Network service quality assurance method, electronic device, and program product
By aggregating and accumulating user session data from network services, the target server IP is selected and quality assurance commands are automatically generated, solving the problem of low efficiency in existing technologies, achieving efficient service quality assurance, and improving the intelligence level of the OLT.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- ZTE CORP
- Filing Date
- 2026-01-14
- Publication Date
- 2026-07-30
AI Technical Summary
Existing technologies are insufficient for efficiently and flexibly identifying server IPs that need optimization and generating service quality assurance instructions, resulting in low efficiency in improving end-to-end service quality for broadband network users.
By acquiring user session data from network services, the data is aggregated and accumulated according to service dimensions and server IP dimensions. Based on target traffic data and configured traffic percentage thresholds, the target server IPs to be protected are selected, and a service quality assurance command with DSCP value is automatically generated.
It achieves efficient and accurate quality assurance of target server IP, improves the end-to-end service quality of broadband network users, simplifies operation and maintenance processes, and enhances the intelligence level of OLT.
Smart Images
Figure CN2026072480_30072026_PF_FP_ABST
Abstract
Description
A method, electronic device, and software product for ensuring network service quality.
[0001] Cross-references to related applications
[0002] This application claims priority to Chinese Patent Application No. 202510112812.3, filed with the Chinese Patent Office on January 23, 2025, the entire contents of which are incorporated herein by reference. Technical Field
[0003] The embodiments disclosed herein relate to, but are not limited to, the fields of computer technology and communication technology. Background Technology
[0004] With the rapid development of communication technology, telecommunications operators have higher demands for maximizing network efficiency, aiming to achieve value-driven operations based on the principles of cost reduction, efficiency improvement, quality enhancement, and revenue generation. In the process of cost reduction, efficiency improvement, quality enhancement, and revenue generation, improving end-to-end service quality for users and ensuring a positive user experience for key users' high-priority apps such as TikTok, WeChat, and Honor of Kings is the development direction for broadband network quality improvement and optimization. Summary of the Invention
[0005] This disclosure provides a method, electronic device, and program product for ensuring network service quality.
[0006] In a first aspect, embodiments of this disclosure provide a method for ensuring network service quality, comprising:
[0007] Obtain user session data for network services;
[0008] The user session data is aggregated and accumulated according to the business dimension and the server network protocol IP dimension to obtain the target traffic data;
[0009] Based on the target traffic data and the configured traffic percentage threshold, the target server IPs to be protected are selected;
[0010] Quality assurance is provided for the network services corresponding to the target server IP.
[0011] Secondly, embodiments of this disclosure also provide an electronic device, including:
[0012] One or more processors;
[0013] A memory having stored one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the network service quality assurance method.
[0014] One or more input / output (I / O) interfaces are connected between the processor and the memory and configured to enable information exchange between the processor and the memory.
[0015] Thirdly, this disclosure also provides a computer program product, which includes a computer program that, when executed by a processor, implements the network service quality assurance method. Attached Figure Description
[0016] In the accompanying drawings of the embodiments disclosed herein:
[0017] Figure 1 is a flowchart of the network service quality assurance method provided in an embodiment of this disclosure;
[0018] Figure 2 is a schematic diagram of a network service quality assurance method provided in an embodiment of this disclosure;
[0019] Figure 3 is a block diagram of the system structure on which the raw data collection provided in the embodiments of this disclosure depends;
[0020] Figure 4 is a schematic diagram of a method for obtaining target traffic data provided in an embodiment of this disclosure;
[0021] Figure 5 is a block diagram of the electronic device provided in an embodiment of this disclosure. Detailed Implementation
[0022] To enable those skilled in the art to better understand the technical solutions of this disclosure, the network service quality assurance method, electronic equipment, and program products provided in the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings.
[0023] The present disclosure will be described more fully below with reference to the accompanying drawings; however, the embodiments shown may be embodied in different forms, and the present disclosure should not be construed as limited to the embodiments set forth below. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will enable those skilled in the art to fully understand the scope of the disclosure.
[0024] The accompanying drawings of the embodiments disclosed herein are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the detailed embodiments to explain this disclosure and do not constitute a limitation thereof. The above and other features and advantages will become more apparent to those skilled in the art from the description of the detailed embodiments with reference to the accompanying drawings.
[0025] This disclosure may be described with reference to plan and / or cross-sectional views using the ideal schematic diagrams of this disclosure. Therefore, the example illustrations may be modified according to manufacturing techniques and / or tolerances.
[0026] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.
[0027] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the disclosure. The term "and / or" as used in this disclosure includes any and all combinations of one or more of the associated enumerated entries. The singular forms "a" and "the" as used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. The terms "comprising," "made of," etc., as used in this disclosure specify the presence of the stated feature, integral, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof.
[0028] Unless otherwise specified, all terms used in this disclosure (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined in this disclosure.
[0029] To improve end-to-end service quality, service identification is essential, which requires data support. Intelligent big data platforms utilize deep packet inspection (DIP) on the network access side to perform deep parsing of control plane and user plane traffic data within the network access pipeline, generating user session-level data. This network data serves as a crucial reference for in-depth analysis of bandwidth user network data.
[0030] With data, an intuitive way to analyze and identify server IP (Internet Protocol) addresses that need optimization is to simply randomly select the server IP addresses corresponding to the service and perform optimization. However, in the current network, there are many destination IPs for users accessing services, and the access traffic of these destination IPs varies. It is difficult to guarantee the quality of all user service IPs with limited OLT (Optical Line Terminal) resources. Therefore, quickly and accurately analyzing the data and personalized filtering of server IPs that need optimization, while maximizing service quality with limited resources, is a significant challenge.
[0031] Given the server IPs that need optimization, the current tuning method involves manually configuring the IPs and service quality assurance basic commands in the OLT for tagging, completing priority scheduling based on IP DSCP (Differentiated Services Code Point) to achieve service assurance. However, this manual process is cumbersome, labor-intensive, and inefficient. Therefore, accurately, flexibly, and efficiently combining the IPs to be optimized and basic commands to generate the final service assurance commands, and improving the intelligence level of the OLT, also faces many challenges.
[0032] This embodiment of the scheme acquires user session data of network services; aggregates and accumulates the user session data according to the service dimension and the server network protocol IP dimension to obtain target traffic data; filters the target server IPs to be guaranteed based on the target traffic data and the configured traffic proportion threshold; and performs quality assurance on the network services corresponding to the target server IPs. This embodiment fully utilizes the data advantages of the big data era, performs precise data analysis, determines the target server IPs to be optimized, and can efficiently and quickly customize and issue service quality assurance commands. It maximizes the guarantee of service quality for target server IPs with limited resources, thereby improving the end-to-end service quality of broadband network users.
[0033] The solutions disclosed herein can be applied to quality assurance for any network service, including but not limited to broadband services, especially for home broadband services. For example, they can be applied to, but are not limited to, OLTs. The implementing entity of the solutions disclosed herein may include, but is not limited to, the OLT.
[0034] The embodiments of this disclosure will be described in detail below.
[0035] This disclosure provides a method for ensuring network service quality, as shown in Figures 1 and 2, which includes steps S11-S14:
[0036] S11. Obtain user session data for network services.
[0037] In this embodiment of the disclosure, the network service can be any network service (which can be simply referred to as a service), and can be multiple network services (such as service 1, service 2, ..., service n, etc., where n is a positive integer). Multiple network services can include, but are not limited to, network services such as Douyin and WeChat.
[0038] In the embodiments of this disclosure, each network service may include one or more server IPs. For example, service 1 may include IP1-1, IP1-2, ..., IP1-a, service 2 may include IP2-1, IP2-2, ..., IP2-b, and service n may include IPn-1, IPn-2, ..., IPn-c, where a, b, and c are positive integers, and a, b, and c may be the same or different.
[0039] In this embodiment of the disclosure, obtaining user session data of network services may include:
[0040] Obtain the raw data of the collected network services;
[0041] User session data is obtained based on the raw data.
[0042] In this embodiment of the disclosure, a data collection device, such as a DPI (deep packet inspection) probe, can be deployed in the operator's network to collect raw data of network services in real time. For example, raw data of home broadband users accessing the Internet can be collected to collect users' real service traffic, form user session data, and store it in a big data analysis system.
[0043] In this embodiment, the DPI probe can be located in a BRAS (Broadband Remote Access Server) or an OLT. For example, a DPI blade can be built into the OLT. Specifically, the DPI can be deployed on the uplink port of the OLT. Prior to this, an OLT mirroring data device can be deployed in the operator's bearer equipment. This OLT mirroring data device can mirror data passing through the bearer equipment, mirroring the raw data of the home broadband user's service traffic to the collection device (such as the DPI blade). Refer to the system architecture block diagram shown in Figure 3, which represents the basis for collecting raw data. This system architecture includes a home user terminal, an ONU (Optical Network Unit), an OLT, a BRAS, a local exit device, and an ISP (Internet Service Provider).
[0044] In this embodiment of the disclosure, the collected raw data can be decoded by a preset user session data device to obtain user session data. The user session data may include, but is not limited to, various service indicator information during the user's Internet access process. For example, the service indicator information may include, but is not limited to, any one or more of the following: user Internet access time information, service type, traffic, duration, latency, packet loss information, retransmission information, etc.
[0045] In this embodiment of the disclosure, the big data analysis system can preprocess the obtained user session data, for example, including but not limited to data filtering and data cleaning of the raw data.
[0046] In this embodiment of the disclosure, user session data can be filtered by a preset data preprocessing device to remove non-user-used heartbeat data, low-traffic data, and some known foreign traffic data, thereby obtaining business data that can better represent the user's perception.
[0047] S12. Aggregate and accumulate user session data according to business dimensions and server network protocol IP dimensions to obtain target traffic data.
[0048] In this embodiment of the disclosure, as shown in FIG4, the aggregation and accumulation of user session data according to the business dimension and the server network protocol IP dimension to obtain target traffic data may include steps S21-S22:
[0049] S21. Aggregate user session data according to a preset time granularity from the business dimension and the server IP dimension to obtain the basic traffic data of each server IP corresponding to each business within the time granularity.
[0050] In this embodiment of the disclosure, the time granularity can be set according to requirements. There is no limitation on the detailed granularity. For example, it may include, but is not limited to, one hour, one day, one week, etc.
[0051] In this embodiment of the disclosure, after preprocessing operations such as filtering and cleaning the user session data, statistics can be performed on a daily granularity. The user session data can be aggregated according to the business dimension and the server IP dimension, and the basic traffic data of each server IP corresponding to each business can be counted and stored in a preset daily business table.
[0052] S22. Accumulate the basic traffic data within the time granularity to obtain the cumulative traffic data within a preset time period.
[0053] In this embodiment of the disclosure, the basic traffic data can be used as sample data to calculate the cumulative traffic data within a preset time period. The preset time period can be set according to needs, and there is no limitation on the specific duration. For example, it can include, but is not limited to, 7 days, 30 days, etc.
[0054] In this embodiment of the disclosure, cumulative traffic data for 7 days can be accumulated based on the basic traffic data.
[0055] S23. Based on the cumulative traffic data, the target traffic data is calculated according to the preset statistical dimensions.
[0056] In this embodiment of the disclosure, the statistical dimension may include, but is not limited to: the business dimension, the dimension of each server IP corresponding to each business, and the dimension of all server IPs corresponding to each business.
[0057] In this embodiment of the disclosure, the target traffic data can be obtained by statistically analyzing the cumulative traffic data based on the aforementioned statistical dimensions. The target traffic data may include, but is not limited to: the total traffic for each type of network service, the total traffic for each server IP corresponding to each type of network service, and the cumulative traffic for all server IPs corresponding to each type of network service.
[0058] In this embodiment, the total traffic for each type of network service can be calculated in a preset window function using the service column as the grouping condition. The total traffic for each server IP corresponding to each type of network service can be calculated in a window function using the service column as the grouping condition and sorting the traffic of each server IP corresponding to each type of network service in a preset order (e.g., descending order). The cumulative traffic for all server IPs corresponding to each type of network service can be obtained by summing the total traffic for each server IP corresponding to each type of network service, using the service column as the grouping condition.
[0059] In this embodiment of the disclosure, the above statistical method has the following advantages:
[0060] 1. Facilitates statistical ranking: Pre-calculates the total traffic of the business, the total traffic of the business's server IPs, and the cumulative traffic of the business's server IPs. This makes it easier to compile a list of server IPs with the highest traffic share by comparing the ratio of the total traffic of the business's server IPs and the cumulative traffic of the business's server IPs to the total traffic of the business. This simplifies the data analysis process and improves the overall performance of the system.
[0061] 2. Reduce redundant calculations: By using database window functions for grouped calculations, the total traffic of the business, the total traffic of the business's server IP, and the cumulative traffic of the business's server IP can be calculated in a single calculation, avoiding subsequent redundant calculations and improving calculation efficiency.
[0062] S13. Filter out the target server IPs to be protected based on the target traffic data and the configured traffic percentage threshold.
[0063] In this embodiment of the disclosure, the traffic percentage threshold may include, but is not limited to, a first traffic percentage threshold and a second traffic percentage threshold.
[0064] In this embodiment of the disclosure, filtering the target server IPs to be protected based on target traffic data and a configured traffic percentage threshold may include:
[0065] Target server IPs are selected based on the total traffic of each type of network service, the cumulative traffic of all server IPs corresponding to each type of network service, and a first traffic percentage threshold, and / or, target server IPs are selected based on the total traffic of each type of network service, the total traffic of each server IP corresponding to each type of network service, and a second traffic percentage threshold.
[0066] In this embodiment of the disclosure, the first traffic percentage threshold and the second traffic percentage threshold can be the same or different, and can be set accordingly as needed.
[0067] In the embodiments of this disclosure, the two schemes described above can be used individually or in combination. When used in combination, either of the above implementation schemes can be used as a verification scheme for another implementation scheme, or as a supplementary scheme for another implementation scheme. For example, target server IPs can be first filtered based on the total traffic of each type of network service, the cumulative traffic of all server IPs corresponding to each type of network service, and a first traffic percentage threshold. If no target server IPs are selected, target server IPs can then be filtered based on the total traffic of each type of network service, the total traffic of each server IP corresponding to each type of network service, and a second traffic percentage threshold.
[0068] In the embodiments disclosed herein, the above-mentioned implementation schemes will be described in detail below.
[0069] In this embodiment of the disclosure, selecting target server IPs based on the total traffic of each type of network service, the cumulative traffic of all server IPs corresponding to each type of network service, and a first traffic percentage threshold may include:
[0070] Calculate the first ratio of the cumulative traffic of all server IPs corresponding to the target service to the total traffic of the target service;
[0071] Compare the first ratio with the first traffic percentage threshold;
[0072] In response to a first ratio being less than or equal to a first traffic percentage threshold, all server IPs corresponding to the target service are used as the target server IPs.
[0073] In this embodiment of the disclosure, the first filtering method for the target server IP can be expressed as: the cumulative traffic of all server IPs / the total traffic of the service <= the first traffic percentage threshold. If the cumulative traffic of all server IPs and the total traffic of any service (such as the target service mentioned above) satisfy the above inequality, then the server IP of the service can be determined as the target server IP and can be added to the list of server IPs that need to be optimized. If the cumulative traffic of all server IPs and the total traffic of the service do not satisfy the above inequality, then the server IP of the service can be determined as not the target server IP and can be ignored.
[0074] In this embodiment of the disclosure, selecting target server IPs based on the total traffic of each type of network service, the total traffic of each server IP corresponding to each type of network service, and a second traffic percentage threshold may include:
[0075] Calculate the second ratio of the total traffic of each server IP corresponding to the target service to the total traffic of the target service;
[0076] Compare the second ratio with the second flow percentage threshold;
[0077] In response to the second ratio being greater than or equal to the second traffic percentage threshold, all server IPs corresponding to the target service are used as the target server IPs.
[0078] In this embodiment of the disclosure, the second filtering method for the target server IP can be expressed as: total traffic of each server IP / total service traffic >= second traffic percentage threshold. If the cumulative traffic of each server IP and the total service traffic of any service (such as the target service mentioned above) satisfy the above inequality, then the server IP of the service can be determined as the target server IP and can be added to the list of server IPs that need to be optimized. If the cumulative traffic of each server IP and the total service traffic of any service do not satisfy the above inequality, then the server IP of the service can be determined as not the target server IP and can be ignored.
[0079] In this embodiment of the disclosure, the selection of target server IPs based on the total traffic of each type of network service, the cumulative traffic of all server IPs corresponding to each type of network service, and a first traffic percentage threshold, and the selection of target server IPs based on the total traffic of each type of network service, the total traffic of each server IP corresponding to each type of network service, and a second traffic percentage threshold, include:
[0080] Calculate the third ratio of the cumulative traffic of all server IPs corresponding to the target service to the total traffic of the target service;
[0081] Compare the third ratio with the first traffic share threshold;
[0082] In response to the third ratio being greater than the first traffic proportion threshold, a fourth ratio is calculated between the total traffic of each server IP corresponding to the target service and the total traffic of the target service.
[0083] Compare the fourth ratio with the second flow rate threshold;
[0084] In response to the fourth ratio being greater than or equal to the second traffic percentage threshold, all server IPs corresponding to the target service are used as the target server IPs.
[0085] In this embodiment of the disclosure, the above-described scheme allows for a second filtering method to be used if the target server IP fails to be filtered out using the first filtering method, thereby preventing omissions.
[0086] In this embodiment of the disclosure, since filtering is performed only by the first filtering method, the inequality in the first filtering method cannot be satisfied when the service has only one server IP address and the configured first traffic percentage threshold is less than 100%. The above solution avoids the omission of high-traffic server IP services by adding a second filtering method.
[0087] S14. Ensure the quality of network services corresponding to the target server IP.
[0088] In this embodiment of the disclosure, quality assurance for network services corresponding to the target server IP may include:
[0089] Based on the target server IP and the configured DSCP (Differentiated Services Code Point) value, automatically assemble and generate business quality assurance commands according to a preset template;
[0090] The service quality assurance command is automatically sent to the device corresponding to the target server IP and bound to the corresponding port to achieve quality assurance for the network services corresponding to the target server IP.
[0091] In this embodiment of the disclosure, the server IP data to be optimized can be combined with the DSCP value configured by the system to dynamically assemble and generate a service quality assurance instruction and send it to the device to complete the automatic configuration of the device and realize the quality assurance of network services corresponding to the target server IP.
[0092] In this embodiment of the disclosure, the DSCP value of a tuple (e.g., a quintuple) can be configured, and the service assurance priority of the corresponding tuple can be increased.
[0093] In this embodiment of the disclosure, a service quality assurance command is automatically generated according to a preset template based on the target server IP and the configured DSCP value, including:
[0094] Encapsulate the target server IP into a rule in the ACL (Access Control List) command;
[0095] The encapsulated rules are assembled into ACL commands according to a preset template;
[0096] Replace the DSCP value with the priority-mark template to generate the priority-mark command;
[0097] The preset configuration command, ACL command, and priority-mark command are concatenated to generate the business quality assurance command.
[0098] In this embodiment of the disclosure, the generated service quality assurance command is automatically sent to the device and bound to the port corresponding to the user, thereby achieving quality assurance for the specific services of the specific user.
[0099] In this embodiment of the disclosure, the configuration of the already effective service quality assurance command can be viewed on the device side.
[0100] In this embodiment of the disclosure, after ensuring the quality of service for the network services corresponding to the target server IP, the method may further include:
[0101] Collect service indicator information of network services corresponding to the target server IP;
[0102] In response to business metric information indicating that the quality of network services corresponding to the target server IP has not been improved or has not reached the preset improvement target, the traffic percentage threshold and / or DSCP value are adjusted.
[0103] In this embodiment, raw data of network services after quality assurance can be collected, and service indicator information can be extracted from the raw data. The quality of each service indicator information can be checked to see if it meets the standards. Based on the test results, the quality of the corresponding network services can be judged. If the compliance rate of the service indicator information does not change, it means that the quality of the network services has not been improved. If the compliance rate of the service indicator information increases, but still does not reach the preset target compliance rate, it means that the quality of the network services has not reached the preset improvement target. In any of the above situations, the traffic proportion threshold and / or DSCP value can be adjusted, and the scheme of this application embodiment can be re-executed with the adjusted traffic proportion threshold and / or DSCP value until the quality of the network services corresponding to the target server IP is improved.
[0104] In this embodiment of the disclosure, the business indicator information may include, but is not limited to, any one or more of the following: user's internet service type, traffic, duration, latency, packet loss, retransmission, etc.
[0105] The present disclosure includes at least the following advantages:
[0106] 1. It can collect users' online data in real time and identify users' online business behavior.
[0107] 2. It can perform in-depth analysis of internet data and identify the server IPs of services that require quality assurance.
[0108] 3. It can automatically generate and issue business quality assurance commands, realizing the function of automatically assembling, generating and issuing business quality assurance commands according to system configuration. This avoids the drawbacks of manual command assembly in related technical solutions, which are cumbersome, inefficient and have a high error rate. It improves the intelligence level of the OLT and enables closed-loop analysis of changes in business quality before and after.
[0109] 4. For the first time in the industry, it has achieved accurate identification and analysis of services on the OLT side, automatically generated and issued service quality assurance commands, and realized service assurance for service flow.
[0110] 5. A novel data hierarchical calculation and filtering algorithm is provided for filtering target server IPs. This algorithm can accurately filter out server IPs that need to be optimized without configuring all server IPs of the business in the OLT, thus saving OLT configuration resources.
[0111] 6. By accurately analyzing user internet access data in real time and performing layered data calculations, the server IP addresses of services requiring optimization can be precisely identified, improving the accuracy and efficiency of data analysis. The automatic generation and issuance of service quality assurance commands based on service configuration and the IP addresses of servers requiring optimization simplifies the operational process for maintenance personnel and enhances the intelligence level of the OLT network.
[0112] This disclosure also provides an electronic device 100, as shown in FIG5, including:
[0113] One or more processors 101;
[0114] The memory 102 stores one or more programs, which, when executed by the one or more processors 101, enable the one or more processors 101 to implement the network service quality assurance method.
[0115] One or more input / output (I / O) interfaces are connected between the processor 101 and the memory 102 and configured to enable information interaction between the processor 101 and the memory 102.
[0116] This disclosure also provides a computer program product, which includes a computer program that, when executed by a processor, implements the network service quality assurance method.
[0117] In this disclosure, any of the aforementioned network service quality assurance methods can be applied to the electronic device or computer program product embodiments, and will not be described in detail here.
[0118] Those skilled in the art will understand that all or some of the functional modules / units disclosed above can be implemented as software, firmware, hardware, or suitable combinations thereof.
[0119] In hardware implementations, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be executed by several physical components working together.
[0120] Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit (CPU), digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH) or other disk storage; read-only optical disc (CD-ROM), digital versatile disc (DVD) or other optical disc storage; magnetic cartridges, magnetic tapes, disk storage or other magnetic storage; and any other media that can be used to store desired information and can be accessed by a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.
[0121] This disclosure has disclosed exemplary embodiments, and although specific terminology has been used, it is for general illustrative purposes only and should not be construed as limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.
Claims
1. A method for ensuring network service quality, comprising: Obtain user session data for network services; The user session data is aggregated and accumulated according to the business dimension and the server network protocol IP dimension to obtain the target traffic data; Based on the target traffic data and the configured traffic percentage threshold, the target server IPs to be protected are selected; Quality assurance is provided for the network services corresponding to the target server IP.
2. The network service quality assurance method according to claim 1, wherein, The process of aggregating and accumulating the user session data according to business dimensions and server network protocol IP dimensions to obtain target traffic data includes: The user session data is aggregated from the business dimension and the server IP dimension according to a preset time granularity to obtain the basic traffic data of each server IP corresponding to each business within the time granularity. The basic traffic data within the time granularity is accumulated to obtain the cumulative traffic data within a preset time period; Based on the cumulative traffic data, the target traffic data is calculated according to preset statistical dimensions.
3. The network service quality assurance method according to claim 1, wherein, The target traffic data includes: the total traffic of each type of network service, the total traffic of each server IP corresponding to each type of network service, and the cumulative traffic of all server IPs corresponding to each type of network service; the traffic percentage threshold includes a first traffic percentage threshold and a second traffic percentage threshold. The process of filtering out the target server IPs to be protected based on the target traffic data and the configured traffic percentage threshold includes: The target server IP is selected based on the total traffic of each type of network service, the cumulative traffic of all server IPs corresponding to each type of network service, and the first traffic percentage threshold, and / or the target server IP is selected based on the total traffic of each type of network service, the total traffic of each server IP corresponding to each type of network service, and the second traffic percentage threshold.
4. The network service quality assurance method according to claim 3, wherein, The step of filtering the target server IP based on the total traffic of each type of network service, the cumulative traffic of all server IPs corresponding to each type of network service, and the first traffic percentage threshold includes: Calculate the first ratio of the cumulative traffic of all server IPs corresponding to the target service to the total traffic of the target service; Compare the first ratio with the first traffic percentage threshold; In response to the first ratio being less than or equal to the first traffic percentage threshold, all server IPs corresponding to the target service are used as the target server IPs.
5. The network service quality assurance method according to claim 3, wherein, The step of filtering the target server IP based on the total traffic of each type of network service, the total traffic of each server IP corresponding to each type of network service, and the second traffic percentage threshold includes: Calculate a second ratio between the total traffic of each server IP corresponding to the target service and the total traffic of the target service; Compare the second ratio with the second traffic share threshold; In response to the second ratio being greater than or equal to the second traffic percentage threshold, all server IPs corresponding to the target service are used as the target server IPs.
6. The network service quality assurance method according to claim 3, wherein, The process of filtering the target server IP based on the total traffic of each type of network service, the cumulative traffic of all server IPs corresponding to each type of network service, and the first traffic percentage threshold, and filtering the target server IP based on the total traffic of each type of network service, the total traffic of each server IP corresponding to each type of network service, and the second traffic percentage threshold, includes: Calculate the third ratio of the cumulative traffic of all server IPs corresponding to the target service to the total traffic of the target service; Compare the third ratio with the first traffic percentage threshold; In response to the third ratio being greater than the first traffic proportion threshold, a fourth ratio is calculated between the total traffic of each server IP corresponding to the target service and the total traffic of the target service. Compare the fourth ratio with the second flow rate percentage threshold; In response to the fourth ratio being greater than or equal to the second traffic percentage threshold, all server IPs corresponding to the target service are used as the target server IPs.
7. The network service quality assurance method according to claim 1, wherein, The quality assurance for network services corresponding to the target server IP includes: Based on the target server IP and the configured Differential Service Code Point (DSCP) value, a service quality assurance command is automatically generated according to a preset template. The service quality assurance command is automatically sent to the device corresponding to the target server IP and bound to the corresponding port to achieve quality assurance for the network services corresponding to the target server IP.
8. The network service quality assurance method according to claim 7, wherein, The step of automatically assembling and generating service quality assurance commands according to a preset template based on the target server IP and the configured Differential Service Code Point (DSCP) value includes: Encapsulate the target server IP into a rule in the Access Control List (ACL) command; The encapsulated rules are assembled into ACL commands according to a preset template; Replace the DSCP value with the priority-mark template to generate the priority-mark command; The service quality assurance command is generated by concatenating the preset configuration command, the ACL command, and the priority-mark command.
9. The network service quality assurance method according to claim 7, wherein, After ensuring the quality of network services corresponding to the target server IP, the method further includes: Collect service indicator information of the network services corresponding to the target server IP; In response to the business indicator information indicating that the quality of the network service corresponding to the target server IP has not been improved or has not reached the preset improvement target, the traffic percentage threshold and / or the DSCP value are adjusted.
10. An electronic device, comprising: One or more processors; A memory having stored one or more programs thereon, which, when executed by one or more processors, cause the one or more processors to implement the network service quality assurance method according to any one of claims 1-9; One or more input / output (I / O) interfaces are connected between the processor and the memory and configured to enable information exchange between the processor and the memory.
11. A computer program product comprising a computer program that, when executed by a processor, implements the network service quality assurance method according to any one of claims 1-9.