CN111935512A - Scalable video CDN distribution system and method - Google Patents
Scalable video CDN distribution system and method Download PDFInfo
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- CN111935512A CN111935512A CN202011114882.6A CN202011114882A CN111935512A CN 111935512 A CN111935512 A CN 111935512A CN 202011114882 A CN202011114882 A CN 202011114882A CN 111935512 A CN111935512 A CN 111935512A
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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
- H04N21/239—Interfacing the upstream path of the transmission network, e.g. prioritizing client content requests
- H04N21/2393—Interfacing the upstream path of the transmission network, e.g. prioritizing client content requests involving handling client requests
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/30—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability
- H04N19/34—Scalability techniques involving progressive bit-plane based encoding of the enhancement layer, e.g. fine granular scalability [FGS]
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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]
- H04N21/60—Network structure or processes for video distribution between server and client or between remote clients; Control signalling between clients, server and network components; Transmission of management data between server and client, e.g. sending from server to client commands for recording incoming content stream; Communication details between server and client
- H04N21/63—Control signaling related to video distribution between client, server and network components; Network processes for video distribution between server and clients or between remote clients, e.g. transmitting basic layer and enhancement layers over different transmission paths, setting up a peer-to-peer communication via Internet between remote STB's; Communication protocols; Addressing
- H04N21/647—Control signaling between network components and server or clients; Network processes for video distribution between server and clients, e.g. controlling the quality of the video stream, by dropping packets, protecting content from unauthorised alteration within the network, monitoring of network load, bridging between two different networks, e.g. between IP and wireless
- H04N21/64723—Monitoring of network processes or resources, e.g. monitoring of network load
- H04N21/64738—Monitoring network characteristics, e.g. bandwidth, congestion level
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- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The invention relates to the technical field of video monitoring, in particular to a scalable video CDN (content distribution network) distribution system and a method, wherein the system comprises a real-time water level line evaluation module, a real-time water level line evaluation module and a real-time video stream system, wherein the real-time water level line evaluation module is used for receiving an external playing request and outputting the change trend of the water level of a current video stream system; and the video stream node tree branching and cutting module is connected with the real-time water level line evaluation module and is used for receiving the change trend of the water level of the current video stream system and performing node branching, node cutting and recovery and node maintenance according to the change trend of the water level. The invention realizes the dynamic expansion and contraction of the video stream CDN by utilizing a dynamic waterline evaluation AI model and a multi-branch tree branching and cutting method.
Description
Technical Field
The invention relates to the technical field of video monitoring, in particular to a scalable video CDN (content delivery network) distribution system and a scalable video CDN distribution method.
Background
In a video monitoring scene, generally, in the existing technology, after a camera is connected to a cloud, the distribution of a video stream is mainly performed through a Content Delivery Network (CDN). Although the transmission is greatly improved compared with the conventional network transmission through the CDN, the following technical defects also exist:
1. when a user wants to see a real-time stream, internal nodes can be forwarded layer by layer, delay, blocking, troubleshooting and complexity are caused, and poor experience is brought to the user;
2. even if only one person watches the video stream, layer-by-layer transfer among different CDN nodes can be generated, a large amount of bandwidth resource waste is brought, and meanwhile, a manufacturer needs to lay a large amount of CDN and IDC resources to bear a large amount of hardware cost and bandwidth resource waste.
Disclosure of Invention
The invention provides a scalable video CDN delivery system and a method thereof, which can overcome the defects of the prior art.
The invention discloses a scalable video CDN delivery system, which comprises:
the real-time water level line evaluation module is used for receiving an external playing request and outputting the change trend of the water level of the current video streaming system;
and the video stream node tree branching and cutting module is connected with the real-time water level line evaluation module and is used for receiving the change trend of the water level of the current video stream system and performing node branching, node cutting and recovery and node maintenance according to the change trend of the water level.
Preferably, the real-time water line evaluation module includes a low water line evaluation sub-module, a medium water line evaluation sub-module, and a high water line evaluation sub-module.
Preferably, the low water line evaluation submodule is connected with a low water line AI prediction model, the middle water line evaluation submodule is connected with a middle water line AI prediction model, and the high water line evaluation submodule is connected with a high water line AI prediction model; the low water line AI prediction model, the mid water line AI prediction model and the high water line AI prediction model each contain the underlying parameters on which the water line depends.
Preferably, the basic parameters on which the water level line depends include cpu utilization, memory usage, disk io, network card throughput, dynamic delay measurement and calculation between different stream nodes, and historical evaluation data.
Preferably, the video stream node tree branching and clipping module includes:
the video stream node branching module is used for branching video stream nodes when the system enters a water level rising mode;
the video stream node cutting and recycling module is used for cutting and recycling the video stream nodes when the system enters a water level descending mode;
and the video streaming node maintaining module is used for maintaining the video streaming nodes when the system enters a water level alleviating mode.
The invention also provides a scalable video CDN distribution method, which comprises the following steps:
firstly, sending an external play request to a real-time water level line evaluation module;
secondly, the real-time water level line evaluation module evaluates the change trend of the water level of the current video stream system and sends the real-time evaluation result to the video stream node tree branching and cutting module;
and thirdly, the video stream node tree branching and cutting module performs corresponding video stream node branching, cutting, recycling and maintaining according to the real-time evaluation result.
And in the second step, the real-time water level line evaluation module judges that the water level of the video streaming system belongs to one of the three models by taking basic parameters in the low water level line AI prediction model, the middle water level line AI prediction model and the high water level line AI prediction model as standards, and outputs the change trend of the current water level of the video streaming system.
In the third step, when the video stream node is branched, namely the system enters a water level raising mode, the video stream root node branches into the multi-level video stream child nodes.
In the third step, when the system enters a water level descending mode, the video stream node cutting and recovery is that the child node of the next-stage video stream is recovered, the stream mounted under the node is removed, and then the stream is seamlessly transferred to the parent node of the previous stage and finally converged into the root node of the video stream.
According to the method, the problems of low CDN resource utilization rate and user experience are solved by utilizing a dynamic water level line evaluation AI model and a multi-branch tree branching and cutting method, when the system is evaluated to be in a low water level state, the flow is not forwarded by nodes, and a user is directly connected with a plug flow root node; and when the system is evaluated to be in a high water level state, in order to support massive concurrent requests for watching real-time streams, the original video stream is used as a root node, the subordinate is expanded into a plurality of leaf nodes, when the system load is further increased, new subordinate leaf nodes are derived from the leaf nodes, when the system water level is reduced, the leaf nodes at the bottommost layer are dynamically recycled and finally converged to the root node, and therefore dynamic expansion of the video stream CDN is achieved.
Drawings
FIG. 1 is a block diagram of a scalable video CDN distribution system according to an embodiment;
FIG. 2 is a diagram illustrating node forking of a video stream according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating clipping and recycling of nodes in a video stream according to an embodiment of the present invention.
Detailed Description
For a further understanding of the invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples. It is to be understood that the examples are illustrative of the invention and not limiting.
Referring to fig. 1, fig. 1 is a block diagram of a scalable video CDN delivery system according to an embodiment of the present invention, where the embodiment provides a scalable video CDN delivery system, including:
the real-time water level line evaluation module is used for receiving an external playing request and outputting the change trend of the water level of the current video streaming system;
and the video stream node tree branching and cutting module is connected with the real-time water level line evaluation module and is used for receiving the change trend of the water level of the current video stream system and performing node branching, node cutting and recovery and node maintenance according to the change trend of the water level.
In the patent of the invention, the "water level" refers to the description of the pressure state of the system load in the current system, and the low load, the medium load and the high load of the system correspond to the low water level, the medium water level and the high water level.
In this embodiment, the real-time water level line evaluation module includes a low water level line evaluation submodule, a medium water level line evaluation submodule, and a high water level line evaluation submodule.
In the embodiment, the low water level line evaluation submodule is connected with a low water level line AI prediction model, the middle water level line evaluation submodule is connected with a middle water level line AI prediction model, and the high water level line evaluation submodule is connected with a high water level line AI prediction model; the low water line AI prediction model, the mid water line AI prediction model and the high water line AI prediction model each contain the underlying parameters on which the water line depends.
In this embodiment, the basic parameters on which the water level line depends include a cpu utilization rate, a memory usage amount, a disk io, a network card throughput, a dynamic delay measurement and calculation between different stream nodes, and historical evaluation data.
According to the water level line AI prediction model of the embodiment, the future water level output is predicted to be the probability of rising, falling or stable or overflowing according to the historical data of the basic parameters and the current basic data, for each predicted probability result, an adjustment factor can be set, the weight of the water level can be changed, and a specific AI prediction model can adopt a neural network method or other classical prediction algorithms and the like.
In this embodiment, the video stream node tree branching and cutting module includes:
the video stream node branching module is used for branching video stream nodes when the system enters a water level rising mode;
the video stream node cutting and recycling module is used for cutting and recycling the video stream nodes when the system enters a water level descending mode;
and the video streaming node maintaining module is used for maintaining the video streaming nodes when the system enters a water level alleviating mode.
The embodiment also provides a scalable video CDN delivery method, which includes the following steps:
firstly, sending an external play request to a real-time water level line evaluation module;
secondly, the real-time water level line evaluation module evaluates the change trend of the water level of the current video stream system and sends the real-time evaluation result to the video stream node tree branching and cutting module;
and thirdly, the video stream node tree branching and cutting module performs corresponding video stream node branching, cutting, recycling and maintaining according to the real-time evaluation result.
In the second step, the real-time water level line evaluation module uses basic parameters in the low water level line AI prediction model, the middle water level line AI prediction model and the high water level line AI prediction model as standards to judge that the water level of the video streaming system belongs to one of the three models and output the variation trend of the current water level of the video streaming system.
Referring to fig. 2, fig. 2 is a schematic diagram of node branching of a video stream in the embodiment of the present invention, and in step three, when a system enters a water level raising mode, a video stream root node branches into multiple stages of video stream sub-nodes to cope with a pressure that rises continuously, so as to easily implement bottom layer expansion on a large amount of real-time streams.
The video stream root node of the machine room 1 branches out of the video stream child nodes in the machine room 1, and the video stream child nodes in the machine room 1 branch out of the video stream child nodes in the machine room 2 according to actual conditions.
Referring to fig. 3, fig. 3 is a schematic diagram of video stream node clipping and recovery according to an embodiment of the present invention, in step three, when the video stream node clipping and recovery is performed when the system enters a water level down mode, a child node of a next-stage video stream is recovered, a stream mounted under the node is removed, and then the stream is seamlessly migrated to a parent node of a previous stage and finally converged to a root node of the video stream.
If the video stream child node of the machine room 2 is recovered, the stream mounted under the node is removed, and then the stream is seamlessly transferred to the video stream child node of the machine room 1; likewise, the video stream child node of the room 1 can be cropped and recycled to the video stream root node of the room 1. The entire crop recovery process is transparent to the end user, who does not feel the stuck and terminal video stream.
The CDN is an intelligent virtual network constructed on the basis of the existing network, and by means of edge servers deployed in various places and functional modules of load balancing, content distribution, scheduling and the like of a central platform, a user can obtain required content nearby, network congestion is reduced, and the access response speed and hit rate of the user are improved. The key technology of the CDN is mainly content storage and distribution technology.
In the embodiment, the problems of low resource utilization rate of the CDN and user experience are solved by utilizing a dynamic water level line evaluation AI model and a multi-branch tree branching and cutting method, when the system is evaluated to be in a low water level state, the flow is not forwarded by nodes, and a user is directly connected with a plug flow root node; and when the system is evaluated to be in a high water level state, in order to support massive concurrent requests for watching real-time streams, the original video stream is used as a root node, the subordinate is expanded into a plurality of leaf nodes, when the system load is further increased, new subordinate leaf nodes are derived from the leaf nodes, when the system water level is reduced, the leaf nodes at the bottommost layer are dynamically recycled and finally converged to the root node, and therefore dynamic expansion of the video stream CDN is achieved.
In the embodiment, two methods are introduced in the traditional scheme, the first method is to introduce a real-time waterline evaluation intelligent AI model in a video stream scheduling system to dynamically evaluate the load state of the system and predict the load trend; and the second method is to introduce a multi-branch tree branching and cutting algorithm, realize the dynamic increase and reduction of nodes, maximize the utilization of resources and save 70% of the cost of manufacturers.
The present invention and its embodiments have been described above schematically, without limitation, and what is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Therefore, if the person skilled in the art receives the teaching, without departing from the spirit of the invention, the person skilled in the art shall not inventively design the similar structural modes and embodiments to the technical solution, but shall fall within the scope of the invention.
Claims (9)
1. A scalable video CDN delivery system, comprising: the method comprises the following steps:
the real-time water level line evaluation module is used for receiving an external playing request and outputting the change trend of the water level of the current video streaming system;
and the video stream node tree branching and cutting module is connected with the real-time water level line evaluation module and is used for receiving the change trend of the water level of the current video stream system and performing node branching, node cutting and recovery and node maintenance according to the change trend of the water level.
2. The scalable video CDN delivery system of claim 1, wherein: the real-time water level line evaluation module comprises a low water level line evaluation submodule, a medium water level line evaluation submodule and a high water level line evaluation submodule.
3. The scalable video CDN delivery system of claim 2, wherein: the low water level line evaluation submodule is connected with a low water level line AI prediction model, the middle water level line evaluation submodule is connected with a middle water level line AI prediction model, and the high water level line evaluation submodule is connected with a high water level line AI prediction model; the low water line AI prediction model, the mid water line AI prediction model and the high water line AI prediction model each contain the underlying parameters on which the water line depends.
4. The scalable video CDN delivery system of claim 3, wherein: the basic parameters on which the water level line depends include cpu utilization, memory usage, disk io, network card throughput, dynamic delay measurement and calculation among different stream nodes, and historical evaluation data.
5. The scalable video CDN delivery system of claim 1, wherein: the video stream node tree branching and cropping module comprises:
the video stream node branching module is used for branching video stream nodes when the system enters a water level rising mode;
the video stream node cutting and recycling module is used for cutting and recycling the video stream nodes when the system enters a water level descending mode;
and the video streaming node maintaining module is used for maintaining the video streaming nodes when the system enters a water level alleviating mode.
6. A scalable video CDN delivery method is characterized by comprising the following steps:
firstly, sending an external play request to a real-time water level line evaluation module;
secondly, the real-time water level line evaluation module evaluates the change trend of the water level of the current video stream system and sends the real-time evaluation result to the video stream node tree branching and cutting module;
and thirdly, the video stream node tree branching and cutting module performs corresponding video stream node branching, cutting, recycling and maintaining according to the real-time evaluation result.
7. The scalable video CDN delivery method of claim 6, wherein: and in the second step, the real-time water level line evaluation module judges that the water level of the video streaming system belongs to one of the three models by taking basic parameters in the low water level line AI prediction model, the middle water level line AI prediction model and the high water level line AI prediction model as standards, and outputs the change trend of the current water level of the video streaming system.
8. The scalable video CDN delivery method of claim 7, wherein: in the third step, when the video stream node is branched, namely the system enters a water level raising mode, the video stream root node branches into the multi-level video stream child nodes.
9. The scalable video CDN delivery method of claim 6, wherein: in the third step, when the system enters a water level descending mode, the video stream node cutting and recovery is that the child node of the next-stage video stream is recovered, the stream mounted under the node is removed, and then the stream is seamlessly transferred to the parent node of the previous stage and finally converged into the root node of the video stream.
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| CN104038358A (en) * | 2013-03-06 | 2014-09-10 | 中兴通讯股份有限公司 | Content scheduling method and content scheduling device |
| CN106572181A (en) * | 2016-11-08 | 2017-04-19 | 深圳市中博睿存科技有限公司 | Object storage interface load balancing method and system based on cluster file system |
| US20190007473A1 (en) * | 2017-06-19 | 2019-01-03 | Wangsu Science & Technology Co., Ltd. | Peer-to-peer network live streaming system and node management method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104038358A (en) * | 2013-03-06 | 2014-09-10 | 中兴通讯股份有限公司 | Content scheduling method and content scheduling device |
| CN106572181A (en) * | 2016-11-08 | 2017-04-19 | 深圳市中博睿存科技有限公司 | Object storage interface load balancing method and system based on cluster file system |
| US20190007473A1 (en) * | 2017-06-19 | 2019-01-03 | Wangsu Science & Technology Co., Ltd. | Peer-to-peer network live streaming system and node management method |
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