A method, apparatus, device and storage medium for configuring a load resource

Abstract

The application discloses a load resource configuration method and device, equipment and a storage medium, relates to the technical field of load balancing, and is applied to a cloud computing platform and comprises the following steps: collecting state data for current traffic requests in an elastic load balancing system and a business system in real time; processing the state data by using a preset load prediction large model to predict traffic requests in a future preset time period and obtaining a traffic prediction result; wherein the preset load prediction large model is a model constructed based on historical traffic requests and historical state data; and performing resource configuration operation of resource expansion or resource reduction on the elastic load balancing system and the business system based on the traffic prediction result, so as to process subsequent traffic requests. In this way, the application predicts future traffic requests by means of a large model, and performs resource expansion or resource reduction according to the traffic prediction result, so that the matching degree of load resources and real-time traffic requests can be improved, and the business lagging condition can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of load balancing, and in particular to a load resource configuration method, device, equipment and storage medium. BACKGROUND

[0002] Cloud computing abstracts various resources such as computing, network and storage, providing users with extremely convenient resource usage and flexible resource expansion capabilities; large business systems usually serve a large number of users and process massive data, so they have a high dependence on load balancing. Load balancing is crucial to ensuring the stability, performance and reliability of these systems. In general, some application systems often experience traffic peaks on certain holidays or specific operational activity nodes, such as shopping websites, etc. However, due to the inability to estimate how high the traffic peak will be and when the traffic peak will arrive, it is often necessary to manually increase or decrease machines based on regional, time period, terminal and other data analysis. In addition to wasting manpower, it also wastes resources. Elastic load balancing systems solve multiple problems related to network traffic management, system availability and resource utilization, including uneven traffic distribution, single point of failure, system scalability, performance optimization and cost effectiveness. In summary, elastic load balancing systems help organizations better manage their online services and applications by providing high availability, flexibility and scalability, ensuring that they can reliably serve changing user needs. However, traditional elastic load balancing systems determine expansion or contraction based on the size of the current traffic. If there is a sudden surge in traffic, the expansion speed may not keep up with the growth of traffic, causing business to lag.

[0003] Therefore, how to improve the matching degree of the elastic load balancing system and real-time traffic to reduce business lag is a problem to be solved in the field. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a load resource configuration method, device, equipment and storage medium, which can predict future traffic requests with the help of a large model, and perform resource expansion or resource contraction based on the traffic prediction results, thereby improving the matching degree of the load resource and real-time traffic requests, and reducing business lag. The specific scheme is as follows:

[0005] In a first aspect, the present application provides a load resource configuration method applied to a cloud computing platform, comprising:

[0006] Real-time collection of state data for current traffic requests in the elastic load balancing system and the business system;

[0007] processing the state data by using a preset load prediction large model to predict traffic requests of the elastic load balancing system and the business system in a future preset time period, to obtain corresponding traffic prediction results; wherein the preset load prediction large model is a model for predicting traffic requests in a future preset time period, which is constructed based on historical traffic requests and historical state data of the elastic load balancing system and the business system;

[0008] performing resource configuration operations of resource expansion or resource contraction on the elastic load balancing system and the business system based on the traffic prediction results, so as to process subsequent traffic requests by using the elastic load balancing system after resource configuration and the business system after resource configuration.

[0009] Optionally, the state data of the elastic load balancing system and the business system for the current traffic request collected in real time includes:

[0010] The state data of each node of the elastic load balancing system and the business system for the current traffic request is collected in real time by a preset monitoring component; the state data includes processor usage, memory usage, disk input and output, and network inflow and outflow traffic.

[0011] Optionally, after the state data of the elastic load balancing system and the business system for the current traffic request is collected in real time, the method further includes:

[0012] The state data is displayed in real time by a preset display interface.

[0013] If there is abnormal data greater than a preset state threshold in the state data, an alarm for the abnormal data is triggered to remind relevant personnel.

[0014] Optionally, after the state data of the elastic load balancing system and the business system for the current traffic request is collected in real time, the method further includes:

[0015] The preset load prediction large model is adjusted by using the state data, to obtain an adjusted large model.

[0016] Correspondingly, the processing of the state data by using the preset load prediction large model to predict traffic requests of the elastic load balancing system and the business system in a future preset time period, to obtain corresponding traffic prediction results, includes:

[0017] The state data is processed by using the adjusted large model to predict traffic requests of the elastic load balancing system and the business system in a future preset time period, to obtain corresponding traffic prediction results.

[0018] Optionally, the resource configuration operation of resource expansion or resource contraction on the elastic load balancing system and the service system based on the traffic prediction result comprises:

[0019] If the traffic prediction result meets a preset expansion condition, a resource configuration operation of resource expansion on the elastic load balancing system and the service system is performed by a first preset control component; the preset expansion condition is that the number of traffic requests represented by the traffic prediction result is greater than the number of current traffic requests, and a preset condition of representing much greater is met;

[0020] If the traffic prediction result meets a preset contraction condition, a resource configuration operation of resource contraction on the elastic load balancing system and the service system is performed by a second preset control component; the preset contraction condition is that the number of traffic requests represented by the traffic prediction result is less than the number of current traffic requests, and a preset condition of representing much less is met.

[0021] Optionally, the resource configuration operation of resource expansion on the elastic load balancing system and the service system by the first preset control component comprises:

[0022] adding a first preset node to the elastic load balancing system and the service system by the first preset control component to complete the resource configuration operation of resource expansion on the elastic load balancing system and the service system;

[0023] or, adjusting a number of nodes in the elastic load balancing system and the service system by the first preset control component using first preset node configuration information to complete the resource configuration operation of resource expansion on the elastic load balancing system and the service system; the first preset node configuration information represents improving the performance of the node.

[0024] Optionally, the resource configuration operation of resource contraction on the elastic load balancing system and the service system by the second preset control component comprises:

[0025] removing a second preset node from the elastic load balancing system and the service system by the second preset control component to complete the resource configuration operation of resource contraction on the elastic load balancing system and the service system;

[0026] or, adjusting a number of nodes in the elastic load balancing system and the service system by the second preset control component using second preset node configuration information to complete the resource configuration operation of resource contraction on the elastic load balancing system and the service system; the second preset node configuration information represents reducing the performance of the node.

[0027] In a second aspect, the application provides a load resource configuration device, applied to a cloud computing platform, comprising:

[0028] a state data collection module, configured to collect state data in real time for current traffic requests in the elastic load balancing system and the business system;

[0029] a traffic prediction module, configured to process the state data by using a preset load prediction large model, to predict traffic requests in a future preset time period of the elastic load balancing system and the business system, and obtain a corresponding traffic prediction result; wherein the preset load prediction large model is a model for predicting traffic requests in the future preset time period, which is constructed based on historical traffic requests and historical state data of the elastic load balancing system and the business system;

[0030] a resource configuration module, configured to perform resource expansion or resource reduction resource configuration operations on the elastic load balancing system and the business system based on the traffic prediction result, so as to process subsequent traffic requests by using the elastic load balancing system after resource configuration and the business system after resource configuration.

[0031] In a third aspect, the application provides an electronic device, comprising:

[0032] a memory, configured to save a computer program;

[0033] a processor, configured to execute the computer program to implement the load resource configuration method as described above.

[0034] In a fourth aspect, the application provides a computer readable storage medium, configured to save a computer program, which is executed by a processor to implement the load resource configuration method as described above.

[0035] Therefore, the application can collect state data for current traffic requests in the elastic load balancing system and the business system in real time; then process the state data by using a preset load prediction large model to predict traffic requests of the elastic load balancing system and the business system in a future preset time period, and obtain a corresponding traffic prediction result; wherein the preset load prediction large model is a model for predicting traffic requests in a future preset time period, which is constructed based on historical traffic requests and historical state data of the elastic load balancing system and the business system; and then perform resource configuration operation of resource expansion or resource contraction on the elastic load balancing system and the business system based on the traffic prediction result, so as to process subsequent traffic requests by using the elastic load balancing system after resource configuration and the business system after resource configuration. In this way, the application can predict traffic requests in a future preset time period by means of a large model, and expand or contract node resource configuration of the elastic load balancing system and the business system according to the traffic prediction result; in this way, the matching degree between the subsequent elastic load balancing system and the business system and real-time traffic requests can be improved, and the situation of business lag caused by sudden large traffic can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0037] Figure 1 A load resource configuration method flow chart disclosed by the present application;

[0038] Figure 2 A specific load resource configuration method flow chart disclosed by the present application;

[0039] Figure 3 A load resource configuration device structure schematic diagram disclosed by the present application;

[0040] Figure 4 A structure diagram of an electronic device disclosed by the present application. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0042] Referring to Figure 1 As shown in the drawings, the embodiments of the present application disclose a load resource configuration method, applied to a cloud computing platform, comprising:

[0043] In step S11, state data of the current traffic request in the elastic load balancing system and the business system is collected in real time.

[0044] In the embodiments of the present application, the cloud computing platform can collect the state data of the current traffic request in the elastic load balancing system and the business system in real time; in specific embodiments, the collection of the state data of the current traffic request in the elastic load balancing system and the business system in real time can include: collecting the state data of the current traffic request in each node of the elastic load balancing system and the business system in real time through a preset monitoring component; the state data includes processor usage, memory usage, disk input and output, network inflow and outflow traffic. Specifically, the cloud computing platform can collect detailed load data and performance indicators of each node of the elastic load balancing system and the business system in real time through a preset monitoring component, including but not limited to CPU (Central Processing Unit, central processor) usage, memory usage, network inflow and outflow traffic including rate, Queries-per-second (QPS) and other data.

[0045] In a specific embodiment, after the state data of the current traffic request in the elastic load balancing system and the business system is collected in real time, the method can further include: displaying the state data in real time through a preset display interface; if there is abnormal data greater than a preset state threshold in the state data, triggering an alarm for the abnormal data to remind relevant personnel. Specifically, after the state data of the current traffic request in the elastic load balancing system and the business system is collected in real time, the state data can be displayed in real time through a preset display interface; accordingly, the cloud computing platform can identify abnormal data in the state data based on a preset state threshold, and can trigger an alarm for the abnormal data to timely remind relevant personnel to handle the abnormal data in the state data.

[0046] In step S12, the state data is processed by using a preset load prediction large model to predict the traffic request of the elastic load balancing system and the business system in a future preset time period, to obtain a corresponding traffic prediction result; wherein the preset load prediction large model is a model for predicting the traffic request in the future preset time period, which is constructed based on the historical traffic request and historical state data of the elastic load balancing system and the business system.

[0047] In this embodiment, the cloud computing platform is pre-provided with a load prediction large model, which is specifically a load prediction large model constructed based on the historical traffic requests and historical state data of the elastic load balancing system and the business system, and is specifically used for predicting the traffic requests of the elastic load balancing system and the business system in a future preset time period.

[0048] In a specific embodiment, after the real-time collection of the state data for the current traffic request in the elastic load balancing system and the business system, the method can further include: adjusting the preset load prediction large model using the state data to obtain an adjusted large model; and correspondingly, the processing of the state data using the preset load prediction large model to predict the traffic requests of the elastic load balancing system and the business system in a future preset time period to obtain a corresponding traffic prediction result includes: processing the state data using the adjusted large model to predict the traffic requests of the elastic load balancing system and the business system in a future preset time period to obtain a corresponding traffic prediction result. Specifically, in order to improve the prediction accuracy of the pre-provisioned load prediction large model in the future, the state data of the elastic load balancing system and the business system for the current traffic request collected in real time can be used to train and adjust the load prediction large model, and an adjusted large model can be obtained. Further, the adjusted large model can be used to predict the traffic requests in a future preset time period based on the state data collected in real time, and a corresponding traffic prediction result can be obtained.

[0049] Step S13, based on the traffic prediction result, performing resource configuration operation of resource expansion or resource contraction on the elastic load balancing system and the business system, so as to use the resource configured elastic load balancing system and the resource configured business system to process subsequent traffic requests.

[0050] In this embodiment, the traffic prediction result can be obtained by predicting the traffic requests in a future preset time period based on the preset load prediction large model, and then the resource configuration operation of resource expansion or resource contraction can be performed on the elastic load balancing system and the business system based on the traffic prediction result, so as to process the traffic requests in a future preset time period through the resource configured elastic load balancing system and the business system.

[0051] In a specific embodiment, the resource configuration operation of resource expansion or resource contraction of the elastic load balancing system and the service system based on the traffic prediction result can include: if the traffic prediction result represents a preset expansion condition, performing resource configuration operation of resource expansion of the elastic load balancing system and the service system by a first preset control component; the preset expansion condition is that the number of traffic requests represented by the traffic prediction result is greater than the number of current traffic requests, and a preset condition of representing much greater is met; if the traffic prediction result represents a preset contraction condition, performing resource configuration operation of resource contraction of the elastic load balancing system and the service system by a second preset control component; the preset contraction condition is that the number of traffic requests represented by the traffic prediction result is less than the number of current traffic requests, and a preset condition of representing much less is met. Specifically, after obtaining the traffic prediction result, it can be judged whether the traffic prediction result meets the preset expansion condition, whether the number of traffic requests represented by the traffic prediction result in the future preset time period is much greater than the number of current traffic requests, for example, the number difference is greater than a certain value, which can be regarded as meeting the expansion condition; when the traffic prediction result meets the preset expansion condition, the resource configuration operation of resource expansion of the elastic load balancing system and the service system can be performed by the first preset control component. Correspondingly, if the traffic prediction result represents the preset contraction condition, the resource configuration operation of resource contraction of the elastic load balancing system and the service system can be performed by the second preset control component.

[0052] In another specific embodiment, the resource configuration operation of resource expansion of the elastic load balancing system and the business system by the first preset control component can include: adding a first preset node to the elastic load balancing system and the business system by the first preset control component to complete the resource configuration operation of resource expansion of the elastic load balancing system and the business system; or, adjusting a plurality of nodes in the elastic load balancing system and the business system by the first preset control component using first preset node configuration information to complete the resource configuration operation of resource expansion of the elastic load balancing system and the business system; the first preset node configuration information represents improving the performance of the node. Specifically, the resource expansion operation can include horizontal expansion and vertical expansion, wherein adding a first preset node to the elastic load balancing system and the business system by the first preset control component expands the two-node cluster to a four-node cluster, disperses the business processing pressure of a single node, and can complete the resource configuration operation of corresponding horizontal resource expansion. Correspondingly, adjusting a plurality of nodes in the elastic load balancing system and the business system by the first preset control component using first preset node configuration information improves the machine specification configuration of the related node, which can improve the business processing capacity of the node, and thus can complete the resource configuration operation of vertical resource expansion.

[0053] In a specific embodiment, the resource configuration operation of resource contraction of the elastic load balancing system and the business system by the second preset control component can include: removing a second preset node from the elastic load balancing system and the business system by the second preset control component to complete the resource configuration operation of resource contraction of the elastic load balancing system and the business system; or, adjusting a plurality of nodes in the elastic load balancing system and the business system by the second preset control component using second preset node configuration information to complete the resource configuration operation of resource contraction of the elastic load balancing system and the business system; the second preset node configuration information represents reducing the performance of the node. Specifically, when the traffic prediction result represents traffic reduction or low-peak period, the contraction operation can be performed to reduce the number of nodes or reduce the node specification of the elastic load balancing system and the business system, and release unnecessary computing resources. In this way, the waste of resources and excessive cost can be avoided, so that the cloud computing platform can adjust the resource use according to the actual demand, and maximize the cost-effectiveness.

[0054] Therefore, the application can predict the traffic request in the future preset time period by means of the large model, and expand or shrink the node resource configuration of the elastic load balancing system and the business system according to the traffic prediction result; in this way, the matching degree between the subsequent elastic load balancing system and the business system and the real-time traffic request can be improved, the business system can maintain high efficiency and stability when facing changing loads, and on-demand allocation of resources is also achieved, which provides continuous high-quality services for users, saves costs for enterprises, and improves competitiveness.

[0055] As shown in Figure 2 The embodiment of the application discloses a load resource configuration method, applied to a cloud computing platform, comprising:

[0056] In this embodiment, the elastic load balancing system coordinates the node resources of itself and the cluster resources of the cloud servers in the application system (business system) to process the traffic requests of the clients. The cloud computing platform can collect detailed load data and performance indicators of each node in real time through a monitoring module, including but not limited to CPU usage, memory usage, network inflow and outflow traffic including rate, query rate per second (QPS), and other key parameters. It can be understood that these monitoring data combined with corresponding historical data can provide rich information resources for the large model, enabling the large model to accurately analyze and predict the load trend in the future period of time. The large model can identify future traffic peak and trough patterns by using machine learning algorithms and data analysis techniques, so as to make decisions on resource allocation in advance. The large model can predict the traffic request in the future period of time in combination with the state of the elastic load balancing system and the business system at the current time. Based on the traffic prediction result, the business system can dynamically adjust the resource allocation strategy, and automatically execute the expansion or shrinkage operation of the elastic load balancing system and the business system through the control module. Specifically, when predicting that a traffic peak period is coming, the cloud computing platform will calculate the required expansion amount in advance, and notify the control module to perform necessary resource expansion according to this prediction, so as to ensure that the system can run smoothly when the traffic peak arrives, and avoid performance degradation or service interruption. Similarly, after the traffic trough period arrives, the cloud computing platform can predict that there will be a period of traffic reduction in the future by means of the large model, and notify the control module to perform corresponding shrinkage operation according to the actual access amount, so as to optimize resource utilization and reduce costs. Through this intelligent load prediction and resource management mechanism, the business system can maintain high efficiency and stability when facing changing loads, and the cost-effectiveness can be maximized, providing continuous high-quality services for users.

[0057] Further, in the process of managing resources, when a forthcoming traffic peak is predicted, the cloud computing platform will start the expansion process in advance, adding more computing resources to meet the increased demand. By horizontally expanding the number of nodes of the elastic load balancing system and the business system or vertically expanding the node specifications, the business system can handle more requests, while improving the system's availability and fault tolerance, effectively improving the response speed and throughput of the business system. On the contrary, when a traffic decrease or trough is predicted, the cloud computing platform will perform a shrinkage operation to avoid waste of resources and excessive cost. By reducing the number of nodes of the elastic load balancing system and the business system or reducing the node specifications, unnecessary computing resources are released. In this way, the cloud computing platform can adjust its resource usage according to actual demand, maximizing cost-effectiveness. To further reduce costs, when business access is in a trough, the nodes of the elastic load balancing system and the business system can be migrated to some physical nodes, and the unnecessary physical nodes can be shut down to save energy. These nodes will be started again when expansion is needed next. The entire resource allocation strategy adjustment process is dynamic and automated, without human intervention, significantly improving the operation and maintenance efficiency and the response ability of the system. In addition, the cloud computing platform also has built-in security checks and fault recovery mechanisms to ensure the security of data and the stability of the system during resource allocation. Specifically, when the monitoring module detects abnormal situations or unpredictable errors, such as when the accuracy of the load prediction large model decreases, or the business system encounters an unprecedented traffic pattern, the monitoring module will immediately trigger an alarm and notification. These alarms can be communicated to relevant staff through various channels, including email, SMS, instant message or a dedicated operation and maintenance platform. After receiving the alarm, the staff will quickly take action to make necessary adjustments to the business system based on their professional knowledge and experience. This may include manually adjusting resource allocation strategies, starting backup systems, performing emergency repairs or working with the development team to solve deeper system problems.

[0058] Correspondingly, in the resource configuration process of resource expansion or resource contraction, the business system can be horizontally or vertically expanded, and the elastic load balancing system can also be horizontally or vertically expanded. Among them, horizontal expansion refers to increasing the number of nodes in the cluster. For example, the original two-node cluster can be expanded to a four-node cluster. This expansion method can effectively disperse the pressure of a single node and improve the concurrent processing capability of the system. By increasing the number of nodes, the business system can handle more requests while improving the availability and fault tolerance of the system. Vertical expansion refers to upgrading the machine specification configuration of a single node. For example, upgrading a 2-core 4G (computer storage unit) memory virtual machine to a 4-core 8G memory. This expansion method can improve the processing capability of a single node and is suitable for scenarios with high requirements for single-node performance. Vertical expansion can effectively improve the response speed and throughput of the business system, but may be limited by hardware resources.

[0059] As can be seen, by virtue of the prediction capability of the large model and in combination with advanced resource management techniques, the application not only ensures that the business system remains efficient and stable in the face of changing loads, but also realizes on-demand allocation of resources, provides continuous high-quality services for users, saves costs for enterprises, and improves competitiveness; and by combining the advantages of automation and manual management, the prediction capability of the large model is utilized while the flexibility of manual management is retained, thereby providing a comprehensive, reliable and efficient resource management framework for the business system.

[0060] As shown in Figure 3 The embodiments of the application disclose a load resource configuration device applied to a cloud computing platform, comprising:

[0061] A state data collection module 11 is configured to collect state data in real time for current traffic requests in the elastic load balancing system and the business system.

[0062] A traffic prediction module 12 is configured to process the state data using a preset load prediction large model to predict traffic requests in the elastic load balancing system and the business system in a future preset time period, and obtain a corresponding traffic prediction result. The preset load prediction large model is a model for predicting traffic requests in a future preset time period, which is constructed based on historical traffic requests and historical state data of the elastic load balancing system and the business system.

[0063] A resource configuration module 13 is configured to perform resource configuration operations of resource expansion or resource contraction on the elastic load balancing system and the business system based on the traffic prediction result, so as to process subsequent traffic requests using the elastic load balancing system after resource configuration and the business system after resource configuration.

[0064] Therefore, the application can predict the traffic request in the future preset time period by means of the large model, and expand or shrink the node resource configuration of the elastic load balancing system and the business system according to the traffic prediction result; in this way, the matching degree between the subsequent elastic load balancing system and the business system and the real-time traffic request can be improved, and the situation of business lag caused by sudden large traffic can be reduced.

[0065] In a specific embodiment, the state data collection module 11 can include:

[0066] A state data collection unit is configured to collect, in real time, state data of each node in the elastic load balancing system and the business system for the current traffic request through a preset monitoring component; the state data includes processor usage, memory usage, disk input and output, and network inflow and outflow traffic.

[0067] In a specific embodiment, the device can further include:

[0068] A state data display module is configured to display, in real time, the state data through a preset display interface.

[0069] An alarm triggering module is configured to trigger an alarm for abnormal data greater than a preset state threshold in the state data, so as to remind relevant personnel.

[0070] In another specific embodiment, the device can further include:

[0071] A model adjustment module is configured to adjust the preset load prediction large model using the state data to obtain an adjusted large model.

[0072] Correspondingly, the traffic prediction module 12 can include:

[0073] A traffic prediction unit is configured to process the state data using the adjusted large model to predict the traffic request of the elastic load balancing system and the business system in a future preset time period, and obtain a corresponding traffic prediction result.

[0074] In a specific embodiment, the resource configuration module 13 can include:

[0075] A resource expansion submodule is configured to perform a resource configuration operation of resource expansion on the elastic load balancing system and the business system through a first preset control component when the traffic prediction result represents a preset expansion condition; the preset expansion condition is that the number of traffic requests represented by the traffic prediction result is greater than the number of current traffic requests, and satisfies a preset condition of representing much greater.

[0076] The resource shrinking submodule is configured to perform a resource configuration operation of resource shrinking on the elastic load balancing system and the service system by a second preset control component when the traffic prediction result meets a preset resource shrinking condition; the preset resource shrinking condition is that the number of traffic requests represented by the traffic prediction result is less than the current number of traffic requests and a preset condition of representing being much smaller is met.

[0077] In another specific embodiment, the resource expanding submodule can include:

[0078] The first resource expanding unit is configured to add a first preset node to the elastic load balancing system and the service system by the first preset control component to complete a resource configuration operation of resource expanding on the elastic load balancing system and the service system.

[0079] The second resource expanding unit is configured to adjust a plurality of nodes in the elastic load balancing system and the service system by the first preset control component using first preset node configuration information to complete a resource configuration operation of resource expanding on the elastic load balancing system and the service system; the first preset node configuration information represents improving the performance of the nodes.

[0080] In another specific embodiment, the resource shrinking submodule can include:

[0081] The first resource shrinking unit is configured to remove a second preset node from the elastic load balancing system and the service system by a second preset control component to complete a resource configuration operation of resource shrinking on the elastic load balancing system and the service system.

[0082] The second resource shrinking unit is configured to adjust a plurality of nodes in the elastic load balancing system and the service system by the second preset control component using second preset node configuration information to complete a resource configuration operation of resource shrinking on the elastic load balancing system and the service system; the second preset node configuration information represents reducing the performance of the nodes.

[0083] Further, the application also discloses an electronic device, Figure 4 is an electronic device 20 structure diagram according to an exemplary embodiment, the content in the figure cannot be considered as any limitation on the use range of the application.

[0084] Figure 4A structural schematic diagram of an electronic device 20 is provided in the embodiments of the present application. The electronic device 20 can specifically include at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25 and a communication bus 26. The memory 22 is configured to store a computer program, and the processor 21 is configured to load and execute the computer program to implement the related steps in the load resource configuration method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in the embodiments of the present application can be specifically an electronic computer.

[0085] In the embodiments of the present application, the power supply 23 is configured to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 is capable of creating a data transmission channel between the electronic device 20 and external devices, and the communication protocol followed by the communication interface 24 can be any communication protocol applicable to the technical solution of the present application, which is not limited specifically herein; the input / output interface 25 is configured to obtain external input data or output data to the outside, and the specific interface type can be selected according to the specific application needs, which is not limited specifically herein.

[0086] In addition, the memory 22 as a resource storage carrier can be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., and the resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage mode can be temporary storage or permanent storage.

[0087] The operating system 221 is configured to manage and control each hardware device on the electronic device 20 and the computer program 222, and can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program capable of completing the load resource configuration method executed by the electronic device 20 disclosed in any of the foregoing embodiments, the computer program 222 can further include a computer program capable of completing other specific work.

[0088] Further, the present application further discloses a computer readable storage medium for storing a computer program; wherein the computer program is executed by a processor to implement the load resource configuration method disclosed in the foregoing embodiments. The specific steps of the method can refer to the corresponding contents disclosed in the foregoing embodiments, which will not be repeated here.

[0089] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts of each embodiment can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can refer to the method part.

[0090] Those skilled in the art will further appreciate that the units and algorithm steps of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various examples have been described herein in terms of their functionality, which has been described generally and symbolically in flow charts. Having thus described the functionality of the examples, a person of ordinary skill in the art will be able to implement such functionality in hardware and / or software, and will recognize that the bounds of the examples are not limited by one approach or the other. The various examples can be realized in a centralized fashion in one computer system or network, or in a distributed fashion where different elements are spread across several computer systems or sub-networks. Any kind of computer system or other apparatus adapted for carrying out the methods described herein is suited. A typical combination of hardware and software could be a general purpose computer system with a computer program that, when being loaded and executed, carries out the methods described herein.

[0091] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in random access memory (RAM), flash memory, read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, hard disk can be used as a storage medium.

[0092] Finally, it should be noted that the terms "first", "second", and the like, herein do not denote any order, quantity, combination, or importance, but rather are used to distinguish one element from another, and do not imply singular or plural. Moreover, the terms "include", "have", or any other variant thereof are intended to encompass non-exclusive inclusions, such that processes, methods, articles, or apparatuses that comprise a set of elements not expressly listed are also within the scope of the present application. In addition, the articles "a" and "an" are used herein to refer to one or to more than one (i.e., to one or at least one) of the grammatical object of the article. By way of example, "an element" means one element or one or more elements.

[0093] The above detailed description of the technical solutions provided by the present application has been described in detail, and the principles and implementation modes of the present application have been described in the above examples. The above example is only used to help understand the method and core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed; in view of the above, the content of the specification should not be understood as limiting the present application.

Claims

1. A load resource allocation method, characterized in that, Applied to cloud computing platforms, including: Collect real-time status data of current traffic requests from the elastic load balancing system and business systems; The state data is processed using a pre-defined load prediction model to predict the traffic requests of the elastic load balancing system and the business system within a future preset time period, thereby obtaining the corresponding traffic prediction results. The pre-defined load prediction model is a model constructed based on the historical traffic requests and historical state data of the elastic load balancing system and the business system for predicting traffic requests within a future preset time period. Based on the traffic prediction results, resource configuration operations are performed on the elastic load balancing system and the business system to expand or shrink resources, so that the elastic load balancing system and the business system after resource configuration can process subsequent traffic requests. The method of collecting real-time status data of the elastic load balancing system and the business system for the current traffic request also includes: The preset load prediction model is adjusted using the state data to obtain the adjusted model. Accordingly, the process of using a preset load prediction model to process the state data to predict the traffic requests of the elastic load balancing system and the business system within a preset time period, and to obtain the corresponding traffic prediction results, includes: The adjusted large model is used to process the state data to predict the traffic requests of the elastic load balancing system and the business system within a preset time period in the future, and the corresponding traffic prediction results are obtained.

2. The load resource allocation method according to claim 1, characterized in that, The real-time collection of status data for current traffic requests from the elastic load balancing system and the business system includes: The system collects real-time status data of each node in the elastic load balancing system and business system in response to current traffic requests through preset monitoring components; the status data includes processor utilization, memory utilization, disk input / output, and network inflow and outflow traffic.

3. The load resource allocation method according to claim 1, characterized in that, After collecting the status data of the elastic load balancing system and the business system for the current traffic request in real time, it also includes: The status data is displayed in real time through a preset display interface; If there is abnormal data in the status data that exceeds a preset status threshold, an alarm will be triggered for the abnormal data to alert relevant personnel.

4. The load resource allocation method according to any one of claims 1 to 3, characterized in that, The resource configuration operation of scaling up or down the elastic load balancing system and the business system based on the traffic prediction results includes: If the traffic prediction result meets the preset expansion conditions, then the first preset control component performs resource configuration operations to expand the resources of the elastic load balancing system and the business system; the preset expansion conditions are that the number of traffic requests represented by the traffic prediction result is greater than the number of current traffic requests, and the preset condition of the representation being much greater than is met. If the traffic prediction result meets the preset scaling-down conditions, then the second preset control component performs resource scaling-down configuration operations on the elastic load balancing system and the business system; the preset scaling-down conditions are that the number of traffic requests represented by the traffic prediction result is less than the number of current traffic requests, and the preset condition of the representation being much smaller than is met.

5. The load resource configuration method according to claim 4, characterized in that, The resource configuration operation for expanding the resources of the elastic load balancing system and the business system through the first preset control component includes: The first preset node is added to the elastic load balancing system and the business system through the first preset control component to complete the resource configuration operation for resource expansion of the elastic load balancing system and the business system; Alternatively, the first preset control component can use the first preset node configuration information to adjust several nodes in the elastic load balancing system and the business system to complete the resource configuration operation for resource expansion of the elastic load balancing system and the business system; the first preset node configuration information represents the improvement of node performance.

6. The load resource configuration method according to claim 5, characterized in that, The resource configuration operation of scaling down the elastic load balancing system and the business system through the second preset control component includes: The second preset node is removed from the elastic load balancing system and the business system by the second preset control component, so as to complete the resource configuration operation of resource scaling down for the elastic load balancing system and the business system. Alternatively, the second preset control component can use the second preset node configuration information to adjust several nodes in the elastic load balancing system and the business system to complete the resource configuration operation of resource scaling down for the elastic load balancing system and the business system; the second preset node configuration information represents the reduction of node performance.

7. A load resource allocation device, characterized in that, Applied to cloud computing platforms, including: The status data collection module is used to collect status data of the elastic load balancing system and business systems for the current traffic requests in real time. The traffic prediction module is used to process the status data using a preset load prediction model to predict the traffic requests of the elastic load balancing system and the business system within a preset time period in the future, and to obtain the corresponding traffic prediction results; wherein, the preset load prediction model is a model built based on the historical traffic requests and historical status data of the elastic load balancing system and the business system for predicting traffic requests within a preset time period in the future. The resource configuration module is used to perform resource expansion or reduction operations on the elastic load balancing system and the business system based on the traffic prediction results, so as to use the resource-configured elastic load balancing system and the resource-configured business system to process subsequent traffic requests. The device further includes: The model adjustment module is used to adjust the preset load prediction large model using the state data to obtain the adjusted large model; Accordingly, the traffic prediction module includes: The traffic prediction unit is used to process the state data using the adjusted large model to predict the traffic requests of the elastic load balancing system and the business system within a preset time period in the future, and to obtain the corresponding traffic prediction results.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the load resource configuration method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the load resource configuration method as described in any one of claims 1 to 6.

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