Bandwidth resource scheduling method and related device
By combining the life cycle of streaming media data and user behavior data, traffic prediction is carried out, and the most suitable suppliers are screened based on bandwidth unit price and usage data, the problems of inaccurate traffic prediction and high cost in the prior art are solved, and efficient bandwidth resource scheduling and cost reduction are achieved.
Patent Information
- Application Number
- CN202510337354.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-20
- Publication Date
- 2025-05-30
AI Technical Summary
Existing bandwidth resource scheduling methods can easily lead to waste or insufficient bandwidth when traffic prediction is inaccurate, and relying on a single CDN provider may lead to high costs.
By obtaining the life cycle data and user behavior data of streaming media data, combining the bandwidth unit price data and bandwidth usage data of all suppliers, traffic forecasting and supplier screening are performed, target suppliers with the lowest cost in meeting the predicted traffic demand, and the bandwidth scheduling ratio is allocated.
Improve the accuracy of traffic prediction, realize flexible scheduling of bandwidth resources, improve bandwidth utilization, and reduce operational costs.
Smart Images
Figure CN120075159A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of resource scheduling, and in particular, to a bandwidth resource scheduling method and related devices. Background Art
[0002] The currently common bandwidth resource scheduling method is as follows: Statistically analyze the traffic data used for watching streaming media data in the same historical period, and then schedule the bandwidth resources required for this traffic data from a single CDN (Content Delivery Network) provider. However, even in the same period, the traffic data required for different online video contents may be different. For example, if a popular TV drama was launched in January last year and no TV drama was launched in January this year, then the traffic data required in January last year and January this year is likely to be different, resulting in inaccurate traffic prediction. Providing traffic services based on the traffic data in January last year for January this year will cause bandwidth waste. On the contrary, if no TV drama was launched in January last year and a popular TV drama was launched in January this year, there will be a problem of insufficient bandwidth. In addition, using the bandwidth resources provided by a single CDN provider may have the problem of high cost. Summary of the Invention
[0003] In view of the above problems, this application provides a bandwidth resource scheduling method and related devices to achieve the purpose of flexibly scheduling bandwidth resources, improving bandwidth utilization rate and reducing costs. The specific solutions are as follows:
[0004] The first aspect of this application provides a bandwidth resource scheduling method, including:
[0005] Obtain the life cycle data and user behavior data of the streaming media data, as well as the bandwidth unit price data and bandwidth usage data of all providers;
[0006] Predict the traffic demand based on the life cycle data and the user behavior data to obtain the target predicted traffic data;
[0007] Determine a target provider from all the providers according to the target predicted traffic data, the bandwidth unit price data and bandwidth usage data of all the providers respectively, where the target provider is the provider with the lowest cost under the condition of meeting the target predicted traffic data;
[0008] Allocate the target predicted traffic data to the target provider to obtain the predicted value of the bandwidth scheduling ratio of the target provider.
[0009] In a possible implementation, the determining a target provider from all the providers according to the target predicted traffic data, the bandwidth unit price data and bandwidth usage data of all the providers respectively includes:
[0010] Based on the target predicted traffic data, the bandwidth unit price data and bandwidth usage data of each of all the suppliers, determine the hypothetical values of the bandwidth scheduling ratios of each of all the suppliers when allocating the target predicted traffic data to all the suppliers;
[0011] Determine the target supplier from all the suppliers according to the hypothetical values of the bandwidth scheduling ratios of each of all the suppliers.
[0012] In a possible implementation, the allocating the target predicted traffic data to the target supplier to obtain the predicted value of the bandwidth scheduling ratio of the target supplier includes:
[0013] Determine whether the target predicted traffic data is greater than a preset traffic threshold;
[0014] If not, determine the initial bandwidth scheduling ratio of the target supplier according to the bandwidth usage data of the target supplier and the target predicted traffic data, and use the initial bandwidth scheduling ratio of the target supplier as the predicted value of the bandwidth scheduling ratio of the target supplier.
[0015] In a possible implementation, it further includes:
[0016] If the target predicted traffic data is greater than the preset traffic threshold, determine the peak traffic control point data according to the target predicted traffic data and the bandwidth unit price data of the target supplier;
[0017] Determine the initial bandwidth scheduling ratio of the target supplier according to the bandwidth usage data of the target supplier and the target predicted traffic data;
[0018] Modify the initial bandwidth scheduling ratio of the target supplier according to the peak traffic control point data to obtain the predicted value of the bandwidth scheduling ratio of the target supplier.
[0019] In a possible implementation, the determining the initial bandwidth scheduling ratio of the target supplier according to the bandwidth usage data of the target supplier and the target predicted traffic data includes:
[0020] Obtain the real-time data of the bandwidth demand for the target supplier;
[0021] Determine the initial bandwidth scheduling ratio of the target supplier according to the real-time data of the bandwidth demand, the bandwidth usage data of the target supplier and the target predicted traffic data.
[0022] In a possible implementation, it further includes:
[0023] Determine the predicted bandwidth settlement cost required to purchase the target predicted traffic data based on the predicted value of the bandwidth scheduling ratio, the bandwidth unit price data, and the bandwidth usage data of the target supplier.
[0024] The second aspect of the present application provides a bandwidth resource scheduling device, including:
[0025] A data acquisition module, configured to acquire the life cycle data and user behavior data of streaming media data, as well as the bandwidth unit price data and bandwidth usage data of all suppliers;
[0026] A traffic prediction module, configured to predict the traffic demand based on the life cycle data and the user behavior data to obtain target predicted traffic data;
[0027] A supplier screening module, configured to determine a target supplier from all the suppliers according to the target predicted traffic data, the bandwidth unit price data and the bandwidth usage data of all the suppliers, where the target supplier is the supplier with the lowest cost under the condition of meeting the target predicted traffic data;
[0028] A bandwidth scheduling module, configured to allocate the target predicted traffic data to the target supplier to obtain a predicted value of the bandwidth scheduling ratio of the target supplier.
[0029] The third aspect of the present application provides a computer program product, including computer-readable instructions, which, when running on an electronic device, enable the electronic device to implement the bandwidth resource scheduling method in the first aspect or any implementation manner of the first aspect.
[0030] The fourth aspect of the present application provides an electronic device, including at least one processor and a memory connected to the processor, where:
[0031] The memory is used to store a computer program;
[0032] The processor is configured to execute the computer program so that the electronic device can implement the bandwidth resource scheduling method in the first aspect or any implementation manner of the first aspect.
[0033] The fifth aspect of the present application provides a computer storage medium, which carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, the electronic device can be enabled to implement the bandwidth resource scheduling method in the first aspect or any implementation manner of the first aspect.
[0034] With the above technical solution, the bandwidth resource scheduling method provided by this application takes into account that the demand for traffic is different when streaming media data is in different life cycle stages, and the behavior of users towards streaming media data can reflect the traffic demand of users. Therefore, this application can obtain the life cycle data and user behavior data of streaming media data, and predict the traffic demand based on the life cycle data and user behavior data to obtain the target predicted traffic data. This application performs traffic prediction based on the life cycle data and user behavior data of streaming media data, improving the accuracy of traffic prediction.
[0035] Furthermore, this application obtains the bandwidth unit price data and bandwidth usage data of each supplier respectively. Based on the target predicted traffic data, the bandwidth unit price data and bandwidth usage data of each supplier, it determines the target supplier that meets the target predicted traffic data and has the lowest cost from all suppliers, and allocates the target predicted traffic data to the target supplier to obtain the predicted value of the bandwidth scheduling ratio of the target supplier. It can be seen that during the scheduling process of bandwidth resources, this application can screen the most suitable target supplier for bandwidth resource scheduling according to the bandwidth unit price data and bandwidth usage data, realizing flexible scheduling of suppliers, improving bandwidth utilization, and reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages and aspects of the various embodiments of the present disclosure will become more apparent. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the original components and elements are not necessarily drawn to scale.
[0037] Figure 1 It is a schematic diagram of a system architecture provided by this application;
[0038] Figure 2 It is an optional hardware structure schematic diagram of the terminal 100 provided by this application;
[0039] Figure 3 It is a structural schematic diagram of a server 200 provided by this application;
[0040] Figure 4 It is a flowchart of a bandwidth resource scheduling method provided by this application;
[0041] Figure 5 It is a structural schematic diagram of a bandwidth resource scheduling device provided by this application;
[0042] Figure 6 It is a structural schematic diagram of an electronic device provided by this application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0043] The embodiments of the present application will be described below in conjunction with the accompanying drawings in the embodiments of the present application. The terms used in the embodiments of the present application are only used to explain the specific embodiments of the present application, rather than to limit the present application.
[0044] The embodiments of the present application will be described below in conjunction with the accompanying drawings. Those of ordinary skill in the art will understand that with the development of technology and the emergence of new scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.
[0045] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such terms can be interchanged under appropriate circumstances, which is only a way of distinguishing objects with the same attributes when describing the embodiments of the present application. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, so that a process, method, system, product or device comprising a series of units does not have to be limited to those units, but may include other units not clearly listed or inherent to these processes, methods, products or devices.
[0046] See Figure 1 , Figure 1 which shows a schematic diagram of a system architecture. The system may include a terminal 100 and a server 200. Among them, the server 200 may include one or more servers ( Figure 1 illustrated by including one server as an example), and the server 200 may provide the method provided by the embodiments of the present application for one or more terminals.
[0047] Among them, an application program may be installed on the terminal 100. The above application program and web page may provide an interface. The terminal 100 may receive relevant parameters input by the user on the interface and send the above parameters to the server 200. The server 200 may obtain a processing result based on the received parameters and return the processing result to the terminal 100.
[0048] It should be understood that in some alternative implementations, the terminal 100 may also complete the action of obtaining the processing result based on the received parameters by itself, without the need for the cooperation of the server. The embodiments of the present application do not limit this.
[0049] Next, describe Figure 1 the product form of the terminal 100 in
[0050] The terminal 100 in the embodiments of the present application may be a mobile phone, a tablet computer, a wearable device, a vehicle-mounted device, an Augmented Reality (AR) / Virtual Reality (VR) device, a laptop computer, an Ultra-mobile Personal Computer (UMPC), a netbook, a Personal Digital Assistant (PDA), etc. The embodiments of the present application do not impose any restrictions thereon.
[0051] Figure 2 Fig. shows an optional schematic diagram of the hardware structure of the terminal 100.
[0052] Referring Figure 2 As shown, the terminal 100 may include a radio frequency unit 110, a memory 120, an input unit 130, a display unit 140, a camera 150 (optional), an audio circuit 160 (optional), a speaker 161 (optional), a microphone 162 (optional), a headphone jack 163 (optional), a processor 170, an external interface 180, a power supply 190, and other components. Those skilled in the art can understand that Figure 2 This is merely an example of a terminal or a multifunctional device, and does not constitute a limitation on the terminal or the multifunctional device. It may include more or fewer components than shown in the figure, or combine certain components, or different components.
[0053] The input unit 130 can be used to receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the portable multifunctional device. Specifically, the input unit 130 may include a touch screen 131 (optional) and / or other input devices 132. The touch screen 131 can collect touch operations of the user thereon or nearby (such as operations of the user using a finger, a joint, a stylus, or any suitable object on or near the touch screen), and drive the corresponding connection device according to a pre-set program. The touch screen can detect the touch actions of the user on the touch screen, convert the touch actions into touch signals and send them to the processor 170, and can receive and execute commands sent by the processor 170; the touch signals at least include contact coordinate information. The touch screen 131 can provide an input interface and an output interface between the terminal 100 and the user. In addition, various types such as resistive, capacitive, infrared, and surface acoustic wave can be used to implement the touch screen. In addition to the touch screen 131, the input unit 130 may further include other input devices. Specifically, the other input devices 132 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.
[0054] Among them, the input device 132 can receive input data and so on.
[0055] The display unit 140 can be used to display information input by the user or information provided to the user, various menus of the terminal 100, an interactive interface, file display, and / or the playback of any multimedia file.
[0056] The memory 120 can be used to store instructions and data. The memory 120 mainly includes a storage instruction area and a storage data area. The storage data area can store various data, such as multimedia files, texts, etc.; the storage instruction area can store software units such as an operating system, applications, instructions required for at least one function, or their subsets and extended sets. It can also include a non-volatile random access memory; it provides the processor 170 with management of hardware, software, and data resources in the computing processing device, supports control software and applications. It is also used for the storage of multimedia files and the storage of running programs and applications.
[0057] The processor 170 is the control center of the terminal 100. It connects various parts of the entire terminal 100 through various interfaces and lines. By running or executing instructions stored in the memory 120 and calling data stored in the memory 120, it executes various functions of the terminal 100 and processes data, thereby performing overall control of the terminal device. Optionally, the processor 170 may include one or more processing units; preferably, the processor 170 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor may not be integrated into the processor 170. In some embodiments, the processor and the memory can be implemented on a single chip. In some embodiments, they can also be separately implemented on independent chips. The processor 170 can also be used to generate corresponding operation control signals, send them to corresponding components of the computing processing device, read and process data in the software, especially read and process data and programs in the memory 120, so that each functional module therein executes corresponding functions, thereby controlling the corresponding components to act according to the requirements of the instructions.
[0058] Among them, the memory 120 can be used to store software codes related to the bandwidth resource scheduling method. The processor 170 can execute the steps of the bandwidth resource scheduling method and can also schedule other units (such as the above-mentioned input unit 130 and display unit 140) to implement corresponding functions.
[0059] The radio frequency unit 110 (optional) can be used for receiving and transmitting information or signals during a call. For example, after receiving the downlink information from the base station, it is sent to the processor 170 for processing. Additionally, the uplink data is sent to the base station. Generally, the RF circuit includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (LNA), a duplexer, etc. In addition, the radio frequency unit 110 can also communicate with network devices and other devices via wireless communication. This wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobile Communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.
[0060] Among them, in the embodiment of the present application, the radio frequency unit 110 can send data to the server 200 and receive the processing result sent by the server 200.
[0061] It should be understood that the radio frequency unit 110 is optional and can be replaced by other communication interfaces, such as a network interface.
[0062] The terminal 100 also includes a power supply 190 (such as a battery) for powering each component. Preferably, the power supply can be logically connected to the processor 170 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system.
[0063] The terminal 100 also includes an external interface 180. This external interface can be a standard Micro USB interface or a multi-pin connector, and can be used to connect the terminal 100 to other devices for communication, or to connect a charger to charge the terminal 100.
[0064] Although not shown, the terminal 100 may also include a flash, a Wireless Fidelity (WiFi) module, a Bluetooth module, sensors with different functions, etc., which will not be elaborated here. Some or all of the methods described below can be applied to the terminal 100 as Figure 2 shown.
[0065] Next, the product form of the server 200 will be described. Figure 1 The product form of the server 200 in Figure 1
[0066] Figure 3 A schematic structural diagram of a server 200 is provided, as Figure 3 shown. The server 200 includes a bus 201, a processor 202, a communication interface 203, and a memory 204. The processor 202, the memory 204, and the communication interface 203 communicate with each other through the bus 201.
[0067] The bus 201 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity in representation, Figure 3 only a thick line is used to represent it in Figure 3 , but it does not mean that there is only one bus or one type of bus.
[0068] The processor 202 can be any one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a Micro Processor (MP), or a Digital Signal Processor (DSP), etc.
[0069] The memory 204 can include a volatile memory, such as a Random Access Memory (RAM). The memory 204 can also include a non-volatile memory, such as a Read-Only Memory (ROM), a flash memory, a Hard Disk Drive (HDD), or a Solid State Drive (SSD).
[0070] Among them, the memory 204 can be used to store software codes related to the bandwidth resource scheduling method, and the processor 202 can execute the steps of the bandwidth resource scheduling method of the chip, or can also schedule other units to implement corresponding functions.
[0071] It should be understood that the above-mentioned terminal 100 and server 200 can be centralized or distributed devices, and the processors in the above-mentioned terminal 100 and server 200 (such as processor 170 and processor 202) can be hardware circuits (such as Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), general-purpose processor, DSP, microprocessor or microcontroller, etc.), or a combination of these hardware circuits. For example, the processor can be a hardware system with the function of executing instructions, such as CPU, DSP, etc., or a hardware system without the function of executing instructions, such as ASIC, FPGA, etc., or a combination of the above-mentioned hardware system without the function of executing instructions and the hardware system with the function of executing instructions.
[0072] This application provides a bandwidth resource scheduling method. Optionally, the bandwidth resource scheduling method provided by this application can be applied to a streaming media data platform.
[0073] In a possible scenario, after the streaming media data publishing platform publishes (or goes online) the streaming media data, it schedules the bandwidth resources required for the streaming media data from the CDN provider so that users can watch the streaming media data and interact.
[0074] It should be noted that the above scenario is only an example and does not limit this application.
[0075] To make those skilled in the art better understand this application, the bandwidth resource scheduling method of the embodiments of this application will be introduced in detail below with reference to the accompanying drawings.
[0076] Refer to Figure 4 , Figure 4 which is a schematic flowchart of a bandwidth resource scheduling method provided by an embodiment of this application. The method may include:
[0077] Step S401, obtain the life cycle data and user behavior data of the streaming media data, as well as the bandwidth unit price data and bandwidth usage data of each provider.
[0078] Optionally, the streaming media data can be video data, audio data or image data.
[0079] The above life cycle data characterizes the life cycle stage of the streaming media data. In this embodiment, the life cycle data is positively correlated with the traffic data required for the corresponding life cycle stage.
[0080] For example, the life cycle stages include the streaming data release stage, the streaming data popularity stage, and the streaming data decline stage. Among them, the streaming data release stage refers to the period when the streaming data is just released (or goes online) and the user attention gradually rises. During this period, the traffic demand of users shows an upward trend, so the life cycle data is 2; the streaming data popularity stage refers to the period when the user attention shows a peak after the streaming data is released for a period of time. During this period, the traffic demand of users is at the peak, so the life cycle data is 3; the streaming data decline stage refers to the period when the user attention gradually decreases after the streaming data is released for a long time. During this period, the traffic demand of users shows a downward trend, so the life cycle data is 1.
[0081] Of course, the above life cycle stages and life cycle data are only examples. In addition, they can be other, and this application does not make a limit.
[0082] Optionally, the user behavior data can be one or more of the following data: the viewing data of users (such as the number of views, viewing duration, viewing completion rate, etc.), interaction data (such as the number of likes, shares, comments, favorites, etc.), the number of playbacks, the number of viewers, the number of retentions, and the conversion rate (such as the proportion of clicking on the promotion link, the proportion of purchasing products, etc.).
[0083] It should be understood that in addition to the above-listed user behavior data, other user behavior data can also be included, and this application does not limit.
[0084] The supplier in this embodiment refers to the CDN supplier (similarly hereinafter), which can provide bandwidth resources for users to watch streaming data and interact.
[0085] The above bandwidth unit price data refers to the cost of unit bandwidth. For example, the cost of 1 bps (bps refers to bits per second). The bandwidth unit price data of different suppliers may be different, and it may be specifically affected by factors such as regional differences, billing models, preferential activities, bandwidth types, and duration supply and demand relationships.
[0086] The above bandwidth usage data refers to the consumption of bandwidth. According to this bandwidth usage data, the remaining bandwidth of the corresponding supplier can be determined, that is, the maximum bandwidth that the corresponding supplier can provide.
[0087] Step S402, predict the traffic demand according to the life cycle data and the user behavior data to obtain the target predicted traffic data.
[0088] There are multiple implementation manners for this step, and two of them are provided here but not limited to the following.
[0089] First, input the lifecycle data and user behavior data into a pre-configured traffic prediction neural network model to obtain the target predicted traffic data output by the traffic prediction neural network model. Here, the traffic prediction neural network model is trained using the training lifecycle data and training user behavior data labeled with traffic data labels as the training data.
[0090] Second, pre-construct a traffic prediction mathematical model, substitute the lifecycle data and user behavior data into the traffic prediction mathematical model to obtain the target predicted traffic data.
[0091] Optionally, the traffic prediction mathematical model is as follows:
[0092] Formula (1);
[0093] Where, represents the target predicted traffic data obtained based on the lifecycle data and user behavior data at time t, that is, the predicted traffic data at time t + 1; represents the lifecycle data of the streaming media data at time t, represents the lifecycle stage where the streaming media data is at time t, represents the influence coefficient on traffic, represents the user behavior data at time t, represents the dynamic influence function on traffic, represents the time-varying dynamic coefficient of the traffic fluctuation at time t, represents the time-varying dynamic coefficient of the user behavior at time t.
[0094] In the above formula (1), , , and are all preset or statistically obtained functional relationships.
[0095] Taking the application of the embodiments of the present application to a streaming media data platform as an example, optionally, the above functional relationships can be pre-stored in the background system of the streaming media data platform and retrieved from the background system for use when formula (1) needs to be used.
[0096] Of course, in addition to the above methods of establishing neural network models and mathematical models, other methods can also be used to obtain the target predicted traffic data, such as the method of pre-training large language models, etc., which are not specifically limited in the present application.
[0097] Step S403: Determine the target supplier from all suppliers according to the target predicted traffic data, the bandwidth unit price data and bandwidth usage data of each supplier.
[0098] Here, the target supplier refers to a supplier that provides target predicted traffic data to meet the needs of users to watch streaming media data and interact.
[0099] It should be understood that in some scenarios, it may be the case that a single supplier cannot meet the target predicted traffic data. For example, there are a total of n (n≥1, and n is an integer) suppliers, and the remaining bandwidth of these n suppliers does not meet the target predicted traffic data. Then, multiple suppliers are required to provide the target predicted traffic data together. Therefore, the target supplier determined in this embodiment includes at least one supplier.
[0100] In order to minimize costs as much as possible while meeting the target predicted traffic data, this embodiment needs to consider the bandwidth unit price data when determining the target supplier, so as to obtain at least one supplier with the lowest cost when the bandwidth usage data meets the target predicted traffic data as the target supplier. That is, preferably, the target supplier in this embodiment is the supplier with the lowest cost when meeting the target predicted traffic data.
[0101] Step S404: Allocate the target predicted traffic data to the target supplier to obtain the predicted value of the bandwidth scheduling ratio of the target supplier.
[0102] The predicted value of the bandwidth scheduling ratio of the above target supplier is used for this application to perform bandwidth resource scheduling in a timely manner according to the predicted value of the bandwidth scheduling ratio.
[0103] For the bandwidth resource scheduling method provided by this application, considering that the traffic requirements of streaming media data are different in different life cycle stages, and the behavior of users towards streaming media data can reflect the traffic requirements of users. Therefore, this application can obtain the life cycle data and user behavior data of the streaming media data, and predict the traffic requirements based on the life cycle data and user behavior data to obtain the target predicted traffic data. This application performs traffic prediction based on the life cycle data and user behavior data of the streaming media data, improving the accuracy of traffic prediction.
[0104] Furthermore, this application obtains the bandwidth unit price data and bandwidth usage data of all suppliers respectively. According to the target predicted traffic data, the bandwidth unit price data and bandwidth usage data of all suppliers respectively, it determines the target supplier that meets the target predicted traffic data and has the lowest cost from all suppliers, and allocates the target predicted traffic data to the target supplier to obtain the predicted value of the bandwidth scheduling ratio of the target supplier. It can be seen that in the process of bandwidth resource scheduling of this application, it can screen the most suitable target supplier for bandwidth resource scheduling according to the bandwidth unit price data and bandwidth usage data, realizing flexible scheduling of suppliers, improving bandwidth utilization rate, and reducing costs.
[0105] In some embodiments of the present application, the process of the foregoing step S403, "determining a target supplier from all suppliers according to the target predicted traffic data, the bandwidth unit price data of each of all suppliers, and the bandwidth usage data", is introduced.
[0106] In this embodiment, according to the target predicted traffic data, the bandwidth unit price data of each of all suppliers, and the bandwidth usage data, it is possible to determine the hypothetical values of the bandwidth scheduling ratios of each of all suppliers when allocating the target predicted traffic data to all suppliers. Here, the "hypothetical value" means that it is not really necessary to allocate the target predicted traffic data to all suppliers, but assuming that the target predicted traffic data is allocated to all suppliers, then, the bandwidth scheduling ratio that each supplier needs to provide.
[0107] Through the above assumption, in this embodiment, according to the hypothetical values of the bandwidth scheduling ratios of each of all suppliers, it is possible to determine which suppliers among all suppliers can provide as much bandwidth as possible and have low bandwidth unit price data. Accordingly, the target supplier can be obtained, that is, in this embodiment, the target supplier can be determined from all suppliers according to the hypothetical values of the bandwidth scheduling ratios of each of all suppliers.
[0108] In a possible implementation, the process of "determining the hypothetical values of the bandwidth scheduling ratios of each of all suppliers when allocating the target predicted traffic data to all suppliers according to the target predicted traffic data, the bandwidth unit price data of each of all suppliers, and the bandwidth usage data" can be implemented by the first bandwidth scheduling mathematical model shown in the following formula (2).
[0109] Formula (2);
[0110] Wherein, represents the hypothetical value of the bandwidth scheduling ratio of the th supplier at the moment of t + 1, represents the bandwidth unit price data of the th supplier, represents the bandwidth consumption discount coefficient of the th supplier, represents the bandwidth usage data of the th supplier, represents the small deviation coefficient, represents the bandwidth discount attenuation coefficient of the th supplier, represents the Laplace distribution function of the bandwidth consumption of the th supplier (used to reflect the traffic fluctuation of the CDN), represents the number of all suppliers participating in the bandwidth resource scheduling.
[0111] The above , , and All of them are preset values, which are generally fixed in the same application scenario. Similar to the functional relationship in the previous formula (1), the four preset values here can also be pre-stored in the background system of the streaming media data platform, and then retrieved from the background system when formula (2) is needed.
[0112] Above and All are real-time data at time t.
[0113] It should also be noted that the above formula (2) is only an example, and the process of "determining the hypothetical value of the bandwidth scheduling ratio of all suppliers when allocating the target predicted traffic data to all suppliers based on the target predicted traffic data, the bandwidth unit price data of all suppliers, and the bandwidth usage data" can also be implemented in other ways, such as neural network models, large language models, etc., which are not limited here.
[0114] There are multiple implementation methods for the above process of "determining the target supplier from all suppliers according to the respective hypothetical values of the bandwidth scheduling ratios of all suppliers", including but not limited to the following two.
[0115] Optionally, all suppliers may be sorted in descending order according to their respective bandwidth scheduling ratio hypothetical values to obtain a supplier sorting result, and then a preset number of suppliers ranked high in the supplier sorting result may be determined as target suppliers.
[0116] That is, taking the preset number as m as an example, the first m suppliers with the largest assumed bandwidth scheduling ratio values among all suppliers can be determined as target suppliers.
[0117] Optionally, a ratio threshold may be preset, and the bandwidth scheduling ratio hypothetical values of all suppliers are compared with the preset ratio threshold to screen out suppliers whose bandwidth scheduling ratio hypothetical values are greater than or equal to the preset ratio threshold from all suppliers, and determine the screened out suppliers as target suppliers.
[0118] In summary, this embodiment dynamically screens suppliers based on target predicted traffic data, bandwidth unit price data of all suppliers, and bandwidth usage data. Target suppliers with sufficient bandwidth to meet the target predicted traffic data and the lowest cost can be screened out. Supplier scheduling is more flexible, bandwidth resource allocation is more reasonable, and it can reduce costs and improve bandwidth utilization.
[0119] In some other embodiments of the present application, the process of the foregoing step S404, "allocating the target predicted traffic data to the target supplier to obtain the predicted value of the bandwidth scheduling ratio of the target supplier", is introduced.
[0120] In an alternative embodiment, this embodiment may obtain the real-time data of the bandwidth demand for the target supplier, and determine the initial bandwidth scheduling ratio of the target supplier according to the real-time data of the bandwidth demand, the bandwidth usage data of the target supplier, and the target predicted traffic data, and use the initial bandwidth scheduling ratio of the target supplier as the predicted value of the bandwidth scheduling ratio of the target supplier.
[0121] Here, the real-time data of the bandwidth demand refers to the real-time demand data of the user for the bandwidth resources provided by the target supplier. For example, when the user uses the bandwidth resources provided by the target supplier to watch streaming media data, the user may feedback network lag, and according to the real-time network lag information, it can be determined that the user has a high demand for bandwidth; for another example, when the user uses the bandwidth resources provided by the target supplier to watch streaming media data, the user may have an action of switching to high-definition picture quality or a download action, and according to the real-time user actions, it can be determined that the user has a high demand for bandwidth.
[0122] Optionally, the process of "determining the initial bandwidth scheduling ratio of the target supplier according to the real-time data of the bandwidth demand, the bandwidth usage data of the target supplier, and the target predicted traffic data" can be implemented by the second bandwidth scheduling mathematical model shown in the following formula (3).
[0123] Formula (3);
[0124] Among them, represents the initial bandwidth scheduling ratio of the th supplier (all suppliers in this formula are target suppliers) at the moment of t + 1, and are adjustment coefficients, represents the bandwidth adjustment frequency, represents the time evolution influence factor of the bandwidth resource, represents the capacity attenuation factor, represents the target predicted traffic data, represents the bandwidth capacity, represents the real-time data of the bandwidth demand for the th supplier, represents the th supplier's bandwidth usage data, represents the attenuation coefficient of the bandwidth resource with time.
[0125] The above 、 、 、 , , and are all real-time data at time t. , , , , , and are all preset values, which can be pre-stored in the background system of the streaming media data platform and used when needed.
[0126] In another alternative embodiment, considering that the target predicted traffic data may exceed the preset traffic threshold and peak shaving control is required, for this reason, this embodiment also provides another process of "allocating the target predicted traffic data to the target supplier to obtain the predicted value of the bandwidth scheduling ratio of the target supplier".
[0127] Specifically, this embodiment can determine whether the target predicted traffic data is greater than the preset traffic threshold. If not, it is determined that peak shaving control is not required. At this time, the initial bandwidth scheduling ratio of the target supplier can be determined according to the bandwidth usage data of the target supplier and the target predicted traffic data, and the initial bandwidth scheduling ratio of the target supplier is used as the predicted value of the bandwidth scheduling ratio of the target supplier.
[0128] Optionally, the process of "determining the initial bandwidth scheduling ratio of the target supplier according to the bandwidth usage data of the target supplier and the target predicted traffic data" may include: inputting the bandwidth usage data of the target supplier and the target predicted traffic data into a pre-trained bandwidth scheduling model to obtain the initial bandwidth scheduling ratio of the target supplier output by the bandwidth scheduling model. Here, the bandwidth scheduling model is trained using the training bandwidth usage data and training traffic data labeled with the initial bandwidth scheduling ratio label as the training data.
[0129] Preferably, the process of "determining the initial bandwidth scheduling ratio of the target supplier according to the bandwidth usage data of the target supplier and the target predicted traffic data" may include: obtaining the real-time data of the bandwidth demand for the target supplier, and determining the initial bandwidth scheduling ratio of the target supplier according to the real-time data of the bandwidth demand, the bandwidth usage data of the target supplier, and the target predicted traffic data.
[0130] Here, the process of "determining the initial bandwidth scheduling ratio of the target supplier according to the real-time data of the bandwidth demand, the bandwidth usage data of the target supplier, and the target predicted traffic data" can refer to the introduction in the previous embodiment and will not be elaborated here.
[0131] Optionally, if the target predicted traffic data is greater than a preset traffic threshold, it is determined that peak shaving control needs to be performed. At this time, the peak traffic control point data can be determined according to the target predicted traffic data and the bandwidth unit price data of the target supplier. The initial bandwidth scheduling ratio of the target supplier can be determined according to the bandwidth usage data of the target supplier and the target predicted traffic data. The initial bandwidth scheduling ratio of the target supplier is corrected according to the peak traffic control point data to obtain the predicted value of the bandwidth scheduling ratio of the target supplier.
[0132] Optionally, the process of "determining the peak traffic control point data according to the target predicted traffic data and the bandwidth unit price data of the target supplier" can be implemented by the following formula (4).
[0133] Formula (4);
[0134] Where, represents the peak traffic control point data at time t + 1, represents the th supplier (all suppliers in this formula are target suppliers)' bandwidth unit price data, represents a piecewise function, characterizing the peak control condition for traffic exceeding, represents a small deviation coefficient, ensuring that the part exceeding the traffic range can be effectively reduced during traffic fluctuations, represents the total demand for bandwidth traffic, and the specific value can be obtained according to the target predicted traffic data, represents the target predicted traffic data, represents the th supplier's exponential decay rate of bandwidth load, represents the number of target suppliers.
[0135] The above 、 and are all preset values, which can be pre-stored in the background system of the streaming media data platform for use when needed.
[0136] The process of "determining the initial bandwidth scheduling ratio of the target supplier according to the bandwidth usage data of the target supplier and the target predicted traffic data" is the same as the previous process. For details, please refer to the previous introduction and will not be elaborated here.
[0137] In summary, this embodiment can dynamically perform peak shaving control according to the target predicted traffic data, avoiding over-consumption of bandwidth resources leading to load imbalance, and avoiding additional bandwidth costs and resource waste.
[0138] In some other embodiments of the present application, in combination with the predicted value of the bandwidth scheduling ratio allocated above, this embodiment further provides a method for calculating the predicted bandwidth settlement cost. Specifically, this embodiment can determine the predicted bandwidth settlement cost required to purchase the target predicted traffic data based on the predicted value of the bandwidth scheduling ratio of the target supplier, the bandwidth unit price data, and the bandwidth usage data.
[0139] Optionally, the process of "determining the predicted bandwidth settlement cost required to purchase the target predicted traffic data based on the predicted value of the bandwidth scheduling ratio of the target supplier, the bandwidth unit price data, and the bandwidth usage data" can be implemented by the following formula (5).
[0140] Formula (5);
[0141] Wherein, represents the predicted bandwidth settlement cost at time t + 1, represents the bandwidth unit price data of the th supplier (all suppliers in this formula are target suppliers), represents the predicted value of the bandwidth scheduling ratio of the th supplier at time t + 1, represents the bandwidth usage data of the th supplier, represents the correction function of the bandwidth scheduling optimization to the settlement cost, represents the number of target suppliers.
[0142] The above and are both real-time data at time t.
[0143] The above is a preset function and can be pre-stored in the background system of the streaming media data platform for use when needed.
[0144] In summary, this embodiment can fully integrate the bandwidth unit price data, bandwidth usage data, and target predicted traffic data of the target supplier to obtain the bandwidth settlement cost, reducing the operating cost.
[0145] The above introduced a bandwidth resource scheduling method provided by the embodiments of the present application. Next, the device for executing the above bandwidth resource scheduling method will be introduced.
[0146] Please refer to Figure 5 ., Figure 5 which is a schematic structural diagram of a bandwidth resource scheduling device provided by an embodiment of the present application. As Figure 5 shown, the device may include:
[0147] A data acquisition module 501, configured to acquire the life cycle data and user behavior data of streaming media data, as well as the bandwidth unit price data and bandwidth usage data of all suppliers respectively;
[0148] A traffic prediction module 502, configured to predict the traffic demand based on the life cycle data and user behavior data to obtain target predicted traffic data;
[0149] A supplier screening module 503, configured to determine a target supplier from all suppliers according to the target predicted traffic data, the bandwidth unit price data and bandwidth usage data of all suppliers respectively, where the target supplier is the supplier with the lowest cost under the condition of meeting the target predicted traffic data;
[0150] A bandwidth scheduling module 504, configured to allocate the target predicted traffic data to the target supplier to obtain a predicted value of the bandwidth scheduling ratio of the target supplier.
[0151] In a possible implementation, the above-mentioned supplier screening module may specifically be used for:
[0152] Determine the imaginary values of the bandwidth scheduling ratios of all suppliers when allocating the target predicted traffic data to all suppliers according to the target predicted traffic data, the bandwidth unit price data and bandwidth usage data of all suppliers respectively;
[0153] Determine the target supplier from all suppliers according to the imaginary values of the bandwidth scheduling ratios of all suppliers respectively.
[0154] In a possible implementation, the above-mentioned bandwidth scheduling module may specifically be used for:
[0155] Judge whether the target predicted traffic data is greater than a preset traffic threshold;
[0156] If not, determine the initial bandwidth scheduling ratio of the target supplier according to the bandwidth usage data and target predicted traffic data of the target supplier, and use the initial bandwidth scheduling ratio of the target supplier as the predicted value of the bandwidth scheduling ratio of the target supplier.
[0157] In a possible implementation, the above-mentioned bandwidth scheduling module may also be used for:
[0158] If the target predicted traffic data is greater than the preset traffic threshold, determine the peak traffic control point data according to the target predicted traffic data and the bandwidth unit price data of the target supplier;
[0159] Determine the initial bandwidth scheduling ratio of the target supplier according to the bandwidth usage data and target predicted traffic data of the target supplier;
[0160] Modify the initial bandwidth scheduling ratio of the target supplier according to the peak traffic control point data to obtain the predicted value of the bandwidth scheduling ratio of the target supplier.
[0161] In a possible implementation, the process of the above bandwidth scheduling module determining the initial bandwidth scheduling ratio of the target supplier according to the bandwidth usage data and target predicted traffic data of the target supplier may include:
[0162] Obtain the real-time data of the bandwidth demand for the target supplier;
[0163] Determine the initial bandwidth scheduling ratio of the target supplier according to the real-time data of the bandwidth demand, the bandwidth usage data of the target supplier, and the target predicted traffic data.
[0164] In a possible implementation, the bandwidth resource scheduling device provided in the embodiments of the present application may further include: a cost settlement module.
[0165] The cost settlement module is used to determine the predicted bandwidth settlement cost required to purchase the target predicted traffic data according to the predicted value of the bandwidth scheduling ratio of the target supplier, the bandwidth unit price data, and the bandwidth usage data.
[0166] The bandwidth resource scheduling device provided in the embodiments of the present application corresponds to the bandwidth resource scheduling method provided above. For details, refer to the above introduction and will not be elaborated here.
[0167] An electronic device is also provided in the embodiments of the present application. Refer to Figure 6 As shown, it shows a schematic structural diagram of an electronic device suitable for implementing the electronic device in the embodiments of the present application. The electronic device in the embodiments of the present application may include, but is not limited to, fixed terminals such as mobile phones, laptop computers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), desktop computers, and the like. Figure 6 The electronic device shown is only an example and should not bring any limitations to the functions and usage scopes of the embodiments of the present application.
[0168] As Figure 6 shown, the electronic device may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage device 608 into the random access memory (RAM) 603. When the electronic device is powered on, various programs and data required for the operation of the electronic device are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The input / output (I / O) interface 605 is also connected to the bus 604.
[0169] Typically, the following devices can be connected to the I / O interface 605: input devices 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 608 including, for example, a memory card, a hard disk, etc.; and a communication device 609. The communication device 609 can allow the electronic device to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 6 an electronic device with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices can be implemented or had.
[0170] An embodiment of the present application also provides a computer program product, including computer-readable instructions, which, when running on an electronic device, enable the electronic device to implement any one of the bandwidth resource scheduling methods provided by the embodiments of the present application.
[0171] An embodiment of the present application also provides a computer-readable storage medium, which carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, can enable the electronic device to implement any one of the bandwidth resource scheduling methods provided by the embodiments of the present application.
[0172] In addition, it should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the device embodiments provided in the present application, the connection relationships between the modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines.
[0173] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general hardware. Of course, it can also be implemented by dedicated hardware including application-specific integrated circuits, dedicated CPUs, dedicated memories, dedicated components, etc. Generally, functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structures for implementing the same function can also be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, software program implementation is a better embodiment in more cases. Based on such an understanding, the technical solution of the present application, in essence or the part that makes a contribution to the prior art, can be embodied in the form of a software product. The computer software product is stored in a readable storage medium, such as a floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disc of a computer, and includes several instructions to enable a computer device (which can be a personal computer, training device, or network device, etc.) to execute the methods described in various embodiments of the present application.
[0174] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product.
[0175] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a dedicated computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, training device, or data center to another website, computer, training device, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store, or a data storage device such as a training device or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive (SSD)).
Claims
1. A bandwidth resource scheduling method, characterized in that: include: Obtain lifecycle data and user behavior data of streaming media data, as well as bandwidth unit price data and bandwidth usage data of all providers; Predicting traffic demand based on the life cycle data and the user behavior data to obtain target predicted traffic data; Determine a target supplier from among all the suppliers according to the target predicted traffic data, the bandwidth unit price data and the bandwidth usage data of all the suppliers, wherein the target supplier is the supplier with the lowest cost while satisfying the target predicted traffic data; The target predicted traffic data is distributed to the target supplier to obtain a bandwidth scheduling ratio prediction value of the target supplier.
2. The bandwidth resource scheduling method according to claim 1, characterized in that: The step of determining a target supplier from among all the suppliers according to the target predicted traffic data, the bandwidth unit price data and the bandwidth usage data of all the suppliers respectively includes: Determine, according to the target predicted traffic data, the bandwidth unit price data and bandwidth usage data of each of the suppliers, the hypothetical values of the bandwidth scheduling ratios of each of the suppliers when allocating the target predicted traffic data to the suppliers; The target supplier is determined from among all the suppliers according to the respective assumed bandwidth scheduling ratios of all the suppliers.
3. The bandwidth resource scheduling method according to claim 1, characterized in that: The step of allocating the target predicted traffic data to the target supplier to obtain a predicted value of the bandwidth scheduling ratio of the target supplier includes: Determine whether the target predicted flow data is greater than a preset flow threshold; If not, the initial bandwidth scheduling ratio of the target supplier is determined according to the bandwidth usage data of the target supplier and the target predicted traffic data, and the initial bandwidth scheduling ratio of the target supplier is used as the predicted value of the bandwidth scheduling ratio of the target supplier.
4. The bandwidth resource scheduling method according to claim 3, characterized in that: Also includes: If the target predicted traffic data is greater than the preset traffic threshold, determining the peak traffic control point data according to the target predicted traffic data and the bandwidth unit price data of the target supplier; Determining an initial bandwidth scheduling ratio of the target supplier according to the bandwidth usage data of the target supplier and the target predicted traffic data; The initial bandwidth scheduling ratio of the target supplier is corrected according to the peak flow control point data to obtain a predicted value of the bandwidth scheduling ratio of the target supplier.
5. The bandwidth resource scheduling method according to claim 3 or 4, characterized in that: The determining, according to the bandwidth usage data of the target provider and the target predicted traffic data, an initial bandwidth scheduling ratio of the target provider comprises: Obtaining real-time data on bandwidth demand for the target provider; An initial bandwidth scheduling ratio of the target supplier is determined according to the real-time bandwidth demand data, the bandwidth usage data of the target supplier and the target predicted traffic data.
6. The bandwidth resource scheduling method according to claim 1, characterized in that: Also includes: The predicted bandwidth settlement fee required to purchase the target predicted traffic data is determined based on the predicted value of the bandwidth scheduling ratio, the bandwidth unit price data and the bandwidth usage data of the target supplier.
7. A bandwidth resource scheduling device, characterized in that: include: A data acquisition module is used to acquire life cycle data and user behavior data of streaming media data, as well as bandwidth unit price data and bandwidth usage data of all suppliers; A traffic prediction module, used to predict traffic demand based on the life cycle data and the user behavior data to obtain target predicted traffic data; A supplier screening module, used to determine a target supplier from all the suppliers according to the target predicted traffic data, the bandwidth unit price data and the bandwidth usage data of all the suppliers, wherein the target supplier is the supplier with the lowest cost under the condition of satisfying the target predicted traffic data; The bandwidth scheduling module is used to distribute the target predicted traffic data to the target supplier to obtain the bandwidth scheduling ratio prediction value of the target supplier.
8. A computer program product, characterized in that The method comprises computer-readable instructions, and when the computer-readable instructions are executed on an electronic device, the electronic device implements the bandwidth resource scheduling method as claimed in any one of claims 1 to 6.
9. An electronic device, characterized in that: The method comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the electronic device can implement the bandwidth resource scheduling method as described in any one of claims 1 to 6.
10. A computer storage medium, characterized in that: The storage medium carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, the electronic device can implement the bandwidth resource scheduling method as described in any one of claims 1 to 6.