtain a load characteristic data set, which is used for subsequent load prediction and expansion decisions.

[0034] Based on a predetermined future time zone window, load saturation prediction is performed on the multiple clusters according to the load characteristic data set to obtain multiple predicted load saturations.

[0035] The scheduled future time zone window is a time period prediction window used to estimate load changes in the future. The window can be flexibly set according to business needs, such as 5 minutes, 30 minutes, 1 hour, etc. The selection of the prediction window affects the real-time and accuracy of the expansion decision. A window that is too large may cause a response delay, and a window that is too small may cause the expansion to be too frequent.

[0036] Load saturation is the ratio of system load to resource upper limit, expressed as a percentage. For example, when CPU usage exceeds 80%, the system may be in a saturated state, which means that the load is close to the upper limit that the system can bear. Extract the current load (such as CPU usage, memory usage) and constraints (such as node resource upper limit) from the load feature data set, predict the load change trend in the predetermined future time zone window based on historical data, obtain the predicted load, compare the predicted load with the node resource upper limit, calculate the future load saturation, and generate multiple predicted load saturations for multiple clusters.

[0037] Furthermore, the step of performing load saturation prediction on the multiple clusters based on the predetermined future time zone window and the load characteristic data set to obtain multiple predicted load saturations includes:

[0038] Based on the load feature data set, feature identification is performed to obtain multiple current loads and multiple constrained loads corresponding to the multiple clusters; based on the predetermined future time zone window, load change trends of the multiple clusters are predicted according to the multiple current loads to obtain multiple predicted loads; based on the multiple predicted loads and the multiple constrained loads, the multiple predicted load saturations are calculated.

[0039] The current load and constrained load of each cluster are extracted from the load feature data set. The current load reflects the resource usage of the cluster at the current moment. Common loads include CPU usage, memory usage, network usage, etc.; the constrained load refers to the maximum load capacity that a cluster or node can carry. For example, the constrained load of CPU usage is the total number of CPU cores, and the constrained load of memory is the total memory capacity. Each cluster has its own resource limitations, so the constrained loads of different clusters may be different.

[0040] In order to predict future load change trends, a variety of methods can be used, such as time series analysis, regression models, machine learning models, etc. For example, a linear regression model can be used to perform linear fitting predictions based on the change trends of historical load data. By analyzing the historical data and trends of the current load, the load of each cluster in the predetermined future time zone window can be predicted to obtain multiple predicted loads.

[0041] The predicted load saturation indicates the load occupancy of the cluster in the future time zone, reflecting the intensity of the cluster load. It is calculated by dividing the predicted load by the constrained load. For each cluster, the predicted load is compared with the constrained load of the cluster to calculate the predicted load saturation. If the predicted load saturation of a cluster reaches the preset threshold, it means that the resources of the cluster are close to saturation, and capacity expansion or other load management measures need to be considered.

[0042] The multiple predicted load saturations are input into a cluster expansion determiner to obtain a cluster expansion instruction, wherein the cluster expansion instruction is a global expansion instruction or a local expansion instruction.

[0043] The cluster expansion determiner includes a local expansion determiner and a global expansion determiner, which are used to determine whether to perform an expansion operation and the type of expansion, including local expansion or global expansion. The local expansion determiner is used to determine whether the load of certain clusters has reached a state requiring local expansion; when the load saturation of multiple clusters exceeds the threshold at the same time, the global expansion determiner is activated to determine whether the entire multi-cluster architecture needs to be globally expanded.

[0044] According to the output of the cluster expansion determiner, if the global load saturation exceeds the global threshold, a global expansion instruction will be issued, local expansion instructions will be disabled, and the entire multi-cluster architecture will be expanded to balance the overall load; if only the load of a certain cluster exceeds the threshold and does not meet the conditions for global expansion, a local expansion instruction will be issued to expand only the specific cluster. This decision-making mechanism ensures that the system's resources can be dynamically expanded to cope with load changes.

[0045] Furthermore, the inputting the multiple predicted load saturations into the cluster expansion determiner to obtain a cluster expansion instruction, wherein the cluster expansion instruction is a global expansion instruction or a local expansion instruction, includes:

[0046] Activate the local capacity expansion determiner within the cluster capacity expansion determiner; input the multiple predicted load saturations into the local capacity expansion determiner, wherein the local capacity expansion determiner includes a load saturation threshold; determine whether the multiple predicted load saturations are greater than the load saturation threshold; if any one of the multiple predicted load saturations is greater than the load saturation threshold, the local capacity expansion determiner outputs the local capacity expansion instruction.

[0047] Activate the local expansion determiner. The local expansion determiner is part of the cluster expansion determiner. When making a cluster expansion determination, the local expansion determiner first analyzes the load data of each cluster and prepares to make a determination on the local expansion demand.

[0048] The multiple predicted load saturations obtained are input into the local expansion determiner. The local expansion determiner includes a load saturation threshold, which is a pre-set key parameter representing the maximum load that the cluster can withstand. For example, a load saturation threshold can be set to 80%, that is, when the load saturation exceeds 80%, the resources of the cluster begin to approach saturation.

[0049] Check the predicted load saturation of each cluster one by one to determine whether these predicted load saturations are greater than the set load saturation threshold. For example, if the predicted load saturation of a cluster is 85% and the threshold is 80%, then the load saturation of the cluster is determined to be too high. If the predicted load saturation is greater than the threshold, it is considered that the cluster may soon reach saturation and capacity expansion is needed to ensure stable operation of the system; if the predicted load saturation is less than the threshold, it is considered that the cluster is within the tolerable range and does not need to be expanded for the time being.

[0050] When the predicted load saturation of any cluster is greater than the load saturation threshold, the local expansion determiner will make a decision on local expansion and issue a local expansion instruction to ensure that resources of the corresponding cluster are expanded in a timely manner.

[0051] Furthermore, the local capacity expansion determiner outputs the local capacity expansion instruction, including:

[0052] The predicted load saturation greater than the load saturation threshold is recorded as abnormal load saturation; the abnormal load saturation and the multiple predicted load saturations are calculated to obtain the abnormal saturation quantity ratio; it is determined whether the abnormal saturation quantity ratio is greater than a predetermined proportion threshold; if the abnormal saturation quantity ratio is greater than the predetermined proportion threshold, the global expansion determiner in the cluster expansion determiner is activated; the centralized value of the multiple predicted load saturations is calculated to obtain the global load saturation, and the global load saturation is input into the global expansion determiner, wherein the global expansion determiner includes a global load saturation threshold; it is determined whether the global load saturation is greater than the global load saturation threshold; if the global load saturation is greater than the global load saturation threshold, the global expansion determiner outputs the global expansion instruction and prohibits the local expansion instruction.

[0053] All predicted load saturations greater than the preset load saturation threshold are defined as abnormal load saturations. These load saturations reflect that the cluster load exceeds the safe range, which means that the resource pressure of these clusters is high, which may lead to performance degradation or even service interruption.

[0054] Count the number of abnormal load saturations and divide it by the number of predicted load saturations to calculate the ratio of abnormal saturations. This ratio reflects how many clusters have loads exceeding the saturation threshold among all clusters. If this ratio is too high, it means that the overall system load pressure is high and local expansion may not be able to solve the problem. You need to consider global expansion.

[0055] The predetermined proportion threshold is a pre-set threshold used to determine whether global expansion is required. This value can be set according to the system's load bearing capacity and business needs, such as 30% or 40%. If the proportion of abnormal saturation is less than the predetermined threshold, it is considered that local expansion can solve the problem; if the proportion is greater than the threshold, it means that the load pressure is too large and global expansion is required.

[0056] When the proportion of abnormal saturation is greater than the predetermined proportion threshold, it is considered that the global load pressure is large and local expansion cannot completely solve the problem. Therefore, the global expansion determiner in the cluster expansion determiner is activated to determine whether the entire system needs to be expanded.

[0057] The predicted load saturation of multiple clusters is calculated as a centralized value. For example, the average of multiple predicted load saturations is calculated to reflect the average level of the overall load to obtain the global load saturation, which is the overall load situation of the entire system and reflects the overall resource utilization of the system. It is the centralized value of the load saturation of multiple clusters and is used to evaluate the load pressure of the entire system in the future time zone.

[0058] The global load saturation is input into the global capacity expansion determiner, wherein the global capacity expansion determiner includes a global load saturation threshold, which is a preset threshold for evaluating the overall capacity expansion demand, and indicates the load pressure limit that the system can withstand at the global level. For example, the global load saturation threshold is set to 80%. If the global load saturation exceeds this threshold, it means that the overall system resources are insufficient, which may affect performance or cause instability.

[0059] The calculated global load saturation is compared with the global load saturation threshold. If the global load saturation is lower than or equal to the threshold, it means that the system as a whole is still at a safe load level. The load problem of the local cluster can continue to be solved by local expansion, and global expansion is not required.

[0060] On the contrary, if the global load saturation is greater than the threshold, it is judged that the load pressure on the entire multi-cluster architecture is too large and cannot be alleviated by local expansion alone. Global expansion is required. In this case, the global expansion determiner outputs a global expansion instruction. This instruction requires the system to expand resources at the overall architecture level, including increasing computing resources in multiple clusters, or adjusting resource allocation between clusters to balance the overall load of the system.

[0061] When the cluster expansion instruction is a global expansion instruction, a global expansion parsing channel is activated, and the multi-cluster architecture is globally expanded according to the global expansion parsing channel to obtain a multi-cluster optimized first architecture.

[0062] When the cluster expansion instruction is a global expansion instruction, the global expansion analysis channel is activated. The global expansion analysis channel is a logic module responsible for global analysis of the load of the entire multi-cluster architecture and generating expansion strategies, including a global expansion decision model and a global expansion fitness analysis model. The global expansion decision model generates a global expansion decision plan based on the current and predicted load conditions; the global expansion fitness analysis model evaluates the fitness of each expansion decision, and continuously optimizes the expansion strategy through iterative optimization, and finally generates the optimal expansion plan.

[0063] According to the optimal expansion plan, the entire multi-cluster architecture is globally expanded, for example, additional clusters are added. After the expansion is completed, the optimized multi-cluster optimized first architecture is obtained, that is, the state after the global expansion.

[0064] Furthermore, when the cluster expansion instruction is a global expansion instruction, a global expansion resolution channel is activated, and the multi-cluster architecture is globally expanded according to the global expansion resolution channel to obtain a multi-cluster optimized first architecture, including:

[0065] A global expansion feature analysis is performed according to the multi-cluster architecture to obtain a global expansion capacity; the global expansion analysis channel includes a global expansion decision model and a global expansion fitness analysis model; based on the global expansion capacity, multiple global expansion decisions are obtained according to the global expansion decision model; based on the global expansion fitness analysis model, the multiple global expansion decisions are iteratively optimized to obtain a global expansion strategy; the multi-cluster architecture is globally expanded according to the global expansion strategy to obtain the multi-cluster optimized first architecture.

[0066] Perform a comprehensive resource and load analysis on the multi-cluster architecture to obtain the global expansion capacity. The specific calculation method is to multiply the sum of multiple constrained loads by the global load saturation threshold, and then subtract the sum of multiple predicted loads from the product. The obtained global expansion capacity represents the resources that can be scheduled or reallocated under the current architecture.

[0067] The global expansion analysis channel is a logic module that is responsible for performing a global analysis of the load of the entire multi-cluster architecture and generating an expansion strategy, which includes a global expansion decision model and a global expansion fitness analysis model. The global expansion decision model generates a global expansion decision plan based on the current and predicted load conditions; the global expansion fitness analysis model evaluates the effectiveness of the expansion strategy through the global expansion fitness analysis function, and continuously optimizes the expansion strategy through iterative optimization, and finally generates the optimal expansion plan.

[0068] The global expansion decision model generates different expansion plans based on the global expansion capacity and the resource usage of each cluster. Each decision plan takes into account the current resource configuration, load pressure and global load balancing requirements of the cluster, and generates multiple global expansion decisions suitable for different situations.

[0069] In order to select the optimal expansion plan, all generated global expansion decisions are gradually optimized based on the global expansion fitness analytical model. In each round of iteration, the current expansion plan is evaluated by the fitness analytical function, and the decision parameters are adjusted on this basis. Specifically, the generated multiple global expansion decisions are used as the initial solution, and the fitness analytical function is used to evaluate the effect of each initial expansion decision, and the global expansion fitness is calculated. Based on the fitness evaluation, each expansion plan is adjusted and optimized. After multiple rounds of iterative optimization, an expansion strategy with the highest fitness is found. This strategy is the global expansion strategy, which achieves the best balance between load balancing, resource utilization efficiency and expansion cost.

[0070] After obtaining the global expansion strategy, the actual global expansion operation is performed according to the optimal strategy, and cluster addition is executed. After the global expansion operation is completed, a new architecture state is entered, that is, the first architecture of multi-cluster optimization. This architecture represents the new state of the system after the global expansion.

[0071] Furthermore, the global expansion fitness analytical model includes a global expansion fitness analytical function, and the global expansion fitness analytical function is:

[0072]

[0073] Among them, GSA represents the global expansion fitness, exp represents the exponential function with the natural constant e as the base, csw and ccw represent the predetermined fitness analysis weight conditions, scx represents the predicted global load balance, sxo represents the expected global load balance, scc represents the predicted global expansion cost, and sco represents the expected global expansion cost.

[0074] Specifically, the global expansion fitness analytical function is as follows:

[0075]

[0076] The global expansion fitness GSA is an indicator for evaluating the effectiveness of a certain expansion plan. The higher its value, the better the adaptability of the plan; exp represents an exponential function with the natural constant e as the base. The reason for using the exponential function is that it can effectively amplify or reduce the evaluation results, especially when the fitness is high or low, making the results more significant; csw and ccw represent the predetermined fitness analysis weight conditions, which are used to adjust the influence of the predicted load balance and expansion cost in the fitness evaluation; scx represents the predicted global load balance. Ideally, the higher the load balance, the better the system performance; scc represents the predicted global expansion cost, which indicates the possible expenses incurred by implementing the expansion plan, usually including hardware cost, software cost and possible maintenance cost; sxo represents the expected global load balance, which is a target value indicating the load balance level that is expected to be achieved under ideal circumstances; sco represents the expected global expansion cost, which is also a target value indicating the expansion cost that is expected to be achieved under ideal circumstances.

[0077] In general, the global expansion fitness analytical function is an evaluation tool that combines the effectiveness and cost-effectiveness of load balancing. It not only ensures the improvement of load balancing, but also minimizes the cost of expansion. The weight coefficient in the function allows the importance of different factors to be adjusted, so that the final decision is more in line with the actual needs and goals of the system. This evaluation method ensures the scientificity and rationality of the expansion decision, and helps the system make timely and effective resource expansion when facing high load pressure.

[0078] When the cluster expansion instruction is a local expansion instruction, the local expansion decision model is activated, and the multi-cluster architecture is locally expanded according to the local expansion decision model to obtain a multi-cluster optimized second architecture.

[0079] When the cluster expansion instruction is a local expansion instruction, the local expansion decision model is activated. The local expansion model mainly analyzes the load saturation of a specific cluster and generates a local expansion decision. This model is different from global expansion, and its scope of action is limited to individual clusters with excessively high loads. By analyzing the load saturation of each cluster, clusters with loads exceeding the threshold are listed as target clusters that need to be expanded, and the scale of expansion is determined. The detailed configuration parameters of the target cluster are collected to obtain cluster configuration feature data. The cluster configuration feature data and the local expansion capacity are input into the local expansion decision model to generate a local expansion decision.

[0080] According to the generated local expansion decision, the designated local expansion cluster is expanded. The expansion operation is to add computing nodes of the local expansion cluster. After the local expansion, the optimized multi-cluster optimized second architecture is obtained, that is, the state after the local expansion.

[0081] Furthermore, when the cluster expansion instruction is a local expansion instruction, activating a local expansion decision model, and performing local expansion on the multi-cluster architecture according to the local expansion decision model to obtain a multi-cluster optimized second architecture, including:

[0082] Perform local expansion feature analysis according to the multi-cluster architecture to determine the local expansion cluster and the local expansion capacity; collect configuration parameters of the local expansion cluster to obtain cluster configuration feature data; input the cluster configuration feature data and the local expansion capacity into the local expansion decision model to obtain a local expansion decision; based on the multi-cluster architecture, expand the local expansion cluster according to the local expansion decision to obtain the multi-cluster optimized second architecture.

[0083] The local expansion feature analysis is to analyze the predicted load saturation of each cluster in the multi-cluster architecture and identify clusters whose predicted load saturation exceeds the threshold. These clusters are under great load pressure and require additional resources to cope with. Clusters whose predicted load saturation is greater than the preset load saturation threshold are recorded as local expansion clusters. The local expansion capacity is the amount of resources that need to be added to the local expansion cluster. The specific calculation method is to multiply the constrained load of the local expansion cluster by the load saturation threshold, and then subtract the predicted load of the local expansion cluster from the product.

[0084] In order to ensure that the expansion can meet the actual needs of the cluster, it is necessary to collect detailed configuration parameters of the local expansion cluster, including but not limited to: the number of nodes, that is, the current number of computing nodes in the cluster; node resource configuration, that is, the resource situation of each node, including the number of CPU cores, memory, storage capacity, etc.; network bandwidth, that is, the network connection rate between nodes in the cluster and the outside; load distribution strategy, that is, the distribution method of tasks or requests within the cluster, whether there is an unbalanced load, etc. Collect these configuration parameters through monitoring tools or management systems to form a complete cluster configuration feature data set.

[0085] The local expansion decision model is a model used to generate local expansion plans. The model comprehensively considers the current configuration of the cluster and the calculated local expansion capacity to decide how to expand. By analyzing the cluster configuration feature data and the local expansion capacity, the local expansion decision model outputs a local expansion decision. When the number of clusters remains unchanged, computing nodes of the local expansion cluster can be added to enable the cluster to better bear the load.

[0086] According to the local expansion decision, the actual expansion operation is performed on the selected local expansion cluster. After the local expansion is completed, the second multi-cluster optimization architecture is formed. This architecture represents the state after the expansion. After the expansion, the load pressure of the expansion cluster is relieved, and the resource usage of the entire system is more balanced.

[0087] Load balancing management and control is performed according to the multi-cluster optimization first architecture or the multi-cluster optimization second architecture.

[0088] The first architecture of multi-cluster optimization is the architecture after global expansion, which means that clusters are added in multiple clusters. At this time, global load balancing is used to reasonably distribute the load; the second architecture of multi-cluster optimization is the architecture after partial expansion, which means that only some clusters are expanded. Therefore, when performing load balancing, focus on these partially expanded clusters to ensure that the expanded resources are fully utilized. Through this load balancing management and control, it is possible to achieve optimal resource utilization, reduce latency, and improve overall system performance and reliability.

[0089] In summary, the dynamic load balancing method for multi-cluster intelligent expansion provided by the embodiment of the present application has the following technical effects:

[0090] By collecting the load characteristics of the multi-cluster architecture in real time, the current load status of each cluster and computing node can be mastered. This real-time monitoring can ensure timely response to load changes, and lay a data foundation for subsequent load prediction and expansion decisions, improving the accuracy of load management; based on the load characteristic data set, the load saturation of each cluster in the predetermined future time zone window is predicted, and multiple predicted load saturations are obtained, which improves the responsiveness of the system and enables timely expansion decisions under high load conditions to avoid performance degradation or service interruption caused by sudden load increases; based on the predicted results of load saturation, the cluster expansion determiner is used to decide whether to perform global expansion Whether it is local expansion or global expansion, such flexibility allows the system to make appropriate resource adjustments based on actual load requirements; during global expansion, the resource capacity of multiple clusters is comprehensively improved to form a multi-cluster optimization first architecture; during local expansion, the focus is on solving the load problem of a specific cluster to form a multi-cluster optimization second architecture. This dual expansion mechanism improves the adaptability of the system; after the expansion is completed, load balancing management and control are performed based on the new optimization architecture to ensure that the load of all clusters is reasonably distributed to avoid overload or idleness of a cluster. Through dynamic load balancing, the overall performance and resource utilization can be improved to ensure that each cluster can maintain a stable service quality during peak load periods.

[0091] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic load balancing system with multi-cluster intelligent expansion, characterized by: The system comprises: A load feature collection module, the load feature collection module is used to collect load features according to a multi-cluster architecture to obtain a load feature data set, wherein the multi-cluster architecture includes a plurality of clusters, each cluster includes a plurality of computing nodes; A load saturation prediction module, the load saturation prediction module is used to perform load saturation prediction on the multiple clusters according to the load characteristic data set based on a predetermined future time zone window to obtain multiple predicted load saturations; A capacity expansion instruction parsing module, the capacity expansion instruction parsing module is used to input the multiple predicted load saturations into the cluster capacity expansion determiner to obtain a cluster capacity expansion instruction, wherein the cluster capacity expansion instruction is a global capacity expansion instruction or a local capacity expansion instruction; A global expansion module, wherein the global expansion module is used to activate a global expansion resolution channel when the cluster expansion instruction is a global expansion instruction, and perform global expansion on the multi-cluster architecture according to the global expansion resolution channel to obtain a multi-cluster optimized first architecture; A local expansion module, wherein the local expansion module is used to activate a local expansion decision model when the cluster expansion instruction is a local expansion instruction, and perform local expansion on the multi-cluster architecture according to the local expansion decision model to obtain a multi-cluster optimized second architecture; A load balancing management and control module is used to perform load balancing management and control according to the multi-cluster optimization first architecture or the multi-cluster optimization second architecture.

2. The system according to claim 1, characterized in that The load saturation prediction module is used to perform load saturation prediction on the multiple clusters based on the predetermined future time zone window and the load characteristic data set to obtain multiple predicted load saturations, including: Performing feature recognition based on the load feature data set to obtain multiple current loads and multiple constrained loads corresponding to the multiple clusters; Based on the predetermined future time zone window, predicting load change trends of the multiple clusters according to the multiple current loads to obtain multiple predicted loads; Based on the plurality of predicted load amounts and the plurality of constrained load amounts, the plurality of predicted load saturations are calculated.

3. The system according to claim 1, characterized in that The capacity expansion instruction parsing module is used to input the multiple predicted load saturations into the cluster capacity expansion determiner to obtain a cluster capacity expansion instruction, wherein the cluster capacity expansion instruction is a global capacity expansion instruction or a local capacity expansion instruction, including: activating a local capacity expansion determiner within the cluster capacity expansion determiner; inputting the plurality of predicted load saturations into the local expansion determiner, wherein the local expansion determiner includes a load saturation threshold; Determining whether the multiple predicted load saturations are greater than the load saturation threshold; If any one of the multiple predicted load saturations is greater than the load saturation threshold, the local capacity expansion determiner outputs the local capacity expansion instruction.

4. The system according to claim 3, characterized in that The local capacity expansion determiner outputs the local capacity expansion instruction, including: Recording the predicted load saturation greater than the load saturation threshold as abnormal load saturation; Calculate the quantity ratio of the abnormal load saturation and the multiple predicted load saturations to obtain the quantity ratio of abnormal saturation; Determine whether the abnormal saturation quantity ratio is greater than a predetermined ratio threshold; If the proportion of the abnormal saturation number is greater than the predetermined proportion threshold, activating the global expansion determiner in the cluster expansion determiner; Calculating a centralized value of the plurality of predicted load saturations to obtain a global load saturation, and inputting the global load saturation into the global capacity expansion determiner, wherein the global capacity expansion determiner includes a global load saturation threshold; Determining whether the global load saturation is greater than the global load saturation threshold; If the global load saturation is greater than the global load saturation threshold, the global capacity expansion determiner outputs the global capacity expansion instruction and prohibits the local capacity expansion instruction.

5. The system according to claim 1, wherein: The global expansion module is used to activate a global expansion resolution channel when the cluster expansion instruction is a global expansion instruction, and perform global expansion on the multi-cluster architecture according to the global expansion resolution channel to obtain a multi-cluster optimized first architecture, including: Perform global expansion feature analysis according to the multi-cluster architecture to obtain global expansion capacity; The global expansion analysis channel includes a global expansion decision model and a global expansion fitness analysis model; Based on the global expansion capacity, and according to the global expansion decision model, multiple global expansion decisions are obtained; Based on the global expansion fitness analytical model, iteratively optimize the multiple global expansion decisions to obtain a global expansion strategy; The multi-cluster architecture is globally expanded according to the global expansion strategy to obtain the multi-cluster optimized first architecture.

6. The system according to claim 5, characterized in that The global expansion fitness analytical model includes a global expansion fitness analytical function, and the global expansion fitness analytical function is: Among them, GSA represents the global expansion fitness, exp represents the exponential function with the natural constant e as the base, csw and ccw represent the predetermined fitness analysis weight conditions, scx represents the predicted global load balance, sxo represents the expected global load balance, scc represents the predicted global expansion cost, and sco represents the expected global expansion cost.

7. The system according to claim 1, characterized in that The local expansion module is used to activate the local expansion decision model when the cluster expansion instruction is a local expansion instruction, and perform local expansion on the multi-cluster architecture according to the local expansion decision model to obtain a multi-cluster optimized second architecture, including: Perform local expansion feature analysis according to the multi-cluster architecture to determine the local expansion cluster and the local expansion capacity; Collecting configuration parameters of the local expansion cluster to obtain cluster configuration feature data; Inputting the cluster configuration feature data and the local expansion capacity into the local expansion decision model to obtain a local expansion decision; Based on the multi-cluster architecture, the local expansion cluster is expanded according to the local expansion decision to obtain the multi-cluster optimized second architecture.

8. A dynamic load balancing method for multi-cluster intelligent expansion, characterized in that: Based on the implementation of the multi-cluster intelligent expansion dynamic load balancing system according to any one of claims 1 to 7, the method comprises: Collecting load characteristics according to a multi-cluster architecture to obtain a load characteristic data set, wherein the multi-cluster architecture includes a plurality of clusters, and each cluster includes a plurality of computing nodes; Based on a predetermined future time zone window, performing load saturation prediction on the multiple clusters according to the load characteristic data set to obtain multiple predicted load saturations; Inputting the multiple predicted load saturations into a cluster expansion determiner to obtain a cluster expansion instruction, wherein the cluster expansion instruction is a global expansion instruction or a local expansion instruction; When the cluster expansion instruction is a global expansion instruction, activating a global expansion parsing channel, and performing global expansion on the multi-cluster architecture according to the global expansion parsing channel to obtain a multi-cluster optimized first architecture; When the cluster expansion instruction is a local expansion instruction, activating a local expansion decision model, and performing local expansion on the multi-cluster architecture according to the local expansion decision model to obtain a multi-cluster optimized second architecture; Load balancing management and control is performed according to the multi-cluster optimization first architecture or the multi-cluster optimization second architecture.

Images