Disclosure of Invention
The invention provides a method and a system for realizing resource scheduling based on cloud computing, which mainly aim to improve the scheduling effect of realizing resource scheduling by the cloud computing.
In order to achieve the above object, the present invention provides a method for implementing resource scheduling based on cloud computing, including:
Acquiring resource use requirements of resource users on resources to be scheduled, acquiring resource use data of the resources to be scheduled, and constructing a resource prediction model of the resources to be scheduled based on the resource use data;
Acquiring a resource state of the resource to be scheduled, predicting a resource to-be-used state of the resource to be scheduled by using the resource prediction model based on the resource state, and analyzing a task to be scheduled of the resource to be scheduled based on the resource to-be-used state;
Analyzing the resource constraint condition of the resource to be scheduled, analyzing the task priority and the task dependency relationship of the task to be scheduled based on the resource use requirement, and constructing a resource scheduling strategy of the resource to be scheduled based on the resource constraint condition, the task priority and the task dependency relationship;
constructing a distributed scheduling architecture of the resources to be scheduled, and establishing redundant task nodes of task nodes corresponding to the distributed scheduling architecture;
And constructing communication protocol stacks of the task nodes and the redundant task nodes, distributing the resource scheduling strategy to the task nodes to obtain resource scheduling task nodes, and executing resource scheduling of the resources to be scheduled based on the resource scheduling task nodes, the redundant task nodes and the communication protocol.
Optionally, the constructing a resource prediction model of the resource to be scheduled based on the resource usage data includes:
Preprocessing the resource use data to obtain preprocessed use data;
extracting data sequence characteristics of the preprocessing usage data;
Performing feature conversion on the data sequence features to obtain conversion features;
determining the model type of the resource to be scheduled;
and constructing a resource prediction model of the resource to be scheduled based on the conversion characteristics, the preprocessing use data and the model type.
Optionally, the extracting the data sequence features of the preprocessing usage data includes:
serializing the preprocessing use data to obtain sequence use data;
calculating the data weight of the sequence using data;
analyzing local trends of the sequence usage data based on the data weights;
And extracting data sequence characteristics of the sequence use data based on the local trend.
Optionally, the calculating the data weight of the sequence usage data includes:
Selecting a bandwidth parameter of the sequence usage data;
based on the bandwidth parameter, data weights for the sequence usage data are calculated using the following formula:
Wherein,Represent the firstIndividual sequence usage data and thThe sequences use the data weights of the data,Represent the firstThe data is used by the sequence of the data,Represent the firstThe data is used by the sequence of the data,Representing the bandwidth parameter.
Optionally, the analyzing the task to be scheduled of the resource to be scheduled based on the resource to be used state includes:
calculating the state difference of the resources to be scheduled based on the state to be used of the resources and the state of the resources corresponding to the resources to be scheduled;
Analyzing the resource influence weight of the state difference on the resource use requirement corresponding to the resource to be scheduled;
Determining an influence elimination strategy of the state difference based on the resource influence weight;
And constructing the task to be scheduled of the resource to be scheduled based on the influence elimination strategy.
Optionally, the analyzing the task priority and task dependency relationship of the task to be scheduled based on the resource usage requirement includes:
identifying a priority factor of the task to be scheduled based on the resource usage requirement;
Analyzing the priority influence relation between the priority factor and the task to be scheduled;
Calculating task priority of the task to be scheduled based on the priority influence relation;
constructing a task dependency graph of the task to be scheduled;
and analyzing the task dependency relationship of the task to be scheduled based on the task dependency graph.
Optionally, the calculating the task priority of the task to be scheduled based on the priority impact relationship includes:
analyzing the priority factor weight of the priority factor corresponding to the task to be scheduled based on the priority influence relation;
identifying a priority factor status value for the priority factor;
Based on the priority factor weight and the priority factor state value, calculating the task priority of the task to be scheduled by using the following formula:
Wherein,Indicating the task priority of the task to be scheduled,Representing task to be scheduledThe priority factor weights of the individual priority factors,Representing task to be scheduledThe priority factor status values of the individual priority factors,Represents the adjustment coefficient of the device,Indicating the number of corresponding priority factors for the tasks to be scheduled.
Optionally, the building the distributed scheduling architecture of the resources to be scheduled includes:
analyzing the distributed demand of the resource to be scheduled;
determining a distributed component of the resource to be scheduled based on the distributed demand;
Constructing a distributed scheduling algorithm of the resources to be scheduled;
and establishing a distributed scheduling architecture of the resources to be scheduled based on the distributed component and the distributed scheduling algorithm.
Optionally, the constructing a communication protocol stack of the task node and the redundant task node includes:
Defining communication requirements of the task nodes and the redundant task nodes;
determining a communication protocol of the task node and the redundant task node based on the communication requirement;
Designing a communication protocol architecture of the communication protocol;
And constructing a communication protocol stack of the task node and the redundant task node based on the communication protocol architecture.
In order to solve the above problems, the present invention further provides a resource scheduling system based on cloud computing, the system comprising:
The resource prediction model construction module is used for acquiring the resource use requirement of a resource user on a resource to be scheduled, acquiring the resource use data of the resource to be scheduled, and constructing a resource prediction model of the resource to be scheduled based on the resource use data;
The to-be-scheduled task construction module is used for acquiring the resource state of the to-be-scheduled resource, predicting the resource to-be-used state of the to-be-scheduled resource by using the resource prediction model based on the resource state, and analyzing the to-be-scheduled task of the to-be-scheduled resource based on the resource to-be-used state;
The resource scheduling policy construction module is used for analyzing the resource constraint condition of the resource to be scheduled, analyzing the task priority and the task dependency relationship of the task to be scheduled based on the resource use requirement, and constructing the resource scheduling policy of the resource to be scheduled based on the resource constraint condition, the task priority and the task dependency relationship;
The distributed scheduling architecture construction module is used for constructing a distributed scheduling architecture of the resources to be scheduled and constructing redundant task nodes of task nodes corresponding to the distributed scheduling architecture;
And the resource scheduling module is used for constructing a communication protocol stack of the task node and the redundant task node, distributing the resource scheduling strategy to the task node to obtain a resource scheduling task node, and executing resource scheduling of the resource to be scheduled based on the resource scheduling task node, the redundant task node and the communication protocol.
The method and the device for scheduling the resource can realize prediction of the resource to be scheduled based on the resource utilization data, so that timeliness of resource allocation is improved, the method and the device for scheduling the resource can identify future utilization conditions of the resource to be scheduled by utilizing the resource prediction model to predict the resource to be used state of the resource to be scheduled based on the resource state, so that timeliness of resource allocation is improved, the method and the device for scheduling the resource to be scheduled can analyze task priority and task dependency relationship of the task to be scheduled based on the resource utilization requirement, further, the method and the device for scheduling the resource to be scheduled can realize distributed processing of the task to be scheduled by constructing a distributed scheduling framework of the resource to be scheduled, and finally, the method and the device for scheduling the resource to be scheduled can realize efficient processing of the task to be scheduled based on the resource constraint condition, the task priority and the task dependency relationship, and the resource to be scheduled can realize efficient processing of the task to be scheduled based on the communication protocol of the task node to be scheduled, and the communication protocol to be scheduled to be executed by the communication node to be scheduled, and the resource to be scheduled can realize stable processing of the task to the resource to be scheduled. Therefore, the method and the system for realizing resource scheduling based on cloud computing can improve the scheduling effect of realizing resource scheduling by cloud computing.
Detailed Description
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 invention.
The embodiment of the application provides a method for realizing resource scheduling based on cloud computing. The execution body for implementing the resource scheduling method based on cloud computing includes, but is not limited to, at least one of a server, a terminal and the like capable of being configured to execute the method provided by the embodiment of the application. In other words, the cloud computing-based resource scheduling method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The server side comprises, but is not limited to, a single server, a server cluster, a cloud server or a cloud server cluster and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a method for implementing resource scheduling based on cloud computing according to an embodiment of the present invention is shown. In this embodiment, the method for implementing resource scheduling based on cloud computing includes:
S1, acquiring resource use requirements of resource users on resources to be scheduled, acquiring resource use data of the resources to be scheduled, and constructing a resource prediction model of the resources to be scheduled based on the resource use data.
In the embodiment of the invention, the resource use requirement refers to the requirement in the resource use process of the resource user, such as response time, availability, data security and the like, and the resource use data refers to the record of the resource use condition in the past period in the cloud computing environment, wherein the record of the resource use condition comprises data such as CPU use rate, memory use condition, storage use condition, network use condition, virtual machine/container use condition, application performance index, resource requirement peak value, resource allocation record and the like.
Optionally, the embodiment of the invention constructs the resource prediction model of the resource to be scheduled based on the resource use data, so as to realize the prediction of the resource use, thereby improving the timeliness of resource allocation. The resource prediction model is used for predicting future use conditions of resources.
Optionally, as an embodiment of the present invention, the constructing a resource prediction model of the resource to be scheduled based on the resource usage data includes preprocessing the resource usage data to obtain preprocessed usage data, extracting a data sequence feature of the preprocessed usage data, performing feature conversion on the data sequence feature to obtain a conversion feature, determining a model type of the resource to be scheduled, and constructing a resource prediction model of the resource to be scheduled based on the conversion feature, the preprocessed usage data and the model type.
The preprocessing use data refers to a data set obtained by performing data cleaning, data normalization and missing value processing on the resource use data, the data sequence features refer to feature attributes of the preprocessing use data, which are converted with time, the conversion features refer to features obtained by converting the data features, such as uneven distribution of the use log conversion processing data, and the model types refer to types of use prediction models of the resources to be scheduled, such as types of linear regression, time sequence analysis, machine learning algorithms and the like.
Further, in an alternative embodiment of the present invention, the extracting the data sequence feature of the preprocessing usage data includes serializing the preprocessing usage data to obtain the sequence usage data, calculating a data weight of the sequence usage data, analyzing a local trend of the sequence usage data based on the data weight, and extracting the data sequence feature of the sequence usage data based on the local trend.
The sequence usage data refers to a data set obtained by arranging the preprocessing usage data according to time, the data weights refer to weight coefficients used for weighted regression, the contribution degree of each data point to regression results is determined, and the local trend refers to the trend or direction of the data in a certain specific interval of the sequence.
Further, in an alternative embodiment of the present invention, the calculating the data weight of the sequence usage data includes selecting a bandwidth parameter of the sequence usage data, and calculating the data weight of the sequence usage data based on the bandwidth parameter using the following formula:
Wherein,Represent the firstIndividual sequence usage data and thThe sequences use the data weights of the data,Represent the firstThe data is used by the sequence of the data,Represent the firstThe data is used by the sequence of the data,Representing the bandwidth parameter.
The bandwidth parameter refers to a neighborhood or window of the bandwidth parameter, which influences the smoothness and adaptability of the model.
S2, acquiring a resource state of the resource to be scheduled, predicting a resource to-be-used state of the resource to be scheduled by using the resource prediction model based on the resource state, and analyzing a task to be scheduled of the resource to be scheduled based on the resource to-be-used state.
In the embodiment of the invention, the resource state refers to the state of all physical and virtual resources, including the states of CPU utilization, memory use condition, storage capacity, network bandwidth and the like.
Optionally, the embodiment of the invention predicts the resource to-be-used state of the resource to be scheduled by using the resource prediction model based on the resource state, and can identify the future use condition of the resource to be scheduled, thereby improving the timeliness of resource allocation. The resource to-be-used state refers to a state of predicting future use of the resource, such as a predicted CPU usage rate, a memory usage condition, and the like.
Further, according to the embodiment of the invention, based on the resource to-be-used state, the task to be scheduled of the resource to be scheduled is analyzed, so that the task can be explicitly scheduled, and a data basis is provided for later scheduling strategy formulation. The task to be scheduled refers to a task needing to be scheduled for resources.
The method comprises the steps of analyzing a to-be-scheduled task of a to-be-scheduled resource based on the to-be-used resource state, calculating a state difference of the to-be-scheduled resource based on the to-be-used resource state and the to-be-scheduled resource corresponding resource state, analyzing a resource influence weight of the state difference on a to-be-scheduled resource corresponding resource use requirement, determining an influence elimination strategy of the state difference based on the resource influence weight, and constructing the to-be-scheduled task of the to-be-scheduled resource based on the influence elimination strategy.
The state difference refers to a difference between the state to be used of the resource and the state of the resource corresponding to the resource to be scheduled, for example, the utilization rate of the state CPU of the resource is 50%, the utilization rate of the state CPU of the resource to be used is 80%, the state difference is a difference of 30% of the utilization rate of the CPU, the resource influence weight refers to the influence degree of the state difference on the resource use requirement, and the influence elimination strategy refers to a strategy how to reduce the influence of the state difference on the resource use requirement, for example, the state difference of the CPU occupancy rate can be improved by eliminating unnecessary use programs.
S3, analyzing the resource constraint condition of the resource to be scheduled, analyzing the task priority and the task dependency relationship of the task to be scheduled based on the resource use requirement, and constructing the resource scheduling strategy of the resource to be scheduled based on the resource constraint condition, the task priority and the task dependency relationship.
In the embodiment of the invention, the resource constraint condition refers to a constraint condition of resources to be considered when designing a resource scheduling strategy. For example, certain tasks may require a specific type or number of resources to execute, ensuring that the designed policy satisfies these constraints to avoid resource conflicts and resource wastage.
Optionally, the embodiment of the invention analyzes the task priority and the task dependency relationship of the task to be scheduled based on the resource use requirement so as to provide basis for resource scheduling. The task priority refers to the task priority of the task to be scheduled, and the task dependency relationship refers to a relationship that resource competition or mutual influence may exist between the tasks to be scheduled.
Optionally, as an embodiment of the present invention, the analyzing task priority and task dependency relationship of the task to be scheduled based on the resource usage requirement includes identifying a priority factor of the task to be scheduled based on the resource usage requirement, analyzing a priority influence relationship between the priority factor and the task to be scheduled, calculating task priority of the task to be scheduled based on the priority influence relationship, constructing a task dependency graph of the task to be scheduled, and analyzing task dependency relationship of the task to be scheduled based on the task dependency graph.
The priority factor refers to factors affecting tasks to be scheduled, such as resource utilization rate, response time, importance and urgency of tasks, task history performance and the like, the priority influence relationship refers to the influence relationship between the priority factor and the priority of the tasks to be scheduled, and the task dependency graph refers to a representation method for showing dependency relationship graphics between tasks.
Further, in an alternative embodiment of the present invention, the calculating the task priority of the task to be scheduled based on the priority impact relationship includes analyzing a priority factor weight of the task to be scheduled corresponding to a priority factor based on the priority impact relationship, identifying a priority factor status value of the priority factor, and calculating the task priority of the task to be scheduled based on the priority factor weight and the priority factor status value by using the following formula:
Wherein,Indicating the task priority of the task to be scheduled,Representing task to be scheduledThe priority factor weights of the individual priority factors,Representing task to be scheduledThe priority factor status values of the individual priority factors,Represents the adjustment coefficient of the device,Indicating the number of corresponding priority factors for the tasks to be scheduled.
The priority factor weight refers to the influence degree of the priority factor on the task priority, and the adjustment coefficient refers to a value dynamically adjusted according to the system state and other factors.
Further, according to the embodiment of the invention, the resource scheduling policy of the resource to be scheduled is constructed based on the resource constraint condition, the task priority and the task dependency relationship, so that the efficiency of task processing can be improved, and the resource waste can be reduced. The resource scheduling policy refers to a policy of scheduling resources.
Further, as an embodiment of the present invention, the constructing the resource scheduling policy of the resource to be scheduled based on the resource constraint condition, the task priority and the task dependency relationship is mainly implemented by determining a task scheduling sequence and a starting time of the resource to be scheduled corresponding to the task to be scheduled.
S4, constructing a distributed scheduling architecture of the resources to be scheduled, and establishing redundant task nodes of task nodes corresponding to the distributed scheduling architecture.
The embodiment of the invention can realize the distributed processing of the scheduling task by constructing the distributed scheduling architecture of the resources to be scheduled, thereby improving the safety of resource scheduling. The distributed scheduling architecture refers to an architecture of distributed processing resource scheduling.
The method comprises the steps of analyzing distributed demands of the resources to be scheduled, determining distributed components of the resources to be scheduled based on the distributed demands, constructing a distributed scheduling algorithm of the resources to be scheduled, and building the distributed scheduling architecture of the resources to be scheduled based on the distributed components and the distributed scheduling algorithm.
The distributed requirements refer to targets of a system, such as resource utilization rate, scheduling performance, reliability, expandability and the like, the distributed components refer to components for realizing resource scheduling distributed processing, such as a scheduling center, a task execution node and a communication mechanism, and the distributed scheduling algorithm refers to a scheduling algorithm adopted by a framework, such as a task priority processing algorithm.
In the embodiment of the invention, the redundant task node refers to a standby node for receiving work when the task node corresponding to the distributed scheduling architecture fails.
S5, constructing a communication protocol stack of the task node and the redundant task node, distributing the resource scheduling strategy to the task node to obtain a resource scheduling task node, and executing resource scheduling of the resource to be scheduled based on the resource scheduling task node, the redundant task node and the communication protocol.
According to the embodiment of the invention, the communication protocol stacks of the task nodes and the redundant task nodes are constructed, so that the mutual communication of the nodes can be realized, and the task processing efficiency is improved. The communication protocol stack refers to a protocol stack for realizing mutual communication of nodes.
As one embodiment of the invention, the construction of the communication protocol stacks of the task nodes and the redundant task nodes comprises the steps of defining the communication requirements of the task nodes and the redundant task nodes, determining the communication protocols of the task nodes and the redundant task nodes based on the communication requirements, designing the communication protocol architecture of the communication protocols, and constructing the communication protocol stacks of the task nodes and the redundant task nodes based on the communication protocol architecture.
The communication requirements refer to requirements that communication needs to meet, such as requirements of transmission speed, data integrity, security and the like, the communication protocol includes protocols such as TCP/IP, UDP, webSocket, and the communication protocol architecture refers to an architecture of the communication protocol, including communication endpoints, communication channels, communication flows and the like.
In detail, the construction of the communication protocol stacks of the task nodes and the redundant task nodes based on the communication protocol architecture can be realized through operations such as protocol analysis, protocol encapsulation, protocol transmission and the like.
Optionally, in the embodiment of the present invention, the resource scheduling task node refers to a node that has been allocated to a resource scheduling task.
Finally, according to the embodiment of the invention, based on the resource scheduling task node, the redundant task node and the communication protocol, the efficient and stable scheduling of the resource to be scheduled can be realized by performing the resource scheduling of the resource to be scheduled.
The method and the device for scheduling the resource can realize prediction of the resource to be scheduled based on the resource utilization data, so that timeliness of resource allocation is improved, the method and the device for scheduling the resource can identify future utilization conditions of the resource to be scheduled by utilizing the resource prediction model to predict the resource to be used state of the resource to be scheduled based on the resource state, so that timeliness of resource allocation is improved, the method and the device for scheduling the resource to be scheduled can analyze task priority and task dependency relationship of the task to be scheduled based on the resource utilization requirement, further, the method and the device for scheduling the resource to be scheduled can realize distributed processing of the task to be scheduled by constructing a distributed scheduling framework of the resource to be scheduled, and finally, the method and the device for scheduling the resource to be scheduled can realize efficient processing of the task to be scheduled based on the resource constraint condition, the task priority and the task dependency relationship, and the resource to be scheduled can realize efficient processing of the task to be scheduled based on the communication protocol of the task node to be scheduled, and the communication protocol to be scheduled to be executed by the communication node to be scheduled, and the resource to be scheduled can realize stable processing of the task to the resource to be scheduled. Therefore, the resource scheduling method based on cloud computing can improve the scheduling effect of the cloud computing for realizing resource scheduling.
Fig. 2 is a functional block diagram of a resource scheduling system implemented based on cloud computing according to an embodiment of the present invention.
The cloud computing-based resource scheduling system 200 can be installed in electronic equipment. Depending on the implementation function, the cloud computing-based resource scheduling system 200 may include a resource prediction model building module 201, a task to be scheduled building module 202, a resource scheduling policy building module 203, a distributed scheduling architecture building module 204, and a resource scheduling module 205. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the resource prediction model construction module 201 is configured to obtain a resource usage requirement of a resource user on a resource to be scheduled, collect resource usage data of the resource to be scheduled, and construct a resource prediction model of the resource to be scheduled based on the resource usage data;
The task to be scheduled construction module 202 is configured to obtain a resource status of the resource to be scheduled, predict a resource to be used status of the resource to be scheduled by using the resource prediction model based on the resource status, and analyze a task to be scheduled of the resource to be scheduled based on the resource to be used status;
The resource scheduling policy construction module 203 is configured to analyze a resource constraint condition of the resource to be scheduled, analyze a task priority and a task dependency relationship of the task to be scheduled based on the resource usage requirement, and construct a resource scheduling policy of the resource to be scheduled based on the resource constraint condition, the task priority and the task dependency relationship;
The distributed scheduling architecture construction module 204 is configured to construct a distributed scheduling architecture of the resources to be scheduled, and establish redundant task nodes corresponding to task nodes of the distributed scheduling architecture;
The resource scheduling module 205 is configured to construct a communication protocol stack of the task node and the redundant task node, distribute the resource scheduling policy to the task node, obtain a resource scheduling task node, and execute resource scheduling on the resource to be scheduled based on the resource scheduling task node, the redundant task node, and the communication protocol.
In detail, each module in the cloud computing-based resource scheduling system 200 in the embodiment of the present invention adopts the same technical means as the cloud computing-based resource scheduling method in the drawings when in use, and can produce the same technical effects, which are not described herein.
The embodiment of the invention provides electronic equipment for realizing a resource scheduling method based on cloud computing.
Referring to fig. 3, the electronic device may include a processor 30, a memory 31, a communication bus 32, and a communication interface 33, and may further include a computer program stored in the memory 31 and executable on the processor 30, such as a resource scheduling method program implemented based on cloud computing.
The processor may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory (for example, executes a resource scheduler based on cloud computing, etc.), and invokes data stored in the memory to perform various functions of the electronic device and process the data.
The memory includes at least one type of readable storage medium including flash memory, removable hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), etc. that are provided on the electronic device. Further, the memory may also include both internal storage units and external storage devices of the electronic device. The memory can be used for storing application software installed in the electronic equipment and various data, such as code for realizing a resource scheduling program based on cloud computing, and the like, and can be used for temporarily storing data which is output or is to be output.
The communication bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, or the like. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory and at least one processor or the like.
The communication interface is used for communication between the electronic equipment and other equipment, and comprises a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
For example, although not shown, the electronic device may further include a power source (such as a battery) for powering the respective components, and preferably, the power source may be logically connected to the at least one processor through a power management system, so as to perform functions of charge management, discharge management, and power consumption management through the power management system. The power supply may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The cloud computing-based implementation resource scheduler stored by the memory in the electronic device is a combination of instructions that, when executed in the processor, may implement:
Acquiring resource use requirements of resource users on resources to be scheduled, acquiring resource use data of the resources to be scheduled, and constructing a resource prediction model of the resources to be scheduled based on the resource use data;
Acquiring a resource state of the resource to be scheduled, predicting a resource to-be-used state of the resource to be scheduled by using the resource prediction model based on the resource state, and analyzing a task to be scheduled of the resource to be scheduled based on the resource to-be-used state;
Analyzing the resource constraint condition of the resource to be scheduled, analyzing the task priority and the task dependency relationship of the task to be scheduled based on the resource use requirement, and constructing a resource scheduling strategy of the resource to be scheduled based on the resource constraint condition, the task priority and the task dependency relationship;
constructing a distributed scheduling architecture of the resources to be scheduled, and establishing redundant task nodes of task nodes corresponding to the distributed scheduling architecture;
And constructing communication protocol stacks of the task nodes and the redundant task nodes, distributing the resource scheduling strategy to the task nodes to obtain resource scheduling task nodes, and executing resource scheduling of the resources to be scheduled based on the resource scheduling task nodes, the redundant task nodes and the communication protocol.
Specifically, the specific implementation method of the above instruction by the processor may refer to descriptions of related steps in the corresponding embodiment of the drawings, which are not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
Acquiring resource use requirements of resource users on resources to be scheduled, acquiring resource use data of the resources to be scheduled, and constructing a resource prediction model of the resources to be scheduled based on the resource use data;
Acquiring a resource state of the resource to be scheduled, predicting a resource to-be-used state of the resource to be scheduled by using the resource prediction model based on the resource state, and analyzing a task to be scheduled of the resource to be scheduled based on the resource to-be-used state;
Analyzing the resource constraint condition of the resource to be scheduled, analyzing the task priority and the task dependency relationship of the task to be scheduled based on the resource use requirement, and constructing a resource scheduling strategy of the resource to be scheduled based on the resource constraint condition, the task priority and the task dependency relationship;
constructing a distributed scheduling architecture of the resources to be scheduled, and establishing redundant task nodes of task nodes corresponding to the distributed scheduling architecture;
And constructing communication protocol stacks of the task nodes and the redundant task nodes, distributing the resource scheduling strategy to the task nodes to obtain resource scheduling task nodes, and executing resource scheduling of the resources to be scheduled based on the resource scheduling task nodes, the redundant task nodes and the communication protocol.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, system and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.