WO2022242470A1 - Procédé, appareil et dispositif de planification de cdn et support d'enregistrement - Google Patents

Procédé, appareil et dispositif de planification de cdn et support d'enregistrement Download PDF

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Publication number
WO2022242470A1
WO2022242470A1 PCT/CN2022/091350 CN2022091350W WO2022242470A1 WO 2022242470 A1 WO2022242470 A1 WO 2022242470A1 CN 2022091350 W CN2022091350 W CN 2022091350W WO 2022242470 A1 WO2022242470 A1 WO 2022242470A1
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Prior art keywords
cdn
token
target
determining
performance value
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PCT/CN2022/091350
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English (en)
Chinese (zh)
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黄胜兰
李孟杰
黄跃龙
张宓
李小成
马茜
严冰
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北京字跳网络技术有限公司
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Publication of WO2022242470A1 publication Critical patent/WO2022242470A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/215—Flow control; Congestion control using token-bucket
    • 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
    • H04N21/27—Server based end-user applications
    • H04N21/274—Storing end-user multimedia data in response to end-user request, e.g. network recorder
    • H04N21/2747—Remote storage of video programs received via the downstream path, e.g. from the server
    • 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
    • H04N21/47—End-user applications
    • H04N21/472—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content

Definitions

  • the present disclosure relates to the technical field of data processing, for example, to a content delivery network (Content Delivery Network, CDN) scheduling method, device, device and storage medium.
  • CDN Content Delivery Network
  • CDN provided by any CDN manufacturer on the market it is difficult for the CDN provided by any CDN manufacturer on the market to support all video traffic alone. Therefore, for the video provider, the only option is to use the CDN provided by multiple CDN vendors for video transmission at the same time, and the CDN provided by each CDN vendor is allocated a certain percentage of CDN traffic. The CDN provided by all CDN vendors undertakes 100% of the video traffic.
  • the present disclosure provides a CDN scheduling method, device, equipment and storage medium, which can flexibly schedule the CDN and maximize the performance of the CDN.
  • the present disclosure provides a CDN scheduling method, including:
  • the present disclosure also provides a CDN scheduling device, including:
  • the feature group division module is configured to divide multiple users into multiple feature groups according to the set feature information
  • a performance value determination module configured to determine the performance values corresponding to each of the multiple CDNs for the multiple feature groups
  • the token delivery module is set to deliver the token corresponding to each CDN into the token bucket according to the set ratio;
  • the target token determining module is configured to determine the target token from the token bucket according to the performance value, and schedule the CDN corresponding to the target token.
  • the present disclosure also provides an electronic device, the electronic device comprising:
  • a storage device configured to store one or more programs
  • the one or more processing devices When the one or more programs are executed by the one or more processing devices, the one or more processing devices implement the above CDN scheduling method.
  • the present disclosure also provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processing device, the above CDN scheduling method is implemented.
  • FIG. 1 is a flowchart of a CDN scheduling method provided by an embodiment of the present disclosure
  • FIG. 2 is a schematic structural diagram of a CDN scheduling device provided by an embodiment of the present disclosure
  • Fig. 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.
  • the term “comprise” and its variations are open-ended, ie “including but not limited to”.
  • the term “based on” is “based at least in part on”.
  • the term “one embodiment” means “at least one embodiment”; the term “another embodiment” means “at least one further embodiment”; the term “some embodiments” means “at least some embodiments.” Relevant definitions of other terms will be given in the description below.
  • a scheduling algorithm that randomly allocates CDNs according to the proportion of CDN purchased traffic is mainly used.
  • CDN_A, CDN_B, and CDN_C agree that their respective traffic ratios are 20%, 50%, and 30%.
  • CDN_A is returned with a 20% probability
  • CDN_B is returned with a 50% probability
  • CDN_C is returned with a 30% probability.
  • the overall traffic ratio will meet the agreed traffic ratio.
  • the related technology has the following defects: 1. Although such an allocation ratio can ensure that the overall allocation ratio conforms to the initially given CDN traffic ratio, it ignores that the performance of CDNs in different regions is different. Usually, due to the different geographic locations of Edge-CDN nodes set by different CDN vendors, the server central processing unit (Central Processing Unit, CPU) and bandwidth performance of different Edge-CDN nodes are different. When CDNs provided by different CDN vendors provide services in the same area, their performance is not always equal. Since the method of allocating the CDN based on the agreed traffic ratio is adopted in the related art, it does not take into account how to use the above information to provide users with better CDN performance as a whole. 2. The importance of different user requests is different.
  • CPU Central Processing Unit
  • the playback experience of different groups of people is of different importance to the video provider. For example, new users are less sticky to the APP than old users, so minor playback quality problems may cause new users The APP is not used, so new users should be assigned a CDN with better performance to ensure a smoother playback experience.
  • Fig. 1 is a flowchart of a CDN scheduling method provided by an embodiment of the present disclosure. This embodiment is applicable to the situation of CDN scheduling, and the method can be executed by a CDN scheduling device, which can be implemented by hardware and/or It consists of software, and generally can be integrated into a device with CDN scheduling function, which can be an electronic device such as a server, a mobile terminal, or a server cluster. As shown in Figure 1, the method includes:
  • the setting feature information may include the region where the user is located, the Internet Service Provider (Internet Service Provider, ISP), the type of network used (WiFi or mobile network), etc.
  • ISP Internet Service Provider
  • WiFi Wireless Fidelity
  • Multiple users can be clustered and divided according to the set feature information to obtain multiple feature groups.
  • users with the same set characteristic information exceeding the set ratio can be divided into the same characteristic group.
  • users with more than 80% N of the same set feature information are classified into the same feature group.
  • the CDN may be a CDN provided by multiple CDN vendors for the current application.
  • the performance value can reflect the performance of the feature group on the CDN.
  • the method of determining the performance values corresponding to each of the multiple CDNs for the multiple feature groups may be: obtaining the historical operating parameters of the multiple feature groups on each CDN; determining the multiple feature groups according to the historical operating parameters The performance value of the group on each CDN respectively.
  • Running parameters can include first frame time, freeze information, bit rate, bandwidth, and round-trip delay.
  • the historical running parameters may be the average value of the parameters of each feature group collected in a set historical time period when running on each CDN.
  • the set historical time period may be the last week or the last month. When collecting parameters, it can be collected every set period of time, for example, every 5 minutes.
  • the way to determine the performance values of multiple feature groups on each CDN according to the historical operating parameters can be: call the first equation to linearly solve the historical operating parameters to obtain the performance of multiple feature groups on each CDN value; or, call the second equation to solve the gradient descent of the historical operating parameters to obtain the performance values of multiple feature groups on each CDN.
  • the first equation may be a linear equation and the second equation may be a non-linear equation.
  • Linear equations and nonlinear equations can be called from the function library. In this embodiment, no limitation is imposed on the linear equation and the nonlinear equation.
  • the method of determining the performance values of multiple feature groups on each CDN according to the historical operating parameters can also be: input the historical operating parameters into the trained tree machine learning model to obtain multiple feature groups Performance values on each CDN respectively.
  • the tree-type machine learning model can be obtained by training based on operating parameters such as the first frame time, freeze information, bit rate, bandwidth, and round-trip delay.
  • the set ratio may be the traffic ratio of CDN provided by multiple CDN vendors purchased by the video provider. Assuming that the video provider purchased the CDNs of three CDN manufacturers, namely CDN_A, CDN_B, and CDN_C, and agreed that the respective traffic ratios are 20%, 50%, and 30%, then 2:5:3 is the set ratio.
  • each token corresponds to a CDN, and the ratio of CDN tokens is set. Assuming that the setting ratio is 2:5:3, the token data of CDN_A is 20%*X, the token amount of CDN_B is 50%*X, and the token amount of CDN_C is 30*X.
  • S140 Determine the target token from the token bucket according to the performance value, and schedule the CDN corresponding to the target token.
  • first determine the remaining CDN tokens in the current token bucket then determine the feature group where the user who initiated the data request is located, and then determine that the feature group where the user is located is on the CDN corresponding to the remaining CND tokens A performance value of , and a target token is determined according to the performance value.
  • the way to determine the target token from the token bucket according to the performance value can be: when receiving the data request sent by the user, obtain the CDN corresponding to the remaining token in the current token bucket, and determine it as the remaining CDN; determine the target where the user is located For the feature group, the performance value of the target feature group on each of the remaining CDNs is normalized; and the target token is determined according to the normalized performance value.
  • the sum of the performance values of the normalized CDN is 1.
  • the CDNs corresponding to the remaining tokens in the token bucket include CDN_A, CDN_B, and CDN_C
  • the performance values of the user’s target feature group on CDN_A, CDN_B, and CDN_C are 0.4, 0.8, and 0.6 respectively.
  • the performance values are: 2/9, 4/9 and 3/9.
  • the target token is determined according to the normalized performance value.
  • the method of determining the target token according to the normalized performance value may be: dividing the set value range into at least one sub-range according to the normalized performance value; One-to-one correspondence; generate a random number within the set value range, and determine the target sub-range in which the random number falls; determine the CDN token corresponding to the target sub-range as the target token.
  • the set value range can be a range between 0-1.
  • the sub-ranges may be divided according to the proportion of the normalized performance value in the total performance value.
  • the normalized performance values of CDN_A, CDN_B, and CDN_C are respectively: 2/9, 4/9, and 3/9, and the sub-ranges divided between 0-1 are: (0, 2 /9], (2/9, 6/9], (6/9, 1].
  • the sub-range corresponding to CDN_A is (0, 2/9]
  • the sub-range corresponding to CDN_B is (2/9, 6/ 9]
  • the sub-range corresponding to CDN_C is (6/9, 1].
  • the random number function can be used to generate a random number between 0-1, for example: if the generated random number is 0.5, it falls into (2/9, 6/9], then the determined target token is the token of CDN_B , so as to schedule CDN_B for the current user.
  • the manner of determining the target token according to the normalized performance value may also be: determining the CDN token with the highest normalized performance value as the target token.
  • the normalized performance values of CDN_A, CDN_B, and CDN_C are: 2/9, 4/9, and 3/9, respectively. Then the normalized performance value of CDN_B is the largest, and the determined target token is the token of CDN_B, so that CDN_B is scheduled for the current user.
  • the target token from the token bucket after determining the target token from the token bucket according to the performance value, it also includes: extracting the target token from the token bucket; The operation of putting cards into the token bucket according to the set ratio.
  • the target token needs to be extracted from the token bucket. Assuming that the target token is the token of CDN_A, the token of CDN_A is extracted from the token bucket, and when there is no CDN_A in the token bucket, the target token is determined according to the remaining CDN tokens in the token bucket. After all the tokens in the token bucket are extracted, the tokens corresponding to each CDN need to be put into the token bucket according to the set ratio again, so as to realize the scheduling of the CDN.
  • multiple users are divided into multiple feature groups according to the set feature information; the performance values corresponding to each CDN of the multiple feature groups are determined; the tokens corresponding to each CDN are divided according to Set the ratio and put it into the token bucket; determine the target token from the token bucket according to the performance value, and schedule the CDN corresponding to the target token.
  • the CDN scheduling method provided by the embodiments of the present disclosure determines the target token from the token bucket according to the performance value corresponding to the feature group and each CDN, so as to schedule the CDN corresponding to the target token, which can flexibly schedule the CDN and ensure that the CDN Maximize performance.
  • Fig. 2 is a schematic structural diagram of a CDN scheduling device provided by an embodiment of the present disclosure. As shown in Figure 2, the device includes:
  • the characteristic group division module 210 is configured to divide multiple users into a plurality of characteristic groups according to the set characteristic information; the performance value determination module 220 is configured to determine that the plurality of characteristic groups correspond to each CDN in the plurality of CDNs respectively performance value; the token placement module 230 is set to put the token corresponding to each CDN into the token bucket according to the set ratio; the target token determination module 240 is set to determine the target from the token bucket according to the performance value Token, and dispatch the CDN corresponding to the target token.
  • the target token determination module 240 is set to:
  • the target token determining module 240 is configured to determine the target token according to the normalized performance value in the following manner:
  • the target token determining module 240 is configured to determine the target token according to the normalized performance value in the following manner:
  • the CDN token with the highest normalized performance value is determined as the target token.
  • the performance value determination module 220 is set to:
  • the operating parameters include the first frame time, freeze information, bit rate, bandwidth, and round-trip delay; determine the multiple feature groups according to the historical operating parameters Performance value on each CDN.
  • the performance value determination module 220 is configured to determine the performance values of multiple feature groups on each CDN according to historical operating parameters in the following manner:
  • a token extraction module set to:
  • the above-mentioned device can execute the methods provided by all the foregoing embodiments of the present disclosure, and has corresponding functional modules and effects for executing the above-mentioned methods.
  • the above-mentioned device can execute the methods provided by all the foregoing embodiments of the present disclosure, and has corresponding functional modules and effects for executing the above-mentioned methods.
  • FIG. 3 it shows a schematic structural diagram of an electronic device 300 suitable for implementing an embodiment of the present disclosure.
  • the electronic device 300 in the embodiment of the present disclosure may include but not limited to mobile phones, notebook computers, digital broadcast receivers, personal digital assistants (Personal Digital Assistant, PDA), tablet computers (PAD), portable multimedia players (Portable Media Player, PMP), mobile terminals such as vehicle-mounted terminals (such as vehicle-mounted navigation terminals), fixed terminals such as digital television (Television, TV), desktop computers, etc., or various forms of servers, such as independent servers or server clusters.
  • PDA Personal Digital Assistant
  • PMP portable multimedia players
  • mobile terminals such as vehicle-mounted terminals (such as vehicle-mounted navigation terminals), fixed terminals such as digital television (Television, TV), desktop computers, etc.
  • servers such as independent servers or server clusters.
  • the electronic device 300 shown in FIG. 3 is only an example, and should not limit the functions and scope of use of the embodiments of the present disclosure.
  • an electronic device 300 may include a processing device (such as a central processing unit, a graphics processing unit, etc.)
  • the device 308 loads programs in the random access storage device (Random Access Memory, RAM) 303 to perform various appropriate actions and processes.
  • RAM Random Access Memory
  • various programs and data necessary for the operation of the electronic device 300 are also stored.
  • the processing device 301, ROM 302, and RAM 303 are connected to each other through a bus 304.
  • An input/output (Input/Output, I/O) interface 305 is also connected to the bus 304 .
  • an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; including, for example, a liquid crystal display (Liquid Crystal Display, LCD) , an output device 307 such as a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309.
  • the communication means 309 may allow the electronic device 300 to perform wireless or wired communication with other devices to exchange data.
  • FIG. 3 shows electronic device 300 having various means, it is not a requirement to implement or possess all of the means shown. More or fewer means may alternatively be implemented or provided.
  • embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for performing a word recommendation method.
  • the computer program may be downloaded and installed from a network via communication means 309, or from storage means 308, or from ROM 302.
  • the processing device 301 When the computer program is executed by the processing device 301, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.
  • the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two.
  • a computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof.
  • Examples of computer readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, RAM, ROM, Erasable Programmable Read-Only Memory (EPROM) or flash memory), optical fiber, portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device .
  • the program code contained on the computer readable medium can be transmitted by any appropriate medium, including but not limited to: electric wire, optical cable, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.
  • the client and the server can communicate using any currently known or future network protocols such as Hypertext Transfer Protocol (HyperText Transfer Protocol, HTTP), and can communicate with digital data in any form or medium
  • the communication eg, communication network
  • Examples of communication networks include local area networks (Local Area Network, LAN), wide area networks (Wide Area Network, WAN), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently existing networks that are known or developed in the future.
  • the above-mentioned computer-readable medium may be included in the above-mentioned electronic device, or may exist independently without being incorporated into the electronic device.
  • the above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: divides multiple users into multiple feature groups according to the set feature information; determines A plurality of feature groups are respectively associated with the performance values corresponding to each CDN in the plurality of CDNs; the token corresponding to each CDN is put into the token bucket according to the set ratio; Determine the target token, and schedule the CDN corresponding to the target token.
  • Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, or combinations thereof, including but not limited to object-oriented programming languages—such as Java, Smalltalk, C++, and Includes conventional procedural programming languages - such as the "C" language or similar programming languages.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer can be connected to the user computer through any kind of network, including a LAN or WAN, or it can be connected to an external computer (eg via the Internet using an Internet Service Provider).
  • each block in a flowchart or block diagram may represent a module, program segment, or portion of code that contains one or more logical functions for implementing specified executable instructions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.
  • the units involved in the embodiments described in the present disclosure may be implemented by software or by hardware. Wherein, the name of the unit does not constitute a limitation of the unit itself in one case.
  • exemplary types of hardware logic components include: Field Programmable Gate Arrays (Field Programmable Gate Arrays, FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (Application Specific Standard Parts, ASSP), System on Chip (System on Chip, SOC), Complex Programmable Logic Device (Complex Programmable Logic Device, CPLD) and so on.
  • a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device.
  • a machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium.
  • a machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard drives, RAM, ROM, EPROM or flash memory, optical fibers, CD-ROMs, optical storage devices, magnetic storage devices, or Any suitable combination of the above.
  • the embodiments of the present disclosure disclose a CDN scheduling method, including:
  • Determining the target token from the token bucket according to the performance value includes:
  • the target token is determined based on the normalized performance value.
  • Determining said target token according to the normalized performance value comprising:
  • Determining said target token according to the normalized performance value comprising:
  • the CDN token with the highest normalized performance value is determined as the target token.
  • Determining the performance values corresponding to each of the multiple CDNs for the multiple feature groups including:
  • the operating parameters include first frame time, freeze information, code rate, bandwidth and round-trip delay;
  • Determining performance values of the multiple feature groups on each CDN according to the historical operating parameters including:

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Multimedia (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Databases & Information Systems (AREA)
  • Human Computer Interaction (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Information Transfer Between Computers (AREA)

Abstract

Un procédé, un appareil et un dispositif de planification de CDN et un support d'enregistrement sont divulgués dans les présentes. Le procédé de planification de CDN comprend les étapes consistant à : diviser, en fonction d'informations de caractéristiques définies, de multiples utilisateurs en de multiples groupes de caractéristiques ; déterminer des multiples groupes de caractéristiques pour qu'ils correspondent à une valeur de performance correspondant à chaque CDN parmi de multiples CDN, respectivement ; introduire un jeton correspondant à chaque CDN dans un seau à jetons selon un rapport défini ; et déterminer un jeton cible parmi le seau à jetons en fonction de la valeur de performance, et planifier un CDN correspondant au jeton cible.
PCT/CN2022/091350 2021-05-21 2022-05-07 Procédé, appareil et dispositif de planification de cdn et support d'enregistrement WO2022242470A1 (fr)

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