CN112764883B - 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 newly added on the basis of a cloud desktop system architecture by adopting a software definition mode; step 2: constructing a cloud desktop system energy management frame; step 3: forming an iteratively evolution system energy consumption model by using an AI deep learning and big data analysis method; step 4: the cloud desktop system is redefined dynamically in real time by means of digital twinning, so that the balance between the system performance and the energy consumption is achieved; the cloud desktop system expansion service is a software-defined cloud desktop system expansion service, can realize energy consumption optimization, improves resource utilization rate, effectively reduces cost, and promotes green 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 in the development of computer technology. Computer virtualization technologies currently include mainly 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, easy management and the like. The cloud desktop system breaks through the application mode of the traditional PC, and provides more flexible, safe and efficient remote desktop experience for users.
In the cloud desktop system in the related technology, a software definition mode is adopted to decouple the calculation, storage, network and application software resources of the traditional terminal, a control method for virtual machine resources is reconstructed, the virtual machine configuration is dynamically adjusted, the resource utilization rate is improved, and efficient, flexible and low-cost collaborative office experience on the high-quality cloud 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 for the virtual machine is reconfigured, full life cycle management is realized, and a custom instruction system is provided, wherein the custom instruction system comprises five major categories of analysis instructions, judgment instructions, scheduling instructions, execution instructions and communication instructions; according to the characteristics of the software definition, the cloud desktop function and the virtual machine management flow are optimized, so that the cloud desktop function and the virtual machine management flow 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 causes the problem of system energy consumption to become more and more prominent, and the servers in an idle state generate a great amount of energy consumption, which wastes resources and energy and increases operation and maintenance costs. Therefore, we propose an energy management method of cloud desktop system based on software definition, which improves the resource utilization rate, reduces redundancy and saves cost.
Disclosure of Invention
The invention provides an energy management method of a cloud desktop system based on software definition, which not only can meet the functional requirements of users on the cloud desktop, but also can reduce energy consumption and operation cost.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
step 1: providing an expansion service of a software-defined cloud desktop system;
step 2: constructing a cloud desktop system energy management frame;
step 3: the deep learning and big data analysis method of AI is used to form a system energy consumption model capable of iterative evolution;
step 4: the real-time dynamic redefinition of the system is realized by means of digital twin means, so that the balance between the system performance and the energy consumption is achieved.
Step 1 is an expansion service of a software-defined cloud desktop system, and on the basis of the cloud desktop system architecture in the related art, a system energy management module is newly added, and a solution of high efficiency and energy saving is provided in a software-defined 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.
Further, when the analysis instruction is executed, the user task type, the difficulty and the energy consumption data required by the system are analyzed, the sensing data are obtained by the energy sensor and stored in the blockchain, and when the user task operation instruction is executed, the energy-saving scheduling strategy is obtained by comparing the energy consumption model 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 comprises a front end, a middle end, a back end and a database.
The front end is used for application access and data display, and creates and generates a user interface, and mainly comprises a user module, 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 involves Portal authentication to improve the security of data interaction. The energy sensor is used for collecting energy consumption data and is the basis for building an energy consumption model; the energy consumption data includes direct data and indirect data.
Further, the direct energy consumption data comprise the power consumption of an electric energy system, a refrigerating system, a lighting system and the like of the physical server; indirect energy consumption data includes consumption of computing resources, storage resources, network resources, and the like.
The middle end 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 the energy consumption model according to a resource scheduling strategy so as to improve the resource utilization efficiency. The network proxy server is used for network deployment management and avoids the phenomenon of link blockage when the scheduling strategy schedules resources.
The back end is used for data management and business 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 an embodiment of the internal association of the system energy consumption, is the basis of energy consumption optimization, and generally comprises three states: standby, running (calculating, storing and transmitting), 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 an executing instruction; the idle state is that a part of modules are in an operation state, so that a part of energy consumption is saved. The data management is scheduling and updating of databases. The database includes awareness data and on-chain data.
Step 3, providing an iteratively evolution system energy consumption model formed by an AI deep learning and big data analysis method, wherein the method comprises the following specific steps:
step 3.1, preparing data: including direct energy consumption data of the physical machine and indirect energy consumption data of the computing, storage and transmission resources.
Step 3.2, establishing an energy consumption model: according to the task difficulty of the user and the minimum energy consumption of the required resources, an energy consumption model is established, and a specific energy consumption model is established:
p i ＝a i +b i x i
wherein, P is the total energy consumption, P 0 The direct energy consumption of the physical machine in dormancy is realized; p is p 1 In connection with the computing node, p 2 In connection with storage nodes, p 3 In connection with a transmission node, a i In connection with virtual machine clusters, b i Related to user task intensity, x i The time period required to perform the task. a, a i 、b i All the prediction parameters are taken from a deep learning system and are prediction parameters generated by learning corresponding contents (cluster efficiency and task work intensity).
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 an initial data set and adjusting an 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 digital twin means, and carrying out real-time monitoring, comparison and adjustment on physical energy 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 in 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.
Step 4.4, real-time dynamic redefinition: and scheduling the virtual machine clusters, the computing, storing 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 a target of a resource scheduling strategy, realizes energy consumption optimization, improves resource utilization rate, effectively reduces cost and promotes green energy conservation development of the cloud desktop system.
Drawings
Fig. 1 is a software-defined-based cloud desktop system framework of the present embodiment.
Fig. 2 is a cloud desktop system energy management framework of the present embodiment.
Fig. 3 is a schematic diagram of an energy consumption model based on AI deep learning in this embodiment.
Fig. 4 is a self-defining flow chart of the energy consumption model in the present embodiment.
Detailed Description
The invention is described in further detail below with reference to the drawings and the 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 newly added on the basis of a cloud desktop system architecture by adopting a software definition mode;
step 2: constructing a cloud desktop system energy management frame;
step 3: forming an iteratively evolution system energy consumption model by using an AI deep learning and big data analysis method;
step 4: the cloud desktop system is redefined dynamically in real time by means of digital twinning, so that the balance between the system performance and the 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 the analysis instruction is executed, the user task type, the difficulty and the energy consumption data required by the system are analyzed, the sensing data are obtained by the energy sensor and stored in the blockchain, and when the user task operation instruction is executed, the energy-saving scheduling strategy is obtained by comparing the energy consumption model formed by the data on the chain, so that the 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 rear end and a database;
the front end is used for application access and data display, and creates and generates a user interface, and mainly comprises a user module, 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 involves Portal authentication so as to improve the security of data interaction; the energy sensor is used for collecting energy consumption data and is the basis for building an energy consumption model; the energy consumption data comprises direct data and indirect data;
the direct energy consumption data comprise the power consumption of an electric energy system, a refrigerating system and a lighting system of a physical server; the indirect energy consumption data comprises consumption of computing resources, storage resources and network resources;
the middle end 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 the resources; the network proxy server is used for network deployment management, so that the phenomenon of link blockage is avoided when resources are scheduled by a scheduling strategy;
the back end is used for data management and business processing and 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 an embodiment of the internal association of the 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 an execution instruction; the idle state is that a part of modules are in an operation state, so that a part of energy consumption is saved; the data management is scheduling and updating of databases.
The database includes awareness 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 shown. The system energy consumption model capable of iterating and evolving is formed by 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, storage and transmission resources.
Step 3.2: establishing an energy consumption model: according to the task difficulty of the user and the minimum energy consumption of the required resources, an energy consumption model is established, and a specific energy consumption model is established:
p i ＝a i +b i x i
wherein, P is the total energy consumption, P 0 The direct energy consumption of the physical machine in dormancy is realized; p is p 1 In connection with the computing node, p 2 In connection with storage nodes, p 3 In connection with the transmission node, alpha i In connection with virtual machine clusters, b i Related to user task intensity, x i The time period required to perform the task. Alpha i 、b i All are taken from the deep learning system and are prediction parameters generated by learning corresponding contents; the corresponding content comprises cluster efficiency and task work intensity;
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 an initial data set and adjusting an 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 custom flow chart of the present embodiment; real-time dynamic redefinition of the system is realized by means of digital twin, and real-time monitoring, comparison and adjustment are carried out on physical energy efficiency data based on an average value of related data obtained from a deep learning platform, so that energy consumption optimization of the system 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 in 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.
Step 4.4, real-time dynamic redefinition: and scheduling the virtual machine clusters, the computing, storing 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 a target of a resource scheduling strategy, realizes energy consumption optimization, improves resource utilization rate, effectively reduces cost, and promotes green energy saving development of a virtual desktop system.
It is to be understood that the above embodiments are merely illustrative of the application of the principles of the present invention, but not in limitation thereof. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the invention, and are also considered to be within the scope of the invention.
Claims (2)
1. The energy management method of the cloud desktop system based on the software definition is characterized by comprising the following steps of:
step 1: a system energy management module is newly added on the basis of a cloud desktop system architecture by adopting a software definition mode;
step 2: constructing a cloud desktop system energy management frame;
step 3: forming an iteratively evolution system energy consumption model by using an AI deep learning and big data analysis method;
step 4: the cloud desktop system is redefined dynamically in real time by means of digital twinning, so that the balance between the system performance and the energy consumption is achieved;
the newly added system energy management module in the step 1 specifically comprises the following steps:
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 the cloud desktop system executes an analysis instruction, analyzing the user task type, difficulty and energy consumption data required by the system, acquiring sensing data by an energy sensor and storing the sensing data in a blockchain, and further obtaining an energy-saving scheduling strategy by comparing an energy consumption model formed by data on the chain when executing a user task operation instruction, so as to dynamically adjust and redefine;
the step 3 specifically comprises the following steps:
step 3.1: preparing data: the method comprises the steps of directly consuming energy data of a physical machine and indirectly consuming energy data of calculation, storage and transmission resources;
step 3.2: establishing an energy consumption model: according to the task difficulty of the user and the minimum energy consumption of the required resources, an energy consumption model is established, and a specific energy consumption model is established:
p i ＝a i +b i x i
wherein, P is the total energy consumption, P 0 The direct energy consumption of the physical machine in dormancy is realized; p is p 1 In connection with the computing node, p 2 In connection with storage nodes, p 3 In connection with a transmission node, a i In connection with virtual machine clusters, b i Related to user task intensity, x i Time length required for executing task a i 、b i All are taken from the deep learning system and are prediction parameters generated by learning corresponding contents; the corresponding content comprises cluster efficiency and task work intensity;
step 3.3, finishing iterative evolution of the energy consumption model through a deployed deep learning platform;
the step 3.3 specifically comprises the following steps:
step 3.31: comparing the models;
step 3.32: updating an initial data set and adjusting an initial model;
step 3.33: testing and obtaining an energy consumption model with optimal cost performance;
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 in a normal deviation range; if the deviation exceeds the normal deviation range, readjusting;
step 4.3, energy-saving resource scheduling strategy: 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;
step 4.4, real-time dynamic redefinition: and scheduling the virtual machine clusters, the computing, storing and network resources according to the energy-saving strategy, dynamically adjusting the resources, and redefining the energy consumption model.
2. The energy management method of a 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, and creates and generates a user interface, and mainly comprises a user module, 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 involves Portal authentication so as to improve the security of data interaction; the energy sensor is used for collecting energy consumption data and is the basis for building an energy consumption model; the energy consumption data comprises direct data and indirect data;
the direct energy consumption data comprise the power consumption of an electric energy system, a refrigerating system and a lighting system of a physical server; the indirect energy consumption data comprises consumption of computing resources, storage resources and network resources;
the middle end 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 the resources; the network proxy server is used for network deployment management, so that the phenomenon of link blockage is avoided when resources are scheduled by a scheduling strategy;
the back end is used for data management and business processing and 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 an embodiment of the internal association of the system energy consumption, is the basis of energy consumption optimization, and 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 an execution instruction; the idle state is that a part of modules are in an operation state, so that a part of energy consumption is saved; the data management is that the database is scheduled and updated;
the database includes awareness data and on-chain data.
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