Edge node management method and device and computing equipment

Abstract

The invention discloses an edge node management method and apparatus, and a computing device. The method comprises the steps of receiving playing session associated data uploaded by a playing end; the play session association data comprises session quality data and a scheduling edge node identifier; aggregating playing session associated data received in a preset time period according to the edge nodes to obtain session quality data corresponding to each edge node; according to the session quality data corresponding to each edge node, determining a session loading quality index of each edge node and recording the session loading quality index; and if it is judged that an offline triggering condition is met according to the recorded session loading quality index of any edge node, performing offline processing on the edge node. Through the above mode, automatic positioning and offline of the problem edge node are realized, a process depending on manual decision and operation can be converted into a process of system automatic analysis and operation, a playing end can be prevented from pulling stream from the problem edge node, and the playing quality of the playing end is ensured.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to an edge node management method, apparatus, computing device, computer storage medium, and computer program product. Background Art

[0002] With the rapid development of computer technology and Internet technology, the live broadcast industry has developed rapidly. Common live broadcast systems are based on content distribution networks. The live stream pushed by the anchor end is provided to the playback end through the content distribution network. The playback end accesses the edge node through the content distribution network to pull the stream. The quality of the edge node directly affects the streaming effect of the playback end.

[0003] In the prior art, if edge nodes in a content distribution network have quality problems, they need to be manually discovered and removed. However, manual processing has some obvious limitations, including long response time, low efficiency, and high error rate. Summary of the invention

[0004] In view of the above problems, the present application is proposed to provide an edge node management method, apparatus, computing device, computer storage medium and computer program product that overcome the above problems or at least partially solve the above problems.

[0005] According to one aspect of the present application, an edge node management method is provided, including:

[0006] Receive playback session associated data uploaded by the playback end; wherein the playback session associated data includes session quality data and a scheduling edge node identifier;

[0007] Aggregate the playback session related data received within a preset period according to the edge nodes to obtain the session quality data corresponding to each edge node;

[0008] According to the session quality data corresponding to each edge node, determine the session load quality index of each edge node and record it;

[0009] If the recorded session loading quality index of any edge node determines that the offline triggering condition is met, the edge node is offlined.

[0010] Optionally, the session quality data includes: the playback duration of the playback session, the service layer callback duration, and the time consumed in rendering the first frame; and the session loading quality indicator includes: the playback session loading failure rate.

[0011] Optionally, the preset time period includes: a historical time period corresponding to the detection time; and aggregating the playback session-related data received within the preset time period according to the edge nodes further includes:

[0012] When the detection time is reached, the playback session-related data received in the corresponding historical period is aggregated according to the edge nodes.

[0013] Optionally, the method further includes: dividing the detection time into multiple detection times according to preset time lengths.

[0014] Optionally, the method further comprises: monitoring whether there are multiple consecutive playback session loading failure rates exceeding a failure rate threshold in the recorded playback session loading failure rates of the edge node;

[0015] If so, it is determined that the offline trigger condition is met; if not, it is determined that the offline trigger condition is not met.

[0016] Optionally, after the edge node is offlined, the method further includes:

[0017] When it is monitored that the offline duration of the edge node reaches the offline duration threshold, the edge node is brought online.

[0018] Optionally, the method further comprises:

[0019] Real-time monitoring of the percentage of offline edge nodes in the content distribution network;

[0020] The offline processing of the edge node further includes:

[0021] If the proportion of offline edge nodes does not exceed the first proportion threshold, the edge node is offlined.

[0022] Optionally, the method further comprises:

[0023] If the proportion of offline edge nodes exceeds the second proportion threshold, a first alarm notification is triggered;

[0024] The number of times the edge nodes of the content distribution network are offline is monitored in real time. If the number of times the edge nodes are offline exceeds the threshold, the second alarm notification is triggered.

[0025] According to another aspect of the present application, an edge node management device is provided, including:

[0026] A receiving module, configured to receive playback session associated data uploaded by a playback terminal; wherein the playback session associated data includes session quality data and a scheduling edge node identifier;

[0027] An aggregation module, used to aggregate the playback session-related data received within a preset period according to the edge nodes to obtain the session quality data corresponding to each edge node;

[0028] An analysis module, configured to determine and record a session loading quality index of each edge node according to the session quality data corresponding to each edge node;

[0029] The execution module is used to perform offline processing on any edge node if it is determined that the offline triggering condition is met according to the recorded session loading quality index of any edge node.

[0030] Optionally, the session quality data includes: playback duration of the playback session, business layer callback duration, and time consumed in rendering the first frame; the session loading quality indicator includes: playback session loading failure rate.

[0031] Optionally, the preset time period includes: a historical time period corresponding to the detection time;

[0032] The aggregation module is further used to: when the detection time is reached, aggregate the playback session related data received in the corresponding historical period according to the edge nodes.

[0033] Optionally, the device further includes: a division module, used to divide the detection time into multiple detection times according to preset time lengths.

[0034] Optionally, the device further comprises:

[0035] The first monitoring module is used to monitor whether there are multiple consecutive playback session loading failure rates exceeding the failure rate threshold in the recorded playback session loading failure rates of the edge node; if so, it is determined that the offline trigger condition is met; if not, it is determined that the offline trigger condition is not met.

[0036] Optionally, the execution module is further used for: after offline processing is performed on the edge node, when it is monitored that the offline duration of the edge node reaches an offline duration threshold, performing online processing on the edge node.

[0037] Optionally, the device further comprises:

[0038] The second monitoring module is used to monitor in real time the proportion of offline edge nodes in the content distribution network;

[0039] The execution module is further used for: if it is determined that the offline trigger condition is met according to the recorded session loading quality index of any edge node, and the proportion of offline edge nodes does not exceed the first proportion threshold, offline processing is performed on the edge node.

[0040] Optionally, the device further comprises:

[0041] An alarm module, configured to trigger a first alarm notification if the proportion of offline edge nodes exceeds a second proportion threshold;

[0042] The second monitoring module is further used to: monitor the number of times edge nodes of the content distribution network go offline in real time;

[0043] The alarm module is further used to: trigger a second alarm notification if the number of times the edge node goes offline exceeds a threshold.

[0044] According to another aspect of the present application, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0045] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned edge node management method.

[0046] According to another aspect of the present application, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to perform operations corresponding to the above-mentioned edge node management method.

[0047] According to another aspect of the present application, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables a processor to perform operations corresponding to the above-mentioned edge node management method.

[0048] According to the edge node management method, device, computing device, computer storage medium and computer program product provided by the embodiment of the present application, the method includes: receiving the playback session associated data uploaded by the playback end; wherein the playback session associated data includes session quality data and scheduling edge node identification; aggregating the playback session associated data received within a preset period according to the edge node to obtain the session quality data corresponding to each edge node; determining and recording the session loading quality index of each edge node according to the session quality data corresponding to each edge node; if it is determined that the offline trigger condition is met according to the recorded session loading quality index of any edge node, the edge node is offline. Through the method of the embodiment of the present application, the playback end actively reports the session quality data, analyzes the playback quality of the edge node based on the session quality data actively reported by the playback end, and offlines the edge node with poor playback quality, thereby realizing the automatic positioning and offline of the problem edge node, and can convert the process that depends on manual decision-making and operation into the process of automatic analysis and operation of the system, and can also avoid the playback end from pulling the stream from the problem edge node, thereby ensuring the playback quality of the playback end.

[0049] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0051] Figure 1 A flow chart of an edge node management method provided by an embodiment of the present application is shown;

[0052] Figure 2 A flow chart of an edge node management method provided by another embodiment of the present application is shown;

[0053] Figure 3 A schematic diagram showing the detection time and historical period provided by an embodiment of the present application is shown;

[0054] Figure 4 A schematic diagram showing a system architecture provided by another embodiment of the present application is shown;

[0055] Figure 5 A functional structure diagram of an edge node management device provided in an embodiment of the present application is shown;

[0056] Figure 6 A schematic diagram of the structure of a computing device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0057] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0058] First, the terms involved in one or more embodiments of the present application are explained.

[0059] Content delivery network: content delivery network, abbreviated as CDN, is a distributed network designed to improve the access speed and availability of content by caching content to edge nodes in various locations.

[0060] Edge node: A terminal server in a CDN network, located between users and source servers, responsible for caching and distributing content to ensure that users can quickly and securely access the required resources.

[0061] Playback session: refers to all interactive activities of a user from the time they start playing media content to the time they stop playing it.

[0062] Session quality data: data used to characterize the quality of playback sessions.

[0063] Figure 1 FIG. 1 is a flow chart showing an edge node management method provided by an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0064] Step S110: receiving the playback session associated data uploaded by the playback end. The playback session associated data includes the session quality data and the scheduling edge node identifier.

[0065] Among them, the playback request of the playback end is scheduled to the edge node, and the scheduling edge node returns the live stream requested by the playback end to the playback end. The playback end receives the live stream and renders the live screen. The scheduling edge node corresponding to the playback end is the edge node from which the playback end pulls the live stream.

[0066] Each playback terminal actively reports the associated data of this playback session. The playback session associated data includes session quality data used to characterize the playback session quality and a scheduling edge node identifier used to identify the scheduling edge node corresponding to the playback terminal. The edge node identifier can be an edge node IP.

[0067] Step S120 , aggregating the playback session-related data received within a preset period according to the edge nodes to obtain session quality data corresponding to each edge node.

[0068] The number of preset time periods may be one or more, and the number, start time, end time, and duration of the preset time periods may be set according to actual business needs.

[0069] The playback session-related data uploaded by each playback terminal are aggregated and analyzed at a specified time. Specifically, all the playback session-related data received within a preset period are aggregated according to the edge node identifier to obtain all the session quality data corresponding to the same edge node identifier, that is, the session quality data corresponding to the edge node is obtained.

[0070] Step S130: determining and recording the session loading quality index of each edge node according to the session quality data corresponding to each edge node.

[0071] For each edge node corresponding to the playback session associated data received within a preset time period, the session quality data corresponding to the edge node is analyzed, the session loading quality index of the edge node is determined, and then the edge node identifier is associated with the session loading quality index for recording.

[0072] Step S140: If it is determined that the offline triggering condition is met according to the recorded session loading quality index of any edge node, the edge node is offlined.

[0073] The playback quality of the edge node is judged according to the recorded session loading quality index of the edge node. If the playback quality of the edge node is poor, it is determined that the offline trigger condition is met and the edge node is offline. After the offline, the edge node does not participate in scheduling.

[0074] In an optional manner, when the session quality data corresponding to the edge node is received only in one preset time period, the analyzed session load quality index can be compared with the preset index threshold for the edge node, and whether the offline trigger condition is met is determined according to the comparison result. When the session quality data corresponding to the edge node is received in multiple preset time periods, the session load quality index determined based on the session quality data in multiple preset time periods is used to determine whether the offline trigger condition is met for the edge node, for example, whether the offline trigger condition is met is determined according to the average value or change trend of multiple session load quality indicators.

[0075] In summary, according to the edge node management method provided by this embodiment, the playback session associated data uploaded by the playback end is received, so as to obtain the playback session associated data actively reported by each playback end for unified analysis; the playback session associated data received within the preset time period is aggregated according to the edge node, so as to obtain the session quality data corresponding to each edge node, so as to analyze the quality of the edge node; according to the session quality data corresponding to each edge node, the session loading quality index of each edge node is determined and recorded, and then according to the recorded session loading quality index of the edge node, it is judged whether the offline trigger condition is met, and the edge node is automatically offline when the offline trigger condition is met. Through the above method, the playback end actively reports the session quality data, analyzes the playback quality of the edge node according to the session quality data actively reported by the playback end, and offlines the edge node with poor playback quality, so as to realize the automatic positioning and offline of the problem edge node, and can convert the process relying on manual decision-making and operation into the process of automatic analysis and operation of the system, and can also avoid the playback end from pulling the stream from the problem edge node, thereby ensuring the playback quality of the playback end.

[0076] Figure 2 FIG. 2 is a flow chart showing an edge node management method provided by another embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0077] Step S210: receiving playback session associated data uploaded by the playback end.

[0078] Among them, the playback request of the playback end is scheduled to the edge node, and the scheduling edge node returns the live stream requested by the playback end to the playback end. The playback end receives the live stream and renders the live screen. The scheduling edge node corresponding to the playback end is the edge node from which the playback end pulls the live stream.

[0079] Each playback terminal actively reports the associated data of the playback session. The playback session associated data includes session quality data for characterizing the quality of the playback session and a scheduling edge node identifier for identifying the scheduling edge node corresponding to the playback terminal.

[0080] In an optional manner, the playback session associated data uploaded by the playback end is stored in a database table, and the data is subsequently read from the database table for analysis.

[0081] Step S220, when the detection time arrives, the playback session associated data received in the corresponding historical period is aggregated according to the edge nodes to obtain the session quality data corresponding to each edge node; according to the session quality data corresponding to each edge node, the session loading quality index of each edge node is determined and recorded.

[0082] In the embodiment of the present application, the preset time period specifically includes a historical time period corresponding to the detection time. When the detection time is reached, all playback session related data received in the corresponding historical time period is obtained from the database table for analysis.

[0083] In an optional manner, multiple detection times are divided according to a preset duration, and the intervals between any two adjacent detection times are all preset durations, which is equivalent to performing aggregation of playback session-related data and quality analysis of edge nodes once every preset duration. Preferably, the preset duration is less than or equal to the duration of the historical period. By setting the interval between detection times to be less than or equal to the duration of the historical period, it is possible to avoid missing playback session-related data and improve the accuracy of edge node quality assessment.

[0084] In the embodiment of the present application, the session loading quality index is specifically: the playback session loading failure rate, that is, the ratio of the number of playback sessions that failed to load to the total number of playback sessions. Determine whether the playback session has failed to load based on the session quality data, and the session quality data specifically includes: the playback time of the playback session, the business layer callback time, and the time taken to render the first frame. Specifically, the judgment rule for playback session loading failure is as follows: the difference between the playback time of the playback session and the business layer callback time is greater than the specified time, and the time taken to render the first frame is zero, indicating that the time taken to load the first frame of the video excluding the influence of the business layer exceeds the specified time, and the playback session loading is judged to have failed.

[0085] Figure 3A schematic diagram of detection time and historical time periods provided by an embodiment of the present application is shown. The horizontal line in the figure represents the time axis. Multiple detection times are evenly divided according to the preset duration. The detection time is used as the end time of the corresponding historical time period, and the duration of the historical time period corresponding to each detection time is consistent. As shown in the figure, the historical time period corresponding to the detection time T1 is time period τ1, the historical time period corresponding to the detection time T2 is time period τ2, and the historical time period corresponding to the detection time T3 is time period τ3. As can be seen from the figure, configuring the interval duration of adjacent detection times to be less than or equal to the duration of the historical time period can avoid the omission of playback session associated data.

[0086] It should be noted that, in addition to the method of evenly dividing the detection time, the detection times with inconsistent time intervals can also be divided. For example, when the service is busy, the detection time is divided by a first preset time length, and when the service is idle, the detection time is divided by a second preset time length, and the first preset time length is different from the second preset time length. Alternatively, the detection time can also be set by receiving a detection time configuration request from the business party, thereby supporting the business party to customize the detection time. Accordingly, the start and end time and duration of the historical period can also be set according to actual business needs.

[0087] In an optional manner, a time series database is used to record the playback session loading failure rate analyzed by the edge node at each detection time, that is, the detection time, edge node identifier, and playback session loading failure rate are associated and recorded in the time series database. For example, a prometheus database is used to record the playback session loading failure rate of the edge node.

[0088] Step S230 , monitoring whether there are multiple consecutive playback session loading failure rates exceeding a failure rate threshold in the recorded playback session loading failure rates of the edge node.

[0089] In the case of multiple detection times, the quality analysis and recording of edge nodes will be performed multiple times. For the same edge node, the playback session loading failure rate may be recorded multiple times. In the embodiment of the present application, the edge node is monitored to see whether N playback session loading failure rates exceeding the failure rate threshold are continuously recorded; if so, it is determined that the offline trigger condition is met and step S240 is executed; if not, it is determined that the offline trigger condition is not met. Among them, N is greater than 1, and the specific value of N and the failure rate threshold can be set according to actual needs.

[0090] For example, when the detection time T1 is reached, all the playback session related data received in the time period τ1 are aggregated by edge nodes to obtain the session quality data corresponding to each edge node, and the playback session loading failure rate is calculated for each edge node, that is, the ratio of the number of playback sessions that failed to load in the time period τ1 to the total number of playback sessions. The recorded data is as follows: {P1-6%, P2-4%, ..., Pn-1%}, where Pi is the edge node identifier and the percentage is the playback session loading failure rate; when the detection time T2 is reached, all the playback session related data received in the time period τ2 are aggregated by edge nodes to obtain the session quality data corresponding to each edge node. The associated data is aggregated by edge nodes to obtain the session quality data corresponding to each edge node. The playback session loading failure rate is calculated for each edge node. The recorded data are as follows: {P1-7%, P3-4%..., Pn-2%}; when the detection time T3 is reached, all the playback session associated data received in the time period τ3 are aggregated by edge nodes to obtain the session quality data corresponding to each edge node. The playback session loading failure rate is calculated for these edge nodes. The recorded data are as follows: {P1-6.5%, P2-2%..., Pn-1%}. Assuming that the value of N is 3 and the failure rate threshold is 5%, it can be seen from the above data records that the playback session loading failure rate of the edge node P1 for three consecutive times exceeds 5%, and it is determined that the offline trigger condition is met, and the edge node P1 is offline.

[0091] Step S240: offline the edge node.

[0092] If the offline trigger condition is met according to the recorded playback session loading failure rate of the edge node, the edge node is removed from the load balancer.

[0093] In an optional manner, after the edge node is offlined, an offline alarm notification is triggered, which carries the edge node identifier and is used to inform the operation and maintenance personnel that the edge node has been offline.

[0094] Step S250: When it is monitored that the offline duration of the edge node reaches the offline duration threshold, the edge node is brought online.

[0095] When the offline duration of an edge node reaches the offline duration threshold, the offline edge node is brought online and participates in scheduling to avoid the edge node being offline for too long, which leads to reduced resource utilization of the content distribution network.

[0096] In an optional manner, the method further includes: monitoring the proportion of offline edge nodes in the content distribution network in real time, and determining whether to stop offline operations on edge nodes of the content distribution network or trigger relevant alarms according to the proportion of offline edge nodes. The specific implementation method is as follows:

[0097] On the one hand, if the recorded session load quality index of any edge node determines that the offline trigger condition is met, and the proportion of offline edge nodes does not exceed the first proportion threshold, the edge node is offline. Among them, the first proportion threshold is used as a judgment standard for whether to stop the offline operation of the edge nodes of the content distribution network. The edge nodes that meet the offline trigger condition can be offline only when the proportion of offline edge nodes is less than or equal to the first proportion threshold. On the contrary, if the proportion of offline edge nodes is greater than the first proportion threshold, at this time, even if the recorded session load quality index of an edge node meets the offline trigger condition, the offline operation of the edge node will not be performed, so as to prevent too many edge nodes from going offline and causing a decrease in resource utilization.

[0098] On the other hand, if the proportion of offline edge nodes exceeds the second proportion threshold, the first alarm notification is triggered. The second proportion threshold, as the judgment standard for initiating an alarm, is less than the first proportion threshold. By triggering the alarm notification, the proportion of offline edge nodes in the content distribution network is fed back to the operation and maintenance personnel in a timely manner.

[0099] In another optional manner, the method further includes: real-time monitoring of the number of times the edge node of the content distribution network is offline, and if the number of times the edge node is offline exceeds a threshold, triggering a second alarm notification. By triggering the alarm notification, the number of times the edge node in the content distribution network is offline is promptly fed back to the operation and maintenance personnel.

[0100] In summary, according to the edge node management method provided by the present embodiment, the playback session associated data actively reported by the playback end in each preset time period is aggregated to obtain the session quality data corresponding to the edge node in each preset time period, and then the playback session loading failure rate of the edge node is analyzed and recorded, and the quality of the edge node is determined according to the multiple playback session loading failure rates recorded for the edge node, and the edge node with poor playback quality is taken offline, thereby realizing automatic positioning and offline of the problem edge node, and being able to convert the process that depends on manual decision-making and operation into the process of automatic analysis and operation of the system, and also being able to avoid the playback end from pulling the stream from the problem edge node, thereby ensuring the playback quality of the playback end; further, the playback end actively reports the session quality data, and the loading failure is analyzed according to the session quality data. The proportion of playback sessions can conveniently obtain indicators for evaluating the quality of edge nodes; further, combined with the recorded loading failure rate of multiple playback sessions of the edge node, it can more accurately evaluate whether the edge node meets the offline triggering condition; further, when the proportion of offline edge nodes in the content distribution network exceeds a certain threshold, the offline operation of the edge nodes of the content distribution network is stopped to avoid too many offline edge nodes leading to low resource utilization; further, the edge nodes are automatically restored after being offline for a period of time to ensure the resource utilization of the content distribution network; further, when the number of offline edge nodes in the content distribution network and the proportion of offline edge nodes exceed the corresponding threshold, the offline situation of edge nodes in the content distribution network is promptly fed back to the operation and maintenance personnel by triggering an alarm notification.

[0101] Figure 4 A schematic diagram of a system architecture provided by another embodiment of the present application is shown below. Figure 4The system architecture shown illustrates the edge node management method of the embodiment of the present application. The user uses the live client 1 to watch the live broadcast. The live client 1 reports data to the first database 2. The data uploaded by the live client 1 includes the playback time of the live session, the service layer callback time, the time taken to render the first frame, and the scheduling edge and node IP. The central service obtains the client data reported in the last 10 minutes from the first database 2 every minute, aggregates according to the edge node IP, obtains the session quality data corresponding to each edge node, and then analyzes and obtains the playback session loading failure rate of each edge node. The playback session loading failure rate obtained by the analysis is stored in the second database 3 as the playback session loading failure rate of the edge node in the current minute. According to the playback session loading failure rate of the edge node stored in the second database 3, it is determined whether the edge node has a playback session loading failure rate of more than 5% for 10 consecutive minutes. If so, it is further determined whether the proportion of offline edge nodes in the CDN exceeds 20%. If the proportion of offline edge nodes does not exceed 20%, the edge node is offline for one hour. If the proportion of offline edge nodes exceeds 20%, the edge node is not offline. In addition, once it is determined that the proportion of offline edge nodes in the CDN exceeds 2% or the number of offline edge nodes of the CDN exceeds 20 times, a corresponding alarm is sent to the operation and maintenance terminal.

[0102] Figure 5 A functional structure diagram of an edge node management device provided in an embodiment of the present application is shown.

[0103] like Figure 5 As shown, the device comprises:

[0104] The receiving module 51 is used to receive the playback session associated data uploaded by the playback end; wherein the playback session associated data includes session quality data and a scheduling edge node identifier;

[0105] Aggregation module 52, used to aggregate the playback session related data received within a preset period according to edge nodes to obtain session quality data corresponding to each edge node;

[0106] An analysis module 53, configured to determine and record a session loading quality index of each edge node according to the session quality data corresponding to each edge node;

[0107] The execution module 54 is used to perform offline processing on any edge node if it is determined that the offline triggering condition is met according to the recorded session loading quality index of any edge node.

[0108] In an optional manner, the session quality data includes: the playback duration of the playback session, the service layer callback duration, and the time consumed in rendering the first frame; the session loading quality index includes: the playback session loading failure rate.

[0109] In an optional manner, the preset time period includes: a historical time period corresponding to the detection time;

[0110] The aggregation module 52 is further configured to aggregate the playback session associated data received in the corresponding historical period according to the edge nodes when the detection time is reached.

[0111] In an optional manner, the device further includes: a division module, used to divide the detection time into multiple detection times according to preset time lengths.

[0112] In an optional manner, the device further comprises:

[0113] The first monitoring module is used to monitor whether there are multiple consecutive playback session loading failure rates exceeding the failure rate threshold in the recorded playback session loading failure rates of the edge node; if so, it is determined that the offline trigger condition is met; if not, it is determined that the offline trigger condition is not met.

[0114] In an optional manner, the execution module 54 is further configured to: after offline processing is performed on the edge node, when it is monitored that the offline duration of the edge node reaches an offline duration threshold, perform online processing on the edge node.

[0115] In an optional manner, the device further comprises:

[0116] The second monitoring module is used to monitor in real time the proportion of offline edge nodes in the content distribution network;

[0117] The execution module 54 is further used for: if it is determined that the offline trigger condition is met according to the recorded session loading quality index of any edge node, and the proportion of offline edge nodes does not exceed the first proportion threshold, offline processing is performed on the edge node.

[0118] In an optional manner, the device further comprises:

[0119] An alarm module, configured to trigger a first alarm notification if the proportion of offline edge nodes exceeds a second proportion threshold;

[0120] The second monitoring module is further used to: monitor the number of times edge nodes of the content distribution network go offline in real time;

[0121] The alarm module is further used to: trigger a second alarm notification if the number of times the edge node goes offline exceeds a threshold.

[0122] In summary, according to the edge node management device provided by this embodiment, the playback session associated data uploaded by the playback end is received, so as to obtain the playback session associated data actively reported by each playback end for unified analysis; the playback session associated data received within the preset time period is aggregated according to the edge node, so as to obtain the session quality data corresponding to each edge node, so as to analyze the quality of the edge node; according to the session quality data corresponding to each edge node, the session loading quality index of each edge node is determined and recorded, and then according to the recorded session loading quality index of the edge node, it is judged whether the offline trigger condition is met, and the edge node is automatically offline when the offline trigger condition is met. Through the above method, the playback end actively reports the session quality data, analyzes the playback quality of the edge node according to the session quality data actively reported by the playback end, and offlines the edge node with poor playback quality, thereby realizing the automatic positioning and offline of the problem edge node, and can convert the process that depends on manual decision-making and operation into the process of automatic analysis and operation of the system, and can also avoid the playback end from pulling the stream from the problem edge node, thereby ensuring the playback quality of the playback end.

[0123] An embodiment of the present application provides a non-volatile computer storage medium, which stores at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to perform operations corresponding to the edge node management method in any of the above method embodiments.

[0124] An embodiment of the present application provides a computer program product, which includes at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to perform operations corresponding to the edge node management method in any of the above method embodiments.

[0125] Figure 6 A schematic diagram of the structure of a computing device provided in an embodiment of the present application is shown, and the specific embodiment of the present application does not limit the specific implementation of the computing device.

[0126] like Figure 6 As shown, the computing device may include: a processor (processor) 602 , a communications interface (Communications Interface) 604 , a memory (memory) 606 , and a communication bus 608 .

[0127] The processor 602, the communication interface 604, and the memory 606 communicate with each other via a communication bus 608. The communication interface 604 is used to communicate with other devices such as a client or other server network elements. The processor 602 is used to execute a program 610, which can specifically execute the relevant steps in the above-mentioned edge node management method embodiment for a computing device.

[0128] Specifically, the program 610 may include program codes, which include computer operation instructions.

[0129] The processor 602 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0130] The memory 606 is used to store the program 610. The memory 606 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0131] The program 610 can be specifically used to enable the processor 602 to execute the edge node management method in any of the above method embodiments. The specific implementation of each step in the program 610 can refer to the corresponding description in the corresponding steps and units in the edge node management embodiment, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the aforementioned method embodiment, which will not be repeated here.

[0132] The algorithm or display provided here are not inherently related to any specific computer, virtual system or other equipment. Various general systems can also be used together with the teaching based on this. According to the above description, it is obvious to construct the structure required for this type of system. In addition, the present application embodiment is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present application described here, and the above description of specific languages ​​is to disclose the best mode of implementation of the present application.

[0133] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0134] Similarly, it should be understood that in order to streamline the present application and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following intention: the claimed application requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present application.

[0135] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0136] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims, any one of the claimed embodiments may be used in any combination.

[0137] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components according to the embodiments of the present application. The application can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for executing a part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0138] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be constructed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. An edge node management method, comprising: Receiving playback session associated data uploaded by the playback end; wherein the playback session associated data includes session quality data and a scheduling edge node identifier; Aggregate the playback session related data received within a preset period according to the edge nodes to obtain the session quality data corresponding to each edge node; Determine and record the session loading quality index of each edge node according to the session quality data corresponding to each edge node; If the recorded session loading quality index of any edge node determines that the offline triggering condition is met, the edge node is offlined.

2. The method according to claim 1, wherein: The session quality data includes: the playback duration of the playback session, the service layer callback duration, and the time consumed in rendering the first frame; the session loading quality index includes: the playback session loading failure rate.

3. The method according to claim 1 or 2, wherein: The preset time period includes: a historical time period corresponding to the detection time; and the aggregating the playback session associated data received within the preset time period according to the edge nodes further includes: When the detection time is reached, the playback session-related data received in the corresponding historical period is aggregated according to the edge nodes.

4. The method according to claim 3, wherein: The method further comprises: Divide the detection time into multiple times according to the preset duration.

5. The method according to claim 4, wherein: The method further comprises: Monitor whether there are multiple consecutive playback session loading failure rates exceeding the failure rate threshold in the recorded playback session loading failure rates of the edge node; If so, it is determined that the offline trigger condition is met; if not, it is determined that the offline trigger condition is not met.

6. The method according to any one of claims 1 to 5, wherein: After the edge node is offlined, the method further includes: When it is monitored that the offline duration of the edge node reaches the offline duration threshold, the edge node is brought online.

7. The method according to any one of claims 1 to 5, wherein: The method further comprises: Real-time monitoring of the percentage of offline edge nodes in the content distribution network; The offline processing of the edge node further includes: If the proportion of offline edge nodes does not exceed the first proportion threshold, the edge node is offlined.

8. The method according to claim 7, wherein: The method further comprises: If the proportion of offline edge nodes exceeds the second proportion threshold, a first alarm notification is triggered; The number of times the edge nodes of the content distribution network are offline is monitored in real time. If the number of times the edge nodes are offline exceeds the threshold, the second alarm notification is triggered.

9. An edge node management device, comprising: A receiving module, configured to receive playback session associated data uploaded by a playback terminal; wherein the playback session associated data includes session quality data and a scheduling edge node identifier; An aggregation module, used to aggregate the playback session-related data received within a preset period according to the edge nodes to obtain the session quality data corresponding to each edge node; An analysis module, configured to determine and record a session loading quality index of each edge node according to the session quality data corresponding to each edge node; The execution module is used to perform offline processing on any edge node if it is determined that the offline triggering condition is met according to the recorded session loading quality index of any edge node.

10. A computing device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the edge node management method according to any one of claims 1 to 8.

11. A computer storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to perform operations corresponding to the edge node management method according to any one of claims 1 to 8.

12. A computer program product, comprising at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the edge node management method according to any one of claims 1 to 8.

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