CN111985831A - Scheduling method and device of cloud computing resources, computer equipment and storage medium - Google Patents

Scheduling method and device of cloud computing resources, computer equipment and storage medium Download PDF

Info

Publication number
CN111985831A
CN111985831A CN202010881117.0A CN202010881117A CN111985831A CN 111985831 A CN111985831 A CN 111985831A CN 202010881117 A CN202010881117 A CN 202010881117A CN 111985831 A CN111985831 A CN 111985831A
Authority
CN
China
Prior art keywords
resource usage
resource
application service
current
scheduling
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010881117.0A
Other languages
Chinese (zh)
Inventor
王钧锋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Teamsun Technology Co ltd
Original Assignee
Beijing Teamsun Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Teamsun Technology Co ltd filed Critical Beijing Teamsun Technology Co ltd
Priority to CN202010881117.0A priority Critical patent/CN111985831A/en
Publication of CN111985831A publication Critical patent/CN111985831A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • 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
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Strategic Management (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Economics (AREA)
  • Artificial Intelligence (AREA)
  • Game Theory and Decision Science (AREA)
  • Educational Administration (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computing Systems (AREA)
  • Medical Informatics (AREA)
  • Mathematical Physics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Development Economics (AREA)
  • Evolutionary Biology (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Debugging And Monitoring (AREA)

Abstract

The embodiment of the invention discloses a scheduling method of cloud computing resources, which comprises the following steps: training a resource scheduling model according to the test data set by adopting a machine learning algorithm; the test data set comprises characteristic information representing that the current resource of the application program meets the appropriate resource usage; inputting the characteristic information correspondingly required by the current application service into the trained resource scheduling model to obtain the appropriate resource usage required by the application service; and scheduling according to the appropriate resource usage and the current resource usage of the current application service. The cloud computing resource scheduling method provided by the embodiment of the invention can realize automatic reasonable allocation of cloud computing resources, and compared with the mode of manual scheduling or arrangement in the prior art, the method has the advantages of low requirement on technical personnel, time saving and high scheduling accuracy.

Description

Scheduling method and device of cloud computing resources, computer equipment and storage medium
Technical Field
The invention relates to the field of cloud computing, in particular to a method and a device for scheduling cloud computing resources, computer equipment and a storage medium.
Background
On a cloud platform or a cloud management platform, technicians usually allocate computing, storage and network resources to an application system according to some strategy algorithms according to needs, so that the system is ensured to continuously and reliably operate. However, the current manual scheduling or scheduling method depends on the experience level of the technician, and problems such as reaction lag, unbalanced resource allocation, and resource waste may occur.
Disclosure of Invention
The embodiment of the invention provides a scheduling method and device of cloud computing resources, computer equipment and a storage medium, which can realize reasonable distribution of the cloud computing resources.
In a first aspect, an embodiment of the present invention provides a method for scheduling cloud computing resources, including:
training a resource scheduling model according to the test data set by adopting a machine learning algorithm; the test data set comprises characteristic information representing that the current resource of the application program meets the appropriate resource usage;
inputting the characteristic information required by the current application service correspondingly into the trained resource scheduling model to obtain the appropriate resource usage required by the application service;
and scheduling according to the appropriate resource usage and the current resource usage of the current application service.
Further, the training of the resource scheduling model according to the test data set by using a machine learning algorithm includes:
presetting an initial test environment;
and testing at least one application service in a preset initial testing environment, adjusting the preset initial testing environment to meet the appropriate resource usage of the current application service in the testing process, and recording current data as a testing data set.
Further, the feature information includes: access pressure, response pressure, and service strength.
Further, the scheduling according to the appropriate resource usage and the current resource usage of the current application service includes:
if the appropriate resource usage is larger than the current resource usage, then the resource usage of the current application service is added;
and if the appropriate resource usage is less than the current resource usage, recovering the resource usage of the current application service.
Further, the suitable resource usage is: the minimum resource usage.
Further, the training of the resource scheduling model according to the test data set by using a machine learning algorithm includes:
inputting the characteristic information of the current application service into a current resource scheduling model to obtain the use amount of training resources; and adjusting the resource scheduling model according to the training resource usage and the proper resource usage in the test data set corresponding to the current application service until the accuracy of the training resource usage and the proper resource usage meets the requirement, so as to obtain the trained resource scheduling model.
In a second aspect, an embodiment of the present invention further provides a scheduling apparatus for cloud computing resources, including:
the resource scheduling model training module is used for training the resource scheduling model according to the test data set by adopting a machine learning algorithm; the test data set comprises characteristic information representing that the current resource of the application program meets the appropriate resource usage;
the proper resource usage obtaining module is used for inputting the characteristic information required by the current application service correspondingly into the trained resource scheduling model to obtain the proper resource usage required by the application service;
and the scheduling module is used for scheduling according to the appropriate resource usage and the current resource usage of the current application service.
Optionally, the resource scheduling model training module is further configured to:
presetting an initial test environment;
and testing at least one application service in a preset initial testing environment, adjusting the preset initial testing environment to meet the appropriate resource usage of the current application service in the testing process, and recording current data as a testing data set.
Optionally, the scheduling module is further configured to:
if the appropriate resource usage is larger than the current resource usage, then the resource usage of the current application service is added;
and if the appropriate resource usage is less than the current resource usage, recovering the resource usage of the current application service.
Optionally, the resource scheduling model training module is further configured to:
inputting the characteristic information of the current application service into a current resource scheduling model to obtain the use amount of training resources; and adjusting the resource scheduling model according to the training resource usage and the proper resource usage in the test data set corresponding to the current application service until the accuracy of the training resource usage and the proper resource usage meets the requirement, so as to obtain the trained resource scheduling model.
In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes: the cloud computing resource scheduling method comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the processor executes the program, the scheduling method of the cloud computing resource is realized according to any embodiment of the invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processing apparatus, implements the method for scheduling cloud computing resources according to any one of the embodiments of the present invention.
Firstly, training a resource scheduling model according to a test data set by adopting a machine learning algorithm; inputting the characteristic information required by the current application service correspondingly into the trained resource scheduling model to obtain the appropriate resource consumption required by the application service; and finally, scheduling according to the appropriate resource usage and the current resource usage of the current application service. The cloud computing resource scheduling method provided by the embodiment of the invention can realize automatic reasonable allocation of cloud computing resources, and compared with the mode of manual scheduling or arrangement in the prior art, the method has the advantages of low requirement on technical personnel, time saving and high scheduling accuracy.
Drawings
Fig. 1 is a flowchart of a scheduling method of cloud computing resources according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a scheduling apparatus for cloud computing resources according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device in a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a scheduling method for cloud computing resources according to an embodiment of the present invention, where the method may be applied to a situation of scheduling cloud computing resources, and the method may be executed by a scheduling apparatus for cloud computing resources, where the apparatus may be composed of hardware and/or software, and may generally be integrated in a device having a scheduling function for cloud computing resources, and the device may be an electronic device such as a server or a server cluster. As shown in fig. 1, the method specifically comprises the following steps:
and step 110, training a resource scheduling model according to the test data set by adopting a machine learning algorithm.
The machine learning covers probability theory knowledge, statistical knowledge, approximate theory knowledge and complex algorithm knowledge, a computer can be used as a tool and simulates a human learning mode, and the learning efficiency is effectively improved by carrying out knowledge structure division on the existing content. The test data set contains characteristic information characterizing that the current resources of the application meet appropriate resource usage, including but not limited to access pressure, response pressure, and service strength. The access pressure can be understood as the pressure of external access data, and the more users accessing the data, the greater the access pressure and the more times of accessing the data, the greater the access pressure; the response time is the time required by the server from receiving the user request to processing; the service strength can be divided into low strength, medium strength and high strength, representing the complexity of the service, for example, the more interactive processes of a service, the more the access pressure is, the stronger the service strength is.
The resource scheduling model may be, but is not limited to, a neural network model constructed based on a machine learning algorithm and trained from the collected data set. In this embodiment, the way of training the resource scheduling model according to the test data set by using the machine learning algorithm may be, but is not limited to: presetting an initial test environment; the method comprises the steps of testing at least one application service in a preset initial testing environment, adjusting the preset initial testing environment in the testing process to meet the appropriate resource usage of the current application service, and recording current data as a testing data set.
Specifically, a pressure test program may be used to test the application service in the test environment, and record the response time, the service strength, and the appropriate resource usage under the current access pressure. Typical initial test environments may be, but are not limited to, 2ghz cpu, 8G memory, and 50G hard disk. For example, the application service may be an application program such as an e-commerce platform, and a response speed test is performed on a shopping cart service of the e-commerce platform, so that a corresponding test data set can be obtained; and performing response speed test on the order service of the E-commerce platform to obtain another corresponding test data set. If the response time requirement of the e-commerce platform shopping cart service is not more than 1s, the response time can be adjusted by adjusting the resource usage until the response time meets the requirement. In the application scenario, the larger the resource usage amount allocated by a service is, the smaller the required response time is, and in the embodiment of the present invention, the minimum allocated resource usage amount is ensured on the premise of meeting the response time, so as to achieve the purpose of saving the resource usage amount.
Further, the process of training the resource scheduling model according to the test data set by using the machine learning algorithm may be: inputting the characteristic information of the current application service into a current resource scheduling model to obtain the use amount of training resources; and adjusting the resource scheduling model according to the training resource usage and the proper resource usage in the test data set corresponding to the current application service until the accuracy of the training resource usage and the proper resource usage meets the requirement, and obtaining the trained resource scheduling model.
Specifically, the input of the resource scheduling model is the characteristic information of the application program in the test data set, and the characteristic information is input into the resource scheduling model for training after being subjected to mathematical processing. In the training process, parameters in the resource scheduling model are adjusted to enable the error between the training resource usage output by the model and the proper resource usage in the corresponding test data set of the application service to be as small as possible until the calculation accuracy of the final model reaches 90% (namely, the accuracy meets the requirement). In the model training, the test data set may be generated for the application service a test, or may be generated for the application service a and B tests, and the more the sources of the test data set are, the higher the accuracy of the trained resource scheduling model is.
And 120, inputting the characteristic information required by the current application service correspondingly into the trained resource scheduling model to obtain the appropriate resource usage required by the application service.
Optionally, the appropriate resource usage required by the application service is the minimum resource usage capable of meeting the current application service, so that the minimum resource is occupied while the requirement is met, and the resource utilization rate is improved to the maximum extent. In practical applications, the appropriate resource usage may also be to reserve part of resources on the basis of the minimum resource usage, so as to improve the stability of the service.
Specifically, the established resource scheduling model can be trained after the test data set is acquired, after the resource scheduling model is trained, the feature information required by the current application service can be input into the trained resource scheduling model, and the output of the resource scheduling model is the appropriate resource usage required by the application service.
And step 130, scheduling according to the appropriate resource usage and the current resource usage of the current application service.
The scheduling method may include, but is not limited to, increasing the amount of resources, recycling the amount of resources, and maintaining the current amount of resources. In this embodiment, the scheduling manner according to the appropriate resource usage and the current resource usage of the current application service may be: if the appropriate resource usage is larger than the current resource usage, the resource usage of the current application service is added; if the appropriate resource usage is less than the current resource usage, recovering the resource usage of the current application service; and if the appropriate resource usage is the same as the current resource usage, maintaining the current resource usage.
In this embodiment, the minimum resource usage required by the application service can be obtained by inputting the access pressure, the response time, and the service strength required by the current application service in the trained resource scheduling model. Because the access pressure is dynamically changed and the access pressures in the peak access period and the idle access period may differ greatly, the cloud computing resource scheduling method provided by the embodiment of the invention can obtain the minimum resource usage required by the application service under the condition that the current access pressure change meets the specified response time, so that the scheduling is performed according to the minimum resource usage and the current resource usage of the application service.
In summary, in the embodiment of the present invention, a machine learning algorithm is first adopted to train a resource scheduling model according to a test data set; inputting the characteristic information required by the current application service correspondingly into the trained resource scheduling model to obtain the appropriate resource consumption required by the application service; and finally, scheduling according to the appropriate resource usage and the current resource usage of the current application service. The cloud computing resource scheduling method provided by the embodiment of the invention can realize automatic reasonable allocation of cloud computing resources, and compared with the mode of manual scheduling or arrangement in the prior art, the method has the advantages of low requirement on technical personnel, time saving and high scheduling accuracy.
Example two
Fig. 2 is a schematic structural diagram of a scheduling apparatus for cloud computing resources according to a second embodiment of the present invention. As shown in fig. 2, the embodiment of the apparatus corresponds to the embodiment of the method provided in the first embodiment, and specific relevant contents are not described herein again, and only differences between the two embodiments are described below. Specifically, the apparatus includes: a resource scheduling model training module 210, a suitable resource usage obtaining module 220, and a scheduling module 230.
The resource scheduling model training module 210 is configured to train the resource scheduling model according to the test data set by using a machine learning algorithm.
The test data set comprises characteristic information for representing that the current resource of the application program meets the appropriate resource usage, wherein the characteristic information comprises access pressure, response pressure and service strength.
The suitable resource usage obtaining module 220 is configured to input the feature information required by the current application service correspondingly into the trained resource scheduling model, and obtain a suitable resource usage required by the application service.
The appropriate resource usage required by the application service is the minimum resource usage capable of meeting the current application service.
The scheduling module 230 is configured to perform scheduling according to the appropriate resource usage and the current resource usage of the current application service.
Optionally, the resource scheduling model training module 210 is further configured to: presetting an initial test environment; the method comprises the steps of testing at least one application service in a preset initial testing environment, adjusting the preset initial testing environment in the testing process to meet the appropriate resource usage of the current application service, and recording current data as a testing data set.
Optionally, the resource scheduling model training module 210 is further configured to: inputting the characteristic information of the current application service into a current resource scheduling model to obtain the use amount of training resources; and adjusting the resource scheduling model according to the training resource usage and the proper resource usage in the test data set corresponding to the current application service until the accuracy of the training resource usage and the proper resource usage meets the requirement, and obtaining the trained resource scheduling model.
Optionally, the scheduling module 230 is further configured to: if the appropriate resource usage is larger than the current resource usage, the resource usage of the current application service is added; and if the appropriate resource usage is less than the current resource usage, recovering the resource usage of the current application service.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present invention. The computer device 312 shown in FIG. 3 is only an example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention. Device 312 is a scheduling device for typical cloud computing resources.
As shown in FIG. 3, computer device 312 is in the form of a general purpose computing device. The components of computer device 312 may include, but are not limited to: one or more processors 316 and storage devices 328, and a bus 318 that couples the various system components including the storage devices 328 and the processors 316.
Bus 318 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.
Computer device 312 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 312 and includes both volatile and nonvolatile media, removable and non-removable media.
Storage 328 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 330 and/or cache Memory 332. The computer device 312 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 334 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk-Read Only Memory (CD-ROM), a Digital Video disk (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 318 by one or more data media interfaces. Storage 328 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
Program 336 having a set (at least one) of program modules 326 may be stored, for example, in storage 328, such program modules 326 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which may comprise an implementation of a network environment, or some combination thereof. Program modules 326 generally carry out the functions and/or methodologies of embodiments of the invention as described herein.
The computer device 312 may also communicate with one or more external devices 314 (e.g., keyboard, pointing device, camera, display 324, etc.), with one or more devices that enable a user to interact with the computer device 312, and/or with any devices (e.g., network card, modem, etc.) that enable the computer device 312 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 322. Also, computer device 312 may communicate with one or more networks (e.g., a Local Area Network (LAN), Wide Area Network (WAN), etc.) and/or a public Network, such as the internet, via Network adapter 320. As shown, network adapter 320 communicates with the other modules of computer device 312 via bus 318. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the computer device 312, including but not limited to: microcode, device drivers, Redundant processing units, external disk drive Arrays, disk array (RAID) systems, tape drives, and data backup storage systems, to name a few.
The processor 316 executes various functional applications and data processing by running programs stored in the storage 328, for example, to implement the scheduling method of cloud computing resources provided by the above-described embodiment of the present invention.
Example four
Embodiments of the present invention provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processing apparatus, implements a scheduling method for cloud computing resources as in the embodiments of the present invention. The computer readable medium of the present invention described above may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: training a resource scheduling model according to the test data set by adopting a machine learning algorithm; the test data set comprises characteristic information representing that the current resource of the application program meets the appropriate resource usage; inputting the characteristic information correspondingly required by the current application service into the trained resource scheduling model to obtain the appropriate resource usage required by the application service; and scheduling according to the appropriate resource usage and the current resource usage of the current application service.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A scheduling method of cloud computing resources is characterized by comprising the following steps:
training a resource scheduling model according to the test data set by adopting a machine learning algorithm; the test data set comprises characteristic information representing that the current resource of the application program meets the appropriate resource usage;
inputting the characteristic information required by the current application service correspondingly into the trained resource scheduling model to obtain the appropriate resource usage required by the application service;
and scheduling according to the appropriate resource usage and the current resource usage of the current application service.
2. The method of claim 1, wherein training a resource scheduling model from a test data set using a machine learning algorithm comprises:
presetting an initial test environment;
and testing at least one application service in a preset initial testing environment, adjusting the preset initial testing environment in the testing process to meet the appropriate resource usage of the current application service, and recording current data as a testing data set.
3. The method of claim 1, wherein the feature information comprises: access pressure, response pressure, and service strength.
4. The method of claim 1, wherein the scheduling according to the suitable resource usage and a current resource usage of a current application service comprises:
if the appropriate resource usage is larger than the current resource usage, then the resource usage of the current application service is added;
and if the appropriate resource usage is less than the current resource usage, recovering the resource usage of the current application service.
5. The method of claim 1, wherein the suitable resource usage is: the minimum resource usage.
6. The method of claim 1, wherein training a resource scheduling model from a test data set using a machine learning algorithm comprises: inputting the characteristic information of the current application service into a current resource scheduling model to obtain the use amount of training resources; and adjusting the resource scheduling model according to the training resource usage and the proper resource usage in the test data set corresponding to the current application service until the accuracy of the training resource usage and the proper resource usage meets the requirement, so as to obtain the trained resource scheduling model.
7. An apparatus for scheduling cloud computing resources, comprising:
the resource scheduling model training module is used for training the resource scheduling model according to the test data set by adopting a machine learning algorithm; the test data set comprises characteristic information representing that the current resource of the application program meets the appropriate resource usage;
the proper resource usage obtaining module is used for inputting the characteristic information required by the current application service correspondingly into the trained resource scheduling model to obtain the proper resource usage required by the application service;
and the scheduling module is used for scheduling according to the appropriate resource usage and the current resource usage of the current application service.
8. The apparatus of claim 7, wherein the resource scheduling model training module is further configured to:
presetting an initial test environment;
and testing at least one application service in a preset initial testing environment, adjusting the preset initial testing environment to meet the appropriate resource usage of the current application service in the testing process, and recording current data as a testing data set.
9. A computer device, the device comprising: the cloud computing resource scheduling method comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the cloud computing resource scheduling method according to any one of claims 1 to 5.
10. A computer-readable storage medium on which a computer program is stored, the program, when executed by a processing apparatus, implementing the method for scheduling cloud computing resources of any of claims 1-5.
CN202010881117.0A 2020-08-27 2020-08-27 Scheduling method and device of cloud computing resources, computer equipment and storage medium Pending CN111985831A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010881117.0A CN111985831A (en) 2020-08-27 2020-08-27 Scheduling method and device of cloud computing resources, computer equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010881117.0A CN111985831A (en) 2020-08-27 2020-08-27 Scheduling method and device of cloud computing resources, computer equipment and storage medium

Publications (1)

Publication Number Publication Date
CN111985831A true CN111985831A (en) 2020-11-24

Family

ID=73440997

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010881117.0A Pending CN111985831A (en) 2020-08-27 2020-08-27 Scheduling method and device of cloud computing resources, computer equipment and storage medium

Country Status (1)

Country Link
CN (1) CN111985831A (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112506652A (en) * 2020-12-01 2021-03-16 中国科学院深圳先进技术研究院 Dynamic resource partitioning method
CN112600906A (en) * 2020-12-09 2021-04-02 中国科学院深圳先进技术研究院 Resource allocation method and device for online scene and electronic equipment
CN114327918A (en) * 2022-03-11 2022-04-12 北京百度网讯科技有限公司 Method and device for adjusting resource amount, electronic equipment and storage medium
CN114629787A (en) * 2020-12-10 2022-06-14 新智云数据服务有限公司 Internet of things resource scheduling method, device, equipment and computer readable medium
CN114896061A (en) * 2022-05-07 2022-08-12 百度在线网络技术（北京）有限公司 Training method of computing resource control model, and computing resource control method and device
CN116956857A (en) * 2023-06-28 2023-10-27 中国人民解放军63921部队 Design method and design system for test task data acquisition scheme

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103248659A (en) * 2012-02-13 2013-08-14 北京华胜天成科技股份有限公司 Method and system for dispatching cloud computed resources
CN108037993A (en) * 2017-11-07 2018-05-15 大国创新智能科技(东莞)有限公司 Cloud computing dispatching method and system based on big data and deep learning neutral net
CN109992404A (en) * 2017-12-31 2019-07-09 中国移动通信集团湖北有限公司 PC cluster resource regulating method, device, equipment and medium
CN110865878A (en) * 2019-11-11 2020-03-06 广东石油化工学院 Intelligent scheduling method based on task multi-constraint in edge cloud collaborative environment
CN111143800A (en) * 2019-12-31 2020-05-12 北京华胜天成科技股份有限公司 Cloud computing resource management method, device, equipment and storage medium

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103248659A (en) * 2012-02-13 2013-08-14 北京华胜天成科技股份有限公司 Method and system for dispatching cloud computed resources
CN108037993A (en) * 2017-11-07 2018-05-15 大国创新智能科技(东莞)有限公司 Cloud computing dispatching method and system based on big data and deep learning neutral net
CN109992404A (en) * 2017-12-31 2019-07-09 中国移动通信集团湖北有限公司 PC cluster resource regulating method, device, equipment and medium
CN110865878A (en) * 2019-11-11 2020-03-06 广东石油化工学院 Intelligent scheduling method based on task multi-constraint in edge cloud collaborative environment
CN111143800A (en) * 2019-12-31 2020-05-12 北京华胜天成科技股份有限公司 Cloud computing resource management method, device, equipment and storage medium

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112506652A (en) * 2020-12-01 2021-03-16 中国科学院深圳先进技术研究院 Dynamic resource partitioning method
CN112506652B (en) * 2020-12-01 2023-10-20 中国科学院深圳先进技术研究院 Dynamic resource partitioning method
CN112600906A (en) * 2020-12-09 2021-04-02 中国科学院深圳先进技术研究院 Resource allocation method and device for online scene and electronic equipment
CN114629787A (en) * 2020-12-10 2022-06-14 新智云数据服务有限公司 Internet of things resource scheduling method, device, equipment and computer readable medium
CN114327918A (en) * 2022-03-11 2022-04-12 北京百度网讯科技有限公司 Method and device for adjusting resource amount, electronic equipment and storage medium
CN114327918B (en) * 2022-03-11 2022-06-10 北京百度网讯科技有限公司 Method and device for adjusting resource amount, electronic equipment and storage medium
CN114896061A (en) * 2022-05-07 2022-08-12 百度在线网络技术（北京）有限公司 Training method of computing resource control model, and computing resource control method and device
CN116956857A (en) * 2023-06-28 2023-10-27 中国人民解放军63921部队 Design method and design system for test task data acquisition scheme
CN116956857B (en) * 2023-06-28 2024-01-26 中国人民解放军63921部队 Design method and design system for test task data acquisition scheme

Similar Documents

Publication Publication Date Title
CN111985831A (en) Scheduling method and device of cloud computing resources, computer equipment and storage medium
WO2020207174A1 (en) Method and apparatus for generating quantized neural network
CN111949850B (en) Multi-source data acquisition method, device, equipment and storage medium
CN112052082B (en) Task attribute optimization method, device, server and storage medium
CN113760674A (en) Information generation method and device, electronic equipment and computer readable medium
CN110765752A (en) Test question generation method and device, electronic equipment and computer readable storage medium
CN114265966A (en) Data processing method and device, electronic equipment and storage medium
CN111898061B (en) Method, apparatus, electronic device and computer readable medium for searching network
CN113592033A (en) Oil tank image recognition model training method, oil tank image recognition method and oil tank image recognition device
CN111625436A (en) Insurance business capacity management method and device, electronic equipment and storage medium
CN112182111A (en) Block chain based distributed system layered processing method and electronic equipment
WO2023173733A1 (en) Data tracking method and apparatus, electronic device and storage medium
CN109067649B (en) Node processing method and device, storage medium and electronic equipment
CN114217790A (en) Interface scheduling method and device, electronic equipment and medium
CN112732979B (en) Information writing method, information writing device, electronic equipment and computer readable medium
CN112258091B (en) Data transmission method, device, equipment and storage medium
CN115016890A (en) Virtual machine resource allocation method and device, electronic equipment and storage medium
CN111694672B (en) Resource allocation method, task submission method, device, electronic equipment and medium
CN113792869A (en) Video processing method and device based on neural network chip and electronic equipment
CN113807056A (en) Method, device and equipment for correcting error of document name sequence number
CN114035804A (en) Code conversion method, device, medium and electronic equipment
CN113204426A (en) Task processing method of resource pool and related equipment
CN110119364B (en) Method and system for input/output batch submission
CN112148448A (en) Resource allocation method, device, equipment and computer readable medium
CN111680754A (en) Image classification method and device, electronic equipment and computer-readable storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination