IP quality data processing method and apparatus for use in CDN, device, and medium

Abstract

An IP quality data processing method and apparatus for use in a CDN, a device, and a medium. The method comprises: acquiring a plurality of data samples from at least one data source associated with a CDN, each of the data samples comprising a client IP address and a service node IP address for performing data transmission in the CDN and transmission performance data associated with the data transmission; determining at least one IP address segment from the plurality of data samples, each IP address segment corresponding to an IP address range of a service node in the CDN; for each IP address segment among the at least one IP address segment, selecting from the plurality of data samples a group of data samples associated with the IP address segment; and for each IP address segment among the at least one IP address segment, determining quality values of a plurality of quality indicators of the IP address segment on the basis of the transmission performance data comprised in the group of data samples associated with the IP address segment.

Description

Method, device, equipment and medium for processing IP quality data in CDN

[0001] This application claims priority to the Chinese invention patent application entitled “IP quality data processing method, device, equipment and medium for CDN” filed on March 21, 2024, with application number 202410330057.1. The entire contents of that application are incorporated herein by reference. Technical Field

[0002] Example embodiments of the present disclosure generally relate to the field of computers, and more particularly, to methods, devices, apparatuses, and computer-readable storage media for processing Internet Protocol (IP) quality data in a content delivery network (CDN). Background Art

[0003] With the development of internet technology, individual users' demand for access to a large number of resources has gradually increased. Content Delivery Network (CDN) pre-warming technology can cache the corresponding resources from the origin server on CDN nodes before the user's first request for the resource. As a result, when the user actually accesses the resource, they can directly obtain the latest, cached data from the CDN node, efficiently and quickly.

[0004] CDNs enable users to access the content they need locally, thereby alleviating network congestion and improving website responsiveness. To further improve content distribution efficiency, some service providers within CDNs may monitor the IP quality of their respective service nodes to guide content scheduling. However, this IP quality assessment has a limited scope. Furthermore, for multiple business scenarios, the corresponding business data is scattered across different datasets without unified metrics. This makes querying and manual aggregation expensive and inadequate to meet user needs. Summary of the Invention

[0005] In a first aspect of the present disclosure, a method for processing IP quality data in a content delivery network (CDN) is provided. The method comprises: collecting a plurality of data samples from at least one data source related to the CDN, each data sample comprising an IP address of a client transmitting data in the CDN, an IP address of a service node, and transmission performance data related to the data transmission; determining at least one IP address segment from the plurality of data samples, each IP address segment corresponding to an IP address range of a service node in the CDN; for each IP address segment in the at least one IP address segment, selecting a group of data samples related to the IP address segment from the plurality of data samples; and for each IP address segment in the at least one IP address segment, determining a quality value of the IP address segment on a plurality of quality indicators based on the transmission performance data included in the group of data samples related to the IP address segment.

[0006] In a second aspect of the present disclosure, an IP quality data processing device for a content delivery network (CDN) is provided. The device comprises: a collection module configured to collect a plurality of data samples from at least one data source related to the CDN, each data sample comprising an IP address of a client transmitting data in the CDN, an IP address of a service node, and transmission performance data related to the data transmission; an execution module configured to determine at least one IP address segment from the plurality of data samples, each IP address segment corresponding to an IP address range of a service node in the CDN; a selection module configured to select a group of data samples related to each IP address segment in the at least one IP address segment from the plurality of data samples; and a determination module configured to determine, for each IP address segment in the at least one IP address segment, a quality value of the IP address segment on a plurality of quality indicators based on the transmission performance data included in the group of data samples related to the IP address segment.

[0007] In a third aspect of the present disclosure, an electronic device is provided. The electronic device includes at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the electronic device to perform the method of the first aspect of the present disclosure.

[0008] In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium and can be executed by a processor to perform the method according to the first aspect of the present disclosure.

[0009] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key features or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other features, advantages and aspects of the various embodiments of the present disclosure will become more apparent hereinafter with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0011] FIG1 shows a schematic diagram of an example environment in which embodiments of the present disclosure can be implemented;

[0012] FIG2 shows a schematic diagram of IP quality data processing in a CDN according to some embodiments of the present disclosure;

[0013] FIG3 shows a schematic diagram of an IP quality data processing and application architecture for a CDN according to some embodiments of the present disclosure;

[0014] FIG4 shows a schematic diagram of data entries according to some embodiments of the present disclosure;

[0015] FIG5 shows a flowchart of an example of an IP quality data processing process in a CDN according to some embodiments of the present disclosure;

[0016] FIG6 shows a block diagram of an apparatus for IP quality data processing in a CDN according to some embodiments of the present disclosure; and

[0017] FIG7 illustrates a block diagram of an electronic device in which one or more embodiments of the present disclosure may be implemented. DETAILED DESCRIPTION

[0018] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0019] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, i.e., "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may be included below.

[0020] Herein, unless explicitly stated otherwise, executing a step “in response to A” does not mean executing the step immediately after “A” but may include one or more intermediate steps.

[0021] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition, use, storage or deletion of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0022] It is understandable that before using the technical solutions disclosed in the various embodiments of the present disclosure, the type, scope of use, usage scenarios, etc. of the information involved in the present disclosure should be informed to relevant users and authorization should be obtained from relevant users in an appropriate manner in accordance with relevant laws and regulations. The relevant users may include any type of right holders, such as individuals, enterprises, and groups.

[0023] For example, in response to receiving an active request from a user, a prompt message is sent to the relevant user to clearly prompt the relevant user that the operation requested to be performed will require obtaining and using the information of the relevant user, so that the relevant user can independently choose whether to provide information to the software or hardware such as the electronic device, application, server or storage medium that executes the operation of the technical solution of the present disclosure based on the prompt message.

[0024] As an optional but non-limiting implementation, in response to receiving an active request from a relevant user, a prompt message may be sent to the relevant user in the form of a pop-up window, in which the prompt message may be presented in text form. Furthermore, the pop-up window may also include a selection control for the user to select "agree" or "disagree" to provide information to the electronic device.

[0025] It is understandable that the above notification and user authorization process are merely illustrative and do not limit the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure. The activation of the digital assistant-related functions of the embodiment of the present disclosure, the data obtained, the processing and storage of the data, etc., shall all obtain the prior authorization of the user and other rights holders associated with the user, and shall comply with the provisions of relevant laws and regulations and the rules of agreement between rights holders.

[0026] As discussed above, traditional IP segment node data faces many business scenarios, and different business data is scattered in different data clusters. In addition, the dimensions, data indicators, and units of different business data are not unified, the corresponding granularity and data latency are inconsistent, and the cost of query and manual aggregation is high. In the face of multiple business type scenarios, it is extremely difficult to use a single data source for quality assessment and quality scheduling of traditional IP segment node data. For example, video services not only rely on the network, but also need to pay attention to the service's parsing capabilities. Faster first frame playback is proportional to user feedback. Using a single data source cannot focus on multiple quality indicators and cannot meet usage requirements.

[0027] In content delivery networks (CDNs), traditional methods rely on CDN vendors reporting node data primarily through self-built service nodes, which is limited to their own links. Using high-frequency dial-up data is prohibitively expensive and fails to provide reliable quality assessment and scheduling. Traditional dynamic link selection logic also prioritizes pre-set network routes, failing to broaden user-side optimization options and optimize access, resulting in a poor user experience.

[0028] In view of this, an embodiment of the present disclosure provides an IP quality data processing method for CDN. The method includes: a data processing system collects multiple data samples from at least one data source related to the CDN, each data sample includes the client IP address, service node IP address and transmission performance data related to data transmission in the CDN; at least one IP address segment is determined from the multiple data samples, each IP address segment corresponds to an IP address range of the service node in the CDN; for each IP address segment in at least one IP address segment, a group of data samples related to the IP address segment is selected from the multiple data samples; and for each IP address segment in at least one IP address segment, based on the transmission performance data included in the group of data samples related to the IP address segment, the quality value of the IP address segment on multiple quality indicators is determined. In this way, the quality of the IP segment can be understood based on the quality value corresponding to the IP segment, or multiple quality indicators can be paid attention to to meet the usage requirements in different business scenarios, and user-side access optimization can be achieved to improve user experience.

[0029] Figure 1 illustrates a schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. In environment 100, CDN 110 is configured to deploy multiple server nodes within a certain range and cache a website's static resources and dynamic content on edge servers close to users. Data processing system 120 is configured to perform multiple data processing tasks and store the data generated by these tasks.

[0030] The data processing system 120 may be any type of device with computing capabilities, including a terminal device or a server device. The terminal device may be any type of mobile, fixed, or portable terminal, including a mobile phone, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a media computer, a multimedia tablet, a personal communication system (PCS) device, or any combination thereof, including accessories and peripherals of these devices, or any combination thereof.

[0031] CDN 110 may include, for example, computing systems / servers such as mainframes, edge computing nodes, computing devices in cloud environments, and the like. The server-side device may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks, and big data and artificial intelligence platforms. The server-side device may include, for example, computing systems / servers such as mainframes, edge computing nodes, computing devices in cloud environments, and the like. In some embodiments, the data processing system 120 may be implemented based on cloud services.

[0032] It should be understood that the structure and functionality of environment 100 are described for exemplary purposes only and do not imply any limitation on the scope of the present disclosure.

[0033] Figure 2 shows a schematic diagram of IP quality data processing in a CDN according to some embodiments of the present disclosure. As shown in Figure 2, data processing tasks that can be performed by the data processing system 120 include data import 250, data processing 255, data synchronization 260, data writeback 265, a test task management module 275, and data query 280.

[0034] The data processing system 120 collects multiple data samples from at least one data source related to the CDN 110 , each data sample including a client IP address performing data transmission in the CDN 110 , a service node IP address, and transmission performance data related to the data transmission.

[0035] The data sources of the multiple data samples imported by the data importer 250 include dial test data 210, on-demand data 215, network group computer room data 220, server data 225, etc.

[0036] In some embodiments, data import 250 imports multiple data samples, and then through data processing 255, data synchronization 260, and data write-back 265, an IP segment quality table 270 is obtained. The IP segment quality table 270 can be represented as a database.

[0037] In some embodiments, the dial test task management module 275 determines the target IP address segment to be evaluated in the CDN 110 and generates a dial test task for the target IP address segment. For example, the task may indicate the service node and client node (node_point) to be tested, as well as the domain name (domain_probe) corresponding to the target IP address segment. The dial test task management module 275 can control the execution of the dial test task for the target IP address segment in the CDN, which may include an Internet Data Center (IDC) dial test or a client simulation dial test. By proactively initiating a dial test task, missing or insufficient data samples for certain IP segments or IP addresses can be compensated. In this way, the data processing system 120 can collect dial test data related to the dial test task from at least one client or at least one service node in the CDN, and obtain at least one data sample from the dial test data. In some embodiments, the data source of the dial test data 210 may include IDC dial tests and client simulation dial tests.

[0038] In some embodiments, data samples (including IP addresses and their corresponding transmission performance data) may be sourced from on-demand streaming, dial-up testing, and reports from CDN vendors. Quality indicators are distributed across various service IP segment quality tables and service interfaces based on the service scenario. IP segment quality table 270 can be determined by data processing system 120 based on quality indicator table 230, data source table 235, task execution table 240, and other factors.

[0039] In some embodiments, the data processing system 120 may pre-set a duration, for example, 5 minutes, and periodically obtain indicator information from the quality indicator table based on the pre-set duration to obtain a quality indicator information data set containing multiple quality indicator information.

[0040] In some embodiments, if there are multiple service nodes or clients in the CDN that need to grab the lock to perform the current task, one service node or client can be selected to perform tasks corresponding to all quality indicators to facilitate subsequent data aggregation.

[0041] FIG3 shows a schematic diagram of an IP quality data processing and application architecture 300 for a CDN according to some embodiments of the present disclosure.

[0042] In some embodiments, client data sources include on-demand 215 and dial test 325. Data sources for on-demand 215 include on-demand SDK 315 and on-demand data module 320. Quality indicators for on-demand SDK 315 may include freeze rate, interruption rate, and so on, while quality indicators for on-demand data module may include freeze rate, interruption rate, and so on. Data sources for dial test 325 include image service 330 and dynamic loading 335. Quality indicators for image service 330 may include download success rate, time to first byte (TTFB), and so on, while quality indicators for dynamic loading 335 may include download success rate, TTFB, and so on. Data sources for the server include dial test 340 and server 225. Data sources for dial test 340 may include B2B service 345 and internal service 350. Quality indicators for B2B service 345 may include success rate, latency, and so on, while quality indicators for internal service 350 may include success rate, latency, and so on. Data sources for server 225 may include status code data 360 and CND-side reported data 365.

[0043] In some embodiments, the information used to create the IP segment quality table 270 includes dimension information and quality indicators. Multiple dimension standards can be pre-set, and the dimension information in the data dimension needs to meet the pre-set multiple dimension standards before it can be stored in the data dimension library. There may be cases where the data dimension does not contain complete dimension information, such as domain name availability, including alias (cname) records, and may not contain CDN service provider information. For example, on-demand video data contains the first frame, but downloaded pictures, etc. do not contain this quality indicator data. The method of setting non-existent information to blank or uniformly encoding non-existent information for compatibility can be used to make the dimension information meet the dimension standards. The processed dimension information is compared with the pre-set multi-dimensional standards to determine whether the processed dimension information meets the pre-set multi-dimensional standards. If it meets the pre-set multi-dimensional standards, the processed dimension information is used as legal dimension information. After all data containing legal dimensions are aggregated, the data dimensions corresponding to the data containing legal dimensions can be converted into some data tables for storage. The example storage form of data dimensions can be seen in Figure 4 below.

[0044] In some embodiments, the data processing system 120 determines at least one IP address segment from a plurality of data samples, where each IP address segment corresponds to an IP address range of a service node in the CDN, and the IP address range can be pre-divided. For each IP address segment in the at least one IP address segment, a group of data samples related to the IP address segment is selected from the plurality of data samples. For each IP address segment in the at least one IP address segment, delivery performance data included in the group of data samples related to the IP address segment is selected.

[0045] Each data sample indicates the performance of data transmission between the client IP address and the service node IP address, including data related to quality indicators such as download success, any lags, and network round-trip time. Based on the transmission performance data collected in the data samples, the data processing system 120 can determine the quality values ​​of the corresponding IP address segment based on multiple quality indicators. In some embodiments, the multiple quality indicators determined by the data processing system 120 can be pre-configured or specified by a user, such as an administrator.

[0046] In some embodiments, the data processing system 120 may use a synchronous aggregator to aggregate the indicator information corresponding to each data containing legal dimension information into an object. An exemplary aggregate object format is detailed in FIG4 below.

[0047] Figure 4 shows a schematic diagram of an example data entry 400 according to some embodiments of the present disclosure. Data entry 400 includes a dimension information portion and a quality indicator portion. The dimension information portion may include information on multiple dimensions such as manufacturer, domain name, and IP segment, while the quality indicator portion may include multiple quality indicators such as availability and latency.

[0048] In some embodiments, the data entry 400 includes at least an IP address segment, data corresponding to at least one data dimension associated with the IP address segment, and quality values ​​of the IP address segment on multiple quality indicators.

[0049] In some embodiments, a synchronous aggregator may be used to aggregate the indicator information corresponding to each data containing legal dimension information into an object. The objects obtained by aggregating multiple data respectively constitute the data entry 400. The indicator data currently obtained may be cached in memory.

[0050] In some embodiments, after all data containing valid dimensions is aggregated, the indicator information corresponding to the data containing valid dimensions can be converted into corresponding data tables and stored by the data reading and writing module of the data processing system 120, and saved in the IP segment quality database. The indicator information includes dimension information and quality indicators, and the data tables corresponding to the data containing valid dimensions can include a dimension information table and a quality indicator table.

[0051] In some embodiments, an IP segment quality database can be obtained by aggregating multi-dimensional and multi-terminal quality data. The data in the IP segment quality database has the characteristics of real-time and multi-dimensional aggregation, and can provide monitoring, operation, and maintenance multi-service capabilities.

[0052] In some embodiments, wherein in each data entry, at least one data dimension associated with the IP address segment includes at least one of the following: service node information corresponding to the IP address segment, client information corresponding to the IP address segment, business type corresponding to the IP address segment, data source for determining the quality value of the IP address segment on multiple quality indicators, or network information corresponding to the IP address segment.

[0053] In some embodiments, the dimension information table, as shown in Table 1, may include service node information corresponding to the IP address segment, which may include the service provider, service domain name, country where the server is located, province where the server is located, operator where the server is located, and IP segment to which the server belongs. The IP segment to which the server belongs may include 1.1.1.0 / 24, 2.2.2.0 / 28, etc. It may also include client information corresponding to the IP address segment, which may include the country where the client is located, province where the client is located, operator where the client is located, etc.

[0054] The dimension information table can also include the business type corresponding to the IP address segment, including business service types such as image, on-demand, download, and dynamic. It can also include the data source used to determine the quality value of the IP address segment on multiple quality indicators, such as Net, video, dial test, device, CDN, etc.

[0055] The dimension information table can also include network information corresponding to the IP address segment, such as: whether the connection is reused, protocol type, status code, network type, whether the connection is used can include unknown, reused, and not reused, the protocol type can include Http, https, and the network type can be Wifi, mobile, idc, lastmail, etc.

[0056] Table 1

[0057] In some embodiments, the quality index table is shown in Table 2 below, and may include at least one of the following items: download success rate, whose index name can be expressed as download_success_rate, and the value range of the download success rate can be [0, 1]; on-demand jam rate, whose index name can be expressed as video_buffered_rate, and the value range of the on-demand jam rate can be [0, 1]; network round-trip time, whose index name can be expressed as rtt, and the unit of network round-trip time can be milliseconds (ms); dns request time, whose index name can be expressed as dns_time; ssl time, whose index name can be expressed as ssl_time; tcp time, whose index name can be expressed as tcp_time; complete request Total time consumption, its indicator name can be expressed as total_time; among them, network round-trip time, DNS request time, SSL time consumption, TCP time consumption, and total time consumption of the complete request can all be in ms; including request header+body, its indicator name can be expressed as recv_total, and the unit can be bytes (bits); download speed, its indicator name can be expressed as download_speed, and the unit can be MB / s. The download speed can be used to verify whether the indicator is the same as total_time / recv_total; sampling ratio, its indicator name can be expressed as sample_multiple, and the value can be selected from one thousandth to one ten-thousandth, etc. The sampling ratio is used to do some data exclusion.

[0058] Table 2

[0059] In some embodiments, in order to generate the IP segment quality table 270, data processing needs to be performed on the imported raw data. The data processing includes an aggregation process where the main key value is the client IP segment and the server node IP segment. Based on the binding relationship between the client IP segment and the server IP segment, other values ​​are selected as attributes, and aggregate sampling processing is performed. The quality data corresponding to the pre-divided server IP address segment after aggregate sampling processing is then stored. For example, the data samples corresponding to the client IP 1.1.1.1 and the server IP 2.2.2.2 and the server IP 3.3.3.3 are distinguished, and the only pair of data samples should be 1.1.1.0-(2.2.2.0) and 1.1.1.0-(3.3.3.0). For example, the data corresponding to client IP 1.1.1.1, client IP 4.4.4.4 and server node IP 2.2.2.2 are distinguished. The only pair of data samples should be 1.1.1.0-(2.2.2.0). In this case, no additional special key value is created.

[0060] In some embodiments, the aggregation process for attribute-related data can be performed by calculating an average value. For example, the value of the download power after aggregation is calculated by multiplying the data value corresponding to the currently-focused quality indicator by the sampling ratio, and then adding them together to obtain the quality value of the current IP segment. The data value corresponding to the currently-focused quality indicator is the cumulative value calculated after multiple samplings. The sampling ratio is a pre-set interval between data sample collections. The sampling ratio is not considered a quality value, but rather a description of the data sample collection. For example, if the currently-focused quality indicators include download success rate, on-demand lag rate, and network round-trip time, the cumulative value of the download success rate is A, the sampling ratio is 30%, the cumulative value of the on-demand lag rate is B, the sampling ratio is 20%, and the cumulative value of the network round-trip time is C (C is in seconds; if it is in milliseconds, unit conversion is required), and the sampling ratio is 10%, then the quality value of the current IP segment can be calculated as: A × 30% + B × 20% + C × 10%. The sampling ratio here can be set according to different application needs.

[0061] In some embodiments, some failures may cause reduced regional availability, so event injection is required. In emergencies, other means can be used to obtain information about a possible network segment cutover at a certain time. When the service is unavailable, the quality of the network segment can be set to unavailable in advance.

[0062] Some example embodiments of the present disclosure will be described below with continued reference to the accompanying drawings.

[0063] The quality values ​​corresponding to multiple quality indicators of multiple IP address segments can form the obtained IP segment quality table 270. In some embodiments, the IP segment quality table 270 can be used to connect to various related services such as alarm, disaster recovery, and quality, and provide data availability in the IP segment dimension. In some embodiments, when the data query 280 of the data processing system 120 receives a data query request, the backend will provide the current data status of similar IPs based on the requested IP status, and the data query request can be expanded into multiple dimensions. In some embodiments, the data processing system 120 can support data queries for different services, business terminals, etc., such as the coverage quality map 285, client feedback quality data center 290, quality scheduling service 295, etc. shown in Figure 2.

[0064] In some embodiments, the IP segment quality table generated by the data processing system 120 can be used in the intelligent alarm, emergency response and recovery platform 370 in Figure 3. The intelligent alarm, emergency response and recovery platform 370 includes an alarm system, an early warning system, a cause analysis system and an emergency center.

[0065] In some embodiments, the IP segment quality table generated by the data processing system 120 can be used in the CDN service middle platform 380 in Figure 3. The services that can be provided by the CDN service middle platform 380 include alarm service, disaster recovery scheduling, quality analysis and quality scheduling.

[0066] In some embodiments, the IP segment quality table generated by the data processing system 120 can also be used for the integrated cloud service 390 in Figure 3, which includes an operation and maintenance center, a data center, disaster recovery, quality scheduling, and a quality center.

[0067] In some embodiments, when an anomaly occurs, the abnormal network status of the IP segment can be used to provide feedback on the current network anomaly area. For example, it could be a large-scale regional problem, a small data center problem, or an anomaly on a single machine. In large-scale network architecture scenarios, due to its real-time nature, the problem discovery capability can be used to identify the scope of the anomaly, allowing for timely alarm and resolution.

[0068] In some embodiments, the data query 280 of the data processing system 120 can also perform a quality scheduling service 295. The IP recommendation capability of the quality scheduling service 295 is similar and can support the manufacturer recommendation capability. It recommends the most suitable service provider based on the quality value of the current IP segment obtained through the equipment distribution of the previously aggregated manufacturers in the current IP segment.

[0069] In some embodiments, different services focus on different quality indicators. For example, download services focus on the first packet and network quality, while on-demand services focus on the first frame time and freeze rate. This allows the quality indicators required by different services to be monitored from the maintained IP segment quality table. Based on the quality values ​​of each IP segment under the corresponding quality indicators, service scheduling, disaster recovery analysis, abnormal condition alarms, and other functions can be performed for related services.

[0070] 5 shows a flow chart of an example of an IP quality data processing process 500 for a CDN according to some embodiments of the present disclosure. The process 500 may be implemented at the data processing system 120. For ease of discussion, the process 500 will be described with reference to the environment 100 of FIG.

[0071] At block 510 , the data processing system 120 collects a plurality of data samples from at least one data source associated with the CDN.

[0072] In some embodiments, each data sample includes an IP address of a client that transmits data in the CDN, an IP address of a service node, and transmission performance data related to the data transmission.

[0073] At block 520 , the data processing system 120 determines at least one IP address segment from the plurality of data samples.

[0074] In some embodiments, each IP address segment corresponds to an IP address range of a service node in the CDN.

[0075] At block 530 , for each IP address segment in the at least one IP address segment, the data processing system 120 selects a set of data samples associated with the IP address segment from the plurality of data samples.

[0076] At block 540 , for each IP address segment in the at least one IP address segment, the data processing system 120 determines a quality value of the IP address segment on a plurality of quality indicators based on the transmission performance data included in a set of data samples associated with the IP address segment.

[0077] In some embodiments, at least one data source includes at least one of the following: actual operation data on at least one client in the CDN, dialing data on at least one client in the CDN, operation data on at least one service node in the CDN, and dialing data on at least one service node in the CDN.

[0078] In some embodiments, a target IP address segment to be evaluated in the CDN is determined; a dialing test task for the target IP address segment is generated; and execution of the dialing test task for the target IP address segment in the CDN is controlled, and wherein collecting multiple data samples includes: collecting dialing test data related to the dialing test task from at least one client or at least one service node in the CDN; and obtaining at least one data sample from the dialing test data.

[0079] In some embodiments, based on the quality values ​​of the IP address segment on multiple quality indicators, an IP segment quality table for the CDN is generated, and the IP segment quality table includes at least one data entry, each data entry includes at least the IP address segment, data corresponding to at least one data dimension associated with the IP address segment, and the quality values ​​of the IP address segment on multiple quality indicators, and the data corresponding to at least one data dimension associated with the IP address segment refers to other data besides the IP segment.

[0080] In some embodiments, wherein in each data entry, at least one data dimension associated with the IP address segment includes at least one of the following: service node information corresponding to the IP address segment, client information corresponding to the IP address segment, business type corresponding to the IP address segment, data source for determining the quality value of the IP address segment on multiple quality indicators, or network information corresponding to the IP address segment.

[0081] In some embodiments, in response to a query request from a client in the CDN, an IP address segment for processing the query request is selected from at least one IP address segment based on at least one IP address segment and a quality value corresponding to each IP address segment on multiple quality indicators.

[0082] In some embodiments, based on at least one IP address segment and the quality values ​​of each IP address segment corresponding to multiple quality indicators, a first IP address segment with quality abnormalities in at least one IP address segment is determined; and an abnormality alarm is provided for the first IP address segment.

[0083] The embodiments of the present disclosure also provide corresponding devices for implementing the above methods or processes.

[0084] 6 shows a block diagram of an apparatus 600 for processing IP quality data in a CDN according to certain embodiments of the present disclosure. Apparatus 600 may be implemented as or included in data processing system 120. Each module / component in apparatus 600 may be implemented by hardware, software, firmware, or any combination thereof.

[0085] As shown in FIG6 , the apparatus 600 includes a collection module 610 configured to collect multiple data samples from at least one data source related to the CDN, each data sample including the IP address of a client transmitting data in the CDN, the IP address of a service node, and transmission performance data related to the data transmission. The apparatus 600 also includes an execution module 620 configured to determine at least one IP address segment from the multiple data samples, each IP address segment corresponding to an IP address range of a service node in the CDN. The apparatus 600 also includes a selection module 630 configured to select, for each IP address segment in the at least one IP address segment, a group of data samples related to the IP address segment from the multiple data samples. The apparatus 600 also includes a determination module 640 configured to determine, for each IP address segment in the at least one IP address segment, the quality value of the IP address segment on multiple quality indicators based on the transmission performance data included in the group of data samples related to the IP address segment.

[0086] In some embodiments, each data sample includes an IP address of a client that transmits data in the CDN, an IP address of a service node, and transmission performance data related to the data transmission.

[0087] In some embodiments, each IP address segment corresponds to an IP address range of a service node in the CDN.

[0088] In some embodiments, at least one data source includes at least one of the following: actual operation data on at least one client in the CDN, dialing data on at least one client in the CDN, operation data on at least one service node in the CDN, and dialing data on at least one service node in the CDN.

[0089] In some embodiments, the execution module 620 is further used to determine the target IP address segment to be evaluated in the CDN; generate a dialing test task for the target IP address segment; and control the execution of the dialing test task for the target IP address segment in the CDN, and wherein collecting multiple data samples includes: collecting dialing test data related to the dialing test task from at least one client or at least one service node in the CDN; and obtaining at least one data sample from the dialing test data.

[0090] In some embodiments, the determination module 640 is further configured to generate an IP segment quality table for the CDN based on the quality values ​​of the IP address segment on multiple quality indicators, the IP segment quality table including at least one data entry, each data entry including at least an IP address segment, data corresponding to at least one data dimension associated with the IP address segment, and the quality values ​​of the IP address segment on multiple quality indicators.

[0091] In some embodiments, wherein in each data entry, at least one data dimension associated with the IP address segment includes at least one of the following: service node information corresponding to the IP address segment, client information corresponding to the IP address segment, business type corresponding to the IP address segment, data source for determining the quality value of the IP address segment on multiple quality indicators, or network information corresponding to the IP address segment.

[0092] In some embodiments, the execution module 620 is also used to respond to a query request from a client in the CDN, and select an IP address segment from at least one IP address segment for processing the query request based on at least one IP address segment and the quality value of each IP address segment corresponding to multiple quality indicators.

[0093] In some embodiments, the determination module 640 is further configured to: determine a first IP address segment with quality abnormalities in at least one IP address segment based on at least one IP address segment and the quality values ​​of each IP address segment corresponding to multiple quality indicators; and provide an abnormality alarm for the first IP address segment.

[0094] The units and / or modules included in the device 600 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units and / or modules can be implemented using software and / or firmware, such as machine executable instructions stored on a storage medium. In addition to or as an alternative to machine executable instructions, some or all of the units and / or modules in the device 600 can be implemented at least in part by one or more hardware logic components. By way of example and not limitation, exemplary types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0095] FIG7 shows a block diagram of an electronic device 700 in which one or more embodiments of the present disclosure may be implemented. It should be understood that the electronic device 700 shown in FIG7 is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein. The electronic device 700 shown in FIG7 may be used to implement the data processing system 120 of FIG1 or the apparatus 600 of FIG6.

[0096] As shown in FIG7 , electronic device 700 is in the form of a general-purpose computing device. Components of electronic device 700 may include, but are not limited to, one or more processors or processing units 710, memory 720, storage device 730, one or more communication units 740, one or more input devices 750, and one or more output devices 760. Processing unit 710 may be a real or virtual processor and is capable of performing various processes according to programs stored in memory 720. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to increase the parallel processing capabilities of electronic device 700.

[0097] The electronic device 700 typically includes a plurality of computer storage media. Such media can be any available media accessible to the electronic device 700, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 720 can be a volatile memory (e.g., registers, cache, random access memory (RAM)), a non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory) or some combination thereof. The storage device 730 can be a removable or non-removable medium and can include a machine-readable medium, such as a flash drive, a disk or any other medium, which can be used to store information and / or data and can be accessed within the electronic device 700.

[0098] The electronic device 700 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not shown in FIG. 7 , a disk drive for reading from or writing to a removable, non-volatile disk (e.g., a “floppy disk”) and an optical drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. The memory 720 may include a computer program product 725 having one or more program modules configured to perform various methods or actions of various embodiments of the present disclosure.

[0099] The communication unit 740 enables communication with other electronic devices via a communication medium. Additionally, the functions of the components of the electronic device 700 can be implemented as a single computing cluster or multiple computing machines that can communicate via a communication connection. Thus, the electronic device 700 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

[0100] Input device 750 may be one or more input devices, such as a mouse, keyboard, or trackball. Output device 760 may be one or more output devices, such as a display, a speaker, or a printer. Electronic device 700 may also communicate with one or more external devices (not shown) via communication unit 740 as needed, such as storage devices, display devices, or the like, with one or more devices that allow a user to interact with electronic device 700, or with any device that allows electronic device 700 to communicate with one or more other electronic devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0101] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein the computer-executable instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above.

[0102] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0103] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, such that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0104] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0105] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple implementations of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part for a module, program segment or instruction, and a part for a module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0106] While various implementations of the present disclosure have been described above, the foregoing description is intended to be illustrative, not exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is selected to best explain the principles of the implementations, their practical applications, or improvements to existing technologies, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A method for processing Internet Protocol (IP) quality data in a content delivery network (CDN), comprising: Collecting a plurality of data samples from at least one data source associated with the CDN, each data sample including an IP address of a client transmitting data in the CDN, an IP address of a service node, and transmission performance data associated with the data transmission; At least one IP address segment determined from the plurality of data samples, each IP address segment corresponding to an IP address range of a service node in the CDN; For each IP address segment of the at least one IP address segment, selecting a group of data samples related to the IP address segment from the plurality of data samples; as well as For each IP address segment in the at least one IP address segment, a quality value of the IP address segment on multiple quality indicators is determined based on transmission performance data included in a group of data samples related to the IP address segment.

2. The method according to claim 1, wherein the at least one data source comprises at least one of the following: Actual running data on at least one client in the CDN, dialing data on at least one client in the CDN, Operational data on at least one service node in the CDN, Dial test data on at least one service node in the CDN.

3. The method according to claim 1, further comprising: Determine the target IP address segment to be evaluated in the CDN; Generate a dial test task for the target IP address segment; as well as Controlling execution of the dial test task for the target IP address segment in the CDN, and The collecting of the plurality of data samples comprises: Collecting dialing test data related to the dialing test task from at least one client or at least one service node in the CDN; as well as At least one data sample is obtained from the dialing data.

4. The method according to claim 1, further comprising: Based on the quality values ​​of the IP address segment on multiple quality indicators, an IP segment quality table for the CDN is generated, wherein the IP segment quality table includes at least one data entry, each data entry includes at least an IP address segment, data corresponding to at least one data dimension associated with the IP address segment, and the quality values ​​of the IP address segment on the multiple quality indicators.

5. The method of claim 4 , wherein in each data entry, the at least one data dimension associated with the IP address segment comprises at least one of the following: The service node information corresponding to the IP address segment, Client information corresponding to the IP address segment, The service type corresponding to the IP address segment, The data source for determining the quality value of the IP address segment on the multiple quality indicators, or The network information corresponding to the IP address segment.

6. The method according to claim 1, further comprising: In response to a query request from a client in the CDN, an IP address segment for processing the query request is selected from the at least one IP address segment based on the at least one IP address segment and the quality value of each IP address segment corresponding to the multiple quality indicators.

7. The method according to claim 1, further comprising: Determining a first IP address segment having quality abnormality in the at least one IP address segment based on the at least one IP address segment and the quality value of each IP address segment corresponding to the multiple quality indicators; as well as Provide an abnormal alarm for the first IP address segment.

8. An IP quality data processing device for CDN, comprising: a collection module configured to collect a plurality of data samples from at least one data source related to the CDN, each data sample including an IP address of a client transmitting data in the CDN, an IP address of a service node, and transmission performance data related to the data transmission; an execution module configured to determine at least one IP address segment from the plurality of data samples, each IP address segment corresponding to an IP address range of a service node in the CDN; A selection module is configured to select, for each IP address segment of the at least one IP address segment, a group of data samples related to the IP address segment from the plurality of data samples; as well as The determination module is configured to determine, for each IP address segment in the at least one IP address segment, a quality value of the IP address segment on multiple quality indicators based on transmission performance data included in a group of data samples related to the IP address segment.

9. An electronic device comprising: at least one processing unit; as well as At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 7 when executed by the at least one processing unit.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program can be executed by a processor to implement the method according to any one of claims 1 to 7.

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