CN112954372A - Streaming media fault monitoring method and device - Google Patents
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- CN112954372A CN112954372A CN202110103914.0A CN202110103914A CN112954372A CN 112954372 A CN112954372 A CN 112954372A CN 202110103914 A CN202110103914 A CN 202110103914A CN 112954372 A CN112954372 A CN 112954372A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/21—Server components or server architectures
- H04N21/218—Source of audio or video content, e.g. local disk arrays
- H04N21/2187—Live feed
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0677—Localisation of faults
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/80—Responding to QoS
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/24—Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth, upstream requests
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
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Abstract
A method and a device for monitoring streaming media faults are provided. The streaming media fault monitoring method comprises the following steps: acquiring preset monitoring indexes of a current dimension in multiple dimensions in a streaming media transmission link, wherein the multiple dimensions are divided according to a minimum service entity and/or a minimum system dimension in the streaming media transmission link; detecting an abnormality in a streaming media transmission link based on a preset monitoring index; in response to the detection of the abnormality, acquiring a stream identifier of the stream media with the abnormal transmission; based on the flow identification, a dimension of a root cause of the anomaly is located. According to the streaming media fault monitoring method and device disclosed by the invention, the abnormal root cause can be positioned, the accuracy of fault detection is improved, and the fault can be conveniently and quickly eliminated.
Description
Technical Field
The present disclosure relates to the field of internet technology. More particularly, the present disclosure relates to a streaming media fault monitoring method and apparatus.
Background
Currently, in the live broadcast service process, the live broadcast end-to-end link has a long depth. When an abnormality occurs in a certain link in the upstream, various links in the downstream are often affected in series. Conventional consumer side alarms often do not have the ability to directly expose problem modules and problem points. How to provide low-delay and high-smoothness live broadcast service quality for large-scale users in a complex network scene, and ensure the watching experience of the users becomes a problem of key consideration of video service providers.
Disclosure of Invention
An exemplary embodiment of the present disclosure is to provide a streaming media fault monitoring method and apparatus, so as to solve at least the problem of streaming media fault monitoring in the related art, and may not solve any of the above problems.
According to an exemplary embodiment of the present disclosure, there is provided a streaming media failure monitoring method, including: acquiring preset monitoring indexes of a current dimension in multiple dimensions in a streaming media transmission link, wherein the multiple dimensions are divided according to a minimum service entity and/or a minimum system dimension in the streaming media transmission link; detecting an abnormality in a streaming media transmission link based on a preset monitoring index; in response to the detection of the abnormality, acquiring a stream identifier of the stream media with the abnormal transmission; based on the flow identification, a dimension of a root cause of the anomaly is located.
Optionally, the plurality of dimensions may be sorted based on a precedence order of occurrence of the service, where a dimension sorted in front is an upstream dimension of a dimension sorted in back.
Optionally, the step of locating the dimension of the root cause of the anomaly may comprise: aiming at the flow identification, acquiring a preset monitoring index of at least one upstream dimension of the current dimension; determining a dimension of a root cause of the anomaly based on a preset monitoring index of the at least one upstream dimension.
Optionally, the step of determining the dimension of the root cause of the anomaly based on the preset monitoring index of the at least one upstream dimension may comprise: determining whether an abnormality exists in the at least one upstream dimension based on a preset monitoring index of the at least one upstream dimension; and determining the dimension with the first abnormality in the at least one upstream dimension according to the dimension ordering as the dimension of the root source of the abnormality.
Optionally, the step of locating the dimension of the root cause of the anomaly may comprise: acquiring a preset monitoring index of a preset upstream dimension of the current dimension aiming at the flow identification; and determining the dimension of the source of the abnormality based on a preset monitoring index of a preset trip dimension.
Optionally, the step of determining the dimension of the root cause of the abnormality based on the preset monitoring index of the preset trip dimension may include: determining whether the preset upstream dimension is abnormal or not based on a preset monitoring index of the preset upstream dimension; and in response to the existence of the abnormity in the preset upstream dimension, determining the preset upstream dimension in which the abnormity exists as the dimension of the root of the abnormity.
Optionally, the multiple dimensions of the streaming media transmission link may include at least one of a stream pushing end, a source station stream receiving end, a source station SRS, a source station TSCODER, and a content distribution network end.
Optionally, the step of detecting an anomaly in the streaming media transmission link based on the preset monitoring indicator may include: comparing a preset monitoring index with a predetermined alarm threshold value; and determining whether the streaming media transmission link has abnormality according to the comparison result.
Optionally, the streaming media failure monitoring method may further include: acquiring historical transmission data of each dimension of the streaming media transmission link; historical data of a preset monitoring index of the streaming media transmission link is calculated based on historical transmission data; and determining an alarm threshold value of the preset monitoring index based on the historical data of the preset monitoring index.
Optionally, the preset monitoring index may include at least one of scale data, abnormal state code data, and abnormal event data.
Optionally, the exception may include an exception to at least one of scale data, exception status code data, exception event data.
Optionally, the size data may include at least one of a number of stream pushing rooms, a number of source station stream receiving cluster rooms, an SRS stream pushing number, and an SRS stream pulling disconnection number, the abnormal state code data may include at least one of a stream pushing client error rate, a source station stream receiving cluster error rate, and an SRS error rate, and the abnormal event data may include at least one of a stream pushing degradation rate, a stream pushing stuck rate, a source station stream receiving cluster packet loss rate, a buffer pile-up condition, and a source return stuck rate.
According to an exemplary embodiment of the present disclosure, there is provided a streaming media failure monitoring apparatus including: the data acquisition unit is configured to acquire preset monitoring indexes of a current dimension in multiple dimensions in a streaming media transmission link, wherein the multiple dimensions are divided according to a minimum service entity in the streaming media transmission link; the anomaly detection unit is configured to detect anomalies in the streaming media transmission link based on preset monitoring indexes; a stream identifier acquiring unit configured to acquire, in response to detection of the abnormality, a stream identifier of the streaming media in which the transmission abnormality occurs; and a root cause locating unit configured to locate a dimension of a root cause of the anomaly based on the flow identification.
Optionally, the plurality of dimensions may be sorted based on a precedence order of occurrence of the service, where a dimension sorted in front is an upstream dimension of a dimension sorted in back.
Optionally, the root cause locating unit may be configured to: aiming at the flow identification, acquiring a preset monitoring index of at least one upstream dimension of the current dimension; determining a dimension of a root cause of the anomaly based on a preset monitoring index of the at least one upstream dimension.
Optionally, the root cause locating unit may be configured to: determining whether an abnormality exists in the at least one upstream dimension based on a preset monitoring index of the at least one upstream dimension; and determining the dimension with the first abnormality in the at least one upstream dimension according to the dimension ordering as the dimension of the root source of the abnormality.
Optionally, the root cause locating unit may be configured to: acquiring a preset monitoring index of a preset upstream dimension of the current dimension aiming at the flow identification; and determining the dimension of the source of the abnormality based on a preset monitoring index of a preset trip dimension.
Optionally, the root cause locating unit may be configured to: determining whether the preset upstream dimension is abnormal or not based on a preset monitoring index of the preset upstream dimension; and in response to the existence of the abnormity in the preset upstream dimension, determining the preset upstream dimension in which the abnormity exists as the dimension of the root of the abnormity.
Optionally, the multiple dimensions of the streaming media transmission link may include at least one of a stream pushing end, a source station stream receiving end, a source station SRS, a source station TSCODER, and a content distribution network end.
Alternatively, the abnormality detection unit may be configured to: comparing a preset monitoring index with a predetermined alarm threshold value; and determining whether the streaming media transmission link has abnormality according to the comparison result.
Optionally, the streaming media failure monitoring apparatus may further include a threshold determination unit configured to: acquiring historical transmission data of each dimension of the streaming media transmission link; historical data of a preset monitoring index of the streaming media transmission link is calculated based on historical transmission data; and determining an alarm threshold value of the preset monitoring index based on the historical data of the preset monitoring index.
Optionally, the preset monitoring index may include at least one of scale data, abnormal state code data, and abnormal event data.
Optionally, the exception may include an exception to at least one of scale data, exception status code data, exception event data.
Optionally, the size data may include at least one of a number of stream pushing rooms, a number of source station stream receiving cluster rooms, an SRS stream pushing number, and an SRS stream pulling disconnection number, the abnormal state code data includes at least one of a stream pushing client error rate, a source station stream receiving cluster error rate, and an SRS error rate, and the abnormal event data includes at least one of a stream pushing degradation rate, a stream pushing stuck rate, a source station stream receiving cluster packet loss rate, a buffer pile-up condition, and a source back stuck rate.
According to an exemplary embodiment of the present disclosure, there is provided an electronic apparatus including: a processor; a memory for storing the processor executable instructions, the processor configured to execute the instructions to implement a streaming media fault monitoring method according to an exemplary embodiment of the disclosure.
According to an exemplary embodiment of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a streaming media failure monitoring method according to an exemplary embodiment of the present disclosure.
According to an exemplary embodiment of the present disclosure, a computer program product is provided, in which instructions are executable by a processor of a computer device to perform a streaming media failure monitoring method according to an exemplary embodiment of the present disclosure.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:
and the method can quickly sense abnormality on a live end-to-end full service link, and directly send out an alarm aiming at an abnormal module and a problem point, so that the problem positioning time is directly saved, and the interference and loss stopping can be directly carried out aiming at alarm information.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure and are not to be construed as limiting the disclosure.
FIG. 1 shows a schematic diagram of an implementation environment in accordance with an example embodiment of the present disclosure.
Fig. 2 shows a flowchart of a streaming media failure monitoring method according to an exemplary embodiment of the present disclosure.
Fig. 3 illustrates a historical state curve showing preset monitoring metrics according to an exemplary embodiment of the present disclosure.
Fig. 4 shows a block diagram of a streaming media failure monitoring apparatus according to an exemplary embodiment of the present disclosure.
Fig. 5 is a block diagram of an electronic device 500 according to an example embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present disclosure better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. The embodiments described in the following examples do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
In this case, the expression "at least one of the items" in the present disclosure means a case where three types of parallel expressions "any one of the items", "a combination of any plural ones of the items", and "the entirety of the items" are included. For example, "include at least one of a and B" includes the following three cases in parallel: (1) comprises A; (2) comprises B; (3) including a and B. For another example, "at least one of the first step and the second step is performed", which means that the following three cases are juxtaposed: (1) executing the step one; (2) executing the step two; (3) and executing the step one and the step two.
Because the depth of a live link is long, when an upstream link of a Content Delivery Network (CDN) is abnormal, the problem point is often not perceived as soon as the original simple attribution capability. Problem troubleshooting needs to be traced step by step, and then a lot of time is consumed when evasive actions are made.
Hereinafter, a streaming media failure monitoring method and apparatus according to an exemplary embodiment of the present disclosure will be described in detail with reference to fig. 1 to 5.
FIG. 1 shows a schematic diagram of an implementation environment in accordance with an example embodiment of the present disclosure. Referring to fig. 1, the implementation environment includes a first terminal 101, a second terminal 102, a streaming server 103, and an electronic device 104.
The first terminal 101 and the second terminal 102 are respectively connected with the streaming media server 103 through a wireless or wired network. Also, clients for providing services by the streaming server 103 may be installed on the first terminal 101 and the second terminal 102. The users corresponding to the first terminal 101 and the second terminal 102 can implement functions such as data transmission, message interaction, and the like through the client. The client can be a short video client or a live client, etc.
The first terminal 101 may be a terminal that transmits streaming media; the second terminal 102 is a terminal that receives streaming media. For example, when the client is a live client, the first terminal 101 is a terminal used by a main broadcast; the second terminal 102 is a terminal used by a viewer.
It should be noted that, instead of installing a client on the first terminal 101 and/or the second terminal 102, the streaming media server 103 may be accessed through a web (network) end, so as to implement functions such as data transmission and message interaction.
In the exemplary embodiment of the present disclosure, by adding one electronic device 104, the electronic device 104 is connected to the first terminal 101, the second terminal 102 and the streaming media server 103 through a wired or wireless network, so that fault monitoring of the streaming media of the first terminal 101, the second terminal 102 and the streaming media server 103 is achieved through the electronic device 104.
The first terminal 101 and the second terminal 102 may be computers, mobile phones, tablet computers or other electronic devices. The streaming media server 103 may be a server, a server cluster composed of several servers, or a cloud computing service center. The electronic device 104 may be a terminal or a server; in the exemplary embodiment of the present disclosure, the electronic device 104 is not particularly limited.
Fig. 2 shows a flowchart of a streaming media failure monitoring method according to an exemplary embodiment of the present disclosure. The streaming media fault monitoring method in the present disclosure may be performed by an electronic device.
Referring to fig. 2, in step S201, a preset monitoring index of a current dimension of a plurality of dimensions in a streaming media transmission link is obtained. Here, the multiple dimensions are partitioned according to a minimum business entity and/or a minimum system dimension in a streaming media transport link (e.g., a live end-to-end service architecture) to guarantee service state awareness of the minimum granularity module. Table 1 shows an example of a plurality of dimensions according to an exemplary embodiment of the present disclosure.
TABLE 1
In an exemplary embodiment of the present disclosure, the multiple dimensions of the streaming media transmission link may include at least one of a stream pushing end, a source station stream receiving end, a source station SRS, a source station TSCODER, and a Content Delivery Network (CDN) end.
In an exemplary embodiment of the present disclosure, the plurality of dimensions are sorted based on the precedence order of the occurrence of the service, and the dimension sorted in the front is an upstream dimension of the dimension sorted in the back.
In an exemplary embodiment of the present disclosure, the preset monitoring index includes at least one of scale data, abnormal state code data, and abnormal event data. The size data refers to the real-time service size of each dimension, such as, but not limited to, the number of concurrent connections, the number of concurrent rooms, and the like. Status codes are the most basic and intuitive feature in the requests and responses of a program, and exception status codes are used to visually indicate exceptions to the status. The abnormal event data refers to the time counted by abnormal events and strategies which are not in accordance with the expectation, such as, but not limited to, a degradation strategy at the push flow end, a retry strategy, the hiton of each link or dimension, and the like. In one example, the preset monitoring metrics include scale data, abnormal state code data, and abnormal event data.
In an exemplary embodiment of the present disclosure, the size data includes at least one of a number of push flow rooms, a number of source station to stream cluster (MCU) rooms, a number of source station to stream cluster rooms, a number of SRS push flows, and a number of SRS pull disconnects. The abnormal state code data includes at least one of a push to stream client error rate, a source station to stream cluster error rate, and an SRS error rate.
The abnormal event data comprises at least one of a push flow degradation rate, a push flow blockage rate, a source station flow receiving cluster packet loss rate, a cache accumulation condition and a back source blockage rate.
Specifically, the real-time data reporting, collection and operation of the core information can be performed for each dimension in the streaming media transmission link, and preset monitoring indexes are obtained from the core information. For example, the core information of the plug-in terminal may include a province where the plug-in terminal is located, may further include a city where the plug-in terminal is located, and may further include a county and district where the plug-in terminal is located. In the embodiment of the present application, the position information of the push stream end includes the province of the push stream end. The core information of the push flow end may further include one or more quality parameters such as a stuck-in rate, a stuck-in duration, a network delay, a broadcast failure rate, a retry number, and the like of the push flow end. The core information of the CDN end may include a CDN manufacturer, a domain name, an Internet Service Provider (ISP), provinces, a platform, a version, and the like.
The real-time data of the core information can be respectively obtained from the first streaming media log, the second streaming media log and the third streaming media log by obtaining the first streaming media log (main play log) of the stream pushing end, the second streaming media log (audience log) of the stream receiving end and the third streaming media log (streaming media log) of the source station SRS.
In the embodiment of the application, the electronic device can acquire the multi-port data by acquiring the first streaming media log, the second streaming media log and the third streaming media log, so that the accuracy of subsequent monitoring can be improved.
The first, second, and third streaming media logs may include real-time logs and summary logs, respectively.
After the real-time data of the core information is acquired, the data can be collected, reported and summarized to obtain preset monitoring indexes such as scale data, abnormal state code data, abnormal event data and the like. In summary calculations, the data may be first cleaned.
Table 2 shows an example of preset monitoring metrics for each dimension according to an exemplary embodiment of the present disclosure.
TABLE 2
In step S202, an anomaly in the streaming media transmission link is detected based on a preset monitoring index.
In an exemplary embodiment of the present disclosure, when detecting an abnormality in the streaming media transmission link based on a preset monitoring index, the preset monitoring index may be first compared with a predetermined alarm threshold, and then it is determined whether there is an abnormality in the streaming media transmission link according to a comparison result.
In an exemplary embodiment of the present disclosure, historical transmission data of each dimension of the streaming media transmission link may also be obtained first, then historical data of a preset monitoring index of the streaming media transmission link is calculated based on the historical transmission data, and an alarm threshold of the preset monitoring index is determined based on the historical data of the preset monitoring index.
Fig. 3 illustrates a historical state curve showing preset monitoring metrics according to an exemplary embodiment of the present disclosure. As an example, when determining the alarm threshold of the preset monitoring index, the historical state curve shown in fig. 3 of each preset monitoring index may be drawn according to the historical data of the preset monitoring index; and then determining an alarm threshold value through a machine learning mode, a manual labeling mode and the like.
In an exemplary embodiment of the present disclosure, the exception includes an exception of at least one of scale data, exception status code data, and exception event data.
In step S203, in response to the detection of the abnormality, a stream identification of the streaming media in which the transmission abnormality occurs is acquired.
In an exemplary embodiment of the present disclosure, the preset monitoring indicators for each dimension may be concatenated based on the flow identification.
In step S204, the dimension of the source of the anomaly is located based on the stream identifier of the streaming media in which the anomaly occurred during transmission.
In an exemplary embodiment of the present disclosure, when the dimension of the root cause of the anomaly is located, a preset monitoring index of at least one upstream dimension of a current dimension may be first obtained for a stream identifier of a stream media in which the anomaly occurs, and then the dimension of the root cause of the anomaly may be determined based on the preset monitoring index of the at least one upstream dimension.
In an exemplary embodiment of the present disclosure, when determining the dimension of the root cause of the anomaly based on the preset monitoring index of the at least one upstream dimension, it may be determined whether there is an anomaly in the at least one upstream dimension based on the preset monitoring index of the at least one upstream dimension, and then the dimension in the at least one upstream dimension in which there is the anomaly first according to the dimension ordering is determined as the dimension of the root cause of the anomaly.
As an example, when there is no anomaly in the first dimension upstream of the current dimension, there is an anomaly in the second dimension, there is an anomaly in the third dimension, or there is no anomaly, the second dimension is determined as the dimension of the root cause of the anomaly.
In the exemplary embodiment of the present disclosure, when the dimension of the abnormal root cause is located, a preset monitoring index of a preset upstream dimension of a current dimension may be first obtained for a stream identifier of a stream media with an abnormal output, and then the dimension of the abnormal root cause is determined based on the preset monitoring index of the preset upstream dimension.
In an exemplary embodiment of the present disclosure, when determining the dimension of the root cause of the abnormality based on the preset monitoring index of the preset upstream dimension, it may be determined whether there is an abnormality in the preset upstream dimension based on the preset monitoring index of the preset upstream dimension, and then, in response to the presence of the abnormality in the preset upstream dimension, the preset upstream dimension in which the abnormality is present may be determined as the dimension of the root cause of the abnormality.
As an example, if the SRS jamming rate sudden increase in the streaming media transmission link is detected based on the preset monitoring index, a stream identifier contributing to the jamming is obtained, and each preset monitoring index of the source station stream receiving end dimension and the stream pushing end dimension is counted according to the stream identifier. If the preset monitoring index of the plug flow end dimension is abnormal, the root cause of the abnormality (such as plug flow stuck rate abnormality) is determined, and finally alarm information about the 'root cause of the abnormality' is given. And if the preset monitoring indexes of the dimension of the receiving end and the dimension of the pushing end of the source station are not abnormal, outputting alarm information about 'sudden increase of the SRS blockage rate'.
As an example, if the SRS has 3 flows to detect the stuck anomaly, the name of the 3 flows of the stuck anomaly is recorded first: path a, path B, and path C. Then, upstream detection is performed according to the plug flow architecture: firstly, detecting whether the A path flow, the B path flow and the C path flow report the blockage or the abnormity on a source station flow receiving cluster module, if the blockage or the abnormity is not reported, sending an SRS blockage alarm, and if the blockage or the abnormity is reported, continuously detecting whether the blockage or the abnormity is reported to an upstream pushing flow client; and if the plug flow client reports the jam or the abnormity, sending out a jam abnormity alarm of the plug flow client, and if the plug flow client does not report the jam or the abnormity, sending out an abnormity alarm of the source station flow receiving cluster.
The streaming media failure monitoring method according to the exemplary embodiment of the present disclosure has been described above with reference to fig. 1 to 3. Hereinafter, a streaming media failure monitoring apparatus and units thereof according to an exemplary embodiment of the present disclosure will be described with reference to fig. 4.
Fig. 4 shows a block diagram of a streaming media failure monitoring apparatus according to an exemplary embodiment of the present disclosure.
Referring to fig. 4, the streaming media failure monitoring apparatus includes a data acquisition unit 41, an abnormality detection unit 42, a stream identification acquisition unit 43, and a root cause localization unit 44.
The data obtaining unit 41 is configured to obtain a preset monitoring index of a current dimension of a plurality of dimensions in the streaming media transmission link, where the plurality of dimensions are divided according to a minimum service entity in the streaming media transmission link.
In an exemplary embodiment of the present disclosure, the plurality of dimensions of the streaming media transmission link may include at least one of a push end, a source station receive end, a source station SRS, a source station TSCODER, and a CDN end.
In an exemplary embodiment of the present disclosure, the plurality of dimensions may be sorted based on a precedence order of occurrence of the business, wherein a dimension sorted in front is an upstream dimension of a dimension sorted in back.
In an exemplary embodiment of the present disclosure, the preset monitoring index may include at least one of scale data, abnormal state code data, and abnormal event data. In one example, the preset monitoring metrics include scale data, abnormal state code data, and abnormal event data.
The anomaly detection unit 42 is configured to detect anomalies in the streaming media transmission link based on preset monitoring indicators.
In an exemplary embodiment of the present disclosure, the abnormality detection unit 42 may be configured to: comparing a preset monitoring index with a predetermined alarm threshold value; and determining whether the streaming media transmission link has abnormality according to the comparison result.
In an exemplary embodiment of the present disclosure, the streaming media failure monitoring apparatus may further include a threshold determining unit (not shown) configured to: acquiring historical transmission data of each dimension of a streaming media transmission link; historical data of preset monitoring indexes of the streaming media transmission link are calculated based on the historical transmission data; and determining an alarm threshold value of the preset monitoring index based on the historical data of the preset monitoring index.
In an exemplary embodiment of the present disclosure, the exception may include an exception of at least one of scale data, exception status code data, exception event data.
In an exemplary embodiment of the present disclosure, the size data includes at least one of a number of breakout rooms, a number of source station flow connection cluster rooms, a number of SRS breakout, and a number of SRS pull disconnection. The abnormal state code data includes at least one of a push to stream client error rate, a source station to stream cluster error rate, and an SRS error rate. The abnormal event data comprises at least one of a push flow degradation rate, a push flow blockage rate, a source station flow receiving cluster packet loss rate, a cache accumulation condition and a back source blockage rate.
The stream identification acquisition unit 43 is configured to acquire, in response to detection of an abnormality, a stream identification of a streaming medium in which the transmission abnormality occurs.
The root cause locating unit 44 is configured to locate a dimension of a root cause of the abnormality based on the stream identification of the streaming media in which the abnormality occurs in the transmission.
In an exemplary embodiment of the present disclosure, the root cause location unit 44 may be configured to: acquiring a preset monitoring index of at least one upstream dimension of the current dimension aiming at the stream identification of the stream media with abnormal transmission; and determining the dimension of the source of the abnormality based on the preset monitoring index of the at least one upstream dimension.
In an exemplary embodiment of the present disclosure, the root cause location unit 44 may be configured to: determining whether an abnormality exists in the at least one upstream dimension based on a preset monitoring index of the at least one upstream dimension; and determining the dimension with the first abnormality in the at least one upstream dimension according to the dimension ordering as the dimension of the root source of the abnormality.
In an exemplary embodiment of the present disclosure, the root cause location unit 44 may be configured to: acquiring a preset monitoring index of a preset upstream dimension of the current dimension aiming at the flow identification; and determining the dimension of the source of the abnormality based on a preset monitoring index of a preset trip dimension.
In an exemplary embodiment of the present disclosure, the root cause location unit 44 may be configured to: determining whether the preset upstream dimension is abnormal or not based on a preset monitoring index of the preset upstream dimension; and in response to the existence of the abnormity in the preset upstream dimension, determining the preset upstream dimension in which the abnormity exists as the dimension of the root of the abnormity.
The streaming media failure monitoring apparatus according to the exemplary embodiment of the present disclosure has been described above with reference to fig. 4. Next, an electronic device according to an exemplary embodiment of the present disclosure is described with reference to fig. 5.
Fig. 5 is a block diagram of an electronic device 500 according to an example embodiment of the present disclosure.
Referring to fig. 5, an electronic device 500 includes at least one memory 501 and at least one processor 502, the at least one memory 501 having stored therein a set of computer-executable instructions that, when executed by the at least one processor 502, perform a method of streaming media failure monitoring according to an exemplary embodiment of the present disclosure.
By way of example, the electronic device 500 may be a PC computer, tablet device, personal digital assistant, smartphone, or other device capable of executing the set of instructions described above. Here, the electronic device 500 need not be a single electronic device, but can be any collection of devices or circuits that can execute the above instructions (or sets of instructions) individually or in combination. The electronic device 500 may also be part of an integrated control system or system manager, or may be configured as a portable electronic device that interfaces with local or remote (e.g., via wireless transmission).
In the electronic device 500, the processor 502 may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a programmable logic device, a special-purpose processor system, a microcontroller, or a microprocessor. By way of example, and not limitation, processors may also include analog processors, digital processors, microprocessors, multi-core processors, processor arrays, network processors, and the like.
The processor 502 may execute instructions or code stored in the memory 501, wherein the memory 901 may also store data. The instructions and data may also be transmitted or received over a network via a network interface device, which may employ any known transmission protocol.
The memory 501 may be integrated with the processor 502, for example, by having RAM or flash memory disposed within an integrated circuit microprocessor or the like. Further, memory 501 may comprise a stand-alone device, such as an external disk drive, storage array, or any other storage device usable by a database system. The memory 501 and the processor 502 may be operatively coupled or may communicate with each other, e.g., through I/O ports, network connections, etc., such that the processor 502 is able to read files stored in the memory.
In addition, the electronic device 500 may also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, mouse, touch input device, etc.). All components of the electronic device 500 may be connected to each other via a bus and/or a network.
According to an exemplary embodiment of the present disclosure, a computer-readable storage medium storing instructions may also be provided, wherein the instructions, when executed by at least one processor, cause the at least one processor to perform a method of streaming media failure monitoring according to the present disclosure. Examples of the computer-readable storage medium herein include: read-only memory (ROM), random-access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random-access memory (DRAM), static random-access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD + R, CD-RW, CD + RW, DVD-ROM, DVD-R, DVD + R, DVD-RW, DVD + RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or compact disc memory, Hard Disk Drive (HDD), solid-state drive (SSD), card-type memory (such as a multimedia card, a Secure Digital (SD) card or a extreme digital (XD) card), magnetic tape, a floppy disk, a magneto-optical data storage device, an optical data storage device, a hard disk, a magnetic tape, a magneto-optical data storage device, a, A solid state disk, and any other device configured to store and provide a computer program and any associated data, data files, and data structures to a processor or computer in a non-transitory manner such that the processor or computer can execute the computer program. The computer program in the computer-readable storage medium described above can be run in an environment deployed in a computer apparatus, such as a client, a host, a proxy device, a server, and the like, and further, in one example, the computer program and any associated data, data files, and data structures are distributed across a networked computer system such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed fashion by one or more processors or computers.
According to an exemplary embodiment of the present disclosure, a computer program product may also be provided, in which instructions are executable by a processor of a computer device to perform a method of streaming media failure monitoring according to an exemplary embodiment of the present disclosure.
The streaming media failure monitoring method and apparatus according to the exemplary embodiment of the present disclosure have been described above with reference to fig. 1 to 5. However, it should be understood that: the streaming media failure monitoring apparatus and units thereof shown in fig. 4 may be respectively configured as software, hardware, firmware, or any combination thereof to perform specific functions, the electronic device shown in fig. 5 is not limited to include the above-shown components, but some components may be added or deleted as needed, and the above components may also be combined.
According to the streaming media fault monitoring method and device disclosed by the invention, the preset monitoring index of the current dimension in the multiple dimensions in the streaming media transmission link is obtained, wherein the multiple dimensions are divided according to the minimum service entity and/or the minimum system dimension in the streaming media transmission link, the abnormity in the streaming media transmission link is detected based on the preset monitoring index, the stream identifier of the streaming media with the abnormity in transmission is obtained in response to the detection of the abnormity, and the dimension of the abnormal root source is positioned based on the stream identifier, so that the positioning of the abnormal root source is realized, the accuracy of fault detection is improved, and the fault can be conveniently and rapidly eliminated.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A streaming media fault monitoring method is characterized by comprising the following steps:
acquiring preset monitoring indexes of a current dimension in multiple dimensions in a streaming media transmission link, wherein the multiple dimensions are divided according to a minimum service entity and/or a minimum system dimension in the streaming media transmission link;
detecting an abnormality in a streaming media transmission link based on a preset monitoring index;
in response to the detection of the abnormality, acquiring a stream identifier of the stream media with the abnormal transmission;
based on the flow identification, a dimension of a root cause of the anomaly is located.
2. The streaming media fault monitoring method according to claim 1, wherein the plurality of dimensions are sorted based on a precedence order of occurrence of the traffic, wherein a dimension sorted in a front order is an upstream dimension of a dimension sorted in a rear order.
3. The streaming media fault monitoring method of claim 1, wherein the step of locating the dimension of the root cause of the anomaly comprises:
aiming at the flow identification, acquiring a preset monitoring index of at least one upstream dimension of the current dimension;
determining a dimension of a root cause of the anomaly based on a preset monitoring index of the at least one upstream dimension.
4. The streaming media fault monitoring method according to claim 3, wherein the step of determining the dimension of the root cause of the anomaly based on the preset monitoring index of the at least one upstream dimension comprises:
determining whether an abnormality exists in the at least one upstream dimension based on a preset monitoring index of the at least one upstream dimension;
and determining the dimension with the first abnormality in the at least one upstream dimension according to the dimension ordering as the dimension of the root source of the abnormality.
5. The streaming media fault monitoring method of claim 1, wherein the step of locating the dimension of the root cause of the anomaly comprises:
acquiring a preset monitoring index of a preset upstream dimension of the current dimension aiming at the flow identification;
and determining the dimension of the source of the abnormality based on a preset monitoring index of a preset upstream dimension.
6. The streaming media fault monitoring method according to claim 5, wherein the step of determining the dimension of the source of the anomaly based on the preset monitoring index of the preset upstream dimension comprises:
determining whether the preset upstream dimension is abnormal or not based on a preset monitoring index of the preset upstream dimension;
and in response to the existence of the abnormity in the preset upstream dimension, determining the preset upstream dimension in which the abnormity exists as the dimension of the root of the abnormity.
7. A streaming media failure monitoring apparatus, comprising:
the data acquisition unit is configured to acquire preset monitoring indexes of a current dimension in multiple dimensions in a streaming media transmission link, wherein the multiple dimensions are divided according to a minimum service entity in the streaming media transmission link;
the anomaly detection unit is configured to detect anomalies in the streaming media transmission link based on preset monitoring indexes;
a stream identifier acquiring unit configured to acquire, in response to detection of the abnormality, a stream identifier of the streaming media in which the transmission abnormality occurs; and
a root cause locating unit configured to locate a dimension of a root cause of the anomaly based on the flow identification.
8. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the streaming media failure monitoring method according to any one of claims 1 to 6.
9. A computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, causes the electronic device to execute the streaming media fault monitoring method according to any one of claims 1 to 6.
10. A computer program product comprising computer programs/instructions, characterized in that when the computer programs/instructions are executed by a processor, the streaming media failure monitoring method of any of claims 1 to 6 is implemented.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113747188A (en) * | 2021-09-03 | 2021-12-03 | 百果园技术(新加坡)有限公司 | Link monitoring system and method for video live broadcast quality |
| CN113840153A (en) * | 2021-08-27 | 2021-12-24 | 云智联网络科技(北京)有限公司 | Method and system for mobile phone broadcasting compatible with various OBS push streams |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107241595A (en) * | 2017-07-14 | 2017-10-10 | 北京奇艺世纪科技有限公司 | A kind of video failure monitoring method, device, system and electronic equipment |
| CN110784355A (en) * | 2019-10-30 | 2020-02-11 | 网宿科技股份有限公司 | Fault identification method and device |
| CN111106962A (en) * | 2019-12-24 | 2020-05-05 | 北京达佳互联信息技术有限公司 | Streaming media fault monitoring method and device, electronic equipment and storage medium |
| CN111918055A (en) * | 2020-06-30 | 2020-11-10 | 上海艾策通讯科技股份有限公司 | Video quality abnormity positioning method and device, storage medium and electronic equipment |
| WO2021017364A1 (en) * | 2019-07-26 | 2021-02-04 | 京信通信系统（中国）有限公司 | Network failure diagnosis method and apparatus, network device, and storage medium |
-
2021
- 2021-01-26 CN CN202110103914.0A patent/CN112954372B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107241595A (en) * | 2017-07-14 | 2017-10-10 | 北京奇艺世纪科技有限公司 | A kind of video failure monitoring method, device, system and electronic equipment |
| WO2021017364A1 (en) * | 2019-07-26 | 2021-02-04 | 京信通信系统（中国）有限公司 | Network failure diagnosis method and apparatus, network device, and storage medium |
| CN110784355A (en) * | 2019-10-30 | 2020-02-11 | 网宿科技股份有限公司 | Fault identification method and device |
| CN111106962A (en) * | 2019-12-24 | 2020-05-05 | 北京达佳互联信息技术有限公司 | Streaming media fault monitoring method and device, electronic equipment and storage medium |
| CN111918055A (en) * | 2020-06-30 | 2020-11-10 | 上海艾策通讯科技股份有限公司 | Video quality abnormity positioning method and device, storage medium and electronic equipment |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113840153A (en) * | 2021-08-27 | 2021-12-24 | 云智联网络科技(北京)有限公司 | Method and system for mobile phone broadcasting compatible with various OBS push streams |
| CN113747188A (en) * | 2021-09-03 | 2021-12-03 | 百果园技术(新加坡)有限公司 | Link monitoring system and method for video live broadcast quality |
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