Service rate evaluation method, apparatus, device, storage medium, and program product

Abstract

The application discloses a service rate evaluation method, device, equipment, storage medium and program product, the method comprises the following steps: collecting service rate data of user objects set in different analysis scenes; the service rate data comprises at least one group of service peak rate of different sampling periods; according to the service rate data corresponding to each analysis scene, the service peak rate ratio distribution matrix and the service peak average rate ratio matrix in the corresponding analysis scene are constructed; according to the service peak rate ratio distribution matrix and the service peak average rate ratio matrix in each analysis scene, the service rate of the corresponding analysis scene is estimated; the application can realize the estimation of the service peak rate of short sampling period and the bandwidth overrun probability by using the existing traffic sampling function of the existing network, and does not need to upgrade the network and construct an analysis platform, so that the accurate bandwidth use performance analysis demand in the packet network can be met, and the cost is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a service rate evaluation method, apparatus, device, storage medium, and program product. Background Technology

[0002] Packet-based networks are currently the most common network type in communication networks. Their bandwidth statistical multiplexing method greatly improves the information transmission efficiency of the entire communication network, leading to their widespread use. When carrying services, packet-based networks do not simultaneously reach their peak bandwidth. Therefore, a smaller bandwidth can carry a larger number of services, but this also causes significant fluctuations in system traffic over time, especially in access networks, where the traffic volume can vary by tens of times per unit time. This presents significant challenges for network planning.

[0003] Currently, the most common method for evaluating system service rates in packet-switched networks is monitoring the system's service rate level. In packet-switched networks, peak service rates are commonly used as the system's service rate value and compared with the system's capacity bandwidth to determine whether system expansion is necessary. In practice, it is often desirable to obtain peak service rates with shorter sampling periods to assess system bandwidth usage. Generally, the shorter the sampling period, the higher the peak service rate. However, sometimes, even with low peak service rates in long sampling periods, peak service rates in short sampling periods can reach or exceed the system's capacity bandwidth, even resulting in packet loss. While most packet-switched networks currently have the capability to monitor peak service rates with long sampling periods, this often fails to meet the needs of refined system analysis and network planning. Short sampling periods can meet the needs of more accurate bandwidth usage performance analysis, but the system cost of data collection is very high, requiring substantial resources. Summary of the Invention

[0004] To address the problems existing in the prior art, embodiments of the present invention provide a service rate assessment method, apparatus, device, storage medium, and program product, which can meet the requirements for accurate bandwidth usage performance analysis in packet-switched networks, while effectively reducing costs.

[0005] In a first aspect, embodiments of the present invention provide a service rate evaluation method, including:

[0006] Collect service rate data of user objects set under different analysis scenarios; wherein, the service rate data includes at least one set of service peak rates with different sampling periods;

[0007] Based on the business rate data corresponding to each of the analysis scenarios, construct the business peak rate ratio distribution matrix and the business peak average rate ratio matrix for the corresponding analysis scenario.

[0008] Based on the peak service rate ratio distribution matrix and the average peak service rate ratio matrix for each analysis scenario, the service rate for the corresponding analysis scenario is estimated.

[0009] As an improvement to the above solution, the step of constructing a peak service rate ratio distribution matrix and a peak service rate average ratio matrix for each analysis scenario based on the service rate data corresponding to each analysis scenario includes:

[0010] For each of the analysis scenarios, the peak rate of the service with the shortest sampling period in each group of service peak rates is used as the base service peak rate. The ratio of the peak rate of the service with different sampling periods in each group of service peak rates to the corresponding base service peak rate is calculated to obtain the service peak rate ratio of each group of service peak rates.

[0011] Construct a peak rate ratio distribution matrix based on the peak rate ratio of each group of services;

[0012] Calculate the average of the base service peak rate for all short sampling periods and the average of the service peak rate for other sampling periods;

[0013] A service peak average rate ratio matrix is ​​constructed based on the average of the basic service peak rates in the short sampling period and the average of the service peak rates in other sampling periods.

[0014] As an improvement to the above scheme, the step of constructing a service peak rate ratio distribution matrix based on the service peak rate ratio of each group of service peak rates includes:

[0015] The peak service rate ratios of different sampling periods are divided into preset ratio ranges, and the proportion of the peak service rate ratios in each ratio range is calculated to obtain the sample proportion of the peak service rate ratios of different sampling periods in different ratio ranges.

[0016] The peak service rate ratio distribution matrix is ​​constructed based on the sample proportion of peak service rate ratios in different ratio ranges for different sampling periods.

[0017] As an improvement to the above scheme, the step of constructing a service peak average rate ratio matrix based on the average of the basic service peak rates of the short sampling period and the average of the service peak rates of other sampling periods includes:

[0018] Calculate the ratio of the average peak service rate of other sampling periods to the average peak service rate of the basic sampling period to obtain the average peak service rate ratio of the corresponding sampling period;

[0019] The average peak rate ratio matrix is ​​constructed based on the average peak rate ratio of services in each sampling period.

[0020] As an improvement to the above scheme, the step of estimating the service rate of the corresponding analysis scenario based on the service peak rate ratio distribution matrix and the service peak average rate ratio matrix under each analysis scenario includes:

[0021] Calculate the average peak value for the short sampling period based on the aforementioned peak-to-average-rate ratio matrix.

[0022] Based on the peak rate ratio distribution matrix, the probability of peak exceeding the limit in a short sampling period is calculated.

[0023] As an improvement to the above scheme, the step of calculating the average peak value for a short sampling period based on the service peak-to-average rate ratio matrix includes:

[0024] Extract the average peak service rate ratio corresponding to the long sampling period from the service peak average rate ratio matrix;

[0025] Collect the average peak service rate of other networks with corresponding long sampling periods under the same analysis scenario, and use it as the reference average peak service rate;

[0026] The average peak value of the short sampling period is estimated based on the ratio of the reference average peak service rate to the extracted average peak service rate.

[0027] As an improvement to the above scheme, the step of calculating the peak exceedance probability in a short sampling period based on the service peak rate ratio distribution matrix includes:

[0028] Collect the peak service rate of other networks in the same analysis scenario with a corresponding peak service rate ratio distribution matrix with a long sampling period, and use it as a reference peak service rate;

[0029] Calculate the ratio of the reference service peak rate to the system capacity bandwidth rate to obtain the reference service peak rate percentage;

[0030] The probability of peak values ​​exceeding the limit in the short sampling period is estimated based on the proportion of the reference service peak rate and the proportion of the service peak rate in the distribution matrix of the service peak rate ratio.

[0031] As an improvement to the above scheme, estimating the probability of short sampling period peak exceeding the limit based on the reference service peak rate ratio and the sample ratio of service peak rates with long sampling periods in the service peak rate ratio distribution matrix includes:

[0032] The proportion of the reference service peak rate is compared and analyzed with the proportion of the service peak rate in the long sampling period in the service peak rate ratio distribution matrix;

[0033] The probability of peak rate exceeding the limit in the short sampling period is obtained by summing up the sample proportions of service peak rates with long sampling periods that are less than the proportion of the reference service peak rate in the service peak rate ratio distribution matrix.

[0034] As an improvement to the above solution, before collecting the business rate data of user objects set under different analysis scenarios, the method further includes:

[0035] Based on the user characteristics of user objects in the grouped network, the grouped network is analyzed and divided into scenarios.

[0036] The user characteristics include one or more of the following: user habits, business application type, usage time, user type, user region, and user environment.

[0037] Secondly, embodiments of the present invention provide a service rate evaluation apparatus, comprising:

[0038] The service rate acquisition module is used to collect service rate data of user objects set under different analysis scenarios; wherein, the service rate data includes at least one set of service peak rates with different sampling periods;

[0039] The matrix construction module is used to construct the peak business rate ratio distribution matrix and the average peak business rate ratio matrix for each analysis scenario based on the business rate data corresponding to each analysis scenario.

[0040] The business rate estimation module is used to estimate the business rate of the corresponding analysis scenario based on the business peak rate ratio distribution matrix and the business peak average rate ratio matrix under each analysis scenario.

[0041] Thirdly, embodiments of the present invention provide a service rate assessment device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the service rate assessment method as described in any one of the first aspects.

[0042] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the service rate evaluation method as described in any one of the first aspects.

[0043] Fifthly, embodiments of the present invention provide a computer program product, including a computer program / instruction that, when executed by a processor, implements the service rate evaluation method as described in any one of the first aspects.

[0044] Compared to existing technologies, the present invention provides a service rate assessment method, apparatus, device, storage medium, and program product that collects service rate data of user objects under different analysis scenarios. The service rate data includes at least one set of service peak rates with different sampling periods. Then, based on the service rate data corresponding to each analysis scenario, a service peak rate ratio distribution matrix and a service peak average rate ratio matrix are constructed for that analysis scenario. Subsequently, the service rate for each analysis scenario is estimated based on the service peak rate ratio distribution matrix and the service peak average rate ratio matrix. This invention can utilize existing network traffic sampling functions to achieve high-precision (i.e., short sampling period) service peak rate and bandwidth over-limit probability estimation without upgrading the network or building an analysis platform, thereby meeting the requirements for accurate bandwidth usage performance analysis in packet-switched networks and effectively reducing costs. Attached Figure Description

[0045] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of a service rate evaluation method provided in Embodiment 1 of the present invention;

[0047] Figure 2 This is a schematic diagram of the peak service rate ratio distribution matrix according to an embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram of the peak-to-average service rate ratio matrix according to an embodiment of the present invention;

[0049] Figure 4 This is a flowchart illustrating the overall process of service rate assessment provided in Embodiment 2 of the present invention;

[0050] Figure 5 This is a structural block diagram of a service rate evaluation device provided in Embodiment 3 of the present invention;

[0051] Figure 6 This is a structural block diagram of a service rate evaluation device provided in Embodiment 4 of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] It should be noted that in this invention, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0054] The following explains some terms and concepts involved in the embodiments of the present invention.

[0055] Peak service rate: refers to the highest service rate per unit time within the evaluation period, usually in Mbps; for example, to evaluate the peak service rate per minute within 1 hour, it is necessary to collect the service traffic for 60 minutes within 1 hour, calculate the service rate for 60 minutes (service traffic per minute / 60 seconds to get Mbps), and then take the maximum value of the service rate for 60 minutes as the peak service rate per minute within 1 hour.

[0056] Average service rate: refers to the average service traffic per unit time during the evaluation period, usually in Mbps; for example, to evaluate the average service rate per minute of service within 1 hour, it is also necessary to collect the service traffic for 60 minutes within 1 hour, calculate the service rate for 60 minutes (service traffic per minute / 60 seconds to get Mbps), and then take the average of the service rates for 60 minutes as the average service rate per minute of service within 1 hour.

[0057] Example 1

[0058] Please see Figure 1 , Figure 1 This is a flowchart of a service rate assessment method provided by an embodiment of the present invention. The service rate assessment method specifically includes:

[0059] S11: Collect service rate data of user objects set under different analysis scenarios; wherein, the service rate data includes at least one set of service peak rates with different sampling periods;

[0060] S12: Based on the business rate data corresponding to each of the analysis scenarios, construct the business peak rate ratio distribution matrix and the business peak average rate ratio matrix for the corresponding analysis scenario;

[0061] S13: Estimate the business rate of the corresponding analysis scenario based on the business peak rate ratio distribution matrix and the business peak average rate ratio matrix under each analysis scenario.

[0062] It is worth noting that the service rate evaluation method described in this embodiment of the invention can be implemented by a server (e.g., a service server). The user object can be a terminal device, including user equipment, terminal apparatus, access terminal, user unit, user station, mobile station, mobile station (MS), mobile terminal (MT), remote terminal, mobile device, user terminal, terminal, wireless communication equipment, user agent, or user apparatus, etc. Examples of terminal devices currently include: mobile phones, tablets, computers with wireless transceiver capabilities, mobile internet devices (MID), virtual reality (VR) devices, augmented reality (AR) devices, in-vehicle devices, wearable devices, terminal devices in 5G networks, and terminal devices in Internet of Things (IoT) systems, etc.

[0063] Specifically, before collecting the business rate data of user objects set under different analysis scenarios, the method further includes:

[0064] Based on the user characteristics of user objects in the grouped network, the grouped network is analyzed and divided into scenarios.

[0065] The user characteristics include one or more of the following: user habits, business application type, usage time, user type, user region, and user environment.

[0066] For example, typical analysis scenarios of the network can be segmented and grouped based on user characteristics such as user habits, business application type, usage time, user type (e.g., company employees, students, farmers), user region (e.g., urban areas, towns, rural areas), and user environment (e.g., residential communities, commercial buildings, universities, enterprises and institutions, villages, etc.), and typical basic data collection user objects can be selected according to the typical analysis scenarios.

[0067] Then, for the selected user objects in each analysis scenario, the sampling time is divided into multiple sampling time periods according to the preset longest analysis period. Within each sampling time period, a set of service peak rates with different sampling periods is collected. For example, if the analysis is selected for three sampling periods: 1 second, 1 minute, and 15 minutes, the longest sampling period (e.g., 15 minutes) is used as the longest analysis period. The entire sampling time is divided into multiple sampling time periods every 15 minutes. Within each sampling time period, a set of service peak rates for the three sampling periods of 1 second, 1 minute, and 15 minutes is collected. This allows the service peak rates for all sampling time periods to be obtained. That is, for each user object, multiple sets of service peak rates with different sampling periods can be collected. Each set of service peak rates with different sampling periods corresponds to a sampling time period. This ensures that the service peak rates can be compared with each other and that a sufficient number of sample groups can be collected to ensure that the sampling data can reflect the general scale. This allows the sampling data to cover the main range of differences in service peak rates under different sampling periods, improving the accuracy and reliability of the service rate evaluation conclusions.

[0068] In this embodiment of the invention, for each analysis scenario, at least one set of service peak rates with different sampling periods are collected from selected user objects in the corresponding analysis scenario. Then, based on the collected service peak rates with different sampling periods, a service peak rate ratio distribution matrix and a service peak average rate ratio matrix are constructed for the corresponding analysis scenario. Based on the service peak rate ratio distribution matrix and the service peak average rate ratio matrix for the corresponding analysis scenario, the service rate of the corresponding analysis scenario is estimated, such as the average peak rate and the probability of peak exceeding the limit in the short sampling period. Thus, the estimation of service peak rates and bandwidth exceeding the limit in the short sampling period can be achieved through the existing traffic sampling function of the existing network without upgrading the network or building an analysis platform. This can meet the requirements for accurate bandwidth usage performance analysis in packet-switched networks and effectively reduce costs.

[0069] Specifically, step S12: Based on the service rate data corresponding to each analysis scenario, construct the service peak rate ratio distribution matrix and the service peak average rate ratio matrix for the corresponding analysis scenario, including:

[0070] For each of the analysis scenarios, the peak rate of the service with the shortest sampling period in each group of service peak rates is used as the base service peak rate. The ratio of the peak rate of the service with different sampling periods in each group of service peak rates to the corresponding base service peak rate is calculated to obtain the service peak rate ratio of each group of service peak rates.

[0071] Construct a peak rate ratio distribution matrix based on the peak rate ratio of each group of services;

[0072] Calculate the average of the base service peak rate for all short sampling periods and the average of the service peak rate for other sampling periods;

[0073] A service peak average rate ratio matrix is ​​constructed based on the average of the basic service peak rates in the short sampling period and the average of the service peak rates in other sampling periods.

[0074] Furthermore, the step of constructing a peak service rate ratio distribution matrix based on the peak service rate ratio of each group of service peak rates includes:

[0075] The peak service rate ratios of different sampling periods are divided according to a preset ratio range, and the proportion of the peak service rate ratios in each ratio range is calculated to obtain the sample proportion of the peak service rate ratios of different sampling periods in different ratio ranges.

[0076] The peak service rate ratio distribution matrix is ​​constructed based on the sample proportion of peak service rate ratios in different ratio ranges for different sampling periods.

[0077] For example, for each analysis scenario, based on the peak rates of each group of services collected in step S11 above, the peak rate of the service with the shortest sampling period is set as the base peak rate of each group of services. For example, if each group of services includes peak rates of services with three sampling periods of 1 second, 1 minute, and 15 minutes, then the peak rate of the 1-second sampling period is used as the base peak rate of each group of services.

[0078] Then, the ratio of the peak service rate to the basic peak service rate in other sampling periods within each group of peak service rates is calculated to obtain the ratio of peak service rates in different sampling periods within that group of peak service rates. Since the smaller the sampling period, the larger the peak service rate, the ratio of the peak service rate to the basic peak service rate in other sampling periods is less than 1, falling within the range of 0 to 1.

[0079] Then, based on the calculated peak rate ratio of services within different sampling periods in each group of service peak rates, the range of the peak rate ratio is divided into segments as needed to obtain multiple ratio intervals. For example, the range of the peak rate ratio [0, 1] is divided into 10 ratio intervals on average.

[0080] Based on the defined ratio intervals, the proportion of peak service rate ratios for different sampling periods within each interval is statistically analyzed for the corresponding analytical scenario. This yields the sample proportion of peak service rate ratios for different sampling periods across different ratio intervals. Specifically, the sample proportion of a peak service rate ratio for a given sampling period within a given ratio interval is calculated as: (Number of peak service rate ratios for that sampling period within that interval) / (Number of peak service rate ratios for that sampling period across all ratio intervals). Based on this sample proportion, a peak service rate ratio distribution matrix D is constructed.

[0081]

[0082] Where, d ni This represents the percentage of samples where the ratio of the peak service rate to the peak basic service rate in the nth sampling period falls within the i-th ratio range.

[0083] Taking the analysis of peak service rates for three sampling periods of 1 second, 1 minute, and 15 minutes as an example, we calculate the ratio of the peak service rate for 1 minute and 15 minutes to the peak service rate for 1 second in each group of peak service rates (i.e., the peak service rate ratio). We also statistically analyze the sample proportion of the peak service rate ratio for 1 minute and 15 minutes within each ratio interval (each interval is set in 10%), constructing a... Figure 2 The peak service rate ratio distribution matrix is ​​shown. From... Figure 2 It can be seen that, throughout the entire sampling period, 10% of the samples had a ratio between 10% and 20% for the peak service rate of 1 minute and the peak service rate of 1 second, while 11% of the samples had a ratio between 40% and 50% for the peak service rate of 15 minutes and the peak service rate of 1 second.

[0084] Furthermore, the step of constructing a service peak-to-average rate ratio matrix based on the average of the basic service peak rates of the short sampling period and the average of the service peak rates of other sampling periods includes:

[0085] Calculate the ratio of the average peak service rate of other sampling periods to the average peak service rate of the basic sampling period to obtain the average peak service rate ratio of the corresponding sampling period;

[0086] The average peak rate ratio matrix is ​​constructed based on the average peak rate ratio of services in each sampling period.

[0087] Similarly, for each analysis scenario, the peak rate of the shortest sampling period in each group of peak rates is taken as the basic peak rate of the business. Then, the average value of the basic peak rate and the average value of the peak rates of other sampling periods are calculated. After that, the ratio of the average value of the peak rates of other sampling periods to the average value of the basic peak rate is calculated as the average peak rate ratio of the corresponding sampling period, and the average peak rate ratio matrix A is constructed based on the average peak rate ratio of the corresponding sampling period.

[0088]

[0089] Among them, a n It represents the ratio of the average peak rate of the service in the nth sampling period (excluding the shortest sampling period) to the average peak rate of the basic service.

[0090] a n = (Sum of peak service rates for all nth sampling periods within the acquisition time) / (Sum of peak service rates for all basic sampling periods within the acquisition time), or a n = (Sum of peak service rates of all nth sampling periods within the acquisition time / m) / (Sum of peak service rates of all basic sampling periods within the acquisition time / m), where m represents the total number of nth sampling periods within the acquisition time.

[0091] Taking the analysis of peak service rates for three sampling periods of 1 second, 1 minute, and 15 minutes as an example, the constructed peak service rate average ratio matrix is ​​as follows: Figure 3 As shown.

[0092] Specifically, step S13: Based on the peak service rate ratio distribution matrix and the average peak service rate ratio matrix for each analysis scenario, estimate the service rate for the corresponding analysis scenario, including:

[0093] Calculate the average peak value for the short sampling period based on the aforementioned peak-to-average-rate ratio matrix.

[0094] Based on the peak rate ratio distribution matrix, the probability of peak exceeding the limit in a short sampling period is calculated.

[0095] Further, the step of calculating the average peak value for a short sampling period based on the service peak-to-average rate ratio matrix includes:

[0096] Extract the average peak service rate ratio corresponding to the long sampling period from the service peak average rate ratio matrix;

[0097] Collect the average peak service rate of other networks with corresponding long sampling periods under the same analysis scenario, and use it as the reference average peak service rate;

[0098] The average peak value of the short sampling period is estimated based on the ratio of the reference average peak service rate to the extracted average peak service rate.

[0099] For example, on other networks within the same analysis scenario, based on the longer sampling period in the aforementioned service peak average rate ratio matrix, the average service peak rate for the corresponding longer sampling period is collected as a reference average service peak rate. It should be understood that the longer sampling period can be any other sampling period preceding the shorter sampling period in the aforementioned service peak average rate ratio matrix, such as the aforementioned 1-minute and 15-minute sampling periods. Users can select these periods according to their actual needs, and no specific limitations are imposed in this embodiment of the invention.

[0100] The average peak value of the short sampling period is estimated by comparing the peak service rate of other networks in the same analysis scenario with the average peak service rate of the long sampling period in the service peak rate ratio matrix. For example, the quotient of the peak service rate of other networks in the same analysis scenario with the average peak service rate of the long sampling period in the service peak rate ratio matrix is ​​used as the average peak value of the short sampling period. The maximum value of the average peak value of the short sampling period is the system capacity bandwidth rate. The estimated average peak value of the short sampling period can be used as a data basis for network planning, construction and optimization.

[0101] To analyze the peak service rate and the long sampling period of 15 minutes Figure 3 Taking the service peak average rate ratio matrix shown as an example, assuming that the average service peak rate of a packet-switched network system of the same analysis scenario and the same type is 400Mbps after 15 minutes, and the average service peak rate ratio of 15 minutes in the service peak average rate ratio matrix is ​​28%, then the average peak rate per second of every 15 minutes in this sampling time can be estimated to be 400Mbps÷28%=1429Mbps.

[0102] Further, the step of calculating the peak rate exceeding the limit probability in a short sampling period based on the service peak rate ratio distribution matrix includes:

[0103] Collect the peak service rate of other networks in the same analysis scenario with a corresponding peak service rate ratio distribution matrix with a long sampling period, and use it as a reference peak service rate;

[0104] Calculate the ratio of the reference service peak rate to the system capacity bandwidth rate to obtain the reference service peak rate percentage;

[0105] The probability of peak values ​​exceeding the limit in the short sampling period is estimated based on the proportion of the reference service peak rate and the proportion of the service peak rate in the distribution matrix of the service peak rate ratio.

[0106] Further, estimating the probability of peak exceeding the limit for short sampling periods based on the reference service peak rate ratio and the sample ratio of service peak rates with long sampling periods in the service peak rate ratio distribution matrix includes:

[0107] The proportion of the reference service peak rate is compared and analyzed with the proportion of the service peak rate in the long sampling period in the service peak rate ratio distribution matrix;

[0108] The probability of peak rate exceeding the limit in the short sampling period is obtained by summing up the sample proportions of service peak rates with long sampling periods that are less than the proportion of the reference service peak rate in the service peak rate ratio distribution matrix.

[0109] Similarly, on other networks in the same analysis scenario, based on the long sampling period in the aforementioned service peak rate ratio distribution matrix, the service peak rate of the corresponding long sampling period is collected as a reference service peak rate; then, the ratio of the currently collected long sampling period reference service peak rate to the system capacity bandwidth rate is calculated as the reference service peak rate percentage; then, this reference service peak rate percentage is compared with the sample percentage of the service peak rate ratio of the long sampling period in the aforementioned service peak rate ratio distribution matrix, and the sample percentages of service peak rate ratios in the aforementioned service peak rate ratio distribution matrix that are less than the reference service peak rate percentage are accumulated to obtain the short sampling period peak exceedance probability; this short sampling period peak exceedance probability can serve as an important data basis for network upgrade and optimization.

[0110] To analyze the peak service rate over a long sampling period of 15 minutes, Figure 2 The peak service rate distribution matrix shown, using a GPON (Gigabit-Capable PON) access network as an example, assumes that the peak service rate of the same type of packet-switched network system collected in the same analysis scenario is 460Mbps in 15 minutes, and the system capacity bandwidth rate of the GPON system is 2300Mbps. The ratio is calculated as: 460Mbps ÷ 2300Mbps = 20%. (Comparison) Figure 2 The cumulative value of the sample proportion less than 20% in the 15min / 1s row is: the sample proportion with a ratio of 0% to 10% + the sample proportion with a ratio of 10% to 20% = 4% + 10% = 14%. Therefore, the peak over-limit probability (i.e., the bandwidth over-limit probability within a 1-second short sampling period) is 14%.

[0111] Compared to existing technologies, the overall process for service rate assessment described in this embodiment of the invention is as follows: Figure 4Steps S21-S26, as shown, construct the peak service rate ratio distribution matrix and the average peak service rate ratio matrix for different sub-scenarios based on the peak service rate of typical sampling periods collected under sub-scenarios. Then, based on the peak service rate ratio distribution matrix and the average peak service rate ratio matrix for the corresponding analysis scenario, estimate the service rate of the corresponding analysis scenario, such as the average peak rate and the probability of peak rate exceeding limits in the short sampling period. This allows for the quantitative calculation of the peak service rate and bandwidth exceeding probability in the short sampling period using the peak service rate of the long sampling period. Thus, by utilizing the existing low-precision (i.e., long sampling period) traffic sampling function of the existing network, the estimation of high-precision (i.e., short sampling period) peak service rate and bandwidth exceeding probability can be achieved. Compared to existing technologies where the deployment cost of high-precision (short sampling period) traffic sampling function in existing packet-switched networks is high, requiring upgrades to existing network equipment (some equipment does not support this function upgrade, and some equipment requires hardware upgrades) and the construction of a traffic analysis platform, resulting in a huge overall investment, this embodiment of the invention does not require network upgrades or the construction of an analysis platform. It can meet the needs of accurate bandwidth usage performance analysis in packet-switched networks, effectively reducing costs and effectively supporting network planning, construction, upgrade optimization, and other work, providing an effective quantitative basis for network operation.

[0112] Example 2

[0113] See Figure 5 , Figure 5 This is a structural block diagram of a service rate assessment device provided in an embodiment of the present invention. The service rate assessment device includes:

[0114] The service rate acquisition module 1 is used to acquire service rate data of user objects set under different analysis scenarios; wherein, the service rate data includes at least one set of service peak rates with different sampling periods;

[0115] Matrix construction module 2 is used to construct the peak business rate ratio distribution matrix and the average peak business rate ratio matrix under the corresponding analysis scenario based on the business rate data corresponding to each analysis scenario.

[0116] The business rate estimation module 3 is used to estimate the business rate of the corresponding analysis scenario based on the business peak rate ratio distribution matrix and the business peak average rate ratio matrix under each analysis scenario.

[0117] In one optional embodiment, the matrix construction module includes:

[0118] The ratio calculation unit is used to calculate the ratio of the peak service rate of different sampling periods in each group of peak service rates to the corresponding basic peak service rate for each analysis scenario, based on the peak service rate of the short sampling period in each group of peak service rates as the basic peak service rate, so as to obtain the peak service rate ratio of each group of peak service rates.

[0119] The first matrix construction unit is used to construct a peak rate ratio distribution matrix based on the peak rate ratio of each group of services.

[0120] The average value calculation unit is used to calculate the average of the basic service peak rate for all short sampling periods and the average of the service peak rate for other sampling periods.

[0121] The second matrix construction unit is used to construct a service peak average rate ratio matrix based on the average of the basic service peak rates of the short sampling period and the average of the service peak rates of other sampling periods.

[0122] In an optional embodiment, the first matrix construction unit includes:

[0123] The sample proportion calculation subunit is used to divide the peak service rate ratio of different sampling periods according to a preset ratio range, and to calculate the proportion of the peak service rate ratio in each ratio range, so as to obtain the sample proportion of the peak service rate ratio of different sampling periods in different ratio ranges.

[0124] The first matrix construction sub-unit is used to construct the service peak rate ratio distribution matrix based on the sample proportion of service peak rate ratios in different ratio ranges for different sampling periods.

[0125] In one optional embodiment, the second matrix construction unit includes:

[0126] The rate average ratio calculation subunit is used to calculate the ratio of the average peak service rate of other sampling periods to the average basic peak service rate of the short sampling period, so as to obtain the average peak service rate ratio of the corresponding sampling period.

[0127] The second matrix construction sub-unit is used to construct the average peak service rate ratio matrix based on the average peak service rate ratio of each sampling period.

[0128] In one optional embodiment, the service rate estimation module includes:

[0129] The average peak value calculation unit is used to calculate the average peak value for a short sampling period based on the service peak value average rate ratio matrix.

[0130] The over-limit probability calculation unit is used to calculate the peak over-limit probability of a short sampling period based on the peak rate ratio distribution matrix of the service.

[0131] In one optional embodiment, the average peak value calculation unit includes:

[0132] The average service peak rate ratio extraction subunit is used to extract the average service peak rate ratio corresponding to the long sampling period in the service peak average rate ratio matrix.

[0133] The reference average service peak rate acquisition subunit is used to acquire the average service peak rate of other networks with corresponding long sampling periods under the same analysis scenario, as a reference average service peak rate.

[0134] The short sampling period average peak value calculation subunit is used to estimate the short sampling period average peak value based on the ratio of the reference average service peak rate and the extracted average service peak rate.

[0135] In one optional embodiment, the over-limit probability calculation unit includes:

[0136] The reference service peak rate acquisition subunit is used to acquire the service peak rate of other networks in the same analysis scenario with a corresponding service peak rate ratio distribution matrix with a long sampling period, as a reference service peak rate.

[0137] The reference service peak rate percentage calculation subunit is used to calculate the ratio of the reference service peak rate to the system capacity bandwidth rate to obtain the reference service peak rate percentage.

[0138] The short sampling period peak exceedance probability calculation subunit is used to estimate the short sampling period peak exceedance probability based on the reference service peak rate ratio and the sample ratio of the service peak rate ratio distribution matrix with the long sampling period service peak rate.

[0139] In an optional embodiment, the short sampling period peak exceedance probability calculation subunit includes:

[0140] The comparative analysis subunit is used to compare and analyze the proportion of the reference service peak rate with the sample proportion of the service peak rate with the long sampling period in the service peak rate ratio distribution matrix.

[0141] The cumulative calculation subunit is used to accumulate the sample proportion of service peak rates with long sampling periods that are less than the proportion of the reference service peak rate in the service peak rate ratio distribution matrix, and obtain the probability of peak exceeding the limit in the short sampling period.

[0142] In an optional embodiment, the device further includes:

[0143] The scenario segmentation module is used to segment the grouped network into analysis scenarios based on the user characteristics of the user objects in the grouped network before collecting the service rate data of user objects set under different analysis scenarios.

[0144] The user characteristics include one or more of the following: user habits, business application type, usage time, user type, user region, and user environment.

[0145] It should be noted that the working process of each module in the service rate evaluation device described in the embodiments of the present invention can refer to the working process of the service rate evaluation method described in Embodiment 1 above, and the technical effect achieved is the same as that of the service rate evaluation method described in Embodiment 1 above, so it will not be repeated here.

[0146] Example 3

[0147] See Figure 6 , Figure 6 This is a structural block diagram of a service rate assessment device provided in an embodiment of the present invention. The service rate assessment device includes a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, it implements the steps in the above-described service rate assessment method embodiments, such as steps S11 to S13.

[0148] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the service rate assessment device.

[0149] The service rate assessment device may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of a service rate assessment device and does not constitute a limitation on the device. It may include more or fewer components than illustrated, or combine certain components, or use different components. For example, the service rate assessment device may also include input / output devices, network access devices, buses, etc.

[0150] The processor 21 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 21 is the control center of the service rate assessment equipment, connecting all parts of the equipment via various interfaces and lines.

[0151] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements various functions of the service rate evaluation device by running or executing the computer programs and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart memory card (SMC), secure digital card (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0152] If the modules / units integrated into the service rate assessment device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 21, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0153] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0154] The above description is a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, many improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating service rate, characterized in that, include: Collect service rate data of user objects set under different analysis scenarios; wherein, the service rate data includes at least one set of service peak rates with different sampling periods; Based on the business rate data corresponding to each analysis scenario, construct the business peak rate ratio distribution matrix and the business peak average rate ratio matrix for the corresponding analysis scenario. Based on the peak service rate ratio distribution matrix and the average peak service rate ratio matrix for each analysis scenario, estimate the service rate for the corresponding analysis scenario. The step of estimating the service rate for each analysis scenario based on the service peak rate ratio distribution matrix and the service peak average rate ratio matrix includes: Calculate the average peak value for the short sampling period based on the aforementioned peak-to-average-rate ratio matrix. Calculate the probability of peak values ​​exceeding the limit in short sampling periods based on the aforementioned peak rate ratio distribution matrix; The step of constructing a peak service rate ratio distribution matrix and a peak service rate average ratio matrix for each analysis scenario based on the service rate data corresponding to each analysis scenario includes: For each of the analysis scenarios, the peak rate of the service with the shortest sampling period in each group of service peak rates is used as the base service peak rate. The ratio of the peak rate of the service with different sampling periods in each group of service peak rates to the corresponding base service peak rate is calculated to obtain the service peak rate ratio of each group of service peak rates. Construct a peak rate ratio distribution matrix based on the peak rate ratio of each group of services; Calculate the average of the basic service peak rate for short sampling periods and the average of the service peak rates for other sampling periods; Calculate the ratio of the average peak service rate of other sampling periods to the average peak service rate of the basic sampling period to obtain the average peak service rate ratio of the corresponding sampling period; The average peak rate ratio matrix is ​​constructed based on the average peak rate ratio of services in each sampling period.

2. The service rate evaluation method as described in claim 1, characterized in that, The step of constructing a peak rate ratio distribution matrix based on the peak rate ratio of each group of services includes: The peak service rate ratios of different sampling periods are divided into preset ratio ranges, and the proportion of the peak service rate ratios in each ratio range is calculated to obtain the sample proportion of the peak service rate ratios of different sampling periods in different ratio ranges. The peak service rate ratio distribution matrix is ​​constructed based on the sample proportion of peak service rate ratios in different ratio ranges for different sampling periods.

3. The service rate evaluation method as described in claim 1, characterized in that, The step of calculating the average peak value for a short sampling period based on the service peak-to-average rate ratio matrix includes: Extract the average peak service rate ratio corresponding to the long sampling period from the service peak average rate ratio matrix; Collect the average peak service rate of other networks with corresponding long sampling periods under the same analysis scenario, and use it as the reference average peak service rate; The average peak value of the short sampling period is estimated based on the ratio of the reference average peak service rate to the extracted average peak service rate.

4. The service rate evaluation method as described in claim 1, characterized in that, The step of calculating the peak rate exceeding the limit probability in a short sampling period based on the service peak rate ratio distribution matrix includes: Collect the peak service rate of other networks in the same analysis scenario with a corresponding peak service rate ratio distribution matrix with a long sampling period, and use it as a reference peak service rate; Calculate the ratio of the reference service peak rate to the system capacity bandwidth rate to obtain the reference service peak rate percentage; The probability of peak values ​​exceeding the limit in the short sampling period is estimated based on the proportion of the reference service peak rate and the proportion of the service peak rate in the distribution matrix of the service peak rate ratio.

5. The service rate evaluation method as described in claim 4, characterized in that, The step of estimating the probability of short-sampling-period peak exceeding the limit based on the reference service peak rate ratio and the sample ratio of service peak rates with long sampling periods in the service peak rate ratio distribution matrix includes: The proportion of the reference service peak rate is compared and analyzed with the proportion of the service peak rate in the long sampling period in the service peak rate ratio distribution matrix; The probability of peak rate exceeding the limit in the short sampling period is obtained by summing up the sample proportions of service peak rates with long sampling periods that are less than the proportion of the reference service peak rate in the service peak rate ratio distribution matrix.

6. The service rate evaluation method as described in claim 1, characterized in that, Before collecting the business rate data of user objects set under different analysis scenarios, the method further includes: Based on the user characteristics of user objects in the grouped network, the grouped network is analyzed and divided into scenarios. The user characteristics include one or more of the following: user habits, business application type, usage time, user type, user region, and user environment.

7. A service rate assessment device, characterized in that, include: The service rate acquisition module is used to collect service rate data of user objects set under different analysis scenarios; wherein, the service rate data includes at least one set of service peak rates with different sampling periods; The matrix construction module is used to construct the peak business rate ratio distribution matrix and the average peak business rate ratio matrix for each analysis scenario based on the business rate data corresponding to each analysis scenario. The business rate estimation module is used to estimate the business rate of the corresponding analysis scenario based on the business peak rate ratio distribution matrix and the business peak average rate ratio matrix under each analysis scenario. The service rate estimation module includes: The average peak value calculation unit is used to calculate the average peak value for a short sampling period based on the service peak value average rate ratio matrix. The over-limit probability calculation unit is used to calculate the peak over-limit probability of a short sampling period based on the service peak rate ratio distribution matrix. The matrix construction module includes: The ratio calculation unit is used to calculate the ratio of the peak service rate of different sampling periods in each group of peak service rates to the corresponding basic peak service rate for each analysis scenario, based on the peak service rate of the short sampling period in each group of peak service rates as the basic peak service rate, so as to obtain the peak service rate ratio of each group of peak service rates. The first matrix construction unit is used to construct a peak rate ratio distribution matrix based on the peak rate ratio of each group of services. The average value calculation unit is used to calculate the average of the basic service peak rate for all short sampling periods and the average of the service peak rate for other sampling periods. The second matrix construction unit is used to construct a service peak average rate ratio matrix based on the average of the basic service peak rates of the short sampling period and the average of the service peak rates of other sampling periods. The second matrix construction unit includes: The rate average ratio calculation subunit is used to calculate the ratio of the average peak service rate of other sampling periods to the average basic peak service rate of the short sampling period, so as to obtain the average peak service rate ratio of the corresponding sampling period. The second matrix construction sub-unit is used to construct the average peak service rate ratio matrix based on the average peak service rate ratio of each sampling period.

8. A service rate assessment device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the service rate assessment method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the service rate evaluation method as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the service rate evaluation method according to any one of claims 1 to 6.

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