CN117354072B - Automatic charging adjustment method and device for data center network bandwidth flow - Google Patents
Automatic charging adjustment method and device for data center network bandwidth flow Download PDFInfo
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Abstract
The application provides a method and a device for automatically charging and adjusting network bandwidth flow of a data center, which are characterized in that historical flow charging data of a user are collected and preprocessed to obtain a plurality of flow data subsequences, flow data in the flow data subsequences are used for determining accumulation residual errors, further accumulation likelihood coefficients are determined, the flow data subsequences are subjected to characteristic reconstruction, a flow characteristic space matrix is determined by the reconstructed flow data subsequences, further a flow characteristic covariance matrix is obtained, a flow characteristic value is determined by the flow characteristic covariance matrix, a flow charging variation index of the flow data subsequences is determined according to the flow characteristic value, a flow charging prediction function value of the flow data subsequences is determined according to the accumulation likelihood coefficients and the flow charging variation index, and the network bandwidth time-consuming by a bandwidth flowmeter of a flow transmission link is adjusted according to the flow charging prediction function value, so that the network stability under different network environments is improved, and the accuracy of charging results is ensured.
Description
Technical Field
The application relates to the technical field of data centers, in particular to a method and a device for automatically charging and adjusting network bandwidth flow of a data center.
Background
A data center is a specially designed and configured facility for centrally storing, processing, and managing a large number of computer servers, network devices, storage systems, and related infrastructure to provide computing, storage, and network services, and traffic billing for data center usage bandwidth is a method based on peak bandwidth and usage traffic billing.
However, in the existing flow charging method, because the network usage habits of the users are different, the flow usage modes are also different, so that the charging result is uncertain, in addition, in the traditional charging mode, the peak value of the network flow only considers the flow change condition in a short time, and does not consider the long-term use condition, which can lead to the charging result having a larger difference from the actual network usage condition, so how to improve the accuracy of the charging result is an urgent problem in the industry.
Disclosure of Invention
The application provides a method and a device for automatically charging and adjusting network bandwidth flow of a data center so as to improve the accuracy of a charging result.
In order to solve the technical problems, the application adopts the following technical scheme:
In a first aspect, the present application provides a method for automatically charging and adjusting a data center network bandwidth flow, including the following steps:
Collecting historical flow charging data of a user of a data center and preprocessing the data to obtain a plurality of flow data subsequences;
Determining accumulation residual errors according to flow data in each flow data subsequence, obtaining a re-accumulation factor of each flow data subsequence according to the accumulation residual errors, and further determining accumulation likelihood coefficients corresponding to each flow data subsequence respectively through the re-accumulation factors;
for each flow data subsequence in the plurality of flow data subsequences, performing feature reconstruction on the flow data subsequence, and mapping flow data in the reconstructed flow data subsequence into a space matrix to obtain a flow feature space matrix;
determining a flow characteristic covariance matrix according to the flow characteristic space matrix, determining a flow characteristic value of the flow data subsequence by the flow characteristic covariance matrix, determining a flow charging iteration index of the flow data subsequence according to the flow characteristic value, and further determining flow charging iteration indexes respectively corresponding to the flow data subsequences;
Determining flow charging prediction function values corresponding to the flow data subsequences according to the accumulation likelihood coefficients and the flow charging variation indexes corresponding to the flow data subsequences, and superposing the flow charging prediction function values of the flow data subsequences to obtain flow charging prediction function values of the flow data sequences;
and adjusting the time-consuming network bandwidth of the bandwidth flowmeter of the flow transmission link according to the flow charging prediction function value.
In some embodiments, collecting and preprocessing historical flow charging data of a user of a data center to obtain a plurality of flow data subsequences specifically includes:
collecting user historical flow charging data of a data center, and dividing the user historical flow charging data into blocks to obtain a flow data sequence;
performing smoothing treatment on the flow data sequence to obtain a flow data smoothing sequence;
and decomposing the flow data smoothing sequence according to the use frequency of the user flow to obtain a plurality of flow data subsequences.
In some embodiments, determining the aggregate residual from the traffic data in each of the traffic data sub-sequences specifically includes:
determining flow data average value corresponding to each flow data subsequence ；
Determining an accumulation residual of each flow data subsequence according to the flow data mean value, wherein the accumulation residual is determined according to the following formula:
Wherein, Represents the/>Accumulation residual of individual traffic data subsequences,/>Represents the/>The/>Flow data value,/>Representing the number of traffic data values in a single traffic data sub-sequence.
In some embodiments, obtaining the re-accumulation factor of each flow data sub-sequence according to the accumulated residual specifically includes:
acquiring accumulated residuals for individual traffic data subsequences ；
Determining the accumulation dissimilarity value corresponding to each flow data subsequence；
Determining a re-accumulation factor of each flow data sub-sequence through the accumulation residual and the accumulation dissimilarity value, wherein the re-accumulation factor is determined according to the following formula:
represents the/> The reaggregation factor of the sub-sequence of the individual flow data,/>Represents the/>The first flow data sub-sequenceFlow data value,/>Representing the number of traffic data values in a single traffic data sub-sequence.
In some embodiments, determining the accumulated likelihood coefficients for each flow data sub-sequence by the re-accumulation factor specifically includes:
acquiring a reaggregation factor of each flow data subsequence;
determining an average re-accumulation factor of each flow data sub-sequence;
determining a refocusing attenuation factor of each flow data sub-sequence;
and determining an accumulation likelihood coefficient according to the re-accumulation factor, the average re-accumulation factor and the re-accumulation attenuation factor.
In some embodiments, determining the flow feature covariance matrix from the flow feature space matrix specifically includes:
Carrying out centering treatment on the flow characteristic space matrix;
Determining covariance of flow data values in the flow characteristic space matrix after the centralization treatment;
And filling covariance of all flow data values in the flow characteristic space matrix to corresponding positions of the matrix according to time sequence to obtain a flow characteristic covariance matrix.
In some embodiments, determining the flow charging iteration index of the flow data subsequence according to the flow characteristic value specifically includes:
Acquiring a flow characteristic value of the flow data subsequence;
determining a flow data mean value in the flow data subsequence;
And determining the flow charging iteration index of the flow data subsequence according to the flow characteristic value and the flow data average value.
In a second aspect, the present application provides an automatic charging adjustment device for bandwidth flow of a data center network, which includes a bandwidth control unit, where the bandwidth control unit includes:
The flow data subsequence determining module is used for collecting user historical flow charging data of the data center, dividing the user historical flow charging data into blocks to obtain a flow data sequence, further smoothing the flow data sequence to obtain a flow data smoothing sequence, and decomposing the flow data smoothing sequence according to the use frequency of the user flow to obtain a plurality of flow data subsequences;
The accumulation likelihood coefficient determining module is used for determining an accumulation residual error from the flow data in each flow data subsequence, obtaining a re-accumulation factor of each flow data subsequence according to the accumulation residual error, and further determining the accumulation likelihood coefficient of each flow data subsequence through the re-accumulation factor;
The flow characteristic space matrix determining module is used for reconstructing each flow data subsequence in the plurality of flow data subsequences according to the flow characteristics, and mapping flow data in the reconstructed flow data subsequence into a space matrix to obtain a flow characteristic space matrix;
The flow charging iteration index determining module is used for determining a flow characteristic covariance matrix according to the flow characteristic space matrix, determining a flow characteristic value of the flow data subsequence according to the flow characteristic covariance matrix, determining a flow charging iteration index of the flow data subsequence according to the flow characteristic value, and further determining flow charging iteration indexes corresponding to the flow data subsequences respectively;
the flow charging prediction function value determining module is used for determining flow charging prediction function values respectively corresponding to the flow data subsequences according to the accumulation likelihood coefficients and the flow charging variation indexes respectively corresponding to the flow data subsequences, and superposing the flow charging prediction function values of the flow data subsequences to obtain flow charging prediction function values of the flow data sequences;
And the bandwidth control module is used for adjusting the time-consuming network bandwidth of the bandwidth flowmeter of the flow transmission link according to the flow charging prediction function value.
In a third aspect, the present application provides a computer device, the computer device including a memory and a processor, the memory storing code, the processor being configured to obtain the code and perform the above-described automatic charging adjustment method for data center network bandwidth traffic.
In a fourth aspect, the present application provides a computer readable storage medium storing a computer program, where the computer program when executed by a processor implements the above-mentioned automatic charging adjustment method for data center network bandwidth flow.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
In the method and the device for automatically adjusting network bandwidth flow of the data center, firstly, historical flow billing data of a user of the data center are collected and preprocessed to obtain a plurality of flow data subsequences, flow data in each flow data subsequence is used for determining accumulation residual errors, a re-accumulation factor of each flow data subsequence is obtained according to the accumulation residual errors, further, accumulation likelihood coefficients corresponding to each flow data subsequence are determined through the re-accumulation factor, characteristic reconstruction is carried out on each flow data subsequence in the plurality of flow data subsequences, flow data in the reconstructed flow data subsequence is mapped into a space matrix to obtain a flow characteristic space matrix, a flow characteristic covariance matrix is determined according to the flow characteristic space matrix, flow characteristic values of each flow data subsequence are determined according to the flow characteristic values, flow billing variation indexes corresponding to each flow data subsequence are further determined, flow billing factors corresponding to each flow data subsequence are respectively corresponding to the accumulation likelihood coefficients and flow data subsequence, flow error is predicted according to the flow data subsequence, and the flow context is predicted, flow context is improved, and the flow meter is predicted according to the flow data subsequence, and the flow context is predicted, and the flow context is not predicted, therefore the flow context is predicted.
Drawings
FIG. 1 is an exemplary flow chart of a method for automatic billing adjustment of data center network bandwidth flows according to some embodiments of the application;
fig. 2 is a schematic diagram of exemplary hardware and/or software of a bandwidth control unit shown in accordance with some embodiments of the present application;
Fig. 3 is a schematic structural diagram of a computer device to which a method for automatically adjusting the charging of a data center network bandwidth flow is applied according to some embodiments of the present application.
Detailed Description
The core of the application is to collect the user history flow charging data of the data center and preprocess the data to obtain a plurality of flow data subsequences; determining accumulation residual errors according to flow data in each flow data subsequence, obtaining a re-accumulation factor of each flow data subsequence according to the accumulation residual errors, and further determining accumulation likelihood coefficients corresponding to each flow data subsequence respectively through the re-accumulation factors; for each flow data subsequence in the plurality of flow data subsequences, performing feature reconstruction on the flow data subsequence, and mapping flow data in the reconstructed flow data subsequence into a space matrix to obtain a flow feature space matrix; determining a flow characteristic covariance matrix according to the flow characteristic space matrix, determining a flow characteristic value of the flow data subsequence by the flow characteristic covariance matrix, determining a flow charging iteration index of the flow data subsequence according to the flow characteristic value, and further determining flow charging iteration indexes respectively corresponding to the flow data subsequences; determining flow charging prediction function values corresponding to the flow data subsequences according to the accumulation likelihood coefficients and the flow charging variation indexes corresponding to the flow data subsequences, and superposing the flow charging prediction function values of the flow data subsequences to obtain flow charging prediction function values of the flow data sequences; and adjusting the time-consuming network bandwidth of the bandwidth flowmeter of the flow transmission link according to the flow billing prediction function value, thereby improving the network stability under different network environments and ensuring the accuracy of the billing result.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments. Referring to fig. 1, which is an exemplary flowchart of a method for automatic billing adjustment of data center network bandwidth flow according to some embodiments of the application, the method 100 for automatic billing adjustment of data center network bandwidth flow mainly includes the steps of:
In step S101, historical flow billing data of a user of a data center is collected and preprocessed, so as to obtain a plurality of flow data subsequences.
In some embodiments, collecting user historical flow charging data of a data center, and performing block division on the user historical flow charging data to obtain a flow data sequence;
and carrying out smoothing treatment on the flow data sequence to obtain a flow data smoothing sequence, and decomposing the flow data smoothing sequence according to the use frequency of the user flow to obtain a plurality of flow data subsequences.
In particular, the data collection tool may be deployed on servers and devices in the data center to collect historical traffic billing data for users of the data center, for example, the prior art Wireshark, an open source tool for network protocol analysis and data packet capture, may be used to allow users to capture network data packets, analyze network communications, and detect and diagnose network problems.
In some embodiments, the traffic data sequence may be obtained by setting a traffic number threshold, and dividing the data into a new block when the traffic data amount reaches the threshold.
It should be noted that, the smoothing process in the present application may use a moving average method in the prior art, where the moving average method calculates an average value of flow data in the flow data sequence, and uses the average value to replace an original data point, so that noise and fluctuation in the data can be reduced, and the data tends to be stable.
In specific implementation, the flow data sequence is decomposed according to the frequency of use of the user flow, for example, the flow data sequence is decomposed according to the frequency of use of the user flow per hour, the flow data sequence is decomposed into 24 sub-blocks, and the decomposed sub-blocks are taken as flow data sub-sequences corresponding to 24 hours.
In step S102, an accumulation residual is determined from the flow data in each flow data sub-sequence, and a re-accumulation factor of each flow data sub-sequence is obtained according to the accumulation residual, so that the accumulation likelihood coefficients respectively corresponding to each flow data sub-sequence are determined according to the re-accumulation factors.
In some embodiments, determining the accumulation residual from the traffic data in each of the sub-sequences of traffic data may be accomplished by:
determining flow data average value corresponding to each flow data subsequence ；
Determining an accumulation residual of each flow data subsequence according to the flow data mean value, wherein the accumulation residual is determined according to the following formula:
Wherein, Represents the/>Accumulation residual of individual traffic data subsequences,/>Represents the/>The/>Flow data value,/>Representing the number of traffic data values in a single traffic data sub-sequence.
In some embodiments, the step of obtaining the re-accumulation factor of each flow data sub-sequence according to the accumulated residual may be implemented by:
acquiring accumulated residuals for individual traffic data subsequences ；
Determining the accumulation dissimilarity value corresponding to each flow data subsequence；
Determining a re-accumulation factor of each flow data sub-sequence through the accumulation residual and the accumulation dissimilarity value, wherein the re-accumulation factor is determined according to the following formula:
represents the/> The reaggregation factor of the sub-sequence of the individual flow data,/>Represents the/>The first flow data sub-sequenceFlow data value,/>Representing the number of traffic data values in a single traffic data sub-sequence.
In some embodiments, determining the accumulation likelihood coefficients corresponding to the respective flow data sub-sequences by the re-accumulation factor may be implemented by:
Obtaining the reaggregation factor of each flow data subsequence ；
Determining an average re-accumulation factor for each flow data sub-sequence based on the re-accumulation factors；
Determining a refocusing attenuation factor for each flow data sub-sequence；
And determining an accumulation likelihood coefficient according to the re-accumulation factor, the average re-accumulation factor and the re-accumulation attenuation factor, wherein the accumulation likelihood coefficient is determined according to the following formula:
represents the/> The cumulative likelihood coefficients for the individual traffic data sub-sequences.
In step S103, for each of the plurality of traffic data sub-sequences, performing feature reconstruction on the traffic data sub-sequence, and mapping traffic data in the reconstructed traffic data sub-sequence to a space matrix to obtain a traffic feature space matrix.
In some embodiments, for each of a plurality of traffic data sub-sequences, feature reconstruction of that traffic data sub-sequence may be accomplished by:
determining a delay time according to the flow data value in the flow data subsequence;
determining an embedding dimension according to the flow data value in the flow data subsequence;
And carrying out characteristic reconstruction on the flow subsequence by the delay time value and the embedding dimension.
The delay time represents a time interval between traffic data values in the traffic data sub-sequence.
In specific implementation, obtaining delay time, mapping data in a flow data sub-sequence into a one-dimensional space, gradually increasing the dimension of the space to obtain a flow embedding vector, for example, increasing the dimension of the space to 2, 3, 4 and the like so as to better represent the dynamic characteristics of the flow time sub-sequence, calculating the distance between each flow data point in each dimension space and the nearest adjacent point, and marking the nearest adjacent points as adjacent points;
A distance threshold value is preset, if the distance between adjacent points is within the threshold value, the actual adjacent points are judged, if the distance between the adjacent points exceeds the threshold value, the actual adjacent points are judged to be false adjacent points, the proportion of the false adjacent points in each dimension, namely the ratio of the number of the false adjacent points to the total number of the adjacent points, is calculated, the embedding dimension is selected according to the ratio of the false adjacent points, and the minimum value of the ratio of the false adjacent points is rounded and used as the embedding dimension.
The determination of the embedding dimension in the present application adopts the false neighboring point method in the prior art, and other methods can also be adopted in other embodiments, which is not limited herein.
In the specific implementation, the flow data subsequence is subjected to feature reconstruction according to the delay time and the embedding dimension, the time interval between flow data points in the reconstructed subsequence is determined by the delay time, the dimension of the reconstructed embedding vector is determined by the embedding dimension, the normalized feature of the flow data subsequence is extracted and converted into the flow embedding vector subsequence, and the flow embedding vector subsequence is a high-dimensional representation of the original flow data subsequence, so that the feature of the flow data can be better acquired for further analysis and processing.
In some embodiments, mapping the flow data in the reconstructed flow data subsequence to a space matrix, and obtaining the flow feature space matrix may be implemented by the following steps:
Acquiring a space matrix, wherein each row of the matrix represents a different time period, and each column of the matrix represents a flow data characteristic;
and mapping the flow data in the flow data subsequence into the space matrix one by one according to the time sequence from small to large to obtain a flow characteristic space matrix.
It should be noted that, the mapping method adopted in the present application is a direct mapping method in the prior art, and other mapping methods may be adopted in other embodiments, which is not limited herein.
In step S104, a flow characteristic covariance matrix is determined according to the flow characteristic space matrix, a flow characteristic value of the flow data subsequence is determined by the flow characteristic covariance matrix, a flow charging iteration index of the flow data subsequence is determined according to the flow characteristic value, and further a flow charging iteration index corresponding to each flow data subsequence is determined.
In some embodiments, determining the flow feature covariance matrix according to the flow feature space matrix may be implemented by:
Carrying out centering treatment on the flow characteristic space matrix;
Determining covariance of flow data values in the flow characteristic space matrix after the centralization treatment;
And filling covariance of all flow data values in the flow characteristic space matrix to corresponding positions of the matrix according to time sequence to obtain a flow characteristic covariance matrix.
It should be noted that, the centering process in the present application is a method for preprocessing data of the flow feature space matrix, where the centering process is to move the average value of the flow data in the flow feature space matrix to the vicinity of the zero point, eliminate the translational difference of the flow data values in the flow feature space matrix, reduce the collinearity, and simplify the calculation of the covariance matrix because the average value of the flow data after centering is zero.
In specific implementation, a space matrix is created, each row of the matrix represents different time periods, each column of the matrix represents flow data characteristics, covariance of all flow data values in the flow characteristic space matrix is filled into corresponding positions of the matrix from small to large according to time sequence, and a flow characteristic covariance matrix is obtained.
In some embodiments, determining the flow characteristic value of the flow data subsequence by the flow characteristic covariance matrix, determining the flow charging iteration index of the flow data subsequence according to the flow characteristic value, and further determining the flow charging iteration index corresponding to each flow data subsequence respectively may be implemented by the following steps:
Calculating eigenvalues of a flow characteristic covariance matrix ；
Acquiring flow data average values respectively corresponding to the flow data subsequences；
Determining flow attribute values for each flow data value in a flow feature covariance matrix；
Determining a flow meter cost iteration index through the characteristic value of the flow characteristic covariance matrix and the flow variation value of each flow data value, wherein the flow meter cost iteration index can be determined according to the following formula:
represents the/> Flow charging iteration index for each flow data sub-sequence.
In step S105, according to the accumulation likelihood coefficient and the flow charging iteration index corresponding to each flow data sub-sequence, determining the flow charging prediction function value corresponding to each flow data sub-sequence, and superposing the flow charging prediction function values of each flow data sub-sequence to obtain the flow charging prediction function value of the flow data sequence.
In specific implementation, the flow billing prediction function value can be implemented by the following steps:
Acquiring accumulation likelihood coefficients respectively corresponding to each flow data subsequence ；
Obtaining flow charging iteration index corresponding to each flow data subsequence；
Determining a flow charging prediction function value according to the accumulation likelihood coefficient and the flow charging iteration index, wherein the flow charging prediction function value is determined according to the following formula:
represents the/> Flow charging prediction function value of each flow data subsequence,/>Represents the/>The/>And traffic data values.
In some embodiments, the process of superposing the flow charging prediction function values of each flow data sub-sequence to obtain the flow charging prediction function value of the flow data sequence may be obtained by adding the flow charging prediction function values of all the flow data sub-sequences, where the flow charging prediction function value is a prediction result obtained by performing function value prediction according to historical flow data of the data center, and reflects the next flow transmission requirement based on the behavior habit of the data center, so that the bandwidth of the flow transmission link may be adjusted by the flow charging prediction function value, thereby improving the network stability in different network environments when network charging, and ensuring the accuracy of the charging result when the network fluctuation condition is large.
In step S106, the time-consuming network bandwidth of the bandwidth flowmeter of the flow transmission link is adjusted according to the flow billing prediction function value.
In some embodiments, a function value proportionality coefficient is obtained by comparing a flow charging prediction function value with a preset function value threshold, and when the method is specifically implemented, the ratio between the flow charging prediction function value and the preset function value threshold can be used as the function value proportionality coefficient, and further, the bandwidth of a flow transmission link is proportionally adjusted through the function value proportionality coefficient, for example, a Proportional-Integral-Derivative (PID) controller is added into a forward path of a router bandwidth control system of a data center, and the function value proportionality coefficient is used as a proportionality parameter in the PID controller, so that the bandwidth occupied by the data center is adjusted in advance according to the function value proportionality coefficient, the network stability under different network environments when network charging is improved, and the accuracy of a charging result can be ensured when the network fluctuation condition is large.
In addition, in another aspect of the present application, in some embodiments, the present application provides an automatic charging adjustment device for bandwidth flow of a data center network, where the device includes a bandwidth control unit, and referring to fig. 2, which is a schematic diagram of exemplary hardware and/or software of the bandwidth control unit according to some embodiments of the present application, the bandwidth control unit 200 includes: the flow data subsequence determining module 201, the aggregate likelihood factor determining module 202, the flow feature space matrix determining module 203, the flow charging iteration index determining module 204, the flow charging prediction function value determining module 205, and the bandwidth control module 206 are respectively described as follows:
the flow data subsequence determining module 201 is mainly used for collecting and preprocessing user historical flow charging data of a data center to obtain a plurality of flow data subsequences;
The accumulation likelihood coefficient determining module 202 in the present application, the accumulation likelihood coefficient determining module 202 is mainly used for determining an accumulation residual error from the flow data in each flow data sub-sequence, obtaining a re-accumulation factor of each flow data sub-sequence according to the accumulation residual error, and further determining the accumulation likelihood coefficient of each flow data sub-sequence according to the re-accumulation factor;
The flow characteristic space matrix determining module 203 in the present application, the flow characteristic space matrix determining module 203 is mainly configured to reconstruct each flow data subsequence of a plurality of flow data subsequences according to flow characteristics, and map flow data in the reconstructed flow data subsequence to a space matrix to obtain a flow characteristic space matrix;
The flow charging iteration index determining module 204 in the present application is mainly configured to determine a flow characteristic covariance matrix according to the flow characteristic space matrix, determine a flow characteristic value of the flow data subsequence according to the flow characteristic covariance matrix, determine a flow charging iteration index of the flow data subsequence according to the flow characteristic value, and further determine a flow charging iteration index corresponding to each flow data subsequence;
the flow charging prediction function value determining module 205 is mainly used for determining flow charging prediction function values corresponding to each flow data subsequence according to the accumulation likelihood coefficient and the flow charging iteration index corresponding to each flow data subsequence, and superposing the flow charging prediction function values of each flow data subsequence to obtain flow charging prediction function values of the flow data sequence;
The bandwidth control module 206, the bandwidth control module 206 of the present application is mainly used for adjusting the network bandwidth of the bandwidth flowmeter of the flow transmission link according to the flow billing prediction function value.
In addition, the application also provides a computer device, which comprises a memory and a processor, wherein the memory stores codes, and the processor is configured to acquire the codes and execute the automatic charging adjustment method for the data center network bandwidth flow.
In some embodiments, reference is made to fig. 3, which is a schematic structural diagram of a computer device for applying a method for automatic charging adjustment of data center network bandwidth flows according to some embodiments of the application. The automatic charging adjustment method for data center network bandwidth flows in the above embodiment may be implemented by a computer device shown in fig. 3, where the computer device includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.
Processor 301 may be a general purpose central processing unit (central processing unit, CPU), application-specific integrated circuit (ASIC), or one or more of the implementations for controlling the automatic billing adjustment of data center network bandwidth flows in the present application.
Communication bus 302 may include a path to transfer information between the above components.
The Memory 303 may be, but is not limited to, a read-only Memory (ROM) or other type of static storage device that can store static information and instructions, a random access Memory (random access Memory, RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-only Memory, EEPROM), a compact disc (compact disc read-only Memory) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 303 may be stand alone and be coupled to the processor 301 via the communication bus 302. Memory 303 may also be integrated with processor 301.
The memory 303 is used for storing program codes for executing the scheme of the present application, and the processor 301 controls the execution. The processor 301 is configured to execute program code stored in the memory 303. One or more software modules may be included in the program code. The method for automatically charging and adjusting the bandwidth flow of the data center network in the above embodiment may be implemented by one or more software modules in the program codes in the processor 301 and the memory 303.
Communication interface 304, using any transceiver-like device for communicating with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), etc.
In a specific implementation, as an embodiment, a computer device may include a plurality of processors, where each of the processors may be a single-core (single-CPU) processor or may be a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
The computer device may be a general purpose computer device or a special purpose computer device. In a specific implementation, the computer device may be a desktop, a laptop, a web server, a personal computer (PDA), a mobile handset, a tablet, a wireless terminal device, a communication device, or an embedded device. Embodiments of the application are not limited to the type of computer device.
In addition, the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the automatic charging adjustment method for the data center network bandwidth flow when being executed by a processor.
In summary, in the method and the device for automatically adjusting the network bandwidth flow of the data center disclosed by the embodiment of the application, firstly, a plurality of flow data subsequences are obtained by collecting and preprocessing the historical flow billing data of the user of the data center, the accumulation residual error is determined by the flow data in the flow data subsequences, the accumulation likelihood coefficient of the flow data subsequences is further determined, the flow data subsequences are subjected to characteristic reconstruction, the flow characteristic space matrix is determined by the reconstructed flow data subsequences, the flow characteristic covariance matrix is further obtained, the flow characteristic value is determined by the flow characteristic covariance matrix, the flow billing variation index of the flow data subsequences is determined according to the flow characteristic value, the flow billing prediction function value of the flow data subsequences is determined according to the accumulation likelihood coefficient and the flow billing variation index, and the time-consuming network bandwidth of the bandwidth flowmeter of the flow transmission link is adjusted according to the flow billing prediction function value, so that the network stability under different network environments is improved, and the accuracy of the billing result is ensured.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. An automatic charging adjustment method for data center network bandwidth flow is characterized by comprising the following steps:
Collecting historical flow charging data of a user of a data center and preprocessing the data to obtain a plurality of flow data subsequences;
Determining accumulation residual errors according to flow data in each flow data subsequence, obtaining a re-accumulation factor of each flow data subsequence according to the accumulation residual errors, and further determining accumulation likelihood coefficients corresponding to each flow data subsequence respectively through the re-accumulation factors;
for each flow data subsequence in the plurality of flow data subsequences, performing feature reconstruction on the flow data subsequence, and mapping flow data in the reconstructed flow data subsequence into a space matrix to obtain a flow feature space matrix;
determining a flow characteristic covariance matrix according to the flow characteristic space matrix, determining a flow characteristic value of the flow data subsequence by the flow characteristic covariance matrix, determining a flow charging iteration index of the flow data subsequence according to the flow characteristic value, and further determining flow charging iteration indexes respectively corresponding to the flow data subsequences;
Determining flow charging prediction function values corresponding to the flow data subsequences according to the accumulation likelihood coefficients and the flow charging variation indexes corresponding to the flow data subsequences, and superposing the flow charging prediction function values of the flow data subsequences to obtain flow charging prediction function values of the flow data sequences;
and adjusting the time-consuming network bandwidth of the bandwidth flowmeter of the flow transmission link according to the flow charging prediction function value.
2. The method of claim 1, wherein collecting and preprocessing historical traffic billing data for a user of the data center to obtain a plurality of traffic data subsequences comprises:
collecting user historical flow charging data of a data center, and dividing the user historical flow charging data into blocks to obtain a flow data sequence;
performing smoothing treatment on the flow data sequence to obtain a flow data smoothing sequence;
and decomposing the flow data smoothing sequence according to the use frequency of the user flow to obtain a plurality of flow data subsequences.
3. The method of claim 1, wherein determining respective accumulation likelihood coefficients for each flow data sub-sequence by the re-accumulation factor comprises:
acquiring a reaggregation factor of each flow data subsequence;
determining an average re-accumulation factor of each flow data sub-sequence;
determining a refocusing attenuation factor of each flow data sub-sequence;
and determining an accumulation likelihood coefficient according to the re-accumulation factor, the average re-accumulation factor and the re-accumulation attenuation factor.
4. The method of claim 1, wherein determining a flow feature covariance matrix from the flow feature space matrix comprises:
Carrying out centering treatment on the flow characteristic space matrix;
Determining covariance of flow data values in the flow characteristic space matrix after the centralization treatment;
And filling covariance of all flow data values in the flow characteristic space matrix to corresponding positions of the matrix according to time sequence to obtain a flow characteristic covariance matrix.
5. The method of claim 1 wherein determining a flow charging iteration index for the flow data subsequence based on the flow characteristic value comprises:
Acquiring a flow characteristic value of the flow data subsequence;
determining a flow data mean value in the flow data subsequence;
And determining the flow charging iteration index of the flow data subsequence according to the flow characteristic value and the flow data average value.
6. The utility model provides a data center network bandwidth flow automatic charging adjusting device which characterized in that, including bandwidth control unit, bandwidth control unit includes:
The flow data subsequence determining module is used for collecting user historical flow charging data of the data center, dividing the user historical flow charging data into blocks to obtain a flow data sequence, further smoothing the flow data sequence to obtain a flow data smoothing sequence, and decomposing the flow data smoothing sequence according to the use frequency of the user flow to obtain a plurality of flow data subsequences;
The accumulation likelihood coefficient determining module is used for determining an accumulation residual error from the flow data in each flow data subsequence, obtaining a re-accumulation factor of each flow data subsequence according to the accumulation residual error, and further determining the accumulation likelihood coefficient of each flow data subsequence through the re-accumulation factor;
The flow characteristic space matrix determining module is used for reconstructing each flow data subsequence in the plurality of flow data subsequences according to the flow characteristics, and mapping flow data in the reconstructed flow data subsequence into a space matrix to obtain a flow characteristic space matrix;
The flow charging iteration index determining module is used for determining a flow characteristic covariance matrix according to the flow characteristic space matrix, determining a flow characteristic value of the flow data subsequence according to the flow characteristic covariance matrix, determining a flow charging iteration index of the flow data subsequence according to the flow characteristic value, and further determining flow charging iteration indexes corresponding to the flow data subsequences respectively;
the flow charging prediction function value determining module is used for determining flow charging prediction function values respectively corresponding to the flow data subsequences according to the accumulation likelihood coefficients and the flow charging variation indexes respectively corresponding to the flow data subsequences, and superposing the flow charging prediction function values of the flow data subsequences to obtain flow charging prediction function values of the flow data sequences;
and the bandwidth control module is used for adjusting the time-consuming network bandwidth of the bandwidth flowmeter of the flow transmission link according to the flow charging prediction function value.
7. A computer device comprising a memory storing code and a processor configured to obtain the code and to perform the data center network bandwidth flow automatic billing adjustment method of any of claims 1 to 5.
8. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the data center network bandwidth flow automatic billing adjustment method of any of claims 1 to 5.
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| CN116346639A (en) * | 2023-03-04 | 2023-06-27 | 西安电子科技大学青岛计算技术研究院 | Network traffic prediction method, system, medium, equipment and terminal |
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| CN116346639A (en) * | 2023-03-04 | 2023-06-27 | 西安电子科技大学青岛计算技术研究院 | Network traffic prediction method, system, medium, equipment and terminal |
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