CN116991581A - Cloud computing environment resource allocation optimization method and system - Google Patents

Cloud computing environment resource allocation optimization method and system Download PDF

Info

Publication number
CN116991581A
CN116991581A CN202310961524.6A CN202310961524A CN116991581A CN 116991581 A CN116991581 A CN 116991581A CN 202310961524 A CN202310961524 A CN 202310961524A CN 116991581 A CN116991581 A CN 116991581A
Authority
CN
China
Prior art keywords
cloud
computing
dynamic
resources
resource
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.)
Withdrawn
Application number
CN202310961524.6A
Other languages
Chinese (zh)
Inventor
黄馨之
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.)
Shanghai Yangmaiyong Network Technology Co ltd
Original Assignee
Shanghai Yangmaiyong Network 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 Shanghai Yangmaiyong Network Technology Co ltd filed Critical Shanghai Yangmaiyong Network Technology Co ltd
Priority to CN202310961524.6A priority Critical patent/CN116991581A/en
Publication of CN116991581A publication Critical patent/CN116991581A/en
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5066—Algorithms for mapping a plurality of inter-dependent sub-tasks onto a plurality of physical CPUs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5072—Grid computing

Landscapes

  • Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Debugging And Monitoring (AREA)

Abstract

The application is applicable to the technical field of cloud computing resource management, and particularly relates to a cloud computing environment resource allocation optimization method and system, wherein the method comprises the following steps: acquiring a cloud client list, and acquiring a corresponding cloud computing history record based on the cloud client list; analyzing based on the cloud computing history record, determining a computing demand type, and dividing cloud computing resources into fixed computing resources and dynamic computing resources; dividing fixed computing resources into cloud clients, counting dynamic computing demands of the cloud clients, and constructing a dynamic demand time schedule; dividing the dynamic computing resources into fixed ratio allocation resources and maneuvering allocation resources, and allocating the fixed ratio allocation resources based on a dynamic demand time sequence table, wherein the maneuvering allocation resources are allocated to corresponding cloud clients when burst computing demands are received. The cloud computing resource management method and the cloud computing resource management system can meet sudden computing demands of all cloud clients, ensure stable operation of all cloud clients, and improve utilization rate of cloud computing resources.

Description

Cloud computing environment resource allocation optimization method and system
Technical Field
The application belongs to the technical field of cloud computing resource management, and particularly relates to a cloud computing environment resource allocation optimization method and system.
Background
Cloud computing (clouding) is one type of distributed computing, which refers to decomposing a huge data computing process program into numerous small programs through a network "cloud", and then processing and analyzing the small programs through a system composed of multiple servers to obtain results and returning the results to users. Early cloud computing, simply referred to as simple distributed computing, solves task distribution, and performs merging of computing results. Thus, cloud computing is also known as grid computing. By this technique, processing of tens of thousands of data can be completed in a short time (several seconds), thereby achieving a powerful network service.
In the current cloud computing service process, the cloud computing resources are difficult to fully utilize due to the influence of uneven distribution of the cloud computing resources, and the waste of the computing resources is caused.
Disclosure of Invention
The embodiment of the application aims to provide a cloud computing environment resource allocation optimization method, which aims to solve the problems that in the current cloud computing service process, the cloud computing resources are difficult to fully utilize and the computing resources are wasted due to the influence of uneven cloud computing resource allocation.
The embodiment of the application is realized in such a way that the cloud computing environment resource allocation optimization method comprises the following steps:
acquiring a cloud client list, and acquiring a corresponding cloud computing history record based on the cloud client list;
analyzing based on cloud computing histories, determining computing demand types of all cloud clients, and dividing cloud computing resources into fixed computing resources and dynamic computing resources based on the computer demand types;
dividing fixed computing resources into cloud clients, counting the dynamic computing demands of the cloud clients based on cloud computing histories, and constructing a dynamic demand time schedule;
dividing the dynamic computing resources into fixed ratio allocation resources and maneuvering allocation resources, and allocating the fixed ratio allocation resources based on a dynamic demand time sequence table, wherein the maneuvering allocation resources are allocated to corresponding cloud clients when burst computing demands are received.
Preferably, the step of analyzing based on the cloud computing history record, determining a computing demand type of each cloud client, and dividing the cloud computing resources into fixed computing resources and dynamic computing resources based on the computing demand type specifically includes:
identifying the cloud computing histories, determining corresponding resource demand types, and classifying the cloud computing histories based on the resource demand types to obtain classification results;
determining the duty ratio of each cloud client to different types of computing demands according to the classification result, and determining the type of the computing demands of each cloud client;
and calculating the resource demand ratio of each cloud client, and dividing the cloud computing resources into fixed computing resources and dynamic computing resources.
Preferably, the step of dividing the fixed computing resources into each cloud client, counting the dynamic computing requirements of each cloud client based on the cloud computing history, and constructing a dynamic requirement time schedule specifically includes:
determining the requirement of each cloud client on the fixed computing resource, determining the fixed requirement ratio of each cloud client, and accordingly distributing the fixed computing resource;
calculating force statistics is carried out based on dynamic calculation demands of all cloud clients in the cloud calculation history record, and calculating force demands at all moments are determined;
and constructing a time sequence according to a preset time step, determining dynamic computing resources required by each cloud client in each time period in the time sequence, and constructing a dynamic demand time sequence table.
Preferably, the step of dividing the dynamic computing resources into proportional allocation resources and dynamic allocation resources, and allocating the proportional allocation resources based on the dynamic demand time sequence table specifically includes:
dividing dynamic computing resources into fixed ratio allocation resources and maneuvering allocation resources according to a preset dynamic resource allocation proportion;
determining the dynamic resource demand ratio required by each cloud client in each time period based on the dynamic demand time sequence table;
and in different time periods, the fixed ratio allocation resources are allocated, and the maneuvering allocation resources are scheduled based on the burst computing demands sent by the cloud clients.
Preferably, the cloud client manifest records all clients allowed to use cloud computing resources.
Preferably, the cloud computing history record at least comprises application program type, application program running time and calculation force using data at each moment.
Another object of an embodiment of the present application is to provide a cloud computing environment resource allocation optimization system, including:
the history acquisition module is used for acquiring a cloud client list and acquiring a corresponding cloud computing history based on the cloud client list;
the resource allocation module is used for analyzing based on the cloud computing history record, determining the computing demand type of each cloud client, and dividing cloud computing resources into fixed computing resources and dynamic computing resources based on the computer demand type;
the fixed resource scheduling module is used for dividing fixed computing resources into cloud clients, counting the dynamic computing demands of the cloud clients based on cloud computing histories, and constructing a dynamic demand time schedule;
and the dynamic resource scheduling module is used for dividing the dynamic computing resources into fixed ratio allocation resources and maneuvering allocation resources, and allocating the fixed ratio allocation resources based on the dynamic demand time sequence table, wherein the maneuvering allocation resources are allocated to the corresponding cloud clients when burst computing demands are received.
Preferably, the resource allocation module includes:
the cloud computing record distribution unit is used for identifying cloud computing histories, determining corresponding resource demand types, and classifying the cloud computing histories based on the resource demand types to obtain classification results;
the demand type identification unit is used for determining the duty ratio of each cloud client to different types of computing demands according to the classification result and determining the computing demand type of each cloud client;
the resource classification unit is used for calculating the resource demand ratio of each cloud client and dividing the cloud computing resources into fixed computing resources and dynamic computing resources.
Preferably, the fixed resource scheduling module includes:
the fixed resource allocation unit is used for determining the requirement of each cloud client on the fixed computing resource, determining the fixed requirement ratio of each cloud client, and accordingly, allocating the fixed computing resource;
the dynamic calculation force statistics unit is used for carrying out calculation force statistics based on the dynamic calculation requirements of each cloud client in the cloud calculation history record and determining calculation force requirements at each moment;
the dynamic demand time sequence statistics unit is used for constructing a time sequence according to a preset time step, determining dynamic computing resources required by each cloud client in each time period in the time sequence and constructing a dynamic demand time sequence table.
Preferably, the dynamic resource scheduling module includes:
the dynamic resource classification unit is used for dividing dynamic computing resources into fixed ratio allocation resources and maneuvering allocation resources according to a preset dynamic resource allocation proportion;
the dynamic demand analysis unit is used for determining the dynamic resource demand ratio required by each cloud client in each time period based on the dynamic demand time sequence table;
and the maneuvering allocation unit is used for allocating the fixed ratio allocation resources in different time periods and scheduling the maneuvering allocation resources based on the burst calculation demands sent by the cloud clients.
According to the cloud computing environment resource allocation optimization method provided by the embodiment of the application, the historical use process of each cloud client is analyzed to determine the fixed calculation power requirement and the dynamic calculation power requirement of each cloud client in each time period, and in the subsequent resource allocation process, resource allocation is carried out, and the excited allocation resources are set, so that the sudden calculation requirements of each cloud client can be met, the stable operation of each cloud client is ensured, and the utilization rate of the cloud calculation resources is improved.
Drawings
FIG. 1 is a flowchart of a cloud computing environment resource allocation optimization method provided by an embodiment of the present application;
FIG. 2 is a flowchart illustrating steps for analyzing based on a cloud computing history, determining a computing demand type of each cloud client, and dividing cloud computing resources into fixed computing resources and dynamic computing resources based on the computing demand type according to an embodiment of the present application;
FIG. 3 is a flowchart showing steps for dividing fixed computing resources into cloud clients, counting dynamic computing demands of the cloud clients based on cloud computing histories, and constructing a dynamic demand time schedule according to the embodiment of the application;
FIG. 4 is a flowchart showing steps for dividing dynamic computing resources into proportional allocation resources and dynamic allocation resources, and allocating the proportional allocation resources based on a dynamic demand schedule according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a cloud computing environment resource allocation optimization system according to an embodiment of the present application;
fig. 6 is a schematic diagram of a resource allocation module according to an embodiment of the present application;
fig. 7 is a schematic diagram of a fixed resource scheduling module according to an embodiment of the present application;
fig. 8 is a schematic diagram of a dynamic resource scheduling module according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
As shown in fig. 1, a flowchart of a cloud computing environment resource allocation optimization method provided by an embodiment of the present application is provided, where the method includes:
s100, acquiring a cloud client list, and acquiring a corresponding cloud computing history record based on the cloud client list.
In this step, a cloud client list is obtained, cloud client devices connected with a cloud server are recorded in the cloud client list, cloud computing resources can be used by different cloud clients, when a plurality of cloud clients exist simultaneously, if the cloud computing resources are not dynamically scheduled, the situation that part of the cloud client computing resources are insufficient or the computing resources are excessive is easy to occur, in order to solve the problem, historical usage records of all the cloud clients are counted to obtain cloud computing historical records, and the cloud computing historical records at least comprise application types, application running time and usage data of computing power at all moments.
S200, analyzing based on cloud computing histories, determining computing demand types of all cloud clients, and dividing cloud computing resources into fixed computing resources and dynamic computing resources based on the computer demand types.
In this step, analysis is performed based on the cloud computing histories, specifically, during analysis, the corresponding application programs in each cloud computing histories are mainly analyzed, the requirements of different application programs on computing resources are different, the requirements of some application programs on computing resources are fixed values and fluctuate within a fixed range, the requirements of some application programs on computing resources fluctuate greatly, a large amount of computing resources are needed to be occupied at times, and the computing resources are not occupied at all times, so that the computing requirement types of each cloud client are determined, the types of the running application programs are determined, statistics is performed, the cloud computing resources are divided into fixed computing resources and dynamic computing resources according to the statistical results, wherein the fixed computing resources are used for coping with the application programs with small fluctuation of the requirements in each cloud client, and the dynamic computing resources are computing resources which need to be frequently allocated.
And S300, dividing the fixed computing resources into all cloud clients, counting the dynamic computing demands of all cloud clients based on cloud computing histories, and constructing a dynamic demand time schedule.
In this step, the fixed computing resources are divided into each cloud client, and based on the application program currently operated by each cloud client, the fixed value of the computing resources required by each cloud client is determined, so that the corresponding fixed computing resources are allocated to the corresponding cloud client, further statistics is performed on the cloud computing history record, the dynamic computing requirements of each cloud client at each moment are determined, for example, in the a-B time period, the computing requirements are a, and the computing requirements in the B-C time period are B, so that determination is performed, and thus, the requirements of each cloud client on the dynamic computing resources in the future time period are estimated through big data analysis, so that a dynamic demand time schedule is constructed.
S400, dividing the dynamic computing resources into proportional allocation resources and maneuvering allocation resources, and allocating the proportional allocation resources based on a dynamic demand time sequence table, wherein the maneuvering allocation resources are allocated to corresponding cloud clients when burst computing demands are received.
In this step, the dynamic computing resources are divided into fixed ratio distributed resources and mobile distributed resources, where the distribution ratio is preset, for example, the distribution ratio is divided according to a ratio of 9:1, the mobile distributed resources have a ratio of 1/10, the fixed ratio distributed resources have a ratio of 9/10, the fixed ratio distributed resources of 9/10 are distributed to the corresponding cloud clients according to the dynamic demand time sequence table, the reserved mobile distributed resources can be randomly distributed to any one cloud client, and when the computing resources of any cloud client are insufficient, burst computing demands can be sent out, and at the moment, the mobile distributed resources are directly distributed to the corresponding cloud clients.
As shown in fig. 2, as a preferred embodiment of the present application, the step of analyzing based on the cloud computing history to determine the computing demand type of each cloud client, and dividing the cloud computing resources into fixed computing resources and dynamic computing resources based on the computer demand type specifically includes:
s201, identifying the cloud computing histories, determining corresponding resource demand types, and classifying the cloud computing histories based on the resource demand types to obtain classification results.
In the step, the cloud computing history is identified, in the cloud computing history, the application programs running in each time period are recorded, the type of the application program for computing resource requirements can be determined based on the application programs, and the cloud computing history is classified based on the resource requirement type, so that a classification result is obtained.
S202, determining the duty ratio of each cloud client to different types of computing demands according to the classification result, and determining the type of the computing demands of each cloud client.
In this step, the duty ratio of each cloud client to different types of computing demands is determined according to the classification result, and the demands for fixed computing resources and the demands for dynamic computing resources in each cloud client are determined to determine the duty ratio, so as to determine the computing demand type of each cloud client.
S203, calculating the resource demand ratio of each cloud client, and dividing the cloud computing resources into fixed computing resources and dynamic computing resources.
In this step, the resource allocation is performed based on the resource demand ratio of each cloud client, and if all cloud clients occupy the computing resource P in total, wherein the fixed computing resource occupies 0.5P and the dynamic computing resource occupies 0.5P, then the cloud computing resource is divided into the fixed computing resource and the dynamic computing resource.
As shown in fig. 3, as a preferred embodiment of the present application, the step of dividing the fixed computing resources into the cloud clients, counting the dynamic computing requirements of the cloud clients based on the cloud computing history, and constructing a dynamic requirement timing table specifically includes:
s301, determining the requirement of each cloud client on the fixed computing resources, determining the fixed requirement ratio of each cloud client, and accordingly distributing the fixed computing resources.
In this step, the requirement of each cloud client for the fixed computing resource is determined, and for the fixed computing resource, the required computing resource can be determined when the corresponding application program runs, so that the resource requirement can be determined according to the application program starting conditions at each moment, and the allocation of the fixed computing resource can be performed.
S302, calculating force statistics is carried out based on dynamic calculation requirements of all cloud clients in the cloud calculation history record, and calculating force requirements at all moments are determined.
In this step, calculation force statistics is performed based on the dynamic calculation requirements of each cloud client in the cloud calculation history, in which the operation conditions of each cloud client are recorded, so that the average requirement of each time period can be determined.
S303, constructing a time sequence according to a preset time step, determining dynamic computing resources required by each cloud client in each time period in the time sequence, and constructing a dynamic demand time sequence table.
In this step, a time sequence is constructed according to a preset time step, and since the computing resource requirement in each time period has been determined, a time sequence can be constructed, the time sequence includes a plurality of time periods, and the resource requirement in each time period can be estimated, so as to construct a dynamic requirement time schedule.
As shown in fig. 4, as a preferred embodiment of the present application, the step of dividing the dynamic computing resources into proportional allocation resources and dynamic allocation resources, and allocating the proportional allocation resources based on the dynamic demand time schedule specifically includes:
s401, dividing the dynamic computing resources into fixed ratio allocation resources and maneuvering allocation resources according to a preset dynamic resource allocation proportion.
In this step, a dynamic resource allocation ratio is determined, for example, 9:1, the proportion is determined in advance to reserve for maneuver, and when the system is used, the system is divided according to the proportion to obtain the proportional allocation resource and the maneuver allocation resource.
S402, determining the dynamic resource demand ratio required by each cloud client in each time period based on the dynamic demand time sequence table.
S403, in different time periods, the fixed ratio allocation resources are allocated, and the maneuvering allocation resources are scheduled based on the burst computing demands sent by the cloud clients.
In this step, the dynamic resource demand ratio required by each cloud client in each time period is determined based on the dynamic demand time schedule, in the dynamic demand time schedule, the dynamic demand in each time period in the future time period is determined, and the fixed ratio allocation resources are reallocated in each time period based on the dynamic demand time schedule, so that for exciting allocation resources, any cloud client can be randomly allocated, when the situation of insufficient calculation resources occurs in any cloud client, burst calculation demands can be sent out, and at the moment, the mobile allocation resources are directly allocated to the corresponding cloud client.
As shown in fig. 5, a cloud computing environment resource allocation optimization system provided by an embodiment of the present application includes:
the history acquisition module 100 is configured to acquire a cloud client manifest, and acquire a corresponding cloud computing history based on the cloud client manifest.
In the system, the history acquisition module 100 acquires a cloud client list, in which cloud client devices connected with a cloud server are recorded, and different cloud clients can use cloud computing resources, when a plurality of cloud clients exist at the same time, if the cloud computing resources are not dynamically scheduled, the situation that part of the cloud client computing resources are insufficient or the computing resources are excessive is easy to occur, and in order to solve the problem, the history use records of the cloud clients are counted to obtain a cloud computing history record, wherein the cloud computing history record at least comprises application types, application running time and use data of computing power at each moment.
The resource allocation module 200 is configured to analyze based on the cloud computing history, determine a computing demand type of each cloud client, and divide the cloud computing resources into fixed computing resources and dynamic computing resources based on the computer demand type.
In the system, the resource allocation module 200 performs analysis based on the cloud computing histories, specifically, during the analysis, mainly analyzes corresponding application programs in each cloud computing histories, the requirements of different application programs on computing resources are different, the requirements of some application programs on computing resources are fixed values and fluctuate within a fixed range, the requirements of some application programs on computing resources fluctuate greatly, a large amount of computing resources are required to be occupied at some time, the computing resources are not occupied at the other time, so that the computing requirement types of each cloud client are determined, the types of the application programs operated by the cloud client are determined, statistics is performed, the cloud computing resources are divided into fixed computing resources and dynamic computing resources according to the statistics results, wherein the fixed computing resources are used for coping with the application programs with small fluctuation of the requirements in each cloud client, and the dynamic computing resources are frequently allocated computing resources.
The fixed resource scheduling module 300 is configured to divide the fixed computing resources into cloud clients, count dynamic computing demands of the cloud clients based on cloud computing histories, and construct a dynamic demand timing schedule.
In the system, the fixed resource scheduling module 300 divides the fixed computing resources into cloud clients, determines fixed values of the computing resources required by the cloud clients based on the application programs currently operated by the cloud clients, so as to allocate the corresponding fixed computing resources to the corresponding cloud clients, further counts the cloud computing histories, determines the dynamic computing requirements of the cloud clients at all times, for example, in the A-B time period, the computing requirements are a, and the computing requirements in the B-C time period are B, so as to determine, and then, through big data analysis, estimates the requirements of the cloud clients on the dynamic computing resources in the future time period, so as to construct a dynamic demand time schedule.
The dynamic resource scheduling module 400 is configured to divide the dynamic computing resources into proportional allocation resources and dynamic allocation resources, and allocate the proportional allocation resources based on the dynamic demand timing table, where the dynamic allocation resources are allocated to the corresponding cloud clients when burst computing demands are received.
In the system, the dynamic resource scheduling module 400 divides dynamic computing resources into fixed ratio distribution resources and dynamic distribution resources, the distribution ratio is preset, for example, the distribution ratio is divided according to a ratio of 9:1, wherein the dynamic distribution resources have a ratio of 1/10, the fixed ratio distribution resources have a ratio of 9/10, the fixed ratio distribution resources of 9/10 are distributed to corresponding cloud clients according to a dynamic demand time schedule, the reserved dynamic distribution resources can be randomly distributed to any one cloud client, and when the computing resources of any one cloud client are insufficient, burst computing demands can be sent out, and at the moment, the dynamic distribution resources are directly distributed to the corresponding cloud client.
As shown in fig. 6, as a preferred embodiment of the present application, the resource allocation module 200 includes:
the cloud computing record allocation unit 201 is configured to identify a cloud computing history record, determine a corresponding resource demand type, and classify the cloud computing history record based on the resource demand type to obtain a classification result.
In this module, the cloud computing record allocation unit 201 identifies a cloud computing history, in which an application running in each time period is recorded, and based on the application, the type of the application on the computing resource demand can be determined, and based on the resource demand type, the cloud computing history is classified, so as to obtain a classification result.
The requirement type identifying unit 202 is configured to determine the duty ratio of each cloud client to different types of computing requirements according to the classification result, and determine the computing requirement type of each cloud client.
In this module, the requirement type identifying unit 202 determines the duty ratio of each cloud client to different types of computing requirements according to the classification result, and determines the requirement of each cloud client for fixed computing resources and the requirement of each cloud client for dynamic computing resources, so as to determine the duty ratio, thereby determining the computing requirement type of each cloud client.
The resource classification unit 203 is configured to calculate a resource demand ratio of each cloud client, and divide the cloud computing resources into fixed computing resources and dynamic computing resources.
In this module, the resource classification unit 203 performs resource allocation based on the resource demand ratio of each cloud client, and if all cloud clients occupy the computing resource P in total, wherein the fixed computing resource occupies 0.5P and the dynamic computing resource occupies 0.5P, then the cloud computing resource is divided into the fixed computing resource and the dynamic computing resource.
As shown in fig. 7, as a preferred embodiment of the present application, the fixed resource scheduling module 300 includes:
the fixed resource allocation unit 301 is configured to determine a requirement of each cloud client for a fixed computing resource, determine a fixed requirement ratio of each cloud client, and allocate the fixed computing resource according to the fixed requirement ratio.
In this module, the fixed resource allocation unit 301 determines the requirement of each cloud client for the fixed computing resource, and for the fixed computing resource, the required computing resource can be determined when the corresponding application program runs, so that the resource requirement can be determined according to the application program starting conditions at each moment, and the allocation of the fixed computing resource can be performed.
The dynamic calculation power statistics unit 302 is configured to perform calculation power statistics based on dynamic calculation requirements of each cloud client in the cloud calculation history, and determine calculation power requirements at each moment.
In this module, the dynamic calculation power statistics unit 302 performs calculation power statistics based on the dynamic calculation requirements of each cloud client in the cloud calculation history, where the operation conditions of each cloud client are recorded, so that the average requirement of each time period can be determined.
The dynamic demand time sequence statistics unit 303 is configured to construct a time sequence according to a preset time step, determine dynamic computing resources required by each cloud client in each time period in the time sequence, and construct a dynamic demand time sequence table.
In this module, the dynamic demand time sequence statistics unit 303 constructs a time sequence according to a preset time step, and since the computing resource demand in each time period has been determined, a time sequence can be constructed, where the time sequence includes a plurality of time periods, and the resource demand in each time period can be estimated, so as to construct a dynamic demand time sequence table.
As shown in fig. 8, as a preferred embodiment of the present application, the dynamic resource scheduling module 400 includes:
the dynamic resource classification unit 401 is configured to divide the dynamic computing resources into fixed ratio allocation resources and maneuver allocation resources according to a preset dynamic resource allocation ratio.
In this module, the dynamic resource classification unit 401 determines a dynamic resource allocation ratio, such as 9:1, the proportion is determined in advance to reserve for maneuver, and when the system is used, the system is divided according to the proportion to obtain the proportional allocation resource and the maneuver allocation resource.
The dynamic demand analysis unit 402 is configured to determine a dynamic resource demand ratio required by each cloud client in each time period based on the dynamic demand timing table.
The maneuver allocation unit 403 is configured to allocate the fixed ratio allocation resources in different time periods, and schedule the maneuver allocation resources based on the sudden computing demands sent by the cloud clients.
In the module, the dynamic resource demand ratio of each cloud client in each time period is determined based on a dynamic demand time sequence table, the dynamic demand in each time period in the future time period is determined in the dynamic demand time sequence table, and the fixed ratio allocation resources are reallocated in each time period based on the dynamic demand time sequence table, so that the excited allocation resources can be randomly allocated to any cloud client, when the situation of insufficient calculation resources occurs in any cloud client, the sudden calculation demand can be sent out, and the mobile allocation resources are directly allocated to the corresponding cloud client.
It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.

Claims (10)

1. A cloud computing environment resource allocation optimization method, the method comprising:
acquiring a cloud client list, and acquiring a corresponding cloud computing history record based on the cloud client list;
analyzing based on cloud computing histories, determining computing demand types of all cloud clients, and dividing cloud computing resources into fixed computing resources and dynamic computing resources based on the computer demand types;
dividing fixed computing resources into cloud clients, counting the dynamic computing demands of the cloud clients based on cloud computing histories, and constructing a dynamic demand time schedule;
dividing the dynamic computing resources into fixed ratio allocation resources and maneuvering allocation resources, and allocating the fixed ratio allocation resources based on a dynamic demand time sequence table, wherein the maneuvering allocation resources are allocated to corresponding cloud clients when burst computing demands are received.
2. The cloud computing environment resource allocation optimization method according to claim 1, wherein the step of analyzing based on the cloud computing history record to determine the computing demand type of each cloud client and dividing the cloud computing resources into fixed computing resources and dynamic computing resources based on the computer demand type specifically comprises:
identifying the cloud computing histories, determining corresponding resource demand types, and classifying the cloud computing histories based on the resource demand types to obtain classification results;
determining the duty ratio of each cloud client to different types of computing demands according to the classification result, and determining the type of the computing demands of each cloud client;
and calculating the resource demand ratio of each cloud client, and dividing the cloud computing resources into fixed computing resources and dynamic computing resources.
3. The cloud computing environment resource allocation optimization method according to claim 1, wherein the step of dividing the fixed computing resources into the cloud clients, counting the dynamic computing demands of the cloud clients based on cloud computing histories, and constructing a dynamic demand time schedule specifically comprises:
determining the requirement of each cloud client on the fixed computing resource, determining the fixed requirement ratio of each cloud client, and accordingly distributing the fixed computing resource;
calculating force statistics is carried out based on dynamic calculation demands of all cloud clients in the cloud calculation history record, and calculating force demands at all moments are determined;
and constructing a time sequence according to a preset time step, determining dynamic computing resources required by each cloud client in each time period in the time sequence, and constructing a dynamic demand time sequence table.
4. The cloud computing environment resource allocation optimization method according to claim 1, wherein the step of dividing the dynamic computing resources into proportional allocation resources and dynamic allocation resources and allocating the proportional allocation resources based on the dynamic demand time schedule specifically comprises:
dividing dynamic computing resources into fixed ratio allocation resources and maneuvering allocation resources according to a preset dynamic resource allocation proportion;
determining the dynamic resource demand ratio required by each cloud client in each time period based on the dynamic demand time sequence table;
and in different time periods, the fixed ratio allocation resources are allocated, and the maneuvering allocation resources are scheduled based on the burst computing demands sent by the cloud clients.
5. The cloud computing environment resource deployment optimization method of claim 1, wherein the cloud client manifest records all clients that are permitted to use cloud computing resources.
6. The cloud computing environment resource deployment optimization method of claim 1, wherein the cloud computing history comprises at least application type, application run time, and usage data of computing power at each moment.
7. A cloud computing environment resource deployment optimization system, the system comprising:
the history acquisition module is used for acquiring a cloud client list and acquiring a corresponding cloud computing history based on the cloud client list;
the resource allocation module is used for analyzing based on the cloud computing history record, determining the computing demand type of each cloud client, and dividing cloud computing resources into fixed computing resources and dynamic computing resources based on the computer demand type;
the fixed resource scheduling module is used for dividing fixed computing resources into cloud clients, counting the dynamic computing demands of the cloud clients based on cloud computing histories, and constructing a dynamic demand time schedule;
and the dynamic resource scheduling module is used for dividing the dynamic computing resources into fixed ratio allocation resources and maneuvering allocation resources, and allocating the fixed ratio allocation resources based on the dynamic demand time sequence table, wherein the maneuvering allocation resources are allocated to the corresponding cloud clients when burst computing demands are received.
8. The cloud computing environment resource deployment optimization system of claim 7, wherein said resource allocation module comprises:
the cloud computing record distribution unit is used for identifying cloud computing histories, determining corresponding resource demand types, and classifying the cloud computing histories based on the resource demand types to obtain classification results;
the demand type identification unit is used for determining the duty ratio of each cloud client to different types of computing demands according to the classification result and determining the computing demand type of each cloud client;
the resource classification unit is used for calculating the resource demand ratio of each cloud client and dividing the cloud computing resources into fixed computing resources and dynamic computing resources.
9. The cloud computing environment resource deployment optimization system of claim 7, wherein said fixed resource scheduling module comprises:
the fixed resource allocation unit is used for determining the requirement of each cloud client on the fixed computing resource, determining the fixed requirement ratio of each cloud client, and accordingly, allocating the fixed computing resource;
the dynamic calculation force statistics unit is used for carrying out calculation force statistics based on the dynamic calculation requirements of each cloud client in the cloud calculation history record and determining calculation force requirements at each moment;
the dynamic demand time sequence statistics unit is used for constructing a time sequence according to a preset time step, determining dynamic computing resources required by each cloud client in each time period in the time sequence and constructing a dynamic demand time sequence table.
10. The cloud computing environment resource deployment optimization system of claim 7, wherein said dynamic resource scheduling module comprises:
the dynamic resource classification unit is used for dividing dynamic computing resources into fixed ratio allocation resources and maneuvering allocation resources according to a preset dynamic resource allocation proportion;
the dynamic demand analysis unit is used for determining the dynamic resource demand ratio required by each cloud client in each time period based on the dynamic demand time sequence table;
and the maneuvering allocation unit is used for allocating the fixed ratio allocation resources in different time periods and scheduling the maneuvering allocation resources based on the burst calculation demands sent by the cloud clients.
CN202310961524.6A 2023-08-01 2023-08-01 Cloud computing environment resource allocation optimization method and system Withdrawn CN116991581A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310961524.6A CN116991581A (en) 2023-08-01 2023-08-01 Cloud computing environment resource allocation optimization method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310961524.6A CN116991581A (en) 2023-08-01 2023-08-01 Cloud computing environment resource allocation optimization method and system

Publications (1)

Publication Number Publication Date
CN116991581A true CN116991581A (en) 2023-11-03

Family

ID=88522807

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310961524.6A Withdrawn CN116991581A (en) 2023-08-01 2023-08-01 Cloud computing environment resource allocation optimization method and system

Country Status (1)

Country Link
CN (1) CN116991581A (en)

Similar Documents

Publication Publication Date Title
EP3673370B1 (en) System and method for distributed resource requirement and allocation
CN109471727B (en) Task processing method, device and system
CN111708627B (en) Task scheduling method and device based on distributed scheduling framework
US20180198855A1 (en) Method and apparatus for scheduling calculation tasks among clusters
CN111985726B (en) Resource quantity prediction method and device, electronic equipment and storage medium
US10255114B2 (en) Abnormality detection apparatus, control method, and program
CN110597858A (en) Task data processing method and device, computer equipment and storage medium
US8627330B2 (en) Workload manager managing a workload of an enterprise data warehouse
CN112689007B (en) Resource allocation method, device, computer equipment and storage medium
CN110955516B (en) Batch task processing method and device, computer equipment and storage medium
CN107682417B (en) Task allocation method and device for data nodes
CN110716800A (en) Task scheduling method and device, storage medium and electronic equipment
CN111553592A (en) Task allocation method and device based on emotion information, computer equipment and medium
CN107038067B (en) Management method and device for processing resources in distributed stream processing
CN110084476B (en) Case adjustment method, device, computer equipment and storage medium
CN110928649A (en) Resource scheduling method and device
CN116991581A (en) Cloud computing environment resource allocation optimization method and system
CN117234733A (en) Distributed system task allocation method, system, storage medium and equipment
US20140047454A1 (en) Load balancing in an sap system
CN110932935A (en) Resource control method, device, equipment and computer storage medium
Ray et al. Is high performance computing (HPC) ready to handle big data?
CN115994029A (en) Container resource scheduling method and device
CN115080253A (en) GPU task allocation method and device, electronic equipment and storage medium
CN112667392A (en) Cloud computing resource allocation method and device, computer equipment and storage medium
CN109918577B (en) Monitoring method and device for social software

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
WW01 Invention patent application withdrawn after publication
WW01 Invention patent application withdrawn after publication

Application publication date: 20231103