CN107908459B - Cloud computing scheduling system - Google Patents
Cloud computing scheduling system Download PDFInfo
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- CN107908459B CN107908459B CN201711155677.2A CN201711155677A CN107908459B CN 107908459 B CN107908459 B CN 107908459B CN 201711155677 A CN201711155677 A CN 201711155677A CN 107908459 B CN107908459 B CN 107908459B
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- 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
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
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- 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]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
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- 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
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/4557—Distribution of virtual machine instances; Migration and load balancing
Abstract
The invention discloses a cloud computing scheduling system which comprises an information acquisition module, a task receiving module, an electronic equipment working condition access module, a data preprocessing module, a cloud computing forecasting sub-module, a cloud computing joint scheduling analysis module, an expert decision analysis module and a man-machine interaction module. The invention adopts a centralized task scheduling method, when a user task arrives, the user task is temporarily stored in a user task set, the optimized output of a task allocation scheme is carried out in a combined group scheduling mode, simultaneously, the monitoring of the working load condition of each virtual machine is realized in the whole calculation process, and the dynamic real-time updating of scheduling decision is realized, thereby effectively improving the bearing capacity of the server, balancing the link load, increasing the network throughput and improving the flexibility.
Description
Technical Field
The invention belongs to the technical field of cloud computing, and particularly relates to a cloud computing scheduling system.
Background
Cloud computing is the integration and development of parallel computing, distributed computing and grid computing, and is a revolution integrating software technology, hardware technology, virtual technology and network technology. The cloud computing needs to realize that resources on the internet are distributed on the network as required like water and electricity, and can be dynamically adjusted reasonably according to the complexity of a request task and the size of a data set, so that the horizontal expansion capability of a system can be improved, and the cost of software and hardware resources is greatly reduced.
The simple explanation of a server cluster is that a plurality of servers are centralized to provide services to the outside, the cluster is hidden from clients, and the clients feel like one server provides services to the cluster. The cluster obtains high-speed computing performance and mass data storage space through a parallel computing technology. It is a necessary trend that server clusters are replacing single servers as load bearing units of application services.
How to reasonably distribute task requests of users on a server cluster and a virtual machine to enable all servers to achieve a relative load balancing state, and meanwhile, the problem that serious waste of resources and energy is caused by starting too many servers with low utilization rate is avoided as much as possible is a difficult problem to be paid attention to.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a cloud computing scheduling system, which can effectively improve the bearing capacity of a server, balance link load, increase network throughput, improve flexibility and provide a process of scheduling the cloud computing system which is optimized as much as possible.
To solve the above technical problem, an embodiment of the present invention provides a cloud computing scheduling system, including
The information acquisition module consists of all function detection modules and is used for detecting the workload parameters of all the virtual machines in a fixed time and quantitative mode;
the task receiving module is used for receiving task demand data of a user and sending the received demand data to the scheduling decision module;
the electronic equipment working condition access module is used for accessing real-time operation working condition information of each electronic equipment;
the data preprocessing module is used for receiving and standardizing the acquired workload parameter data and the electronic equipment operation condition data;
the cloud computing forecasting submodule is used for establishing a short-term forecasting unit by adopting a statistical regression and data driving method, and generating short-term cloud computing system condition forecasting information by utilizing the acquired workload parameters and the electronic equipment operation condition data for the cloud computing joint scheduling analysis module to use;
the cloud computing joint scheduling analysis module is used for optimizing and calculating by adopting a multi-group differential evolution algorithm according to the received task demand data and the load working condition information of the virtual machine which completes the standardized processing to obtain a cloud computing group joint scheduling scheme which is beneficial to improving the efficient operation of the cloud computing system;
the expert decision analysis module is used for receiving the cloud computing group joint scheduling alternative schemes obtained by the cloud computing group joint scheduling analysis module, comparing the change trends of the operation working conditions of the electronic equipment caused by the different cloud computing group joint scheduling alternative schemes, and providing a final scheduling decision scheme;
and the human-computer interaction module consists of a high-performance server and a display terminal thereof.
Preferably, the cloud computing group joint scheduling scheme at least includes performing reasonable allocation and migration of existing virtual machines, and performing establishment of a new virtual machine as needed.
Preferably, the expert decision analysis module comprises
The physical model building module is used for building a physical model of the electric power equipment according to the received working conditions of the electronic equipment and corresponding basic parameter data of the electronic equipment through Flac 3D;
the virtual parameter actuation module is used for changing parameters within a specified range after establishing a relationship with each element in the physical model construction module, so that various simulation analysis methods are driven to calculate and solve different parameters;
the virtual parameter module is used for inserting a logic unit which can directly obtain a corresponding result or information target into the established mathematical model;
the simulation analysis module is used for inputting parameters and algorithms which can be decomposed into design variables, design targets and design constraints, dividing the input parameters and algorithms into units, characteristics and loads, and respectively applying the units, the characteristics and the loads to the specified modules;
the virtual parameter actuation module feeds back results to the simulation analysis module through the circulation execution simulation analysis module, the simulation analysis module extracts the results and sends the results to the virtual parameter module, and the virtual parameter module receives the results and automatically displays result data.
Preferably, the cloud computing forecast submodule comprises
The graph drawing module is used for drawing various curve graphs according to the monitoring data;
the comparison analysis module is used for performing comparison analysis and prediction on the drawn curve and the original actual measurement curve and outputting an analysis prediction result;
and the regression calculation module is used for carrying out regression calculation on the drawn curve through different functions.
The graph drawing module generates a temporal curve and a spatial effect curve which change along with time and space according to the input monitoring data; the temporal curve shows the change condition of the original data of each monitoring point along with time, and the spatial effect curve highlights the change rule of the monitoring results of different measuring points along with the position of the information acquisition module at the same time.
The invention has the following beneficial effects:
by adopting a centralized task scheduling method, when a user task arrives, the user task is temporarily stored in a user task set, the optimized output of a task allocation scheme is carried out in a combined group scheduling mode, meanwhile, in the whole calculation process, the monitoring of the working load condition of each virtual machine is realized, and the dynamic real-time updating of scheduling decisions is realized, so that the bearing capacity of a server can be effectively improved, the link load is balanced, the network throughput is increased, and the flexibility is improved.
Drawings
Fig. 1 is a schematic block diagram of a cloud computing scheduling system according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, an embodiment of the present invention provides a cloud computing scheduling system, which includes
The information acquisition module consists of all function detection modules and is used for detecting the workload parameters of all the virtual machines in a fixed time and quantitative mode;
the task receiving module is used for receiving task demand data of a user, collecting the received demand data to a certain number and then sending the data to the scheduling decision module, wherein the number is not less than 2 but not more than 10;
the electronic equipment working condition access module is used for accessing real-time operation working condition information of each electronic equipment;
the data preprocessing module is used for receiving and standardizing the acquired workload parameter data and the electronic equipment operation condition data;
the cloud computing forecasting submodule is used for establishing a short-term forecasting unit by adopting a statistical regression and data driving method, and generating short-term cloud computing system condition forecasting information by utilizing the acquired workload parameters and the electronic equipment operation condition data for the cloud computing joint scheduling analysis module to use;
the cloud computing joint scheduling analysis module is used for optimizing and calculating by adopting a multi-group differential evolution algorithm according to the received task demand data and the load working condition information of the virtual machine which completes the standardized processing to obtain a cloud computing group joint scheduling scheme which is beneficial to improving the efficient operation of the cloud computing system;
the expert decision analysis module is used for receiving the cloud computing group joint scheduling alternative schemes obtained by the cloud computing group joint scheduling analysis module, comparing the change trends of the operation working conditions of the electronic equipment caused by the different cloud computing group joint scheduling alternative schemes, and providing a final scheduling decision scheme;
and the human-computer interaction module consists of a high-performance server and a display terminal thereof.
The cloud computing group joint scheduling scheme at least comprises the steps of reasonably distributing and migrating the existing virtual machines and establishing new virtual machines according to needs.
The expert decision analysis module comprises
The physical model building module is used for building a physical model of the electric power equipment according to the received working conditions of the electronic equipment and corresponding basic parameter data of the electronic equipment through Flac 3D;
the virtual parameter actuation module is used for changing parameters within a specified range after establishing a relationship with each element in the physical model construction module, so that various simulation analysis methods are driven to calculate and solve different parameters;
the virtual parameter module is used for inserting a logic unit which can directly obtain a corresponding result or information target into the established mathematical model;
the simulation analysis module is used for inputting parameters and algorithms which can be decomposed into design variables, design targets and design constraints, dividing the input parameters and algorithms into units, characteristics and loads, and respectively applying the units, the characteristics and the loads to the specified modules;
the virtual parameter actuation module feeds back results to the simulation analysis module through the circulation execution simulation analysis module, the simulation analysis module extracts the results and sends the results to the virtual parameter module, and the virtual parameter module receives the results and automatically displays result data.
The cloud computing forecast submodule comprises
The graph drawing module is used for drawing various curve graphs according to the monitoring data;
the comparison analysis module is used for performing comparison analysis and prediction on the drawn curve and the original actual measurement curve and outputting an analysis prediction result;
and the regression calculation module is used for carrying out regression calculation on the drawn curve through different functions.
The graph drawing module generates a temporal curve and a spatial effect curve which change along with time and space according to the input monitoring data; the temporal curve shows the change condition of the original data of each monitoring point along with time, and the spatial effect curve highlights the change rule of the monitoring results of different measuring points along with the position of the information acquisition module at the same time.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (6)
1. A cloud computing scheduling system is characterized by comprising
The information acquisition module consists of all function detection modules and is used for detecting the workload parameters of all the virtual machines in a fixed time and quantitative mode;
the task receiving module is used for receiving task demand data of a user, collecting the received demand data to a certain number and then sending the data to the scheduling decision module, wherein the number is not less than 2 but not more than 10;
the electronic equipment working condition access module is used for accessing real-time operation working condition information of each electronic equipment;
the data preprocessing module is used for receiving and standardizing the acquired workload parameter data and the electronic equipment operation condition data;
the cloud computing forecasting submodule is used for establishing a short-term forecasting unit by adopting a statistical regression and data driving method, and generating short-term cloud computing system condition forecasting information by utilizing the acquired workload parameters and the electronic equipment operation condition data for the cloud computing joint scheduling analysis module to use;
the cloud computing joint scheduling analysis module is used for collecting a certain amount of demand data and the virtual machine load working condition information which is subjected to standardized processing in the scheduling decision module, and optimizing and calculating by adopting a multi-group differential evolution algorithm to obtain a cloud computing group joint scheduling scheme which is beneficial to improving the efficient operation of a cloud computing system;
the expert decision analysis module is used for receiving the cloud computing group joint scheduling alternative schemes obtained by the cloud computing group joint scheduling analysis module, comparing the change trends of the operation working conditions of the electronic equipment caused by the different cloud computing group joint scheduling alternative schemes, and providing a final scheduling decision scheme;
and the human-computer interaction module consists of a high-performance server and a display terminal thereof.
2. The cloud computing scheduling system of claim 1, wherein the cloud computing group joint scheduling scheme at least includes performing rational allocation and migration of existing virtual machines, and performing establishment of new virtual machines as needed.
3. The cloud computing scheduling system of claim 1 wherein said expert decision analysis module comprises
The physical model building module is used for building a physical model of the electronic equipment according to the received working conditions of the electronic equipment and corresponding basic parameter data of the electronic equipment through Flac 3D;
the virtual parameter actuation module is used for changing the parameters within a specified range after establishing a relationship with the elements in the physical model construction module, so as to drive various simulation analysis methods to calculate and solve the parameters;
the virtual parameter module is used for inserting a logic unit which can directly obtain a corresponding result or information target into the established physical model;
the simulation analysis module is used for inputting parameters and algorithms which can be decomposed into design variables, design targets and design constraints, dividing the input parameters and algorithms into units, characteristics and loads, and respectively applying the units, the characteristics and the loads to the specified modules;
the virtual parameter actuation module feeds back results to the simulation analysis module through the circulation execution simulation analysis module, the simulation analysis module extracts the results and sends the results to the virtual parameter module, and the virtual parameter module receives the results and automatically displays result data.
4. The cloud computing scheduling system of claim 1 wherein the cloud computing forecast submodule comprises
The graph drawing module is used for drawing various curve graphs according to the monitoring data;
the comparison analysis module is used for performing comparison analysis and prediction on the drawn curve and the original actual measurement curve and outputting an analysis prediction result;
and the regression calculation module is used for carrying out regression calculation on the drawn curve through different functions.
5. The cloud computing scheduling system of claim 4 wherein the graph plotting module generates temporal curves and spatial effect curves that vary over time and space based on the input monitoring data.
6. The cloud computing scheduling system of claim 5 wherein the temporal curve shows the change of the raw data of each monitoring point with time, and the spatial effect curve highlights the change rule of the monitoring results of different measuring points with the position of the information acquisition module at the same time.
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| CN108764580A (en) * | 2018-05-28 | 2018-11-06 | 延安大学 | A kind of assembly shop scheduling system |
| CN111258767B (en) * | 2020-01-22 | 2023-01-03 | 中国人民解放军国防科技大学 | Cloud computing resource intelligent distribution method and device for complex system simulation application |
| CN111639395B (en) * | 2020-05-26 | 2023-07-04 | 成都运达科技股份有限公司 | Device and method for acquiring vibration information of vehicle under transverse track expansion |
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| CN102004671A (en) * | 2010-11-15 | 2011-04-06 | 北京航空航天大学 | Resource management method of data center based on statistic model in cloud computing environment |
| CN102546379A (en) * | 2010-12-27 | 2012-07-04 | ***通信集团公司 | Virtualized resource scheduling method and system |
| CN103679285A (en) * | 2013-11-29 | 2014-03-26 | 河海大学 | Reservoir group combined operation scheduling system and method for improving river and lake relationship |
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