CN114615144A - Network optimization method and system - Google Patents
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- CN114615144A CN114615144A CN202210370313.0A CN202210370313A CN114615144A CN 114615144 A CN114615144 A CN 114615144A CN 202210370313 A CN202210370313 A CN 202210370313A CN 114615144 A CN114615144 A CN 114615144A
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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Abstract
The present disclosure relates to a network optimization method and system, wherein the method comprises: acquiring operation data and node attribute data of each node in a designated computer network, wherein the operation data of each node comprises node computing resource data; determining a first target node set of which the node calculation resource occupancy is greater than a first preset value in each node based on the operation data of each node; determining node attribute parameters of the nodes based on the node attribute data of the nodes, determining a second target node set of the nodes, wherein the node attribute parameters are larger than a second preset value, based on the node attribute parameters of the nodes, and determining a third target node based on the second target node set and the first target node set; and determining the running state of a third target node based on the running data and the node attribute data of the third target node, and performing optimization processing on the third target node when the running state is determined to meet the preset state condition.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computer networks, in particular to a network optimization method and system.
Background
At present, with the continuous enlargement of computer network scale, the continuous improvement of network complexity and the continuous increase of network heterogeneity, network optimization becomes more and more important. Network optimization enables a computer network to operate safely, reliably, and efficiently to achieve optimal network performance.
In the related art, a node to be optimized is determined based on operation state data of each node, such as a computer device, in a local area network, such as a campus network, so as to perform network optimization processing. The inventor finds that the method for selecting and determining the nodes to be optimized is not accurate enough, so that the network optimization effect is poor, and the network performance needs to be further improved.
Disclosure of Invention
In order to solve the above technical problem or at least partially solve the above technical problem, embodiments of the present disclosure provide a network optimization method and system.
In a first aspect, an embodiment of the present disclosure provides a network optimization method, applied to a management server, where the management server is in communication connection with a specified computer network, and the method includes:
acquiring operation data and node attribute data of each node in a designated computer network, wherein the operation data of each node comprises node computing resource data;
determining a first target node set of which the node calculation resource occupancy is greater than a first preset value in each node based on the operation data of each node;
determining node attribute parameters of the nodes based on the node attribute data of the nodes, determining a second target node set of the nodes, wherein the node attribute parameters are larger than a second preset value, based on the node attribute parameters of the nodes, and determining a third target node based on the second target node set and the first target node set;
and determining the running state of the third target node based on the running data and the node attribute data of the third target node, and performing optimization processing on the third target node when the running state is determined to meet a preset state condition.
In one embodiment, the node attribute data for each of the nodes comprises a node type parameter and/or a node connection number, the node connection number indicating the number of other nodes adjacently connected to a node; the determining a node attribute parameter for each of the nodes based on the node attribute data for each of the nodes includes:
calculating the node type parameter and the node connection number of each node based on a pre-constructed node attribute function to determine the node attribute parameter of each node; the node attribute parameters are used for representing the running performance of the node, and the larger the node attribute parameters are, the worse the running performance of the node is.
In one embodiment, the node computing resource data in the operation data of each node includes one or more of CPU resource occupation data, storage resource occupation data, hard disk resource occupation data, and network resource occupation data; the determining, based on the operational data of each of the nodes, a first target node set in which the calculated resource occupancy of the node in each of the nodes is greater than a first preset value includes:
calculating to obtain the node calculation resource occupation amount of each node based on the CPU resource occupation data, the storage resource occupation data, the hard disk resource occupation data and the network resource occupation data of each node;
and constructing the first target node set based on at least one node of the nodes, the calculated resource occupation amount of which is greater than the first preset value.
In one embodiment, the third target node is a node in an intersection of the first set of target nodes and the second set of target nodes.
In one embodiment, the determining the operational status of the third target node based on the operational data and the node attribute data of the third target node comprises:
inputting the running data and the node attribute data of the third target node into a preset state recognition model to obtain a running state parameter of the third target node; the preset state recognition model is obtained by training an original state recognition model based on a convolutional neural network in advance based on sample operation data, sample node attribute data and corresponding sample operation state parameters of a third target node;
and determining the operating state of the third target node based on the operating state parameter of the third target node.
In one embodiment, the determining the operating state of the third target node based on the operating state parameter of the third target node comprises:
when the running state parameter of the third target node is larger than the first state parameter value, determining that the third target node is in a first running state;
when the operating state parameter of the third target node is greater than a second state parameter value, determining that the third target node is in a second operating state, wherein the second state parameter value is greater than the first state parameter value, and the operating performance of the node corresponding to the second operating state is inferior to that of the node corresponding to the first operating state;
when the operating state parameter of the third target node is greater than a third state parameter value, determining that the third target node is in a third operating state; and the third state parameter value is larger than the second state parameter value, and the running performance of the node corresponding to the third running state is inferior to that of the node corresponding to the second running state.
In one embodiment, when it is determined that the operating state satisfies the preset state condition, performing optimization processing on the third target node includes:
and when the third target node is in the third running state, determining that the preset state condition is met, and optimizing the node computing resources of the third target node.
In a second aspect, an embodiment of the present disclosure provides a network optimization system, applied to a management server, where the management server is communicatively connected to a designated computer network, and the system includes:
the data acquisition module is used for acquiring operation data and node attribute data of each node in a designated computer network, wherein the operation data of each node comprises node computing resource data;
the first determining module is used for determining a first target node set of which the node calculation resource occupation amount is greater than a first preset value in each node based on the operation data of each node;
a second determining module, configured to determine a node attribute parameter of each node based on the node attribute data of each node, determine a second target node set, in which the node attribute parameter is greater than a second preset value, in each node based on the node attribute parameter of each node, and determine a third target node based on the second target node set and the first target node set;
and the optimization processing module is used for determining the running state of the third target node based on the running data and the target node attribute data of the third target node, and performing optimization processing on the third target node when the running state is determined to meet a preset state condition.
In a third aspect, the disclosed embodiments provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the network optimization method according to any one of the above embodiments.
In a fourth aspect, an embodiment of the present disclosure provides an electronic device, including:
a processor; and
a memory for storing a computer program;
wherein the processor is configured to perform the network optimization method of any of the above embodiments via execution of the computer program.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
the network optimization method and the system provided by the embodiment of the disclosure are applied to a management server in communication connection with a designated computer network, and are used for acquiring operation data and node attribute data of each node in the designated computer network, wherein the operation data of each node comprises node computing resource data; determining a first target node set of which the node calculation resource occupancy is greater than a first preset value in each node based on the operation data of each node; determining node attribute parameters of the nodes based on the node attribute data of the nodes, determining a second target node set of the nodes, wherein the node attribute parameters are larger than a second preset value, based on the node attribute parameters of the nodes, and determining a third target node based on the second target node set and the first target node set; and determining the running state of the third target node based on the running data and the node attribute data of the third target node, and performing optimization processing on the third target node when the running state is determined to meet a preset state condition. Therefore, a target node set to be optimized is respectively determined based on the operation data and the node attribute data of the nodes, then a final target node to be optimized is determined comprehensively, and then the operation state of the final target node is judged to meet the preset state condition by combining the operation data and the node attribute data of the final target node, so that the third target node, namely the final target node to be optimized, is optimized, the node to be optimized, such as the node with larger influence on the network performance, can be accurately determined, and meanwhile, the state judgment is carried out on the node to be optimized so as to carry out network optimization, so that the optimization effect of the whole computer network is improved, and the network performance is integrally improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a flow chart of a network optimization method according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a computer network architecture according to an embodiment of the present disclosure;
FIG. 3 is a flow chart of a network optimization method according to another embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a network optimization system according to an embodiment of the disclosure;
fig. 5 is a schematic diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.
It is to be understood that, hereinafter, "at least one" means one or more, "a plurality" means two or more. "and/or" is used to describe the association relationship of the associated objects, meaning that there may be three relationships, for example, "a and/or B" may mean: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
Fig. 1 is a flowchart of a network optimization method according to an embodiment of the present disclosure, where the network optimization method according to the embodiment of the present disclosure is applied to a management server, and as shown in fig. 2, the management server is communicatively connected to a specified computer network (a computer network formed by nodes 1 to N), and the specified computer network is, for example, a local area network such as a campus network, but is not limited thereto. The management server is disposed in a designated computer network and is capable of being communicatively connected to each of the nodes, such as node 1 to node N, in the computer network. The method may comprise the steps of:
step S101: the method comprises the steps of obtaining operation data and node attribute data of each node in a designated computer network, wherein the operation data of each node comprises node computing resource data.
For example, each node in the designated computer network, such as the nodes 1 to N, may report respective operation data and node attribute data at regular time or in real time, or the management server sends a data acquisition request to the nodes 1 to N to receive the returned operation data and node attribute data, which is not limited thereto. The operation data of the node includes node computing resource data, such as one or more of CPU resource occupation data, storage resource occupation data, hard disk resource occupation data, and network resource occupation data of the node, but is not limited thereto.
Step S102: and determining a first target node set of which the node calculation resource occupation amount is greater than a first preset value in each node based on the operation data of each node.
For example, in an embodiment, the management server may calculate, based on the CPU resource occupation data, the storage resource occupation data, the hard disk resource occupation data, and the network resource occupation data of each of the nodes, a node calculation resource occupation amount of each of the nodes, and then construct the first target node set based on at least one node of each of the nodes whose node calculation resource occupation amount is greater than the first preset value.
It is understood that the first preset value can be set according to the requirement, and is not limited in this regard. Specifically, each node may be identified based on a node identifier of each node, such as an ID and/or a node name, and the node identifier ID of a node in the first target node set, where the node calculation resource occupancy amount is greater than a first preset value, may be in each node. For example, if the node computing resource occupation of 5 nodes from the node 1 to the node N is greater than a first preset value, a first target node set P is constructed based on the node identification IDs of the 5 nodes.
Step S103: determining a node attribute parameter of each of the nodes based on the node attribute data of each of the nodes, determining a second target node set in which the node attribute parameter is greater than a second preset value in each of the nodes based on the node attribute parameter of each of the nodes, and determining a third target node based on the second target node set and the first target node set.
Illustratively, the node attribute data is, for example, a node type parameter, a node connection number, i.e., the number of other nodes connected adjacently to one node. Different node type parameters indicate the type of a node, such as routing nodes, computing nodes, storage nodes, and the like. The node type parameter can be set by self-definition, and is not limited in this respect. The management server can determine the node attribute parameters of each node based on the node attribute data of each node, such as the node type parameters and the node connection number, wherein the node attribute parameters represent the operation performance of the node, and the larger the node attribute parameters, the worse the operation performance of the node. And determining a second target node set of which the node attribute parameters are larger than a second preset value in each node based on the node attribute parameters of each node.
For example, the second preset value may also be set according to needs, and is not limited in this regard. Specifically, for example, if the node attribute parameters of 4 nodes of the nodes 1 to N are greater than the second preset value, the second target node set Q is constructed based on the node identifiers ID of the 4 nodes. A third target node is then determined based on the first set of target nodes P and the second set of target nodes Q. In one embodiment, the third target node is a node in the intersection M (P ═ Q ═ M) of the first set of target nodes P and the second set of target nodes Q, that is, a node having the same node identification ID in the first set of target nodes P and the second set of target nodes Q. At this point, a target node to be optimized in the computer network is determined, such as one or more nodes with relatively worst performance in the designated computer network.
Step S104: and determining the running state of the third target node based on the running data and the node attribute data of the third target node, and performing optimization processing on the third target node when the running state is determined to meet a preset state condition.
For example, after a target node to be optimized in a computer network is determined, an operating state of the target node to be optimized is determined based on operating data of the target node to be optimized, such as CPU resource occupation data, storage resource occupation data, hard disk resource occupation data, network resource occupation data, and node attribute data, such as node type parameters and node connection number, and when the operating state is determined to satisfy a preset state condition, such as a poor operating state, the target node to be optimized is optimized to improve the operating state.
According to the method, the target node set to be optimized can be determined respectively based on the operation data and the node attribute data of the nodes, then the final target node to be optimized is determined comprehensively, and then the third target node, namely the final target node to be optimized, is optimized when the operation state of the final target node is judged to meet the preset state condition by combining the operation data and the node attribute data of the final target node, so that the node to be optimized, such as the node with a large influence on the network performance, can be determined accurately, and meanwhile, the state judgment is performed on the node to be optimized to perform the network optimization, so that the optimization effect of the whole computer network is improved, and the computer network performance is improved integrally.
In one embodiment, the node attribute data of each of the nodes comprises a node type parameter u and/or a node connection number v representing the number of other nodes connected adjacently to a node. In step S103, determining a node attribute parameter of each node based on the node attribute data of each node, which may specifically include: calculating the node type parameter and the node connection number of each node based on a pre-constructed node attribute function to determine the node attribute parameter of each node; the node attribute parameters are used for representing the running performance of the node, and the larger the node attribute parameters are, the worse the running performance of the node is.
For example, the node attribute function H (u, v) may be established in advance on the management server, and specifically, f ═ H (u, v) may be determined by fitting based on a mapping relationship between a plurality of node type parameters u and a plurality of node connection numbers v of nodes acquired experimentally and a plurality of corresponding node attribute parameters f. The method considers two factors of the type of the node and the connection number of the node, and the constructed node attribute function can accurately represent different node attribute data, such as different node attribute parameters corresponding to the node type parameter and the connection number of the node, namely different node operation performances. In this embodiment, the node attribute parameters of the nodes can be accurately determined based on the pre-constructed node attribute function, so that the target nodes in the second target node set determined based on the node attribute parameters are more accurate, that is, the finally determined nodes to be optimized are more accurate, thereby improving the optimization effect of the whole computer network and integrally further improving the computer network performance.
In an embodiment, referring to fig. 3 in combination, the determining, in step S104, the operating state of the third target node based on the operating data and the node attribute data of the third target node may specifically include the following steps:
step S301: inputting the running data and the node attribute data of the third target node into a preset state recognition model to obtain a running state parameter of the third target node; the preset state recognition model is obtained by training an original state recognition model based on a convolutional neural network in advance based on sample operation data, sample node attribute data and corresponding sample operation state parameters of a third target node.
Illustratively, the convolutional neural network may include an input layer, an intermediate layer and an output layer, wherein one sample operation data, such as CPU resource occupation data, storage resource occupation data, hard disk resource occupation data and network resource occupation data, and sample node attribute data, such as node type parameters and node connection numbers, are input into the input layer of the convolutional neural network in advance, the intermediate layer performs processing, such as feature extraction, and the output layer finally outputs a corresponding predicted operation state parameter, such as a probability value, calculates a difference value between the predicted operation state parameter and the corresponding one sample operation state parameter, and re-inputs another sample operation data and the sample node attribute data when the difference value is greater than a preset value, and thus, training is circulated to update the model parameters of the original convolutional neural network-based state recognition model until the difference value is less than the preset value, and the training is ended. Based on the trained preset state recognition model, the operation data and the node attribute data of the third target node determined in step S103, that is, the node to be optimized finally, are processed to obtain an operation state parameter, such as a probability value, of the third target node.
Step S302: and determining the operating state of the third target node based on the operating state parameter of the third target node.
Specifically, after obtaining the operating state parameter of the third target node, such as the probability value, the operating state of the third target node, that is, the node to be optimized finally, may be determined, and then subsequent steps, such as the optimization processing step, may be performed.
In this embodiment, the running state of the node to be optimized finally is recognized based on the preset state recognition model obtained through pre-training, the sample running data and the sample node attribute data based on the node are trained simultaneously during training, and influence factors of the node attribute data are considered, so that the running state of the node to be optimized finally can be recognized accurately by the preset state recognition model obtained through training, and then the node to be optimized finally is judged accurately to perform network optimization processing, so that the optimization effect of the whole computer network is further improved, and the performance of the computer network is further improved integrally.
In an embodiment, the determining the operating state of the third target node based on the operating state parameter of the third target node in step S302 may specifically include the steps of: when the running state parameter of the third target node is larger than the first state parameter value, determining that the third target node is in a first running state; when the operating state parameter of the third target node is greater than a second state parameter value, determining that the third target node is in a second operating state, wherein the second state parameter value is greater than the first state parameter value, and the operating performance of the node corresponding to the second operating state is inferior to that of the node corresponding to the first operating state; when the operating state parameter of the third target node is greater than a third state parameter value, determining that the third target node is in a third operating state; and the third state parameter value is larger than the second state parameter value, and the running performance of the node corresponding to the third running state is inferior to that of the node corresponding to the second running state.
For example, the third state parameter value, the second state parameter value, and the first state parameter value may be set as needed, which is not limited. The larger these status parameter values, the worse the performance of the node. Therefore, different operation states of the third target node can be determined in a detailed and more accurate mode, so that the management server can make accurate state judgment to perform network optimization processing, and the optimization effect of the whole computer network is further improved.
In an embodiment, when it is determined in step S104 that the operating state satisfies the preset state condition, performing optimization processing on the third target node may specifically include: and when the third target node is in the third running state, determining that the preset state condition is met, and optimizing the node computing resources of the third target node.
That is, when it is determined that the third target node is in the third operating state, that is, the node has the worst operability, it is determined that the preset state condition is satisfied, and at this time, optimization processing is performed on the node computing resources of the third target node, such as CPU resource occupation data, storage resource occupation data, hard disk resource occupation data, network resource occupation data, and the like, such as suspending or ending some tasks to release occupied part of the computing resources, so as to improve the operating performance of the node, further improve the network optimization effect, and improve the network performance.
Further, the performance of different tasks may have some impact on the network performance, and in one embodiment, the method may further include: and when the node computing resources of the third target node are optimized, acquiring task identifiers of a plurality of tasks currently running on the third target node, and suspending or ending the tasks with preset task identifiers in the plurality of tasks. The influence degree of the task with the preset task identification on the network performance is higher than the influence degree of other tasks on the network performance, and the other tasks are the other tasks except the task with the preset task identification in the plurality of tasks. Therefore, some designated tasks with high influence degree on the network performance can be suspended or ended to release occupied part of computing resources for other tasks to use, the running performance of the nodes is improved, meanwhile, the execution efficiency of other tasks is improved, the potential adverse effect of the execution of other tasks on the network performance is reduced, and the network performance is integrally improved.
It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc. Additionally, it will also be readily appreciated that the steps may be performed synchronously or asynchronously, e.g., among multiple modules/processes/threads.
As shown in fig. 4, an embodiment of the present disclosure provides a network optimization system applied to a management server, where the management server is communicatively connected to a designated computer network, and the system includes:
a data obtaining module 401, configured to obtain operation data and node attribute data of each node in a designated computer network, where the operation data of each node includes node computing resource data;
a first determining module 402, configured to determine, based on operation data of each node, a first target node set in which a node calculation resource occupancy amount in each node is greater than a first preset value;
a second determining module 403, configured to determine a node attribute parameter of each node based on the node attribute data of each node, determine a second target node set, where the node attribute parameter is greater than a second preset value, in each node based on the node attribute parameter of each node, and determine a third target node based on the second target node set and the first target node set;
and an optimization processing module 404, configured to determine an operating state of the third target node based on the operating data of the third target node and the target node attribute data, and perform optimization processing on the third target node when it is determined that the operating state meets a preset state condition.
In one embodiment, the node attribute data of each of the nodes includes a node type parameter and/or a node connection number indicating the number of other nodes adjacently connected to one node; the second determining module 403 determines the node attribute parameter of each node based on the node attribute data of each node, including: calculating the node type parameter and the node connection number of each node based on a pre-constructed node attribute function to determine the node attribute parameter of each node; the node attribute parameters are used for representing the running performance of the nodes, and the larger the node attribute parameters are, the worse the running performance of the nodes is.
In one embodiment, the node computing resource data in the operation data of each node includes one or more of CPU resource occupation data, storage resource occupation data, hard disk resource occupation data, and network resource occupation data; the first determining module 402 determines, based on the operation data of each node, a first target node set in which the calculated resource occupancy of the node in each node is greater than a first preset value, including: calculating to obtain the node calculation resource occupation amount of each node based on the CPU resource occupation data, the storage resource occupation data, the hard disk resource occupation data and the network resource occupation data of each node; and constructing the first target node set based on at least one node of the nodes, the calculated resource occupation amount of which is greater than the first preset value.
In one embodiment, the third target node is a node in an intersection of the first set of target nodes and the second set of target nodes.
In one embodiment, the optimization module 404 determines the operating state of the third target node based on the operating data and the node attribute data of the third target node, including: inputting the running data and the node attribute data of the third target node into a preset state recognition model to obtain a running state parameter of the third target node; the preset state recognition model is obtained by training an original state recognition model based on a convolutional neural network in advance based on sample operation data, sample node attribute data and corresponding sample operation state parameters of a third target node; and determining the operating state of the third target node based on the operating state parameter of the third target node.
In one embodiment, the optimization module 404 determines the operating state of the third target node based on the operating state parameter of the third target node, including: when the running state parameter of the third target node is larger than the first state parameter value, determining that the third target node is in a first running state; when the operating state parameter of the third target node is greater than a second state parameter value, determining that the third target node is in a second operating state, wherein the second state parameter value is greater than the first state parameter value, and the operating performance of the node corresponding to the second operating state is inferior to that of the node corresponding to the first operating state; when the operating state parameter of the third target node is greater than a third state parameter value, determining that the third target node is in a third operating state; and the third state parameter value is larger than the second state parameter value, and the running performance of the node corresponding to the third running state is inferior to that of the node corresponding to the second running state.
In one embodiment, when the optimization module 404 determines that the operating state satisfies the preset state condition, performing optimization processing on the third target node includes: and when the third target node is in the third running state, determining that the preset state condition is met, and optimizing the node computing resources of the third target node.
In one embodiment, the optimization module 404 is further operable to: and when the node computing resources of the third target node are optimized, acquiring task identifiers of a plurality of tasks currently running on the third target node, and suspending or ending the tasks with preset task identifiers in the plurality of tasks. The influence degree of the task with the preset task identification on the network performance is higher than the influence degree of other tasks on the network performance, and the other tasks are the other tasks except the task with the preset task identification in the plurality of tasks.
With regard to the system in the above embodiment, the specific manner in which each module performs operations and the corresponding technical effects have been described in detail in the embodiment related to the method, and will not be described in detail herein.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units. The components shown as modules or units may or may not be physical units, i.e. may be located in one place or may also be distributed over a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the wood-disclosed scheme. One of ordinary skill in the art can understand and implement it without inventive effort.
The embodiments of the present disclosure further provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the network optimization method in any one of the above embodiments.
By way of example, and not limitation, such readable storage media can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
An embodiment of the present disclosure also provides an electronic device, including a processor and a memory for storing a computer program. Wherein the processor is configured to perform the network optimization method of any of the above embodiments via execution of the computer program.
An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 5. The electronic device 600 shown in fig. 5 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 that connects the various system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.
Wherein the storage unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention as described in the above-mentioned method embodiment section of the present specification. For example, the processing unit 610 may perform the steps of a network optimization method as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)6201 and/or a cache storage unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the method steps according to the above-mentioned embodiments of the present disclosure.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description is only for the purpose of describing particular embodiments of the present disclosure, so as to enable those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for optimizing a network, the method being applied to a management server, the management server being communicatively coupled to a designated computer network, the method comprising:
acquiring operation data and node attribute data of each node in a designated computer network, wherein the operation data of each node comprises node computing resource data;
determining a first target node set of which the node calculation resource occupancy is greater than a first preset value in each node based on the operation data of each node;
determining node attribute parameters of the nodes based on the node attribute data of the nodes, determining a second target node set of the nodes, wherein the node attribute parameters are larger than a second preset value, based on the node attribute parameters of the nodes, and determining a third target node based on the second target node set and the first target node set;
and determining the running state of the third target node based on the running data and the node attribute data of the third target node, and performing optimization processing on the third target node when the running state is determined to meet a preset state condition.
2. The method according to claim 1, wherein the node attribute data of each of the nodes comprises a node type parameter and/or a node connection number indicating the number of other nodes adjacently connected to one node; the determining a node attribute parameter for each of the nodes based on the node attribute data for each of the nodes includes:
calculating the node type parameter and the node connection number of each node based on a pre-constructed node attribute function to determine the node attribute parameter of each node; the node attribute parameters are used for representing the running performance of the nodes, and the larger the node attribute parameters are, the worse the running performance of the nodes is.
3. The method of claim 2, wherein the node computing resource data in the operational data of each of the nodes comprises one or more of CPU resource occupancy data, storage resource occupancy data, hard disk resource occupancy data, and network resource occupancy data; the determining, based on the operational data of each of the nodes, a first target node set in which the calculated resource occupancy of the node in each of the nodes is greater than a first preset value includes:
calculating to obtain the node calculation resource occupation amount of each node based on the CPU resource occupation data, the storage resource occupation data, the hard disk resource occupation data and the network resource occupation data of each node;
and constructing the first target node set based on at least one node of the nodes, the calculated resource occupation amount of which is greater than the first preset value.
4. A method according to any of claims 1 to 3, wherein the third target node is a node in the intersection of the first set of target nodes and the second set of target nodes.
5. The method according to any one of claims 1 to 3, wherein the determining the operational status of the third target node based on the operational data and the node attribute data of the third target node comprises:
inputting the running data and the node attribute data of the third target node into a preset state recognition model to obtain a running state parameter of the third target node; the preset state recognition model is obtained by training an original state recognition model based on a convolutional neural network in advance based on sample operation data, sample node attribute data and corresponding sample operation state parameters of a third target node;
and determining the operating state of the third target node based on the operating state parameter of the third target node.
6. The method of claim 5, wherein determining the operational status of the third target node based on the operational status parameter of the third target node comprises:
when the running state parameter of the third target node is larger than the first state parameter value, determining that the third target node is in a first running state;
when the operating state parameter of the third target node is greater than a second state parameter value, determining that the third target node is in a second operating state, wherein the second state parameter value is greater than the first state parameter value, and the operating performance of the node corresponding to the second operating state is inferior to that of the node corresponding to the first operating state;
when the running state parameter of the third target node is greater than a third state parameter value, determining that the third target node is in a third running state; and the third state parameter value is larger than the second state parameter value, and the running performance of the node corresponding to the third running state is inferior to that of the node corresponding to the second running state.
7. The method according to claim 6, wherein when it is determined that the operating state satisfies a preset state condition, performing optimization processing on the third target node includes:
and when the third target node is in the third running state, determining that the preset state condition is met, and optimizing the node computing resources of the third target node.
8. A network optimization system for use with a management server communicatively coupled to a designated computer network, the system comprising:
the data acquisition module is used for acquiring operation data and node attribute data of each node in a designated computer network, wherein the operation data of each node comprises node computing resource data;
the first determining module is used for determining a first target node set of which the node calculation resource occupation amount is greater than a first preset value in each node based on the operation data of each node;
a second determining module, configured to determine a node attribute parameter of each node based on the node attribute data of each node, determine a second target node set, in which the node attribute parameter is greater than a second preset value, in each node based on the node attribute parameter of each node, and determine a third target node based on the second target node set and the first target node set;
and the optimization processing module is used for determining the running state of the third target node based on the running data and the target node attribute data of the third target node, and performing optimization processing on the third target node when the running state is determined to meet a preset state condition.
9. A computer-readable storage medium, on which a computer program is stored, the computer program, when being executed by a processor, implementing the network optimization method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a processor; and
a memory for storing a computer program;
wherein the processor is configured to perform the network optimization method of any one of claims 1 to 7 via execution of the computer program.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015113298A1 (en) * | 2014-01-29 | 2015-08-06 | 华为技术有限公司 | Resource configuration method and device |
| CN104918262A (en) * | 2014-03-11 | 2015-09-16 | 华为技术有限公司 | Network optimization method and apparatus |
| US20170093698A1 (en) * | 2015-09-30 | 2017-03-30 | Huawei Technologies Co., Ltd. | Method and apparatus for supporting service function chaining in a communication network |
| US20200302303A1 (en) * | 2018-01-05 | 2020-09-24 | Microsoft Technology Licensing, Llc | Optimization of neural network in equivalent class space |
| CN113011602A (en) * | 2021-03-03 | 2021-06-22 | 中国科学技术大学苏州高等研究院 | Method and device for training federated model, electronic equipment and storage medium |
| CN113115327A (en) * | 2021-03-04 | 2021-07-13 | 广州杰赛科技股份有限公司 | Method, device, equipment and storage medium for dynamically optimizing network performance |
| CN113676370A (en) * | 2021-07-22 | 2021-11-19 | 浪潮商用机器有限公司 | System network performance monitoring method, device, system and storage medium |
| CN114254950A (en) * | 2021-12-27 | 2022-03-29 | 中国电信股份有限公司 | Telecommunication resource data processing method and device, electronic equipment and storage medium |
-
2022
- 2022-04-09 CN CN202210370313.0A patent/CN114615144B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015113298A1 (en) * | 2014-01-29 | 2015-08-06 | 华为技术有限公司 | Resource configuration method and device |
| CN104918262A (en) * | 2014-03-11 | 2015-09-16 | 华为技术有限公司 | Network optimization method and apparatus |
| US20170093698A1 (en) * | 2015-09-30 | 2017-03-30 | Huawei Technologies Co., Ltd. | Method and apparatus for supporting service function chaining in a communication network |
| US20200302303A1 (en) * | 2018-01-05 | 2020-09-24 | Microsoft Technology Licensing, Llc | Optimization of neural network in equivalent class space |
| CN113011602A (en) * | 2021-03-03 | 2021-06-22 | 中国科学技术大学苏州高等研究院 | Method and device for training federated model, electronic equipment and storage medium |
| CN113115327A (en) * | 2021-03-04 | 2021-07-13 | 广州杰赛科技股份有限公司 | Method, device, equipment and storage medium for dynamically optimizing network performance |
| CN113676370A (en) * | 2021-07-22 | 2021-11-19 | 浪潮商用机器有限公司 | System network performance monitoring method, device, system and storage medium |
| CN114254950A (en) * | 2021-12-27 | 2022-03-29 | 中国电信股份有限公司 | Telecommunication resource data processing method and device, electronic equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| ZHU DAN DAN: "The Path Optimization Algorithm of Car Navigation System considering Node Attributes under Time-Invariant Network", 《MOBILE INFORMATION SYSTEMS》 * |
| 唐卫国等: "基于LabWindows的云计算环境安全框架研究", 《计算机测量与控制》 * |
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