CN112764883A - Energy management method of cloud desktop system based on software definition - Google Patents
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Abstract
The invention discloses an energy management method of a cloud desktop system based on software definition, which belongs to the technical field of computer information and comprises the following steps: step 1: a system energy management module is added on the basis of a cloud desktop system architecture in a software definition mode; step 2: constructing an energy management framework of a cloud desktop system; and step 3: forming an iterative evolution system energy consumption model by using an AI deep learning and big data analysis method; and 4, step 4: the cloud desktop system is dynamically redefined in real time by means of a digital twin means, so that balance between system performance and energy consumption is achieved; the cloud desktop system development method based on software definition is an expansion service of the cloud desktop system defined by software, can achieve energy consumption optimization, improves resource utilization rate, effectively reduces cost, and promotes green and energy-saving development of the cloud desktop system.
Description
Technical Field
The invention belongs to the technical field of computer information, and particularly relates to an energy management method of a cloud desktop system based on software definition.
Background
With the rapid development of cloud computing, virtualization technology has become a trend of computer technology development. Computer virtualization technologies currently mainly include server virtualization, application virtualization, and desktop virtualization. The desktop virtualization technology has become the technology with the fastest development and the widest application prospect at present due to the characteristics of low cost, low power consumption, high safety, easiness in management and the like. Through the cloud desktop system, the application mode of the traditional PC is broken through, and more flexible, safe and efficient remote desktop experience is provided for users.
In a cloud desktop system in the related art, a software definition mode is adopted to decouple computing, storage, network and application software resources of a traditional terminal, a control method for virtual machine resources is reconstructed, the configuration of a virtual machine is dynamically adjusted, the resource utilization rate is improved, and high-quality on-cloud cooperative office experience with high efficiency, flexibility and low cost is provided for a designer group. As shown in fig. 1, it includes an application layer, a control layer, an infrastructure layer, and a data layer; the control method of the virtual machine is reconstructed, the full life cycle management of the virtual machine is realized, and a user-defined instruction system is provided, wherein the user-defined instruction system comprises five categories of analysis instructions, judgment instructions, scheduling instructions, execution instructions and communication instructions; according to the characteristics of software definition, the cloud desktop function and the virtual machine management process are optimized, so that the cloud desktop function and the virtual machine management process are more personalized.
However, with the wide application of the cloud desktop system, the number and the scale of the cloud servers are also greatly increased, which directly leads to that the problem of system energy consumption becomes more and more prominent, and the servers in an idle state also generate a great amount of energy consumption, which wastes resources and energy and increases the operation and maintenance cost. Therefore, the energy management method of the cloud desktop system based on the software definition is provided, the resource utilization rate is improved, the redundancy is reduced, and the cost is saved.
Disclosure of Invention
The invention provides an energy management method of a cloud desktop system based on software definition, which can meet the functional requirements of users on a cloud desktop, reduce energy consumption and reduce operation cost.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
step 1: providing an expansion service of a software-defined cloud desktop system;
step 2: constructing an energy management framework of a cloud desktop system;
and step 3: forming an iterative evolution system energy consumption model by using an AI deep learning and big data analysis method;
and 4, step 4: the real-time dynamic redefinition of the system is realized by means of a digital twin means, so that the balance between the system performance and the energy consumption is achieved.
The step 1 is an expansion service of the cloud desktop system defined by software, and on the basis of the cloud desktop system architecture in the related technology, a system energy management module is added, and a high-efficiency and energy-saving solution is provided by adopting a software definition mode.
And adding an energy consumption model at an application layer of the cloud desktop system architecture, adding an energy-saving strategy at a control layer, adding an energy sensor at an infrastructure layer, and adding a perception database and an on-chain database at a data layer.
Furthermore, when an analysis instruction is executed, the type and the difficulty of a user task and energy consumption data required by the system are analyzed, the sensing data are obtained by the energy sensor and stored in the block chain, and then when an operation instruction of the user task is executed, an energy-saving scheduling strategy is obtained by comparing energy consumption models formed by the data on the chain, so that dynamic adjustment and redefinition are carried out.
And 2, constructing an energy management framework of the cloud desktop system, which is a basic link of the energy management method. The energy management framework includes a front end, a middle end, a back end, and a database.
The front end is used for application access and data display, creating and generating a user interface and mainly comprises a user module, an authority control and an energy sensor. The user module is used for managing user information, including user registration, login, user authority and the like. The authority control is used for user authority authentication and relates to Portal authentication so as to improve the safety of data interaction. The energy sensor is used for acquiring energy consumption data and is the basis for establishing an energy consumption model; the energy consumption data includes direct data and indirect data.
Further, the direct energy consumption data comprises power consumption of an electric energy system, a refrigeration system, a lighting system and the like of the physical server; indirect energy consumption data includes consumption of computing resources, storage resources, and network resources, among others.
The middle terminal is used for data interaction and resource scheduling and comprises a resource management system and a network proxy server. The resource management system is used for storing and scheduling computing resources, storage resources and network resources, and can optimize an energy consumption model according to a resource scheduling strategy so as to improve the utilization efficiency of the resources. The network proxy server is used for network deployment management, and avoids the phenomenon of link blockage when scheduling resources by a scheduling strategy.
The back end is used for data management and service processing and mainly comprises a virtual machine cluster, an energy consumption model and data management. The virtual machine cluster realizes the functions of calculation, storage and transmission. The energy consumption model is the embodiment of the internal correlation of system energy consumption, is the basis of energy consumption optimization, and generally comprises three states: standby, running (calculating, storing and transmitting) and idle, wherein the standby state is a sleep mode, and each module is closed to provide the lowest power consumption; the running state is that each module is used for calculating, storing and transmitting the execution instruction; and the idle state is that a part of modules are in the running state, so that a part of energy consumption is saved. The data management is the scheduling and updating of the database. The database includes perception data and on-chain data.
The step 3 provides an iterative evolution system energy consumption model formed by an AI deep learning and big data analysis method, and specifically includes the following steps:
step 3.1, preparing data: including direct energy consumption data of the physical machine and indirect energy consumption data of the computing, storing and transmitting resources.
Step 3.2, establishing an energy consumption model: according to the difficulty degree of a user task and the minimum energy consumption of required resources, establishing an energy consumption model, wherein the specific energy consumption model comprises the following steps:
pi＝ai+bixi
wherein, P is total energy consumption, P0Direct energy consumption of the physical machine during dormancy; p is a radical of1Associated with a computing node, p2Associated with a storage node, p3Associated with a transmission node, aiRelating to virtual machine clusters, biRelated to the intensity of the user's work task, xiThe length of time required to perform the task. a isi、biAll taken from the deep learning system, are prediction parameters generated by learning corresponding contents (cluster efficiency and task working strength).
Step 3.3, iterative evolution of the energy consumption model is completed through a deployed deep learning platform, and the specific steps are as follows:
step 3.31: comparing the models;
step 3.32: updating the initial data set and adjusting the initial model;
step 3.33: and testing and obtaining the energy consumption model with the optimal cost performance.
Step 4, realizing real-time dynamic redefinition of the system by means of a digital twin means, and carrying out real-time monitoring, comparison and adjustment on physical function efficiency data based on an average value of related data obtained from a deep learning platform to realize optimization of system energy consumption; the method comprises the following specific steps:
step 4.1, pre-analysis: performing energy consumption analysis on a work task initiated by a user to obtain analysis data;
step 4.2, comparison: comparing the analysis data with the energy consumption model parameters by adopting a digital twin means, and continuing if the analysis data is within a normal deviation range; if the deviation exceeds the normal deviation range, readjusting;
step 4.3, energy-saving resource scheduling strategy: and comprehensively analyzing the energy efficiency data by combining the user task and the virtual machine load type to form an energy-saving resource scheduling strategy.
And 4.4, real-time dynamic redefinition: and scheduling the virtual machine cluster, calculation, storage and network resources according to the energy-saving strategy, dynamically adjusting the resources and redefining the energy consumption model.
Compared with the prior art, the invention has the beneficial effects that:
the energy management method of the cloud desktop system based on the software definition is an expansion service of the cloud desktop system based on the software definition, adopts AI deep learning and big data analysis to establish a system energy consumption model, forms the target of a resource scheduling strategy, realizes energy consumption optimization, improves the resource utilization rate, effectively reduces the cost, and promotes the green and energy-saving development of the cloud desktop system.
Drawings
Fig. 1 is a cloud desktop system framework based on software definition according to this embodiment.
Fig. 2 is a cloud desktop system energy management framework according to the embodiment.
Fig. 3 is a schematic diagram of an energy consumption model based on AI deep learning according to the embodiment.
Fig. 4 is a flow chart of energy consumption model customization in this embodiment.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
The invention discloses an energy management method of a cloud desktop system based on software definition, which comprises the following steps:
step 1: a system energy management module is added on the basis of a cloud desktop system architecture in a software definition mode;
step 2: constructing an energy management framework of a cloud desktop system;
and step 3: forming an iterative evolution system energy consumption model by using an AI deep learning and big data analysis method;
and 4, step 4: the cloud desktop system is dynamically redefined in real time by means of digital twin, so that balance between system performance and energy consumption is achieved.
As shown in fig. 1, the cloud desktop system framework based on software definition of the present embodiment. And adding an energy consumption model at an application layer of the cloud desktop system architecture, adding an energy-saving strategy at a control layer, adding an energy sensor at an infrastructure layer, and adding a perception database and an on-chain database at a data layer.
When an analysis instruction is executed, the type and the difficulty of a user task and energy consumption data required by a system are analyzed, sensing data are obtained by an energy sensor and stored in a block chain, and then when a user task operation instruction is executed, an energy-saving scheduling strategy is obtained by comparing energy consumption models formed by data on the chain, so that dynamic adjustment and redefinition are carried out.
As shown in fig. 2, the cloud desktop system energy management framework of the present embodiment; the system comprises a front end, a middle end, a back end and a database;
the front end is used for application access and data display, creating and generating a user interface and mainly comprises a user module, an authority control and an energy sensor; the user module is used for managing user information, including user registration, login and user authority; the authority control is used for user authority authentication and relates to Portal authentication so as to improve the security of data interaction; the energy sensor is used for acquiring energy consumption data and is the basis for establishing an energy consumption model; the energy consumption data comprises direct data and indirect data;
the direct energy consumption data comprises the power consumption of an electric energy system, a refrigeration system and a lighting system of the physical server; the indirect energy consumption data comprises consumption of computing resources, storage resources and network resources;
the middle terminal is used for data interaction and resource scheduling and comprises a resource management system and a network proxy server; the resource management system is used for storing and scheduling computing resources, storage resources and network resources, and can optimize an energy consumption model according to a resource scheduling strategy and improve the utilization efficiency of resources; the network proxy server is used for network deployment management to avoid the phenomenon of link blockage when scheduling resources by a scheduling strategy;
the back end is used for data management and service processing, including virtual machine clustering, energy consumption model and data management; the virtual machine cluster realizes the functions of calculation, storage and transmission; the energy consumption model is the embodiment of the internal correlation of system energy consumption, is the basis of energy consumption optimization, and generally comprises three states: standby, running and idle, wherein the standby state is a sleep mode, and each module is closed to provide the lowest power consumption; the running state is that each module is calculating, storing and transmitting to execute the instruction; the idle state is that a part of modules are in a running state, so that a part of energy consumption is saved; the data management is the scheduling and updating of the database.
The database includes perception data and on-chain data.
As shown in fig. 3, the energy consumption model based on AI deep learning of the present embodiment is schematically illustrated. The method for forming the system energy consumption model capable of iterative evolution by using the deep learning and big data analysis method of AI specifically comprises the following steps:
step 3.1: preparing data: including direct energy consumption data of the physical machine and indirect energy consumption data of the computing, storing and transmitting resources.
Step 3.2: establishing an energy consumption model: according to the difficulty degree of a user task and the minimum energy consumption of required resources, establishing an energy consumption model, wherein the specific energy consumption model comprises the following steps:
pi＝ai+bixi
wherein, P is total energy consumption, P0Direct energy consumption of the physical machine during dormancy; p is a radical of1Associated with a computing node, p2Associated with a storage node, p3Associated with the transmitting node, αiRelating to virtual machine clusters, biRelated to the intensity of the user's work task, xiThe length of time required to perform the task. Alpha is alphai、biAll taken from a deep learning system and are prediction parameters generated by learning corresponding contents; the corresponding content comprises cluster efficiency and task working strength;
and 3.3, finishing iterative evolution of the energy consumption model through a deployed deep learning platform.
Step 3.3 further comprises:
step 3.31: comparing the models;
step 3.32: updating the initial data set and adjusting the initial model;
step 3.33: and testing and obtaining the energy consumption model with the optimal cost performance.
As shown in fig. 4, the energy consumption model customization flow chart of the present embodiment; the system real-time dynamic redefinition is realized by means of a digital twin means, and the physical function efficiency data is monitored, compared and adjusted in real time based on the average value of the related data obtained from the deep learning platform, so that the optimization of the system energy consumption is realized; the method comprises the following specific steps:
step 4.1, pre-analysis: performing energy consumption analysis on a work task initiated by a user to obtain analysis data;
step 4.2, comparison: comparing the analysis data with the energy consumption model parameters by adopting a digital twin means, and continuing if the analysis data is within a normal deviation range; if the deviation exceeds the normal deviation range, readjusting;
step 4.3, energy-saving resource scheduling strategy: and comprehensively analyzing the energy efficiency data by combining the user task and the virtual machine load type to form an energy-saving resource scheduling strategy.
And 4.4, real-time dynamic redefinition: and scheduling the virtual machine cluster, calculation, storage and network resources according to the energy-saving strategy, dynamically adjusting the resources and redefining the energy consumption model.
The energy management method of the cloud desktop system based on the software definition is an expansion service of the cloud desktop system based on the software definition, adopts AI deep learning and big data analysis to establish a system energy consumption model, forms the target of a resource scheduling strategy, realizes energy consumption optimization, improves the resource utilization rate, effectively reduces the cost, and promotes the green and energy-saving development of the virtual desktop system.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.
Claims (7)
1. A method for managing energy of a cloud desktop system based on software definition is characterized by comprising the following steps:
step 1: a system energy management module is added on the basis of a cloud desktop system architecture in a software definition mode;
step 2: constructing an energy management framework of a cloud desktop system;
and step 3: forming an iterative evolution system energy consumption model by using an AI deep learning and big data analysis method;
and 4, step 4: the cloud desktop system is dynamically redefined in real time by means of digital twin, so that balance between system performance and energy consumption is achieved.
2. The energy management method of the cloud desktop system based on software definition according to claim 1, wherein: the newly added system energy management module in the step 1 specifically comprises:
and adding an energy consumption model at an application layer of the cloud desktop system architecture, adding an energy-saving strategy at a control layer, adding an energy sensor at an infrastructure layer, and adding a perception database and an on-chain database at a data layer.
3. The energy management method of the cloud desktop system based on software definition according to claim 2, wherein: the step 1 is further as follows:
when the cloud desktop system executes an analysis instruction, the type and the difficulty of a user task and energy consumption data required by the system are analyzed, the energy sensor acquires sensing data and stores the sensing data in a block chain, and then when the user task operation instruction is executed, an energy-saving scheduling strategy is obtained by comparing energy consumption models formed by data on the chain, so that dynamic adjustment and redefinition are carried out.
4. The energy management method of the cloud desktop system based on software definition according to claim 1, wherein: the cloud desktop system energy management framework in the step 2 comprises a front end, a middle end, a rear end and a database;
the front end is used for application access and data display, creating and generating a user interface and mainly comprises a user module, an authority control and an energy sensor; the user module is used for managing user information, including user registration, login and user authority; the authority control is used for user authority authentication and relates to Portal authentication so as to improve the security of data interaction; the energy sensor is used for acquiring energy consumption data and is the basis for establishing an energy consumption model; the energy consumption data comprises direct data and indirect data;
the direct energy consumption data comprises the power consumption of an electric energy system, a refrigeration system and a lighting system of the physical server; the indirect energy consumption data comprises consumption of computing resources, storage resources and network resources;
the middle terminal is used for data interaction and resource scheduling and comprises a resource management system and a network proxy server; the resource management system is used for storing and scheduling computing resources, storage resources and network resources, and can optimize an energy consumption model according to a resource scheduling strategy and improve the utilization efficiency of resources; the network proxy server is used for network deployment management to avoid the phenomenon of link blockage when scheduling resources by a scheduling strategy;
the back end is used for data management and service processing, including virtual machine clustering, energy consumption model and data management; the virtual machine cluster realizes the functions of calculation, storage and transmission; the energy consumption model is the embodiment of the internal correlation of system energy consumption, is the basis of energy consumption optimization, and generally comprises three states: standby, running and idle, wherein the standby state is a sleep mode, and each module is closed to provide the lowest power consumption; the running state is that each module is calculating, storing and transmitting to execute the instruction; the idle state is that a part of modules are in a running state, so that a part of energy consumption is saved; the data management is the scheduling and updating of the database.
The database includes perception data and on-chain data.
5. The energy management method of the cloud desktop system based on software definition according to claim 1, wherein: the step 3 specifically comprises the following steps:
step 3.1: preparing data: including direct energy consumption data of the physical machine and indirect energy consumption data of the computing, storing and transmitting resources.
Step 3.2: establishing an energy consumption model: according to the difficulty degree of a user task and the minimum energy consumption of required resources, establishing an energy consumption model, wherein the specific energy consumption model comprises the following steps:
pi＝ai+bixi
wherein, P is total energy consumption, P0Direct energy consumption of the physical machine during dormancy; p is a radical of1Associated with a computing node, p2Associated with a storage node, p3Associated with a transmission node, aiRelating to virtual machine clusters, biRelated to the intensity of the user's work task, xiThe length of time required to perform the task. a isi、biAll taken from a deep learning system and are prediction parameters generated by learning corresponding contents; the corresponding content comprises cluster efficiency and task working strength;
and 3.3, finishing iterative evolution of the energy consumption model through a deployed deep learning platform.
6. The energy management method of the cloud desktop system based on software definition according to claim 5, wherein: the step 3.3 is specifically as follows:
step 3.31: comparing the models;
step 3.32: updating the initial data set and adjusting the initial model;
step 3.33: and testing and obtaining the energy consumption model with the optimal cost performance.
7. The energy management method of the cloud desktop system based on software definition according to claim 1, wherein: the step 4 specifically comprises the following steps:
step 4.1, pre-analysis: performing energy consumption analysis on a work task initiated by a user to obtain analysis data;
step 4.2, comparison: comparing the analysis data with the energy consumption model parameters by adopting a digital twin means, and continuing if the analysis data is within a normal deviation range; if the deviation exceeds the normal deviation range, readjusting;
step 4.3, energy-saving resource scheduling strategy: and comprehensively analyzing the energy efficiency data by combining the user task and the virtual machine load type to form an energy-saving resource scheduling strategy.
And 4.4, real-time dynamic redefinition: and scheduling the virtual machine cluster, calculation, storage and network resources according to the energy-saving strategy, dynamically adjusting the resources and redefining the energy consumption model.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115658215A (en) * | 2022-12-14 | 2023-01-31 | 网思科技股份有限公司 | Digital twinning visualization operation method, system and readable storage medium |
| CN115658215B (en) * | 2022-12-14 | 2023-03-31 | 网思科技股份有限公司 | Digital twin visualization operation method, system and readable storage medium |
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