CN112737798A - Host resource allocation method and device, scheduling server and storage medium - Google Patents
Host resource allocation method and device, scheduling server and storage medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0896—Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0893—Assignment of logical groups to network elements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
- H04L43/0817—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking functioning
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/16—Threshold monitoring
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/51—Discovery or management thereof, e.g. service location protocol [SLP] or web services
Abstract
The application discloses a host resource allocation method, a host resource allocation device, a scheduling server and a storage medium, and relates to the technical field of communication. Generating a resource capacity evaluation value according to the host operation parameter and generating a service capacity evaluation value according to a service parameter; then inputting the resource capacity evaluation value and the service capacity evaluation value into a pre-trained nonlinear reasonableness prediction model to generate the current resource allocation reasonableness; if the current resource allocation reasonableness is not within the preset range, host resources are reallocated according to the resource capacity evaluation value, the whole process does not affect the service processing of the service host, meanwhile, manual intervention is avoided, and the labor cost is saved.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for allocating host resources, a scheduling server, and a storage medium.
Background
Before a service host of a communication operator processes a service, a background server is required to allocate host resources to the service host in advance, and in the process of specifically processing the service by the service host, the host resources may need to be reallocated according to host operation parameters and service parameters, so that when the service host configures the current service resources for operation, no resource waste is caused and no abnormal operation of the service host is caused.
In the prior art, when resource reallocation is performed, a service host needs to be closed, and after the resource reallocation is completed, the service host is re-opened and then operated, so that hot deployment of resource allocation cannot be realized, service processing is affected, and certain labor cost is also provided.
Disclosure of Invention
In a first aspect, an embodiment of the present application provides a host resource allocation method, including:
receiving host operation parameters and service parameters uploaded by a service host;
generating a resource capacity evaluation value according to the host operation parameter and generating a service capacity evaluation value according to a service parameter;
inputting the resource capacity evaluation value and the service capacity evaluation value into a pre-trained nonlinear reasonableness prediction model to generate the current resource allocation reasonableness;
and if the current resource allocation reasonableness is not in the preset range, reallocating the host resources according to the resource capacity evaluation value.
Optionally, the generating a resource capacity assessment value according to the host operating parameter includes:
and determining a resource capacity evaluation value according to the received peak value of the host operating parameter and a preset safety value of the host operating parameter.
Optionally, the host operating parameter includes at least one of a CPU usage rate, a network bandwidth usage rate, and a memory usage rate.
Optionally, the service parameters include a total number of service accesses, a number of service accesses in a busy time period, and a number of busy time periods;
and determining a service capacity evaluation value according to the total service access number, the service access number in the busy time period, the set number in the busy time period and the current time coefficient.
Optionally, before receiving the uploaded host operating parameter and service parameter of the service host, the method further includes:
and taking the historical resource capacity evaluation value, the historical service capacity evaluation value and the corresponding historical resource distribution reasonableness as training samples to train the nonlinear reasonableness prediction model.
Optionally, if the current resource allocation reasonableness is not within the preset range, reallocating the host resources according to the resource capacity evaluation value includes:
and if the current resource distribution reasonableness is not in the preset range and the resource capacity evaluation value is greater than a preset threshold value, carrying out resource capacity expansion on the service host.
Optionally, if the current resource allocation reasonableness is not within the preset range, reallocating the host resources according to the resource capacity evaluation value includes:
and if the current resource allocation reasonableness is not in a preset range and the resource capacity evaluation value is smaller than a preset threshold value, carrying out resource recovery on the service host.
In a second aspect, an embodiment of the present application further provides a host resource allocation apparatus, including:
the information receiving unit is configured to receive the uploaded host operating parameters and the service parameters of the service host;
the evaluation value generating unit is configured to generate a resource capacity evaluation value according to the host operation parameter and a service capacity evaluation value according to a service parameter;
the reasonability generating unit is configured to input the resource capacity estimated value and the service capacity estimated value into a pre-trained nonlinear reasonability prediction model to generate the current resource allocation reasonability;
and the resource allocation unit is configured to reallocate the host resources according to the resource capacity evaluation value if the current resource allocation reasonableness degree is not in a preset range.
In a third aspect, an embodiment of the present application further provides a scheduling server, including a processor, a memory, and a computer program stored on the memory and executable on the processor, where the computer program, when executed by the processor, implements the steps of the host resource allocation method provided in the first aspect of the embodiment of the present application.
In a fourth aspect, the present application further provides a computer-readable storage medium storing one or more programs, where the one or more programs include instructions that, when executed by a vehicle-mounted terminal including a plurality of application programs, implement the steps of the host resource allocation method as provided in the first aspect of the present application.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
generating a resource capacity evaluation value according to the host operation parameter and generating a service capacity evaluation value according to a service parameter; then inputting the resource capacity evaluation value and the service capacity evaluation value into a pre-trained nonlinear reasonableness prediction model to generate the current resource allocation reasonableness; if the current resource allocation reasonableness is not within the preset range, host resources are reallocated according to the resource capacity evaluation value, the whole process does not affect the service processing of the service host, meanwhile, manual intervention is avoided, and the labor cost is saved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic diagram illustrating an interaction between a dispatch server and a plurality of service hosts according to an embodiment of the present application;
fig. 2 is a flowchart of a host resource allocation method according to an embodiment of the present application;
fig. 3 is a flowchart of a host resource allocation method according to an embodiment of the present application;
fig. 4 is a functional unit diagram of a host resource allocation apparatus according to an embodiment of the present application;
fig. 5 is a functional unit diagram of a host resource allocation apparatus according to an embodiment of the present application;
fig. 6 is a circuit connection block diagram of a dispatch server according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
The embodiment of the present application provides a host resource allocation method, which is applied to a scheduling server 101, wherein as shown in fig. 1, the scheduling server 101 is communicatively connected to a plurality of service hosts 102 so as to perform data interaction. As shown in fig. 2, the method includes:
s21: host operating parameters and business parameters uploaded by the business host 102 are received.
Specifically, the host operating parameters may include CPU operating parameters (e.g., CPU usage), memory operating parameters (memory usage), and network bandwidth operating parameters (e.g., network bandwidth usage).
The CPU utilization rate, the memory utilization rate, and the network bandwidth utilization rate may be collected by the service host 102 through proxy. In addition, the CPU occupation amount, the memory occupation amount and the network bandwidth occupation amount can also be transmitted to the scheduling server 101 through the service host 102, and the scheduling server 101 calculates the CPU occupation amount, the memory occupation amount and the network bandwidth occupation amount to calculate the CPU utilization rate, the memory utilization rate and the network bandwidth utilization rate.
For example, by means of equations
Calculating the CPU utilization rate, wherein R is the CPU utilization rate, NuserCPU footprint, N in user modeniceCPU footprint, N, in low priority user modesysCPU footprint, N in Kernel modeidleCPU footprint when the service host 102 is in the idle state.
By way of another example, the following equations
And calculating the utilization rate of the network bandwidth, wherein N is the utilization rate of the bandwidth, W is the utilization amount of the network bandwidth, and T is the total amount of the network bandwidth.
By way of another example, the following equations
And calculating the memory utilization rate, wherein H is the memory utilization rate, L is the memory utilization amount, and M is the total memory amount.
Additionally, the traffic parameters may include, but are not limited to, total number of traffic accesses, number of traffic accesses during busy time periods, and number of busy time periods.
S22: and generating a resource capacity evaluation value according to the host operation parameter and generating a service capacity evaluation value according to the service parameter.
Specifically, the resource capacity estimation value may be determined according to the received peak value of the host operating parameter and a preset security value of the host operating parameter.
For example, when the host operating parameter is CPU utilization, the calculation formula can be based on
Calculating a CPU capacity evaluation value, wherein CnAs an evaluation value of CPU capacity, RnUsing the peak value, S, for the CPUnIs a preset CPU utilization safety value. In particular, the amount of the solvent to be used,
wherein n is a resourceThe number of the cells.
For another example, when the host operating parameter is network bandwidth utilization, the host operating parameter can be calculated according to the formula
Calculating an estimate of network bandwidth capacity, where KnAs an estimate of bandwidth capacity, NnFor peak bandwidth utilization, SnIs a bandwidth usage security value. In particular, the amount of the solvent to be used,
wherein n is the number of resources.
For another example, when the host operating parameter is memory usage, the method can be based on the formula
Calculating the memory capacity estimation value, wherein Mn is the memory capacity estimation value, HnFor peak memory usage, SnIs a memory usage security value, wherein n is the number of resources.
In addition, the service parameters may include a total number of service accesses, a number of service accesses in a busy time period, and a number of busy time periods; the service capacity evaluation value can be determined according to the total service access number, the service access number in the busy time period, the set number in the busy time period and the current time coefficient.
For example, according to the formula
And calculating a traffic capacity evaluation value, wherein Tn is the traffic capacity evaluation value, Vi is the traffic access evaluation value, V is (the total number of traffic access users, the number of traffic accesses in a busy time period, the current time coefficient)/(1-redundancy ratio), and N is the number of the set busy time periods.
S23: and inputting the resource capacity evaluation value and the service capacity evaluation value into a pre-trained nonlinear reasonableness prediction model to generate the current resource allocation reasonableness.
Specifically, the pre-trained non-linear reasonableness prediction model may be a decision tree algorithm model or a neural network algorithm model or a support vector machine algorithm model. The decision tree algorithm model is a decision tree algorithm, and is a method for approximating a discrete function value. It is a typical classification method that first processes the data, generates readable rules and decision trees using a generalisation algorithm, and then uses the decisions to analyze the new data. In essence, a decision tree is a process of classifying data through a series of rules. The decision tree construction can be performed in two steps, the first step, the generation of the decision tree: a process of generating a decision tree from a training sample set. In general, a training sample data set is a data set which has a history according to actual needs and a certain degree of integration and is used for data analysis processing. Step two, pruning the decision tree: the pruning of the decision tree is a process of checking, correcting and repairing the decision tree generated at the previous stage, and is mainly to use data in a new sample data set (called a test data set) to check a preliminary rule generated in the process of generating the decision tree and prune branches influencing the accuracy of pre-balance. The neural network algorithm model has self-adaptive and self-organizing capabilities, is classified or simulated by utilizing a given sample standard, and changes synapse weight values in the process of learning or training according to training samples so as to adapt to the requirements of the surrounding environment. The SVM model is a supervised learning model related to a related learning algorithm, can analyze data, recognize patterns, and is used for classification and regression analysis.
S24: and judging whether the current resource allocation reasonableness is in a preset range, and if not, executing S25.
S25: and reallocating the host resources according to the resource capacity evaluation value.
According to the host resource allocation method provided by the embodiment of the application, a resource capacity evaluation value is generated according to the host operation parameter, and a service capacity evaluation value is generated according to the service parameter; then inputting the resource capacity evaluation value and the service capacity evaluation value into a pre-trained nonlinear reasonableness prediction model to generate the current resource allocation reasonableness; if the current resource allocation reasonableness is not within the preset range, host resources are reallocated according to the resource capacity evaluation value, the whole process does not affect the service processing of the service host 102, meanwhile, manual intervention is avoided, and the labor cost is saved.
Optionally, before S21, as shown in fig. 3, the method further includes:
s31: and taking the historical resource capacity evaluation value, the historical service capacity evaluation value and the corresponding historical resource distribution reasonableness as training samples to train the nonlinear reasonableness prediction model.
Optionally, the S25 may include:
and if the current resource allocation reasonableness is not in the preset range and the resource capacity evaluation value is greater than a preset threshold value, performing resource capacity expansion on the service host 102.
When the resource capacity assessment value is greater than the preset threshold value, it indicates that the current resource capacity is not sufficient to meet the operation requirement of the current service host 102, and the capacity of the resource capacity of the service host 102 needs to be expanded. Here, the preset threshold may be set to a value that is lower than 90% such as 80%, 85%, 88%, and the like if the safety threshold of the resource capacity assessment value is 90%, so that resource expansion can be performed in advance before the service host 102 is abnormal.
And if the current resource allocation reasonableness is not in the preset range and the resource capacity evaluation value is smaller than a preset threshold value, performing resource recovery on the service host 102.
When the resource capacity evaluation value is smaller than the preset threshold value, which indicates that the resource capacity of the current service host 102 is idle, part of the resource capacity can be recovered, so that the waste of resources can be avoided. Here, the preset threshold may be 30%, 40%, 50%, and so on.
Referring to fig. 4, the present embodiment further provides a host resource allocation apparatus 400, it should be noted that the basic principle and the resulting technical effect of the host resource allocation apparatus 400 provided by the present embodiment are the same as those of the above embodiments, and for a brief description, reference may be made to the corresponding contents in the above embodiments for the sake of brevity. The apparatus 400 includes an information receiving unit 401, an evaluation value generating unit 402, a reasonableness generating unit 403, and a resource allocating unit 404. Wherein the content of the first and second substances,
the information receiving unit 401 is configured to receive the uploaded host operating parameters and service parameters of the service host 102.
Optionally, the host operating parameter includes at least one of a CPU usage rate, a network bandwidth usage rate, and a memory usage rate.
The evaluation value generating unit 402 is configured to generate a resource capacity evaluation value according to the host operating parameter and a traffic capacity evaluation value according to a traffic parameter.
The reasonableness generating unit 403 is configured to input the resource capacity assessment value and the traffic capacity assessment value into a pre-trained non-linear reasonableness prediction model, and generate a current resource allocation reasonableness.
The resource allocation unit 404 is configured to reallocate the host resource according to the resource capacity evaluation value if the current resource allocation reasonableness is not within the preset range.
The host resource allocation apparatus 400 provided in this embodiment of the present application, when executed, may implement the following functions: generating a resource capacity evaluation value according to the host operation parameter and generating a service capacity evaluation value according to a service parameter; then inputting the resource capacity evaluation value and the service capacity evaluation value into a pre-trained nonlinear reasonableness prediction model to generate the current resource allocation reasonableness; if the current resource allocation reasonableness is not within the preset range, host resources are reallocated according to the resource capacity evaluation value, the whole process does not affect the service processing of the service host 102, meanwhile, manual intervention is avoided, and the labor cost is saved.
Alternatively, the evaluation value generating unit 402 may be specifically configured to determine the resource capacity evaluation value according to the received peak value of the host operating parameter and a preset safety value of the host operating parameter.
Optionally, the service parameters include a total number of service accesses, a number of service accesses in a busy time period, and a number of busy time periods; the evaluation value generating unit 402 may be specifically configured to determine the traffic capacity evaluation value according to the total number of traffic accesses, the number of traffic accesses in busy time periods, the number of set busy time periods, and the current time coefficient.
As shown in fig. 5, the apparatus 400 further includes: and the model training unit 501 is configured to train the nonlinear reasonableness prediction model by using the historical resource capacity assessment value, the historical service capacity assessment value and the corresponding historical resource allocation reasonableness as training samples.
Optionally, the resource allocation unit 404 is specifically configured to perform resource expansion on the service host 102 if the current resource allocation reasonableness is not within a preset range and the resource capacity assessment value is greater than a preset threshold; and if the current resource allocation reasonableness is not in the preset range and the resource capacity evaluation value is smaller than a preset threshold value, performing resource recovery on the service host 102.
It should be noted that the execution subjects of the steps of the method provided in embodiment 1 may be the same device, or different devices may be used as the execution subjects of the method. For example, the execution subject of steps 21 and 22 may be device 1, and the execution subject of step 23 may be device 2; for another example, the execution subject of step 21 may be device 1, and the execution subjects of steps 22 and 23 may be device 2; and so on.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Fig. 6 is a schematic structural diagram of a scheduling server according to an embodiment of the present application. Referring to fig. 6, at a hardware level, the dispatch server includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the dispatch server may also include hardware needed for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry standard architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry standard architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 6, but that does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the host resource allocation device on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
receiving host operation parameters and service parameters uploaded by a service host;
generating a resource capacity evaluation value according to the host operation parameter and generating a service capacity evaluation value according to a service parameter;
inputting the resource capacity evaluation value and the service capacity evaluation value into a pre-trained nonlinear reasonableness prediction model to generate the current resource allocation reasonableness;
and if the current resource allocation reasonableness is not in the preset range, reallocating the host resources according to the resource capacity evaluation value.
The method performed by the host resource allocation apparatus according to the embodiment shown in fig. 1 of the present application may be applied to a processor, or may be implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The scheduling server may also execute the method in fig. 1, and implement the functions of the host resource allocation apparatus in the embodiments shown in fig. 1 and fig. 2, which are not described herein again in this embodiment of the present application.
Of course, besides software implementation, the scheduling server of the present application does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution main body of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.
Embodiments of the present application also provide a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by a portable scheduling server including a plurality of application programs, enable the portable scheduling server to perform the method of the embodiment shown in fig. 1, and are specifically configured to:
receiving host operation parameters and service parameters uploaded by a service host;
generating a resource capacity evaluation value according to the host operation parameter and generating a service capacity evaluation value according to a service parameter;
inputting the resource capacity evaluation value and the service capacity evaluation value into a pre-trained nonlinear reasonableness prediction model to generate the current resource allocation reasonableness;
and if the current resource allocation reasonableness is not in the preset range, reallocating the host resources according to the resource capacity evaluation value.
In short, the above description is only a preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Claims (10)
1. A method for host resource allocation, comprising:
receiving host operation parameters and service parameters uploaded by a service host;
generating a resource capacity evaluation value according to the host operation parameter and generating a service capacity evaluation value according to a service parameter;
inputting the resource capacity evaluation value and the service capacity evaluation value into a pre-trained nonlinear reasonableness prediction model to generate the current resource allocation reasonableness;
and if the current resource allocation reasonableness is not in the preset range, reallocating the host resources according to the resource capacity evaluation value.
2. The method of claim 1, wherein generating the resource capacity assessment value based on the host operating parameter comprises:
and determining a resource capacity evaluation value according to the received peak value of the host operating parameter and a preset safety value of the host operating parameter.
3. The method of claim 1 or 2, wherein the host operating parameters comprise at least one of CPU usage, network bandwidth usage, and memory usage.
4. The method of claim 1, wherein the traffic parameters comprise a total number of traffic accesses, a number of traffic accesses during busy time periods, and a number of busy time periods;
and determining a service capacity evaluation value according to the total service access number, the service access number in the busy time period, the set number in the busy time period and the current time coefficient.
5. The method of claim 1, wherein prior to receiving the uploaded host operational parameters and business parameters of the business host, the method further comprises:
and taking the historical resource capacity evaluation value, the historical service capacity evaluation value and the corresponding historical resource distribution reasonableness as training samples to train the nonlinear reasonableness prediction model.
6. The method of claim 1, wherein the re-allocating the host resources according to the resource capacity evaluation value if the current resource allocation reasonableness is not within a preset range comprises:
and if the current resource distribution reasonableness is not in the preset range and the resource capacity evaluation value is greater than a preset threshold value, carrying out resource capacity expansion on the service host.
7. The method of claim 1, wherein the re-allocating the host resources according to the resource capacity evaluation value if the current resource allocation reasonableness is not within a preset range comprises:
and if the current resource allocation reasonableness is not in a preset range and the resource capacity evaluation value is smaller than a preset threshold value, carrying out resource recovery on the service host.
8. A host resource allocation apparatus, comprising:
the information receiving unit is configured to receive the uploaded host operating parameters and the service parameters of the service host;
the evaluation value generating unit is configured to generate a resource capacity evaluation value according to the host operation parameter and a service capacity evaluation value according to a service parameter;
the reasonability generating unit is configured to input the resource capacity estimated value and the service capacity estimated value into a pre-trained nonlinear reasonability prediction model to generate the current resource allocation reasonability;
and the resource allocation unit is configured to reallocate the host resources according to the resource capacity evaluation value if the current resource allocation reasonableness degree is not in a preset range.
9. A scheduling server comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of the host resource allocation method of any one of claims 1 to 7.
10. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a vehicle terminal comprising a plurality of application programs, carry out the steps of the host resource allocation method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114219377A (en) * | 2022-02-22 | 2022-03-22 | 云智慧（北京）科技有限公司 | Service resource allocation method, device and equipment |
| CN114896296A (en) * | 2022-02-24 | 2022-08-12 | 浙江华通云数据科技有限公司 | Cloud service resource configuration method and device, electronic equipment and computer readable medium |
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