JP7444263B2 - Bandwidth estimation device, band estimation method, and program - Google Patents

Description

The present disclosure relates to a technique for calculating a communication band required when a plurality of lines are accommodated in one communication link.

1 and 2 are diagrams for explaining a communication band calculation device 300 described in Patent Document 1. The communication system 10 connects two communication devices (11, 12) with a link 15. Link 15 accommodates multiple lines. For the communication band of each line, an upper limit value (hereinafter referred to as "contract band") is set according to the contract for each line. The communication devices (11, 12) observe the traffic amount of the entire line accommodated in the link 15 at their respective physical ports (11a, 12a), and notify the communication band calculation device 300.

The number of lines accommodated and the contracted bandwidth change over time due to the addition of new lines, contract changes, or deletions, and may change multiple times within the observation period Tv and design period Tx shown in Figure 2. There is. In order to guarantee the communication quality of each line (so that traffic is not discarded), the necessary physical bandwidth (hereinafter referred to as "required bandwidth") is designed for link 15 (hereinafter referred to as "bandwidth design"). to be done.) to be done.

Japanese Patent Application Publication No. 2009-118274

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. “LightGBM: A Highly Efficient Gradient Boosting Decision Tree.” Advances in Neural Information Processing Systems 30 (NIPS 20 17), pp. 3149-3157.

Here, the communication system 10 as shown in FIG. 1 has the following problems regarding band utilization efficiency.
If the required bandwidth of the link 15 is the sum of the contracted bandwidths of all lines accommodated, if the utilization rates of many lines are low, the band usage efficiency will be low. For this reason, the communication system 10 has the problem that it is required to guarantee the communication quality of each line and to set the required bandwidth to an extent that does not reduce the bandwidth usage efficiency.

In order to solve the problem, the following prediction method may be used.
This prediction method predicts the bandwidth of each variable element from observed traffic data, and uses the sum as the required bandwidth. Here, existing line fluctuations are predicted using a linear approximation function formula based on past traffic data. On the other hand, new line fluctuations are predicted using the same probability density function as existing traffic.
Then, the band design is performed at each time within the design period Tx, and the required band Z(t _i ) is calculated for each time t _i .

Patent Document 1 discloses estimating the required bandwidth for each variable element. An object of the present invention is to estimate the required bandwidth with higher accuracy than the method of Patent Document 1. In other words, in order to solve the above problems, the present invention provides a bandwidth estimation device and a bandwidth estimation method that can calculate the required bandwidth of a link in a communication system with high accuracy, guarantee communication quality, and improve bandwidth usage efficiency. , and programs.

In order to achieve the above object, a bandwidth estimation device according to the present invention estimates traffic amount from line information using a machine learning model that has learned observed traffic data and a set of line information, and estimates traffic amount from a plurality of sets of line information. We decided to use the upper limit of the confidence interval based on the distribution of traffic as the required bandwidth.

Specifically, the bandwidth estimating device according to the present invention is a bandwidth estimating device that calculates a required bandwidth of a link in a communication system in which a plurality of lines are accommodated in a link between communication devices, and includes:
a communication unit into which the contracted band for each line and the traffic volume of the link in the observation period up to the present, and the contracted band for each line in the future design period are input;
creating a machine learning model that performs machine learning on the contracted band and the traffic amount for each hour in the observation period and predicts the traffic amount from the contracted band;
applying a contracted band for each line during the design period to the machine learning model to estimate the traffic amount for each time during the design period;
estimating a probability density function from the estimated traffic amount at each time in the design period; and applying a desired confidence interval to the probability density function, and setting the upper limit of the confidence interval to the required band. a processing section;
Equipped with

Further, a bandwidth estimation method according to the present invention is a bandwidth estimation method for calculating a required bandwidth of a link in a communication system in which a plurality of lines are accommodated in a link between communication devices, the method comprising:
obtaining the contracted bandwidth for each line and the traffic volume of the link during the observation period up to the present;
creating a machine learning model that performs machine learning on the contracted band and the traffic amount for each hour in the observation period and predicts the traffic amount from the contracted band;
obtaining a contracted band for each line in a future design period, and estimating the traffic amount for each time in the design period using the machine learning model;
estimating a probability density function from the estimated traffic amount at each time in the design period; and applying a desired confidence interval to the probability density function, and setting the upper limit of the confidence interval to the required band.

The present band estimation device and method generates a model by machine learning past traffic volume and line information, and estimates future traffic volume by applying line information to the model. Then, the required bandwidth is set based on the estimated probability density distribution of the traffic amount. In other words, the present invention provides a bandwidth estimation device, a bandwidth estimation method, and a program that can calculate the required bandwidth of a link in a communication system with high accuracy using machine learning, and can guarantee communication quality and improve bandwidth usage efficiency. can do.

Further, the present invention is a program for causing a computer to function as the band estimating device. The band estimating device of the present invention can also be realized by a computer and a program, and the program can be recorded on a recording medium or provided through a network.

Note that the above inventions can be combined as much as possible.

The present invention can provide a bandwidth estimating device, a bandwidth estimating method, and a program that can calculate the required bandwidth of a link in a communication system with high accuracy, guarantee communication quality, and improve bandwidth usage efficiency.

1 is a diagram illustrating a communication band calculation device related to the present invention. FIG. 3 is a diagram illustrating the operation of a communication band calculation device related to the present invention. It is a table of time series data of line information vector x and scalar value y of traffic amount. FIG. 3 is a diagram illustrating a model used in the band estimating device according to the present invention. 3 is a flowchart illustrating a band estimation method according to the present invention. FIG. 1 is a diagram illustrating a band estimating device according to the present invention. FIG. 1 is a diagram illustrating a band estimating device according to the present invention. It is a figure explaining the effect of the band estimation device concerning the present invention. FIG. 1 is a diagram illustrating a band estimating device according to the present invention.

Embodiments of the invention will be described with reference to the accompanying drawings. The embodiments described below are examples of the present invention, and the present invention is not limited to the following embodiments. Note that components with the same reference numerals in this specification and the drawings indicate the same components.

FIG. 3 is a table of time series data of line information vector x and traffic volume scalar value y. FIG. 4 is a diagram illustrating a model used in the present invention.
The vector x _t at time t represents each row of the x portion of the table in FIG.
x _t = {x _{(t, 0)} , x _{(t, 1)} ,...}
Each element x _{(t, i)} represents the contracted bandwidth of line i at time t.
The traffic amount _yt at time t represents the traffic amount of the entire accommodating line of the link targeted for bandwidth design.
The vector _xt of the observation period Tv and the design period Tx changes over time as lines are newly added, contracts are changed, or deleted. The traffic amount _yt during the observation period Tv is the actually observed traffic amount. On the other hand, as shown in FIG. 4, the traffic amount _yt for the design period Tx is predicted using "Model 1". Furthermore, from the traffic amount y _t predicted by Model 1, a probability density function p(y) that fits the distribution of the traffic amount y _t is predicted using "Model 2".

FIG. 5 is a flowchart illustrating the band estimation method according to the present invention. This bandwidth estimation method is a bandwidth estimation method for calculating the required bandwidth of a link 15 in a communication system 10 in which a plurality of lines i are accommodated in a link 15 between communication devices (11, 12),
Obtaining the contracted band x _{(t, i)} for each line i and the traffic amount y _t of link 15 in the observation period Tv up to now (step S01);
Create a machine learning model (Model 1) that performs machine learning on the contracted bandwidth x _{(t, i)} and traffic volume _yt for each hour during the observation period Tv, and predicts the traffic volume _yt from the contracted bandwidth x _{(t, i).} to do (step S02),
Obtaining the contracted band x _{(t, i)} for each line i in the future design period Tx, and estimating the traffic amount _yt for each hour of the design period Tx using the machine learning model (step S03);
Estimating a probability density function p(y) from the estimated traffic volume _yt for each time of the design period Tx (step S04), and applying a desired confidence interval to the probability density function p(y), and calculating the confidence interval of the confidence interval. Setting the upper limit value to the above-mentioned required band (step S05)
I do.

FIG. 6 is a diagram illustrating the band estimation device 301 and the communication system 10 to be measured according to the present invention. The communication system 10 is a network in which a plurality of lines i are accommodated in a link 15 between two arbitrary communication devices (11, 12). The steps in FIG. 5 will be explained in detail using FIG. 6 as well.

[Step S01]
Bandwidth estimating device 301 acquires time-series data for observation period Tx regarding the contracted band of each line i and the traffic average counter value in arbitrary time units for each communication device (11, 12).
[Step S02]
The band estimating device 301 performs machine learning on the acquired observation period Tx data and creates a model 1 that predicts the traffic amount y _t from the vector x _t . In other words, model 1 is a model that calculates the amount of traffic y _t between the communication devices (11, 12) for a certain period from the contracted band data x _{(t, i)} of each line i for that period. For example, to generate model 1, the algorithm LightGBM (non-patent document 1) is used, which is effective in estimating accuracy and reducing calculation time in creating a model for a regression problem that estimates discrete values from nonlinear data.
Note that x _{(t, i)} is not limited to the contracted band, and may include the average value and variance of the contracted band. Furthermore, by adjusting the parameters of model 1, it is possible to improve the estimation accuracy.
[Step S03]
Bandwidth estimating device 301 acquires time-series data of design period Tv for the contracted band of each line i. Then, the band estimating device 301 uses Model 1 to estimate the traffic amount y' _t in the shaded area in FIG. 3 from the vector x _t of the design period Tv.
[Step S04]
The band estimating device 301 calculates the probability density function p(y) of the traffic amount over the entire design period Tx based on the estimated traffic amount y' _t at each time. In this step, the band estimating device 301 creates a model 2 that predicts the probability density function p(y) from the traffic amount y' _t . Model 2 is a model that estimates a probability density function p(y) from a set of a plurality of traffic amounts y' _t by kernel density estimation (a method of estimating the entire distribution from finite sample points). For example, scikit-learn is used to create model 2. Gaussian distribution is used for kernel density estimation. Further, estimation accuracy may be improved by using an arbitrary probability density function (exponential distribution, etc.) for kernel density estimation based on the distribution of observed traffic amount.
In this way, the band estimating device 301 estimates the probability density function p(y) from y' _t at time t (t∈Tv) of the design period Tx using Model 2 in this step.
[Step 5]
Band estimation device 301 calculates an upper limit value of a confidence interval necessary to guarantee communication quality from probability density function p(y). Specifically, the band estimating device 301 determines the average and standard deviation from the probability density function p(y), and determines the upper limit of the confidence interval of β%. Then, the band estimating device 301 sets the upper limit of this confidence interval as the required band. For example, assume that the confidence interval is β=99.999%.

FIG. 7 is a diagram illustrating the band estimation device 301. The bandwidth estimating device 301 is a bandwidth estimating device that calculates the required bandwidth of the link 15 in the communication system 10 in which a plurality of lines i are accommodated in the link 15 between the communication devices (11, 12), and includes:
The contracted bandwidth x _{(t, i)} for each line i in the current observation period Tx, the traffic volume y _t of link 15, and the contracted bandwidth x _{(t, i)} for each line i in the future design period Tv are input. a communication section 21,
A machine learning model (Model 1) that performs machine learning on the contracted bandwidth x _{(t, i)} and traffic volume _yt for each hour during the observation period Tx, and predicts the traffic volume y' _t from the contracted bandwidth x _{(t, i).} to create,
Applying the contracted band x _{(t, i)} for each line i in the design period Tv to the machine learning model to estimate the traffic amount _y't for each hour of the design period Tv;
Estimating a probability density function p(y) from the estimated traffic volume _y't at each time of the design period Tv, and Applying a desired confidence interval to the probability density function p(y), and determining the upper limit of the confidence interval. an arithmetic processing unit 22 that performs the necessary bandwidth;
Equipped with

The communication unit 21 has a function of performing data communication with an external device via a communication line. The communication unit 21 receives the contracted bandwidth x _{(t, i)} and the traffic amount y _t acquired at the physical ports (11a, 12a) of each communication device (11, 12) in steps S01 and S03 described above. Ru.
The calculation processing section 22 includes a first calculation section to a third calculation section.
The first calculation unit creates model 1 from the traffic amount y _t and line information (vector x _t ), and uses model 1 to calculate the estimated traffic amount y' _t at each time. The first calculation unit is in charge of steps S01 to S03 described above.
The second calculation unit estimates the probability density function p(y) from the estimated traffic amount y' _t at each time. The second calculation unit is in charge of step S04 described above.
The third calculation unit calculates the upper limit of the confidence interval of β% of the estimated probability density function p(y) as the required band. The third calculation unit is in charge of step S05 described above.

Band estimation device 301 also includes data bus 20, input section 23, output section 24, and storage section 25.
The storage unit 25 stores observation data (time series data y of past traffic volume y _t ), accommodated line data (data such as contract bandwidth x _{(t, i)} of each line i), and bandwidth calculation parameters (traffic volume y ' Parameters used in model 1 to estimate _t ) and the band calculation result (required band) are stored.
The input unit 23 receives parameters and programs from an operator, a network controller, or the like.
The output unit 24 has functions such as outputting the calculation status and calculation results to a file or displaying them on a screen.
The data bus 20 has a function of connecting each section to transmit and receive data from each section.

(effect)
In order to explain the effects of the band estimating device 301, time-series data on the amount of traffic during the design period Tx was also acquired.
FIG. 8 is a diagram illustrating the effects of the band estimating device 301. FIG. 8 shows the actual traffic amount _yt , the predicted traffic amount _y't made by the band estimation device 301, and the predicted traffic amount y't made by the communication band calculation device 300 explained in FIGS. 1 and 2 during the design period Tx. The predicted traffic amount _y0't , the required bandwidth W1 calculated by the bandwidth estimation device 301, and the required bandwidth W0 calculated by the communication bandwidth calculation device 300 are described.
Since the required bandwidth W1 calculated by the bandwidth estimating device 301 exceeds the actual traffic amount _yt at all times, packets are not discarded and communication quality can be maintained. Furthermore, since the required bandwidth W1 calculated by the bandwidth estimating device 301 is lower than the required bandwidth W0 calculated by the communication bandwidth calculating device 300, the band usage efficiency can be improved.

Further, by adjusting the parameters of the model 1 according to the traffic pattern of the traffic observed during the observation period Tv, it is possible to create a model according to the characteristics of the traffic. For example, if traffic fluctuates greatly depending on the day of the week or the season, it may be possible to adjust parameters according to the traffic pattern.

As described above, the band estimation device 301 of the present invention uses the traffic amount y _t and the line information x _{(t, i)} during the observation period Tv to create the model 1 that estimates the traffic amount from the line information. , the estimated traffic amount y' _t for each time can be accurately estimated based on the line information x _{(t, i)} for each time within the design period Tx.
Further, the band estimating device 301 of the present invention can estimate a probability density function p(y) from the estimated traffic amount y' _t , and calculate a necessary band to guarantee communication quality based on this probability density function.
Therefore, the bandwidth estimating device 301 of the present invention can accurately estimate the amount of traffic, thereby reducing the necessary bandwidth to guarantee communication quality to the minimum, thereby improving the efficiency of bandwidth use.

(Other embodiments)
Bandwidth estimating device 301 can also be realized by a computer and a program, and the program can be recorded on a recording medium or provided through a network.
FIG. 9 shows a block diagram of system 100. System 100 includes computer 105 connected to network 135.

Network 135 is a data communications network. Network 135 may be a private network or a public network, such as (a) a personal area network covering, for example, a room; (b) a local area network, e.g., covering a building; (c) a local area network, e.g. (d) a metropolitan area network, which covers, for example, a city; (e) a wide network, which covers an area that spans, for example, urban, regional, or national boundaries or (f) the Internet. Communication occurs via electronic and optical signals via network 135.

Computer 105 includes a processor 110 and memory 115 coupled to processor 110. Although computer 105 is depicted herein as a stand-alone device, it is not so limited, but rather may be connected to other devices not shown in a distributed processing system.

Processor 110 is an electronic device comprised of logic circuitry that responds to and executes instructions.

Memory 115 is a tangible computer readable storage medium having a computer program encoded thereon. In this regard, memory 115 stores data and instructions, or program codes, readable and executable by processor 110 to control the operation of processor 110. Memory 115 may be implemented as random access memory (RAM), a hard drive, read only memory (ROM), or a combination thereof. One of the components of memory 115 is program module 120.

Program modules 120 include instructions for controlling processor 110 to perform the processes described herein. Although operations are described herein as being performed by computer 105 or a method or process or subprocess thereof, those operations are actually performed by processor 110.

The term "module" is used herein to refer to functional operations that can be implemented either as a stand-alone component or as an integrated arrangement of multiple subcomponents. Accordingly, program module 120 may be implemented as a single module or as multiple modules operating in concert with each other. Additionally, although program modules 120 are described herein as being installed in memory 115 and thus being implemented in software, program modules 120 may be implemented in hardware (e.g., electronic circuitry), firmware, software, or any combination thereof. It is possible to implement either of these methods.

Although program modules 120 are shown as already loaded into memory 115, they may be configured to reside on storage device 140 for later loading into memory 115. Storage device 140 is a tangible computer readable storage medium that stores program modules 120 . Examples of storage devices 140 include compact disks, magnetic tape, read-only memory, optical storage media, memory units composed of a hard drive or multiple parallel hard drives, and Universal Serial Bus (USB) flash drives. It will be done. Alternatively, storage 140 may be random access memory or other type of electronic storage device located in a remote storage system, not shown, and connected to computer 105 via network 135.

System 100 further includes a data source 150A and a data source 150B, collectively referred to herein as data sources 150, and communicatively connected to network 135. In fact, data sources 150 may include any number of data sources, ie, one or more data sources. Data sources 150 include unstructured data and may include social media.

System 100 further includes a user device 130 operated by user 101 and connected to computer 105 via network 135. User device 130 may include an input device, such as a keyboard or voice recognition subsystem, for allowing user 101 to convey information and command selections to processor 110. User device 130 further includes a display device or an output device such as a printer or a speech synthesizer. A cursor control, such as a mouse, trackball, or touch-sensitive screen, allows user 101 to manipulate a cursor on the display to convey additional information and command selections to processor 110.

Processor 110 outputs results 122 of execution of program module 120 to user device 130. Alternatively, processor 110 may provide output to storage 125, such as a database or memory, or via network 135 to a remote device not shown.

For example, the program module 120 may be a program that performs the flowchart in FIG. System 100 may operate as a bandwidth estimator 301.

(Additional note)
The term "comprising" or "comprising" specifies that the recited feature, integer, step, or component is present, but one or more It should not be interpreted as excluding the presence of other features, integers, steps or components or groups thereof. The terms "a" and "an" are indefinite articles and therefore do not exclude embodiments having a plurality of them.

Note that the present invention is not limited to the above-described embodiments, and can be implemented with various modifications without departing from the gist of the present invention. In short, the present invention is not limited to the higher-level embodiment as it is, but can be embodied by modifying the constituent elements within the scope of the gist at the implementation stage.

Moreover, various inventions can be formed by appropriately combining the plurality of components disclosed in the above embodiments. For example, some components may be deleted from all the components shown in the embodiments. Furthermore, components from different embodiments may be combined as appropriate.

10: Communication system 11, 12: Communication device 11a, 12a: Physical port 15: Link 20: Data bus 21: Communication unit 22: Arithmetic processing unit 23: Input unit 24: Output unit 25: Storage unit 100: System 101: User 105: Computer 110: Processor 115: Memory 120: Program module 122: Result 125: Storage device 130: User device 135: Network 140: Storage device 150: Data source 300: Communication band calculation device 301: Bandwidth estimation device

Claims (3)

In a communication system in which a plurality of lines are accommodated in a link between communication devices, a bandwidth estimation device that calculates a required bandwidth of the link,
a communication unit into which the contracted band for each line and the traffic volume of the link in the observation period up to the present, and the contracted band for each line in the future design period are input;
creating a machine learning model that performs machine learning on the contracted band and the traffic amount for each hour in the observation period and predicts the traffic amount from the contracted band;
applying a contracted band for each line during the design period to the machine learning model to estimate the traffic amount for each time during the design period;
estimating a probability density function from the estimated traffic amount at each time in the design period; and applying a desired confidence interval to the probability density function, and setting the upper limit of the confidence interval to the required band. a processing section;
A band estimation device comprising: In a communication system in which a plurality of lines are accommodated in a link between communication devices, a bandwidth estimation method for calculating a required bandwidth of the link, the method comprising:
obtaining the contracted bandwidth for each line and the traffic volume of the link during the observation period up to the present;
creating a machine learning model that performs machine learning on the contracted band and the traffic amount for each hour in the observation period and predicts the traffic amount from the contracted band;
obtaining a contracted band for each line in a future design period, and estimating the traffic amount for each time in the design period using the machine learning model;
A band for estimating a probability density function from the estimated traffic amount at each time in the design period, and applying a desired confidence interval to the probability density function, and setting the upper limit of the confidence interval to the required band. Estimation method. A program for causing a computer to function as the band estimating device according to claim 1.

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