CN114697683A - Intelligent scheduling method, equipment and computer program product for streaming media file - Google Patents
Intelligent scheduling method, equipment and computer program product for streaming media file Download PDFInfo
- Publication number
- CN114697683A CN114697683A CN202210301208.1A CN202210301208A CN114697683A CN 114697683 A CN114697683 A CN 114697683A CN 202210301208 A CN202210301208 A CN 202210301208A CN 114697683 A CN114697683 A CN 114697683A
- Authority
- CN
- China
- Prior art keywords
- streaming media
- media file
- level
- determining
- node
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000004590 computer program Methods 0.000 title claims abstract description 23
- 235000019633 pungent taste Nutrition 0.000 claims description 2
- 230000001186 cumulative effect Effects 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000013500 data storage Methods 0.000 description 3
- 230000003993 interaction Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 244000208734 Pisonia aculeata Species 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 229910021389 graphene Inorganic materials 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- 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
- H04N21/2181—Source of audio or video content, e.g. local disk arrays comprising remotely distributed storage units, e.g. when movies are replicated over a plurality of video servers
-
- 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
- H04N21/231—Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers, prioritizing data for deletion
- H04N21/23103—Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers, prioritizing data for deletion using load balancing strategies, e.g. by placing or distributing content on different disks, different memories or different servers
-
- 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
-
- 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
- H04N21/2405—Monitoring of the internal components or processes of the server, e.g. server load
-
- 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
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/262—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists
- H04N21/26208—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists the scheduling operation being performed under constraints
Abstract
The application relates to an intelligent scheduling method, equipment and a computer program product for streaming media files. According to the method and the device, characteristics of different CDN service providers can be utilized to cooperate with multiple CDN service providers to carry out global scheduling, download hit rates of respective CDN nodes of different companies are improved, the probability of back source pull is reduced, and for a user, download delay of the user is reduced, so that the streaming media file playing quality is further improved. The method comprises the following steps: acquiring the accumulated playing times of the streaming media file in a preset time period, and determining the heat level of the streaming media file based on the accumulated playing times; determining a file scheduling mode according to the heat level; and selecting a target node of the streaming media file according to the file scheduling mode, and acquiring the streaming media file through the target node.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to an intelligent scheduling method, a computer device, and a computer program product for streaming media files.
Background
CDN technology has been widely used in the current internet field. A CDN (Content Delivery Network) is a distributed Network that is established and overlaid on a bearer Network and is composed of edge node server clusters distributed in different areas. In the face of the condition that the access demand of current internet users is large and frequent, the CDN network can redirect the access request of the user to the edge node server which is nearest to the user and works normally by using the global load balancing technology, and the edge node server directly responds to the request of the user to realize the nearby access of resources. When the edge node server closest to the user (i.e., CDN node) does not have the content that the user wants to access, the CDN node will go to the source station according to the domain name address input by the user to pull the target content that the user wants to access, which is called back to the source. The CDN technology can save the bandwidth cost of the source station, effectively relieve the access pressure of the source station, avoid the condition that the source station is broken down due to mass access, reduce the time delay of a user for obtaining resources due to near access and improve the access speed of the user.
The current streaming media software operators provide services for users by using a CDN technology, and the users realize fast downloading or uploading (e.g., recording songs) of streaming media files, such as audio and video files, through the CDN technology, while in practical applications, some audio and video playing software needs to cooperate with multiple CDN servers due to the large scale of the users, and these CDN nodes generally adopt a random scheduling manner when providing services, and do not perform global cooperative scheduling according to core indexes such as characteristics, capacity, cache hit rate, and the like of each CDN server, resulting in high utilization rate of some CDN nodes and low utilization rate of some CDN nodes, which means that the CDN cannot realize sufficient and effective utilization of network bandwidth resources for software operators, wastes a large amount of bandwidth resources, improves software operation cost, and for users, for many times, it still needs to pull back target resources to source, and improves user delay, the user can not carry out the streaming downloading and playing at the fastest speed, and the playing quality is reduced.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an intelligent scheduling method, a computer device and a computer program product for streaming media files.
In a first aspect, the present application provides an intelligent scheduling method for streaming media files. The method comprises the following steps:
acquiring the accumulated playing times of a streaming media file in a preset time period, and determining the heat level of the streaming media file based on the accumulated playing times;
determining a file scheduling mode according to the heat level;
and selecting a target node of the streaming media file according to the file scheduling mode, and acquiring the streaming media file through the target node.
In a second aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps in the embodiment of the intelligent scheduling method of the streaming media file when executing the computer program.
In a third aspect, the present application also provides a computer program product. The computer program product includes a computer program, and the computer program, when executed by a processor, implements the steps in the above-mentioned method for intelligently scheduling streaming media files.
According to the intelligent scheduling method, the computer equipment and the computer program product for the streaming media file, the accumulated playing times of the streaming media file in a preset time period are obtained, and the heat level of the streaming media file is determined based on the accumulated playing times; determining a file scheduling mode according to the heat level; and selecting a target node of the streaming media file according to the file scheduling mode, and acquiring the streaming media file through the target node. According to the method, the heat level of the streaming media file is determined through a preset file cold-heat level judgment rule, a cold-heat graded scheduling strategy is adopted for the current file according to the heat level, compared with a traditional CDN (Content Delivery Network) node random scheduling mode, global scheduling can be performed by cooperating with multiple CDN service providers by utilizing the characteristics of different CDN service providers (or different CDN nodes), the download hit rate of CDN nodes of different companies is improved, the source return pull probability is reduced, for a software operator, Network bandwidth resources are further fully utilized, the software operation cost is saved, for a user, the file source return pull probability is reduced, the user download delay is reduced, the file download rate is improved, and the streaming media file playing quality is further improved.
Drawings
FIG. 1 is a diagram of an application environment of a method for intelligent scheduling of streaming media files in an embodiment;
FIG. 2 is a flowchart illustrating a method for intelligently scheduling streaming media files according to an embodiment;
FIG. 3 is a flow diagram that illustrates an intelligent scheduling policy in one embodiment;
FIG. 4 is a schematic flow chart illustrating a document cooling/heating degree determination model according to another embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The method for intelligently scheduling streaming media files provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Wherein the terminal 101 communicates with the server 102 via a network. The data storage system may store data that the server 102 needs to process. The data storage system may be integrated on the server 102, or may be located on the cloud or other network server. The terminal 101 may be but not limited to various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart car-mounted devices, and the like. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, and the like. Server 102 may be implemented as a server cluster comprised of multiple servers. In practical applications, the server cluster may be a server cluster provided by multiple CDN service providers.
In one embodiment, as shown in fig. 2, an intelligent scheduling method for streaming media files is provided, which is described by taking the method as an example applied to the terminal 101 in fig. 1, and includes the following steps:
step S201, acquiring the accumulated playing times of the streaming media file in a preset time period, and determining the heat level of the streaming media file based on the accumulated playing times;
the streaming media file refers to a streaming media file that a user wants to acquire, for example, a song a that the user wants to acquire is downloaded on a mobile phone terminal through some music playing software, and the song a may be regarded as the streaming media file that the user wants to acquire. The heat level refers to a hot degree according to the document, and in the present application, the heat level may be divided into three levels, namely, a first level (hot work), a second level (warm work), and a third level (cold work).
Specifically, the terminal responds to an access request of a user for streaming media files, and obtains the accumulated playing times of the streaming media files in a preset time period, for example, the user downloads a song or an official accompaniment file on a mobile phone terminal through a certain music playing App, and after the user clicks the name of the song, the terminal receives the access request and obtains the accumulated playing times of the song in a past period. The preset time period can be flexibly selected according to actual needs, for example, the current click behavior can be started to backward push within a period of time day. The accumulated playing times can be the accumulated playing times of the song on the current software, can also be the accumulated playing times of the whole network, and can be flexibly set according to the actual situation. And then, determining the popularity level of the streaming media file based on the accumulated playing times.
Specifically, by setting a playing frequency interval (a first preset interval), for example, [ m1, n1 ], when the accumulated playing frequency of the streaming media file is located in the first preset interval, the heat level can be judged as a second level (warm work), when the accumulated playing frequency is greater than or equal to a first preset interval upper limit n1, the heat level can be judged as a first level (warm work), and when the accumulated playing frequency is less than a first preset interval lower limit m1, the heat level can be judged as a third level (cold work).
If the streaming media file is not played, the popularity level of the current streaming media file can be determined by referring to the popularity of the played user, and the played user refers to the original publisher of the streaming media file.
Step S202, determining a file scheduling mode of the streaming media file according to the popularity level.
The file scheduling mode is a downloading method used to improve resource utilization and downloading time.
In practical applications, as mentioned above, the heat level may be a plurality of levels, and may include division of hot works, warm works, cold works, and the like. The hot works (i.e. the streaming media files of the first level above) mainly occupy bandwidth costs of an OC (Outer Center, edge data Center/edge machine room/edge node), where the OC node, i.e. the edge node, is a server node closest to the User, and statistics show that cache hit rates of various CDN manufacturers for the hot works are all as high as x% or more, while cache hit rates of the warm works are all below y%, and the hit rate of some CDN manufacturers for the warm works is even below z%, and the reason is mainly that most UGC (User Generated Content) works Generated in social scenes are actually inaccessible in large quantities (exposure is rarely obtained), resulting in many long-tailed access requests (i.e. a small number of access requests whose scheduling response time is significantly higher than the average value), and mainly when these non-hot UGC works are just released, short term access within the author relationship chain results in a long tail access request. When the long-tail access requests are randomly scheduled to each CDN manufacturer, the long-tail access requests are very difficult to process, on one hand, the long-tail access requests also occupy network bandwidth resources, on the other hand, due to the fact that sufficient backup resources are not available, downloading and playing cannot be successfully carried out, the hit of hot works can be affected, the overall cache hit rate of the CDN is greatly reduced, and the cost of the source return bandwidth of a DC (Data Center, main Data Center/core machine room) is increased.
According to different heat grades of the streaming media files, corresponding file scheduling modes are allocated to the streaming media files, and the method mainly comprises the following steps: when the streaming media file is in a third level (cold works), a scheduling mode of directly returning to the source is adopted; when the streaming media file is of a first grade (hot work), a scheduling mode of randomly distributing CDN manufacturers or CDN nodes according to weight is adopted; when the streaming media file is of a second level (temperature works), a weight consistency scheduling mode is adopted.
The scheduling mode for randomly allocating the CDN nodes according to the weight specifically includes: because the processing capacities of the nodes are different due to different configurations (including storage space, caching algorithm, response time and the like) of each CDN node, different weights can be allocated to each node according to the different processing capacities of the nodes, so that the CDN nodes can receive file scheduling requests corresponding to the weights. For example, assuming that there are 3 CDN nodes (a, b, c), weights a (1), b (2), and c (4) are first assigned to each node according to the processing capability of each node, which means that every time a file scheduling request for 7 identical streaming media files is received, 1 of the file scheduling requests is assigned to node a, 2 of the file scheduling requests is assigned to node b, and 4 of the file scheduling requests is assigned to node c, note that the 7 file scheduling requests are randomly assigned, that is, the assignment order may be (c, c, b, a, c, b, c), and when a file scheduling request for an 8 th identical streaming media file comes, the file scheduling request is randomly assigned to the CDN node again according to the above weight to obtain the streaming media file.
The consistent scheduling mode, also called a consistent Hash scheduling mode, refers to that in a distributed storage system, a Hash value is calculated according to a certain fixed attribute of each node, where the fixed attribute may refer to physical attributes such as the size of a storage space of the CDN node (machine room), and then the Hash values calculated by all nodes form a Hash ring. And when the request comes, calculating the hash value of the characteristic according to the characteristic of the request, and then searching the hash value on the hash ring clockwise, wherein the node corresponding to the hash value larger than the hash value of the characteristic of the request is the selected node.
Step S203, selecting a target node of the streaming media file according to the file scheduling mode, and obtaining the streaming media file through the target node.
Specifically, after the heat level of the streaming media file and the corresponding file scheduling mode are determined, the corresponding target node is found from CDN nodes provided by multiple CDN manufacturers according to the file scheduling mode, and the streaming media file is pulled from the target node.
In the embodiment, the heat level of the streaming media file is determined through a preset rule, and according to the heat level, a cold and hot temperature hierarchical scheduling strategy is adopted for the current streaming media file, compared with a traditional Content Delivery Network (CDN) node random scheduling mode, the method can perform global scheduling in cooperation with multiple CDN service providers by using the characteristics of different CDN service providers (or different CDN nodes), so as to improve the download hit rate of respective CDN nodes of different companies and reduce the probability of pull-back.
In an embodiment, the heat level is a first level (a thermal work), and the step S203 includes: and randomly selecting a target node from the plurality of content distribution network nodes, and acquiring the streaming media file through the target node.
The mode may also be referred to as a weighted random selection mode, where the weighted random selection mode refers to randomly selecting a CDN vendor or a CDN node according to a weight.
Specifically, as shown in fig. 3, when the popularity level of the streaming media file is determined to be the first level (hot work), a target node may be allocated to the streaming media file according to a weighted random selection mode, the file is downloaded from the target node, and if the current target node has a cache miss, a next CDN node is correspondingly randomly selected according to the weight of the CDN node until the cache is hit and the file is obtained. Because the amount of hot works is small and the hit rate is extremely high, the hot works should be dispatched to all CDN manufacturers as much as possible, and the hot works can be quickly accessed to achieve the disaster recovery effect under the condition that a single CDN manufacturer has problems.
According to the embodiment, the CDN manufacturers or CDN nodes are randomly selected for the hot works according to the weights, so that the probability of pulling the hot works back to the source is effectively reduced, OC edge bandwidth is fully utilized, the client can perform streaming downloading and playing at the highest speed, and the playing quality is improved.
In one embodiment, the heat level is a third level (cold work), and the step S203 includes: and determining a source station node of the streaming media file as a target node, and acquiring the streaming media file through the source station node.
The source node refers to a publishing node of the streaming media file.
Specifically, as shown in fig. 3, when the hot level of the streaming media file is the third level (cold work), a file scheduling mode of directly scheduling back to the source station is adopted, and resources are directly pulled back to the source station.
According to the embodiment, the strategy of directly pulling back the source is adopted for the cold work, so that the OC edge bandwidth can be effectively saved, and the utilization efficiency of bandwidth resources is improved.
In an embodiment, the heat level is a second level (temperature work), and the step S203 includes: and determining a target node from the plurality of content distribution network nodes by adopting a consistency scheduling mode based on the node weight of each content distribution network node, and acquiring the streaming media file through the target node.
The consistent scheduling mode is that a hash value is calculated according to a certain fixed attribute of each node in a distributed storage system, the fixed attribute may be physical attributes such as the size of a storage space of the CDN node (machine room), and then the hash values calculated by all nodes form a hash ring. And when the request comes, calculating the hash value of the characteristic according to the characteristic of the request, and then clockwise searching the hash value on the hash ring, wherein the node corresponding to the hash value larger than the hash value of the characteristic of the request is the selected node.
Specifically, when the heat level of the streaming media file is the second level (warm work), a load balancing algorithm in the distributed cluster, namely a consistency scheduling algorithm, may be used for scheduling, that is, a consistency Hash ring is used to select the target node according to the weight of each CDN node. Specifically, a distributed cluster is composed of a plurality of physical machine nodes, if a scene in the application is analogized, a network composed of different CND manufacturers is a large distributed cluster actually, each CDN manufacturer is equivalent to a physical machine node in the cluster, based on the advantages of a consistency scheduling algorithm, for streaming media application software used by a large number of users, the same work can be scheduled to the same CDN manufacturer as much as possible, the same file is only scheduled to one manufacturer, scheduling mutual exclusion among files can be realized, the times of source return of different CDN manufacturers are reduced, and the cache hit rate of the long-tail work in each CDN manufacturer is improved to the greatest extent; when some quality problems need to be offline from a CDN manufacturer or a CDN manufacturer needs to be added for service expansion (which is equivalent to physical node arbitration or capacity expansion in a distributed cluster), the algorithm better ensures that data storage changes at least, and greatly saves the overhead of data movement.
According to the embodiment, aiming at the condition that the warm work can be dispatched to different CDN nodes in the traditional method, the CDN nodes which are as uniform as possible can be rapidly distributed for the warm work according to the Hash value of the work and the Hash value of a CDN manufacturer, the number of times of source return caused by the fact that the CDN nodes are not hit is reduced, and the bandwidth utilization rate of the CDN is further improved.
In an embodiment, the method further includes: acquiring node parameters of each content distribution network node; and setting the node weight of each content distribution network node according to the node parameters.
Specifically, as shown in fig. 4, a dynamic conditional weight mechanism is further adopted in the present application, specifically, each CDN vendor node reports a node parameter in real time, where the node parameter includes: cluster hardware configuration, storage space redundancy conditions, cache hit rate, cache algorithm and the like. And updating the node weight of each CDN manufacturer or CDN node in real time according to the node parameters.
According to the embodiment, the weight of the CDN node is updated in real time, so that effective scheduling of the CDN node can be realized subsequently.
In an embodiment, the step S201 includes: and if the accumulated playing times of the streaming media file represent that the streaming media file is not played, determining the heat degree grade of the streaming media file according to the heat degree of a played user corresponding to the streaming media file.
The popularity of the played user refers to the popularity of the publisher of the streaming media file. When the accumulated playing times of the streaming media file represent that the streaming media file is not played, for example, when the accumulated playing times of the streaming media file is zero, the popularity of the played user is used as a reference to determine the popularity level of the current streaming media file. For example, a certain work in the works of the broadcasted user is taken as a reference work, and the popularity level of the current streaming media file is judged by using the popularity of the reference work.
In the above embodiment, when the cumulative playing time of the streaming media file is zero, the popularity of the played user is used as a reference to determine the popularity level of the current streaming media file, so that the popularity determination accuracy of the current streaming media file is improved.
In one embodiment, the popularity of the broadcasted user is determined by the historical broadcast record of the broadcasted user; the determining the popularity rating of the streaming media file according to the popularity of the broadcasted user corresponding to the streaming media file includes: acquiring a reference work with the highest playing frequency of a played user according to the historical playing record; and determining the popularity level of the streaming media file according to the playing times of the reference works in a preset time period.
Specifically, acquiring a historical play record of a played user in a preset time period; the broadcasted user is a publisher of the streaming media file; the historical playing record is the result of the statistics of the playing times of all the released works of the played user in the preset time period; searching and obtaining a reference work with the highest playing times from the historical playing records; and determining the popularity of the played user according to the playing times of the reference works in a preset time period, thereby sequentially determining the popularity level of the streaming media file for reference.
In addition, if the accumulated playing times of the streaming media file is smaller than a third preset value (the third preset value is, for example, 2), that is, the playing times of the streaming media file in a preset time period is very low, and the history playing record of the played user is empty, it is determined that the heat level of the streaming media file is a third level (cold work).
For example, as shown in fig. 4, the present embodiment mainly determines the heat level of the streaming media file based on two data, which are the accumulated playing times of the streaming media file and the historical playing records of the played user. The broadcasted user is a publisher or an original author of the streaming media file, and the historical play record of the broadcasted user refers to the play record of all works of the work publisher in a preset time period.
Specifically, after obtaining the cumulative playing times of the current streaming media file in a preset time period, for example, the cumulative playing times in a past time period, it is further required to obtain a historical playing record of the played user in the preset time period, for example, a historical playing record of all works published by a work publisher in a past time period. If the cumulative playing times of the streaming media file is less than a third preset value (for example, 2) and the history playing record of the played user is empty, it is determined that the hotness level of the streaming media file to be requested is a third level (cold work).
In the above embodiment, when the cumulative playing time of the current work to be requested is less than the third preset value (or is zero), the mode based on the user popularity is adopted to determine the popularity level of the current streaming media file, and the popularity of the work publisher is taken as a reference, so that a data basis can be provided for the scheduling of the subsequent works.
In an embodiment, the determining the popularity rating of the streaming media file according to the number of playing times of the reference work in a preset time period includes:
when the playing times of the reference works in the preset time are in a second preset interval, determining the heat level of the streaming media file as a second level;
when the playing times of the reference works in the preset time are smaller than the lower limit of the interval of the second preset interval, determining the heat level of the streaming media file as a third level;
and when the playing times of the reference works in the preset time are greater than the upper limit of the interval of the second preset interval, determining the heat level of the streaming media file as the first level.
Specifically, as shown in fig. 4, when the cumulative playing frequency of the streaming media file is less than a third preset value (for example, the third preset value is 2 or 1), the popularity of the played user is used as a reference, and if the historical playing record of the played user within a preset time period obtained at this time is not empty, a reference work with the highest playing frequency is found from the historical playing record, and the popularity level of the streaming media file to be requested currently is determined according to the popularity level of the reference work.
When the playing times of the reference works in the preset time are in a second preset interval, for example, the playing times of the reference works are in a second preset interval [ m2, n2 ], determining that the heat level of the streaming media file is a second level (warm works); when the playing times of the reference works in the preset time are smaller than the lower limit m2 of the interval of the second preset interval, determining that the heat level of the streaming media file is a third level (cold works); when the playing times of the reference works in the preset time are greater than or equal to the interval upper limit n2 of the second preset interval, the popularity level of the streaming media file is determined to be the first level (popularity works).
In the embodiment, when the accumulated playing times of the current work to be requested is too low (smaller than the third preset value), the popularity of the work publisher is used as a reference, so that a data base can be provided for the scheduling of the subsequent works.
In one embodiment, the popularity of the broadcasted user is characterized by the historical broadcast record of the broadcasted user; determining the popularity level of the streaming media file according to the popularity of the broadcasted user corresponding to the streaming media file, wherein the determining step comprises the following steps:
and if the historical playing record of the played user does not exist, determining that the hot level of the streaming media file is a third level (cold work).
In the above embodiment, in the situation that the heat level of the current streaming media file is determined based on the heat of the played user, if the history playing record of the played user does not exist, for example, the history playing record of the played user is empty, the accumulated playing times of the current streaming media file is also low, so that the heat level of the current streaming media file can be directly determined as the third level (cold work).
In an embodiment, the determining the popularity rating of the streaming media file based on the accumulated playing times includes: determining the heat level of the streaming media file as a second level when the accumulated playing times of the streaming media file represent that the streaming media file is played and the accumulated playing times of the streaming media file is located in a first preset interval; when the accumulated playing times of the streaming media file is greater than or equal to the interval upper limit of a first preset interval, determining the heat level of the streaming media file as a first level; and when the accumulated playing times of the streaming media file is smaller than the lower interval limit of the first preset interval, determining that the heat level of the streaming media file is a third level.
Specifically, as shown in fig. 4, in the case that the cumulative playing time of the current to-be-requested streaming media file is not zero, the popularity level of the streaming media file can be directly determined according to the cumulative playing time of the current to-be-requested work. For example, when the accumulated playing times of the streaming media file is located in a first preset interval [ m1, n1 ], determining that the heat level of the streaming media file is a second level (warm work); when the accumulated playing times of the streaming media file is greater than or equal to the upper limit n1 of the interval of the first preset interval, the heat level of the streaming media file is determined to be the first level (hot work), and when the accumulated playing times of the streaming media file is less than the lower limit m1 of the interval of the first preset interval, the heat level of the streaming media file is determined to be the third level (cold work).
Optionally, the threshold or the interval in the above embodiments is determined based on empirical values and a large amount of data analysis, and is also determined by using a machine learning model such as xgboost, for example, adding more dimensional data, user images (such as stars or pixels), work interaction data (such as amount of likes or comments), user liveness, and user interaction data, and the like, and based on more data, judging and predicting the heat of the work of the user.
In the above embodiment, the accumulated playing times is directly used as the judgment basis under the condition that the accumulated playing times of the current work to be requested is not zero, so that the popularity level of the current work to be requested is determined, and a data basis can be provided for the scheduling of the subsequent works
It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 5. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The communication interface of the computer device is used for communicating with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method for intelligent scheduling of streaming media files. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer program product is provided comprising a computer program that, when executed by a processor, performs the steps in the method embodiments for intelligent scheduling of streaming media files.
It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (12)
1. An intelligent scheduling method for streaming media files, the method comprising:
acquiring the accumulated playing times of a streaming media file in a preset time period, and determining the heat level of the streaming media file based on the accumulated playing times;
determining a file scheduling mode according to the heat level;
and selecting a target node of the streaming media file according to the file scheduling mode, and acquiring the streaming media file through the target node.
2. The method of claim 1, wherein the heat level is a first level; the selecting a target node of the streaming media file according to the file scheduling mode and acquiring the streaming media file through the target node includes:
and randomly selecting the target node from a plurality of content distribution network nodes, and acquiring the streaming media file through the target node.
3. The method of claim 1, wherein the heat level is a third level; the selecting a target node of the streaming media file according to the file scheduling mode and acquiring the streaming media file through the target node includes:
and determining a source station node of the streaming media file as the target node, and acquiring the streaming media file through the source station node.
4. The method of claim 1, wherein the heat level is a second level; the selecting a target node of the streaming media file according to the file scheduling mode and acquiring the streaming media file through the target node includes:
and determining the target node from a plurality of content distribution network nodes by adopting a consistency scheduling mode based on the node weight of each content distribution network node, and acquiring the streaming media file through the target node.
5. The method of claim 4, further comprising:
acquiring node parameters of each content distribution network node;
and setting the node weight of each content distribution network node according to the node parameter.
6. The method according to any one of claims 1 to 5, wherein the determining the hotness level of the streaming media file based on the accumulated playing times comprises:
and if the accumulated playing times of the streaming media file represent that the streaming media file is not played, determining the heat degree grade of the streaming media file according to the heat degree of a played user corresponding to the streaming media file.
7. The method of claim 6, wherein the played user popularity is characterized by a played user's historical play records; the determining the popularity level of the streaming media file according to the popularity of the played user corresponding to the streaming media file comprises:
acquiring a reference work with the highest playing frequency of the played user according to the historical playing record;
and determining the popularity level of the streaming media file according to the playing times of the reference works in the preset time period.
8. The method of claim 7, wherein determining the popularity rating of the streaming media file according to the number of plays of the reference composition within the preset time period comprises:
when the playing times of the reference works in the preset time are in a second preset interval, determining the heat level of the streaming media file as a second level;
when the playing times of the reference works in the preset time are smaller than the lower limit of the interval of the second preset interval, determining that the popularity level of the streaming media file is a third level;
and when the playing times of the reference works in the preset time are greater than the interval upper limit of the second preset interval, determining that the heat level of the streaming media file is a first level.
9. The method of claim 6, wherein the played user popularity is characterized by a played user's historical play records; the determining the popularity level of the streaming media file according to the popularity of the played user corresponding to the streaming media file comprises:
and if the historical play record of the played user does not exist, determining the heat level of the streaming media file as a third level.
10. The method according to any one of claims 1 to 5, wherein the determining the popularity rating of the streaming media file based on the accumulated playing times comprises:
determining the heat level of the streaming media file as a second level when the accumulated playing times of the streaming media file represent that the streaming media file is played and the accumulated playing times of the streaming media file is located in a first preset interval; when the accumulated playing times of the streaming media file is greater than or equal to the upper limit of the interval of the first preset interval, determining that the heat level of the streaming media file is a first level; and when the accumulated playing times of the streaming media file is smaller than the lower interval limit of the first preset interval, determining that the heat level of the streaming media file is a third level.
11. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 10 when executing the computer program.
12. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 10 when executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210301208.1A CN114697683A (en) | 2022-03-25 | 2022-03-25 | Intelligent scheduling method, equipment and computer program product for streaming media file |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210301208.1A CN114697683A (en) | 2022-03-25 | 2022-03-25 | Intelligent scheduling method, equipment and computer program product for streaming media file |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114697683A true CN114697683A (en) | 2022-07-01 |
Family
ID=82139951
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210301208.1A Pending CN114697683A (en) | 2022-03-25 | 2022-03-25 | Intelligent scheduling method, equipment and computer program product for streaming media file |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114697683A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115334321A (en) * | 2022-08-10 | 2022-11-11 | 北京百度网讯科技有限公司 | Method and device for acquiring access heat of video stream, electronic equipment and medium |
| CN115412740A (en) * | 2022-08-29 | 2022-11-29 | 上海哔哩哔哩科技有限公司 | Live broadcast source return scheduling method and device, computing equipment and computer storage medium |
| CN117119052A (en) * | 2023-10-25 | 2023-11-24 | 腾讯科技（深圳）有限公司 | Data processing method, device, electronic equipment and computer readable storage medium |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103024081A (en) * | 2013-01-04 | 2013-04-03 | 福建星网视易信息系统有限公司 | Peer-to-peer communication terminal dispatching method adaptable to time-effect-guaranteed communication systems |
| CN104702625A (en) * | 2015-03-31 | 2015-06-10 | 北京奇艺世纪科技有限公司 | Method and device for scheduling access request in CDN (Content Delivery Network) |
| CN105516360A (en) * | 2016-01-19 | 2016-04-20 | 苏州帕科泰克物联技术有限公司 | Method and device for load balance of computer |
| CN108259945A (en) * | 2018-04-19 | 2018-07-06 | 腾讯音乐娱乐科技（深圳）有限公司 | The method and apparatus that processing plays the playing request of multi-medium data |
| CN108307198A (en) * | 2018-03-08 | 2018-07-20 | 广州酷狗计算机科技有限公司 | flow service node dispatching method, device and scheduling node |
| CN110995866A (en) * | 2019-12-20 | 2020-04-10 | 深圳市网心科技有限公司 | Node scheduling method, node scheduling device, scheduling server and storage medium |
| CN113038191A (en) * | 2021-02-26 | 2021-06-25 | 北京百度网讯科技有限公司 | Live stream scheduling method and device, electronic equipment and readable storage medium |
| CN113141317A (en) * | 2021-03-05 | 2021-07-20 | 西安电子科技大学 | Streaming media server load balancing method, system, computer equipment and terminal |
| CN113301081A (en) * | 2020-06-11 | 2021-08-24 | 阿里巴巴集团控股有限公司 | Data processing method, device and electronic equipment for content distribution network |
| CN113296924A (en) * | 2020-04-28 | 2021-08-24 | 阿里巴巴集团控股有限公司 | Content distribution method, device, system and storage medium |
| CN113711186A (en) * | 2019-04-09 | 2021-11-26 | 思科技术公司 | Distributed object placement, replication, and retrieval for cloud-scale storage and data transfer |
-
2022
- 2022-03-25 CN CN202210301208.1A patent/CN114697683A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103024081A (en) * | 2013-01-04 | 2013-04-03 | 福建星网视易信息系统有限公司 | Peer-to-peer communication terminal dispatching method adaptable to time-effect-guaranteed communication systems |
| CN104702625A (en) * | 2015-03-31 | 2015-06-10 | 北京奇艺世纪科技有限公司 | Method and device for scheduling access request in CDN (Content Delivery Network) |
| CN105516360A (en) * | 2016-01-19 | 2016-04-20 | 苏州帕科泰克物联技术有限公司 | Method and device for load balance of computer |
| CN108307198A (en) * | 2018-03-08 | 2018-07-20 | 广州酷狗计算机科技有限公司 | flow service node dispatching method, device and scheduling node |
| CN108259945A (en) * | 2018-04-19 | 2018-07-06 | 腾讯音乐娱乐科技（深圳）有限公司 | The method and apparatus that processing plays the playing request of multi-medium data |
| CN113711186A (en) * | 2019-04-09 | 2021-11-26 | 思科技术公司 | Distributed object placement, replication, and retrieval for cloud-scale storage and data transfer |
| CN110995866A (en) * | 2019-12-20 | 2020-04-10 | 深圳市网心科技有限公司 | Node scheduling method, node scheduling device, scheduling server and storage medium |
| CN113296924A (en) * | 2020-04-28 | 2021-08-24 | 阿里巴巴集团控股有限公司 | Content distribution method, device, system and storage medium |
| CN113301081A (en) * | 2020-06-11 | 2021-08-24 | 阿里巴巴集团控股有限公司 | Data processing method, device and electronic equipment for content distribution network |
| CN113038191A (en) * | 2021-02-26 | 2021-06-25 | 北京百度网讯科技有限公司 | Live stream scheduling method and device, electronic equipment and readable storage medium |
| CN113141317A (en) * | 2021-03-05 | 2021-07-20 | 西安电子科技大学 | Streaming media server load balancing method, system, computer equipment and terminal |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115334321A (en) * | 2022-08-10 | 2022-11-11 | 北京百度网讯科技有限公司 | Method and device for acquiring access heat of video stream, electronic equipment and medium |
| CN115334321B (en) * | 2022-08-10 | 2024-04-09 | 北京百度网讯科技有限公司 | Method and device for acquiring access heat of video stream, electronic equipment and medium |
| CN115412740A (en) * | 2022-08-29 | 2022-11-29 | 上海哔哩哔哩科技有限公司 | Live broadcast source return scheduling method and device, computing equipment and computer storage medium |
| CN115412740B (en) * | 2022-08-29 | 2024-03-01 | 上海哔哩哔哩科技有限公司 | Live broadcast back source scheduling method and device, computing equipment and computer storage medium |
| CN117119052A (en) * | 2023-10-25 | 2023-11-24 | 腾讯科技（深圳）有限公司 | Data processing method, device, electronic equipment and computer readable storage medium |
| CN117119052B (en) * | 2023-10-25 | 2024-01-19 | 腾讯科技（深圳）有限公司 | Data processing method, device, electronic equipment and computer readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11677853B2 (en) | Managing preloading of data on client systems | |
| US9952753B2 (en) | Predictive caching and fetch priority | |
| CN114697683A (en) | Intelligent scheduling method, equipment and computer program product for streaming media file | |
| US8751662B2 (en) | System and method for effectively optimizing content segment downloads in an electronic network | |
| US8393544B2 (en) | Intelligent identification of multimedia content for synchronization | |
| JP5655083B2 (en) | Prefetch content items based on social distance | |
| JP5814436B2 (en) | Caching information system and method | |
| US20120144117A1 (en) | Recommendation based caching of content items | |
| RU2619181C2 (en) | System and method for downloadable content transmission optimizing | |
| US20180367632A1 (en) | Apparatus for providing cloud service using cloud service brokerage based on multiple clouds and method thereof | |
| KR20090016119A (en) | Method and system for providing content according to personal preference | |
| CN115022342B (en) | Data processing method, device, electronic equipment and computer readable storage medium | |
| CN110377572A (en) | A kind of spatial cache management method, device, equipment and medium | |
| US20080086607A1 (en) | Method and System for Caching Data | |
| CN112506875B (en) | File storage method, related device and file storage system | |
| CN113784170B (en) | Content data preheating method and device, computer equipment and storage medium | |
| US10992743B1 (en) | Dynamic cache fleet management | |
| US11468417B2 (en) | Aggregated storage file service | |
| Jian et al. | A HDFS dynamic load balancing strategy using improved niche PSO algorithm in cloud storage | |
| CN117033693B (en) | Method and system for cloud processing in mixed mode | |
| CN116955867A (en) | Resource pre-caching method and related device | |
| CN117336522A (en) | Method, device, equipment and medium for realizing edge cache cluster | |
| CN116737375A (en) | Resource allocation method, device, computer equipment and storage medium | |
| CN113538031A (en) | Method and device for training multimedia resource allocation model and multimedia resource allocation | |
| CN115860826A (en) | Low-cost extensible advertisement putting frequency control method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |