Resource adjustment method and device based on multi-dimensional load index, equipment and medium

Abstract

The invention relates to a resource adjustment method and device based on a multi-dimensional load index, equipment and a medium. The resource adjustment method based on the multi-dimensional load index comprises the following steps: collecting multi-dimensional load index data of a cloud service in real time; performing evaluation, analysis and calculation according to the multi-dimensional load index data to obtain a load index; performing decision processing based on the load index and a preset rule to obtain a decision conclusion; and controlling the cloud service to execute resource adjustment operation according to the decision conclusion. According to the embodiment of the invention, the load condition of the system can be comprehensively reflected in combination with the multi-dimensional load index data, and the load change is quickly responded.

Description

Technical Field

[0001] The present disclosure relates to the application field of cloud resource dynamic adjustment technology, and in particular to a resource adjustment method, device, equipment and medium based on multi-dimensional load indicators. Background Art

[0002] Current cloud resource dynamic adjustment technologies primarily rely on a single or a few metrics (such as CPU or memory utilization) for decision-making, which suffers from the following drawbacks: Incompleteness: Relying solely on CPU or memory fails to fully reflect the system's true load. Response delay: Static threshold adjustment strategies based on historical data struggle to cope with sudden traffic bursts, leading to delayed resource expansion or excessive contraction. Lack of adaptability: Fixed weights or rules struggle to adapt to dynamic changes in business scenarios, such as the difference between e-commerce promotions and daily traffic. Summary of the Invention

[0003] In order to solve the above technical problems, the present disclosure provides a resource adjustment method, apparatus, device and medium based on multi-dimensional load indicators.

[0004] In a first aspect, the present disclosure provides a resource adjustment method based on multi-dimensional load indicators, comprising:

[0005] Real-time collection of multi-dimensional load indicator data of cloud services;

[0006] Performing evaluation, analysis, and calculation based on the multi-dimensional load indicator data to obtain a load index;

[0007] Perform decision processing based on the load index and preset rules to obtain a decision conclusion;

[0008] The cloud service is controlled to perform resource adjustment operations according to the decision conclusion.

[0009] In a second aspect, the present disclosure provides a resource adjustment device based on multi-dimensional load indicators, comprising:

[0010] Data collection module, used to collect multi-dimensional load indicator data of cloud services in real time;

[0011] A first processing module is configured to evaluate, analyze, and calculate the multi-dimensional load index data to obtain a load index;

[0012] A second processing module is used to perform decision processing based on the load index and preset rules to obtain a decision conclusion;

[0013] A resource adjustment module is used to control the cloud service to perform resource adjustment operations according to the decision conclusion.

[0014] In a third aspect, the present disclosure provides a resource adjustment device based on multi-dimensional load indicators, including:

[0015] processor;

[0016] a memory for storing executable instructions;

[0017] The processor is used to read executable instructions from the memory and execute the executable instructions to implement the resource adjustment method based on multi-dimensional load indicators of the first aspect.

[0018] In a fourth aspect, the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor implements the resource adjustment method based on multi-dimensional load indicators of the first aspect.

[0019] The technical solution provided by the embodiments of the present disclosure has the following advantages over the prior art:

[0020] The resource adjustment method, apparatus, device and medium based on multi-dimensional load indicators of the embodiments of the present disclosure can collect multi-dimensional load indicator data of cloud services in real time, then evaluate, analyze and calculate the multi-dimensional load indicator data to obtain a load index, and then make decisions based on the load index and preset rules to obtain a decision conclusion. Finally, according to the decision conclusion, the cloud service is controlled to perform resource adjustment operations. Thus, through real-time load monitoring and analysis, combined with multi-dimensional load indicator data, the system load situation is fully reflected, and load changes can be quickly responded to. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0022] Figure 1 A schematic diagram of a flow chart of a resource adjustment method based on multi-dimensional load indicators provided in an embodiment of the present disclosure;

[0023] Figure 2 A flowchart of another resource adjustment method based on multi-dimensional load indicators provided by an embodiment of the present disclosure;

[0024] Figure 3 A schematic diagram of the structure of a resource adjustment device based on multi-dimensional load indicators provided by an embodiment of the present disclosure;

[0025] Figure 4 A schematic diagram of the structure of a resource adjustment device based on multi-dimensional load indicators provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0027] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0028] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.

[0029] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0030] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0031] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0032] In order to solve the above problems, the present disclosure provides a resource adjustment method, device, equipment and medium based on multi-dimensional load indicators. Figures 1 to 2 The resource adjustment method based on multi-dimensional load indicators provided by the embodiment of the present disclosure is described in detail.

[0033] Figure 1 A flow chart of a resource adjustment method based on multi-dimensional load indicators provided by an embodiment of the present disclosure is shown.

[0034] In an embodiment of the present disclosure, the resource adjustment method based on multi-dimensional load indicators may be executed by an electronic device, which may include but is not limited to devices such as computer devices, cloud servers, or cloud server clusters.

[0035] like Figure 1 As shown, the resource adjustment method based on multi-dimensional load indicators may include the following steps.

[0036] S110. Collect multi-dimensional load indicator data of cloud services in real time.

[0037] In an embodiment of the present disclosure, the electronic device can collect multi-dimensional load indicator data of the cloud service in real time.

[0038] Among them, multi-dimensional load indicator data can include the number of new connections per second (Connection Per Second, CPS), the number of queries per second (Queries Per Second, QPS), concurrency, throughput, central processing unit (CPU) utilization and memory utilization.

[0039] Specifically, electronic devices can collect multi-dimensional load indicator data of cloud services in real time, such as CPS, QPS, concurrency, throughput, CPU utilization and memory utilization. Then, electronic devices can calculate the mean corresponding to the multi-dimensional load indicator data every minute through sliding window smoothing technology. For example, the sliding window smoothing technology calculates the mean of all data points in a fixed-size window (that is, a certain number of data points, such as the latest 10 values), and continuously updates the window content over time to obtain a new mean, thereby avoiding instantaneous fluctuations interfering with the load index calculation.

[0040] S120: Perform evaluation, analysis, and calculation based on the multi-dimensional load indicator data to obtain a load index.

[0041] In the embodiment of the present disclosure, the electronic device can perform evaluation, analysis and calculation based on the multi-dimensional load indicator data to obtain a load index.

[0042] Specifically, after calculating the mean value corresponding to the multi-dimensional load index data, the electronic device can evaluate, analyze and calculate the multi-dimensional load index data using a preset calculation formula to obtain a load index.

[0043] S130: Perform decision processing based on the load index and preset rules to obtain a decision conclusion.

[0044] In the embodiment of the present disclosure, the electronic device can perform decision processing based on the load index and preset rules to obtain a decision conclusion.

[0045] The preset rules may be pre-set decision rules. For example, the preset rules may include preset expansion rules and preset contraction rules, including but not limited to increasing or decreasing virtual machine instances, container instances, QPS, concurrency, etc.

[0046] Specifically, the electronic device can perform decision-making according to the obtained load index and preset rules, thereby obtaining a decision conclusion for adjusting resources.

[0047] S140: Control the cloud service to perform resource adjustment operations according to the decision conclusion.

[0048] In an embodiment of the present disclosure, the electronic device may control the cloud service to perform resource adjustment operations according to the decision conclusion.

[0049] Specifically, after obtaining the decision conclusion, the electronic device can control the cloud service to perform resource adjustment operations according to the decision conclusion. For example, if the decision conclusion is to expand the instance, the electronic device can verify the resource situation (such as resource quota). If the resources are sufficient, the relevant functions are called to expand the instance (such as adding virtual machine instances, container instances, QPS, concurrency, etc.); if the resources are insufficient, the instance expansion is not performed, and the log recording module is informed to record relevant logs; for example, if the decision conclusion is to shrink the instance, the electronic device can call the relevant functions to shrink the instance (such as reducing virtual machine instances, container instances, QPS, concurrency, etc.) and inform the log recording module to record relevant operations.

[0050] Therefore, in the embodiment of the present disclosure, multi-dimensional load indicator data of the cloud service can be collected in real time, and then evaluation, analysis and calculation are performed based on the multi-dimensional load indicator data to obtain a load index, and then decision processing is performed based on the load index and preset rules to obtain a decision conclusion. Finally, according to the decision conclusion, the cloud service is controlled to perform resource adjustment operations. Therefore, through real-time load monitoring and analysis, combined with multi-dimensional load indicator data, the system load situation is fully reflected, and load changes can be quickly responded to.

[0051] Optionally, S120 may specifically include: performing a first calculation process on the number of new connections per second, the number of queries per second, and the throughput based on a first calculation formula to obtain a first load index.

[0052] In an embodiment of the present disclosure, the electronic device may perform a first calculation process on the number of new connections per second, the number of queries per second, and the throughput based on a first calculation formula to obtain a first load index.

[0053] Optionally, the first calculation formula can be: LI = a*(average QPS / maximum QPS) + b*(average CPS / maximum CPS) + d*(average throughput / maximum throughput), the numerators are the sliding window averages in the same recent time period, and a+b+c=1 (a, b, c are weight values), and LI is the first load index.

[0054] Specifically, the electronic device can perform a first calculation on the maximum and average values corresponding to the number of new connections per second (CPS), the number of queries per second (QPS) and the throughput according to the above-mentioned first calculation formula, thereby obtaining a first load index (LI).

[0055] Optionally, S120 may specifically include: performing a second calculation process on the concurrency number based on a second calculation formula to obtain a second load index.

[0056] In an embodiment of the present disclosure, the electronic device may perform a second calculation on the concurrency number based on a second calculation formula to obtain a second load index.

[0057] Optionally, the second calculation formula may be: CI=average number of concurrent users / total number of concurrent users, where CI is the second load index (concurrent load index).

[0058] Specifically, the electronic device may perform a second calculation process on the maximum value and the average value corresponding to the concurrency number according to the second calculation formula to obtain a second load index (CI).

[0059] Therefore, through multi-dimensional load evaluation, combined with QPS, CPS, concurrency, throughput, CPU utilization and memory utilization, the system load situation can be fully reflected.

[0060] Optionally, S130 may specifically include: performing decision processing based on the central processing unit utilization, the memory utilization, the first load index, the second load index and the preset rules to obtain a decision conclusion.

[0061] In the embodiment of the present disclosure, the electronic device can perform decision processing based on the central processing unit utilization, the memory utilization, the first load index, the second load index and the preset rules to obtain a decision conclusion.

[0062] In some embodiments, the preset rules may include preset expansion rules (real-time resource addition), as follows:

[0063] If the load index LI is greater than the preset threshold LI1, and the CI load index is greater than the preset threshold CI2 but less than CI1, 30% of the instances are expanded.

[0064] If the load index LI is greater than the preset threshold LI1 and less than CI2, 10% of the instances are expanded.

[0065] If the CI load index is greater than the preset threshold CI1, and the LI load index is greater than LI2 but less than LI1, 30% of the instances are expanded.

[0066] If the CI load index is greater than the preset threshold CI1 and the LI load index is less than LI2, the capacity is expanded by 10%.

[0067] If the CPU utilization is greater than 70% or the memory utilization is greater than 70%, and the LI load index is less than LI2, and the CI load index is less than CI2, the logging module will be notified to record this data and prompt that there is non-business growth in system operating resources.

[0068] LI1 and LI2 are thresholds of the preset LI load index, where LI1>LI2; CI1 and CI2 are thresholds of the CI load index, where CI1>CI2.

[0069] In other embodiments, the preset rules may include preset scaling-down rules (when there is a record of non-human instance change, and the current resource status (e.g., instance order status) is not in the initial state, and the resource status is continuously observed for a period of time (during normal business traffic), the resource status is reduced), as follows:

[0070] If the LI load index is less than LI2, the CI load index is less than CI2, and the CPU usage and memory usage are lower than the preset thresholds, and the data for the past week is continuously observed and all are lower than the thresholds, the instance will be scaled down by 10%.

[0071] Optionally, the resource adjustment method based on multi-dimensional load indicators may further include: monitoring the adjustment effect of the cloud service that performs the resource adjustment operation to obtain a monitoring result; and optimizing the resource adjustment operation based on the monitoring result.

[0072] In an embodiment of the present disclosure, the electronic device may monitor the adjustment effect of the cloud service that performs the resource adjustment operation, obtain a monitoring result, and optimize the resource adjustment operation based on the monitoring result.

[0073] Specifically, after the cloud service performs a resource adjustment operation, such as adjusting the capacity expansion instance, the electronic device can observe the growth rates of QPS, CPS, throughput, and concurrency for a period of time. If the growth rates of QPS, CPS, and throughput values increase significantly after the resource adjustment, the weight values (a, b, c) are adjusted according to the growth ratio (including but not limited to adjustment according to the growth ratio). If the growth rate of concurrency is greater than the growth rate of QPS, CPS, and throughput, the weight values are not adjusted, and the log module records the observation adjustment results, while the scaled-down instance is not processed, thereby optimizing the resource adjustment operation.

[0074] Therefore, through real-time load monitoring and analysis, the system can automatically expand resources when the load is high and shrink resources when the load is low, thereby avoiding resource waste and performance degradation. Dynamic resource adjustment ensures that the cloud platform maintains good service quality even under high load conditions, avoiding performance issues caused by insufficient resources. Through intelligent optimization algorithms, the system continuously adjusts resource management strategies based on historical load data and load trends, improving the accuracy and timeliness of resource scheduling. This automated resource adjustment mechanism reduces manual intervention, lowers operation and maintenance costs, and improves resource utilization efficiency.

[0075] Figure 2 A flow chart of another resource adjustment method based on multi-dimensional load indicators provided by an embodiment of the present disclosure is shown.

[0076] like Figure 2 As shown, the electronic device can use the resource monitoring module to collect multi-dimensional load indicator data of cloud services in real time, including QPS (queries per second), CPS (new creations per second), concurrency, throughput, CPU utilization, and memory utilization. The sliding window smoothing technology is used to calculate the mean value of the multi-dimensional load indicator data per minute. Then, the load evaluation module evaluates and analyzes the multi-dimensional load indicator data to obtain the load index (including the first load index LI and the second load index CI).

[0077] Furthermore, the electronic device can make decisions based on the CPU utilization, memory utilization, the first load index, the second load index and the preset rules through the decision module to obtain decision conclusions, including but not limited to increasing or decreasing virtual machine instances, container instances, QPS, concurrency, etc., that is, including expanding instances and shrinking instances.

[0078] Furthermore, the electronic device can control the cloud service to perform resource adjustment operations according to the decision conclusion through the execution module. For example, if the decision conclusion is to expand the instance, the electronic device can verify the resource situation (such as resource quota). If the resources are sufficient, the relevant functions are called to expand the instance (such as adding virtual machine instances, container instances, QPS, concurrency, etc.); if the resources are insufficient, the instance expansion is not performed, and the log recording module is informed to record relevant logs; for example, if the decision conclusion is to shrink the instance, the electronic device can call the relevant functions to shrink the instance (such as reducing virtual machine instances, container instances, QPS, concurrency, etc.) and inform the log recording module to record relevant operations.

[0079] Furthermore, the electronic device can use the feedback optimization module to observe the growth rates of QPS, CPS, throughput, and concurrency for a period of time after the cloud service performs resource adjustment operations, such as after the expansion instance adjustment. If the growth rates of QPS, CPS, and throughput values increase significantly after the resource adjustment, the weight values (a, b, c) are adjusted according to the growth ratio (including but not limited to adjustment according to the growth ratio). If the growth rate of concurrency is greater than the growth rate of QPS, CPS, and throughput, the weight values are not adjusted, and the log module records the observation adjustment results, while the scaling down instance is not processed, thereby optimizing the resource adjustment operation.

[0080] Furthermore, the electronic device can record the system operation, resource adjustment process and results through the log recording module.

[0081] Figure 3 A schematic structural diagram of a resource adjustment device based on multi-dimensional load indicators provided by an embodiment of the present disclosure is shown.

[0082] like Figure 3 As shown, the resource adjustment device 300 based on multi-dimensional load indicators may include a data collection module 310 , a first processing module 320 , a second processing module 330 and a resource adjustment module 340 .

[0083] The data collection module 310 can be used to collect multi-dimensional load indicator data of cloud services in real time.

[0084] The first processing module 320 may be configured to perform evaluation, analysis, and calculation based on the multi-dimensional load indicator data to obtain a load index.

[0085] The second processing module 330 can be used to perform decision processing based on the load index and preset rules to obtain a decision conclusion.

[0086] The resource adjustment module 340 may be configured to control the cloud service to perform resource adjustment operations according to the decision conclusion.

[0087] Therefore, in the embodiment of the present disclosure, multi-dimensional load indicator data of the cloud service can be collected in real time, and then evaluation, analysis and calculation are performed based on the multi-dimensional load indicator data to obtain a load index, and then decision processing is performed based on the load index and preset rules to obtain a decision conclusion. Finally, according to the decision conclusion, the cloud service is controlled to perform resource adjustment operations. Therefore, through real-time load monitoring and analysis, combined with multi-dimensional load indicator data, the system load situation is fully reflected, and load changes can be quickly responded to.

[0088] In some embodiments of the present disclosure, the multi-dimensional load indicator data includes the number of new connections per second, the number of queries per second, the number of concurrency, throughput, CPU utilization, and memory utilization.

[0089] In some embodiments of the present disclosure, the first processing module 320 may specifically include a first processing unit.

[0090] The first processing unit may be configured to perform a first calculation on the number of new connections per second, the number of queries per second, and the throughput based on a first calculation formula to obtain a first load index.

[0091] In some embodiments of the present disclosure, the first processing module 320 may specifically include a second processing unit.

[0092] The second processing unit may be configured to perform a second calculation on the concurrency number based on a second calculation formula to obtain a second load index.

[0093] In some embodiments of the present disclosure, the second processing module 330 may specifically include a third processing unit.

[0094] The third processing unit can be used to perform decision processing based on the central processing unit utilization, the memory utilization, the first load index, the second load index and the preset rules to obtain a decision conclusion.

[0095] In some embodiments of the present disclosure, the preset rules include preset expansion rules and preset contraction rules.

[0096] In some embodiments of the present disclosure, the resource adjustment device 300 based on multi-dimensional load indicators may include an effect monitoring module and an operation optimization module.

[0097] The effect monitoring module can be used to monitor the adjustment effect of the cloud service that performs the resource adjustment operation to obtain a monitoring result.

[0098] The operation optimization module can be used to optimize resource adjustment operations based on the monitoring results.

[0099] It should be noted that Figure 3 The resource adjustment device 300 shown in FIG. 3 can be executed based on the multi-dimensional load index. Figures 1 to 2 The various steps in the method embodiment shown are implemented Figures 1 to 2 The various processes and effects in the illustrated method embodiment are not described in detail here.

[0100] Figure 4 A schematic structural diagram of a resource adjustment device based on multi-dimensional load indicators provided by an embodiment of the present disclosure is shown.

[0101] In some embodiments of the present disclosure, Figure 4The resource adjustment device based on multi-dimensional load indicators shown may be an electronic device. Specifically, the electronic device may include but is not limited to devices such as computer devices, cloud servers, or cloud server clusters.

[0102] like Figure 4 As shown, the resource adjustment device based on multi-dimensional load indicators may include a processor 401 and a memory 402 storing computer program instructions.

[0103] Specifically, the processor 401 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0104] Memory 402 may include a large-capacity memory for information or instructions. By way of example, and not limitation, memory 402 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk, a magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway device. In certain embodiments, memory 402 is non-volatile solid-state memory. In certain embodiments, memory 402 includes read-only memory (ROM). Where appropriate, the ROM may be mask-programmed ROM, programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0105] The processor 401 reads and executes computer program instructions stored in the memory 402 to perform the steps of the resource adjustment method based on multi-dimensional load indicators provided in the embodiment of the present disclosure.

[0106] In one example, the resource adjustment device based on multi-dimensional load indicators may further include a transceiver 403 and a bus 404. Figure 4As shown, the processor 401 , the memory 402 and the transceiver 403 are connected via a bus 404 and communicate with each other.

[0107] The bus 404 includes hardware, software, or both. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, the bus 404 may include one or more buses. Although embodiments herein describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

[0108] An embodiment of the present disclosure further provides a computer-readable storage medium, which may store a computer program. When the computer program is executed by a processor, the processor implements the resource adjustment method based on multi-dimensional load indicators provided by an embodiment of the present disclosure.

[0109] The above-mentioned storage medium may, for example, include a memory 402 of computer program instructions, and the above-mentioned instructions may be executed by the processor 401 of the resource adjustment device based on multi-dimensional load indicators to complete the resource adjustment method based on multi-dimensional load indicators provided in the embodiment of the present disclosure. Optionally, the storage medium may be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium may be a ROM, a random access memory (Random Access Memory, RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0110] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article or apparatus.

[0111] The foregoing description is intended only to provide specific embodiments of the present disclosure, intended to enable those skilled in the art to understand and implement the present disclosure. Various modifications to these embodiments will be readily 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 disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments described herein, but rather to be construed in the broadest manner consistent with the principles and novel features disclosed herein.

Claims

1. A resource adjustment method based on multi-dimensional load indicators, characterized in that: include: Real-time collection of multi-dimensional load indicator data of cloud services; Performing evaluation, analysis, and calculation based on the multi-dimensional load indicator data to obtain a load index; Perform decision processing based on the load index and preset rules to obtain a decision conclusion; The cloud service is controlled to perform resource adjustment operations according to the decision conclusion.

2. The method according to claim 1, characterized in that The multi-dimensional load indicator data includes the number of new connections per second, the number of queries per second, the number of concurrent connections, the throughput, the CPU utilization and the memory utilization.

3. The method according to claim 2, characterized in that The evaluation, analysis and calculation based on the multi-dimensional load index data to obtain the load index includes: Based on a first calculation formula, a first calculation process is performed on the number of new connections per second, the number of queries per second, and the throughput to obtain a first load index.

4. The method according to claim 2, characterized in that The evaluation, analysis and calculation based on the multi-dimensional load index data to obtain the load index includes: Based on the second calculation formula, a second calculation process is performed on the concurrency number to obtain a second load index.

5. The method according to claim 1, wherein The decision-making process based on the load index and the preset rules to obtain a decision conclusion includes: A decision is made based on the CPU utilization, the memory utilization, the first load index, the second load index and the preset rules to obtain a decision conclusion.

6. The method according to claim 1, characterized in that The preset rules include preset expansion rules and preset contraction rules.

7. The method according to claim 1, characterized in that The method further comprises: Monitoring the adjustment effect of the cloud service that performs the resource adjustment operation to obtain a monitoring result; The resource adjustment operation is optimized based on the monitoring result.

8. A resource adjustment device based on multi-dimensional load indicators, characterized in that: include: Data collection module, used to collect multi-dimensional load indicator data of cloud services in real time; A first processing module is configured to evaluate, analyze, and calculate the multi-dimensional load index data to obtain a load index; A second processing module is used to perform decision processing based on the load index and preset rules to obtain a decision conclusion; A resource adjustment module is used to control the cloud service to perform resource adjustment operations according to the decision conclusion.

9. A resource adjustment device based on multi-dimensional load indicators, characterized in that: include: processor; a memory for storing executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the resource adjustment method based on multi-dimensional load indicators according to any one of claims 1 to 7.

10. A non-volatile computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the processor implements the resource adjustment method based on multi-dimensional load indicators described in any one of claims 1 to 7.

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