A content pre-fetching method based on node business traffic profile

Abstract

The present application relates to the technical field of content distribution networks. In order to solve the problem of low efficiency of pre-fetching content in existing pre-fetching technologies, a content pre-fetching method, computer equipment, computer-readable storage medium, and computer program product based on node business traffic profiles are disclosed. The method includes constructing a node business traffic profile; establishing a dynamic demand model for node business content based on the node business traffic profile, and the dynamic demand model for node business content is used to predict the dynamic demand vector of the node for business content at any time in the next access cycle; generating a node business content pre-fetching list based on the dynamic demand vector of the node for business content at each time in the next access cycle; and pre-fetching content based on the node business content pre-fetching list. The use of this method will improve the accuracy of demand prediction for node business content and the efficiency of content pre-fetching.

Description

Technical Field

[0001] The present application relates to the technical field of content distribution networks, and more specifically, to a content pre-fetching method based on node business traffic profiling, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Focusing on the demand for high-traffic business distribution at nodes, and addressing difficult issues such as high concurrency pressure on central nodes, low forwarding efficiency at backbone nodes, and low transmission rates at edge nodes, the current main approach is to use Content Delivery Networks (CDNs) to cache business content on cache nodes (also known as CDN nodes) close to users, enabling node users to access content locally. Furthermore, based on CDN caching technology, content prefetching has been introduced, alleviating node traffic pressure to a certain extent. However, the dynamic changes in node business demand and the uncertainty of node business traffic have resulted in poor cache prefetching effectiveness, making it difficult to fundamentally reduce node business traffic pressure and ensure the node user business experience.

[0003] Existing caching technologies (i.e., CDN caching technologies) primarily cache service content in cache service devices (i.e., cache nodes) close to the node. Network request redirection forwards service requests to the nearest cache node for response, shortening service access time and alleviating node access pressure. Existing prefetching technologies typically pre-fetch content from specific URLs manually or on a scheduled basis, typically based on user interest or the popularity of cached content, to ensure the specific access needs of node users.

[0004] However, existing caching and prefetching technologies still have the following defects: existing caching technologies cannot solve the network pressure caused by new user requests or access to new business content; existing prefetching technologies usually only consider the interest types of node users or the popularity of cached content, ignoring the correlation between network topology and resource distribution, and ignoring network bandwidth or network business traffic. It is a resource acquisition method that artificially predicts future user access needs, and has problems such as low prefetching resource efficiency, unreasonable prefetching time period, and poor matching of prefetched content. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides a content pre-fetching method, computer equipment, computer-readable storage medium and computer program product based on node business traffic profile, which will improve the accuracy of demand prediction for node business content and content pre-fetching efficiency.

[0006] To achieve the above objectives, according to a first aspect of the present invention, a content prefetching method based on a node service traffic profile is provided, the method comprising:

[0007] Build a node business traffic profile;

[0008] Based on the node traffic profile, a dynamic demand model for node service content is established. This model is used to predict the node's dynamic demand vector for service content at any time during the next access cycle.

[0009] Generate a node service content pre-fetch list based on the dynamic demand vector of the node for service content at each moment in the next access cycle;

[0010] Content is pre-fetched based on the node service content pre-fetch list.

[0011] Furthermore, a node business traffic profile is constructed, including collecting node network traffic and extracting node business information, where the node business information includes traffic source IP address, destination IP address, source port number, destination port number and transport layer protocol type; based on the node business information, the node business is accessed and statistics are performed to obtain the business content and node business rules that the node users are most interested in; based on the business content and node business rules that the node users are most interested in, a node business traffic profile is constructed.

[0012] Furthermore, based on the node business traffic portrait, a dynamic demand model for node business content is established, including determining the node's demand for business content based on the node business traffic distribution and duration in the node business traffic portrait; using the forgetting curve to calculate the weight of the historical access cycle; using an incremental method to describe the change process of the node's demand for business content as the node business traffic changes; based on the weight of the historical access cycle and the change process of the node's demand for business content as the node business traffic changes, a dynamic demand model for node business content is established.

[0013] Furthermore, based on the distribution and duration of the node business traffic in the node business traffic portrait, the node's demand for business content is determined. For example, if a certain business traffic of the node does not appear again for a long time after appearing, or appears intermittently with a long period, it means that the node's demand for business content is lower than the demand threshold or the demand is decreasing and the rate of decrease is getting faster; if a certain business traffic of the node lasts for longer than the set duration threshold, it means that the node's demand for business content is higher than the demand threshold.

[0014] Furthermore, based on the dynamic demand vector of the node for the business content at each moment in the next access cycle, a node business content pre-fetch list is generated, including defining the priority of the business content based on the dynamic demand vector of the node for the business content at each moment in the next access cycle; and adding each business content to the initial pre-fetch list according to the size of the priority to generate a node business content pre-fetch list.

[0015] Furthermore, according to the size of the priority, each business content is added to the initial prefetch list to generate a node business content prefetch list, including calculating the maximum value of the node's demand for the business content to obtain the highest priority; the highest priority content is added to the initial prefetch list; when the number of a certain business content in the initial prefetch list reaches the upper limit, the demand degree of the node represented by the dynamic demand vector for the business content is set to 0, and the priority of the remaining business content is calculated; the above process is repeated until the demand degree of the node represented by the dynamic demand vector for each business content is 0, or the length of the initial prefetch list reaches the maximum, and the node business content prefetch list is generated.

[0016] Furthermore, content is pre-fetched based on the node business content pre-fetch list, including defining the demand value of the content by predicting the frequency of access to the node business content in the next cycle; if the node has sufficient storage space, content is pre-fetched in sequence based on the node business content pre-fetch list; if the node has insufficient storage space, then when the demand value of the cached content is less than the demand value of the content to be pre-fetched, the cached content is deleted and the content to be pre-fetched is pre-fetched from the node business content pre-fetch list, otherwise, the content to be pre-fetched is not pre-fetched; when performing the pre-fetch operation, if the business content to be pre-fetched exists on multiple nodes, a comprehensive judgment is made based on the number of node business requests for the business content to be pre-fetched, the node storage capacity and the node traffic situation, and the node with the largest pre-fetch coefficient is selected to perform the pre-fetch operation.

[0017] According to a second aspect of the present invention, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of any one of the above methods.

[0018] According to a third aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0019] According to a fourth aspect of the present invention, there is also provided a computer program product, comprising a computer program, which implements the steps of any one of the above methods when executed by a processor.

[0020] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0021] (1) The present invention provides a content pre-fetching method based on a node business traffic profile, which constructs a node business traffic profile; based on the node business traffic profile, a dynamic demand model for node business content is established. The model can predict the dynamic demand vector of the node for business content at any time in the next access cycle, and generate a node business content pre-fetching list based on the dynamic demand vector of the node for business content at each time in the next access cycle. Content is pre-fetched based on the node business content pre-fetching list, thereby realizing automatic pre-fetching of node business content. Compared with existing pre-fetching technologies that mainly use manual or timed methods to pre-fetch the content of a specific URL, the present invention performs automatic content pre-fetching based on the node business content pre-fetching list, avoiding waste of human resources and making the pre-fetching process more efficient, thereby achieving the purpose of improving the efficiency of pre-fetching content.

[0022] (2) The present invention provides a content pre-fetching method based on a node business traffic profile, which collects node network traffic, performs accurate statistical analysis on the business information covered by the node, and constructs an accurate node business traffic profile.

[0023] (3) The present invention provides a content pre-fetching method based on node traffic profiles. The method maps the node's demand for content through the node traffic profiles, calculates the weight of the historical access cycle using the forgetting curve, and uses an incremental method to describe the process of the node's demand for content as the node's traffic changes, thereby establishing a dynamic demand model for node content. Compared to existing prediction and recommendation systems that treat historical access cycle information equally and lack quantitative analysis of timeliness, the present invention predicts the demand for node content dynamically over time, resulting in a higher accuracy rate for node content demand prediction.

[0024] (4) The present invention provides a content pre-fetching method based on node business traffic profile. By pre-fetching business content in a timely manner according to priority during the node traffic off-peak period, combined with the demand prediction results for the node's business content, the method achieves the effect of "peak shaving and valley flattening" by pre-fetching content in advance before the user actually requests the content, saving distribution overhead, reducing service latency, and eliminating the decline in service quality caused by dynamic changes in the network. Compared with existing pre-fetching technologies that often ignore the consideration of node network bandwidth, node traffic load, etc., especially the large fluctuations in edge node networks, which can easily cause network congestion and seriously affect network quality and user experience, the present invention maximizes the efficiency of network resource utilization and business content pre-fetching. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 A flowchart of a content pre-fetching method based on a node service traffic profile provided in an embodiment of the present application;

[0027] Figure 2 A flowchart illustrating the steps of establishing a dynamic demand model for node service content provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0030] The terms "first," "second," "third," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0031] like Figure 1 As shown, a content prefetching method based on a node business traffic profile is provided. The method can be executed by a node cache server or by other terminal devices that communicate with the node through a network. The terminal devices can be, but are not limited to, various personal computers, laptops, smartphones, tablet computers, etc. The node cache server can be an independent server or a server cluster consisting of multiple servers. The method is described using the node cache server as an example, and includes the following steps:

[0032] Step 101: Obtain a node service traffic profile.

[0033] Exemplarily, the node cache server collects node network traffic (i.e., the overall traffic of the node network) on a daily basis, and extracts node business information including identifiers such as the traffic source IP address, destination IP address, source port number, destination port number, and transport layer protocol type; performs access statistics on the node business according to the access cycle to obtain the business content that the node users are most interested in, and summarizes the node business rules to construct a node business traffic portrait within the operation time. The node business traffic is the traffic generated by a specific business of the node.

[0034] Step 102: Based on the node business traffic profile, a dynamic demand model of the node business content is established.

[0035] The dynamic demand model of the node service content is used to predict the dynamic demand vector of the node for service content at any time in the next access cycle.

[0036] For example, the node cache server models the dynamic demand for node service content based on the node service traffic profile. By analyzing factors such as the node service traffic distribution and duration in the node service traffic profile, the node's demand for the service and the changing process of demand are mapped.

[0037] Considering the demand for node services, we need to conduct a superposition analysis of multiple cycles. We calculate the weights of historical access cycles based on the forgetting curve, and use an incremental method to describe the change in the node's demand for service content as traffic changes. This allows us to predict the dynamic demand vector of the node for service content at a certain moment in the next access cycle.

[0038] Step 103: Generate a node service content pre-fetch list according to the dynamic demand vector of the node for service content at each moment in the next access cycle.

[0039] For example, the node cache server defines the priority of service content based on the node's dynamic demand vector for service content at each moment in the next access cycle. It then adds each service content to the initial prefetch list based on its priority, resulting in a final node service content prefetch list. During the addition of each service content to the initial prefetch list, the priority calculation method for service content is adjusted based on the proportion of added service content to prevent overloading of high-demand service content.

[0040] Step 104: Pre-fetch content from the node service content pre-fetch list.

[0041] Exemplarily, the node cache server defines the demand value of the content by predicting the frequency of access to the node service content in the next cycle, and performs a pre-fetching operation by comparing the demand value of the cached content with the demand value of the content to be pre-fetched.

[0042] If the node has sufficient storage space, content is pre-fetched sequentially according to the node's service content pre-fetch list. If the node's storage space is insufficient, if the demand value of the cached content is less than the demand value of the content to be pre-fetched, the cached content is deleted and the content to be pre-fetched is pre-fetched from the node's service content pre-fetch list. Otherwise, pre-fetching of the content to be pre-fetched is not performed. In addition, when performing a pre-fetch operation, if the service content to be pre-fetched exists on multiple nodes, a comprehensive judgment is made based on the number of service requests for the service content to be pre-fetched, the node's storage capacity, and the node's traffic situation, and the node with the largest pre-fetch coefficient is selected to perform the pre-fetch operation. The pre-fetch coefficient is a measure of the execution of pre-fetch operations by multiple nodes. A larger pre-fetch coefficient indicates a higher success rate for the pre-fetch operation.

[0043] The pre-fetching operation needs to be based on the node network traffic situation. The node traffic trough is found based on the node business traffic profile. When the traffic is small or low, it is executed according to the node business content pre-fetch list.

[0044] In the above-mentioned content prefetching method based on node business traffic portrait, a node business traffic portrait is constructed; based on the node business traffic portrait, a dynamic demand model for node business content is established. This model can predict the dynamic demand vector of the node for business content at any time in the next access cycle. According to the dynamic demand vector of the node for business content at each time in the next access cycle, a node business content prefetching list is generated, and the content is prefetched on the list, thereby realizing automatic prefetching of node business content and achieving the purpose of improving the efficiency of prefetching content.

[0045] In one embodiment, Figure 2 As shown, the above step 102, based on the node service traffic profile, establishes a dynamic demand model for the node service content, including the following steps:

[0046] Step 201: Determine the node's demand for service content based on the node's service traffic distribution and duration in the node's service traffic profile.

[0047] Step 202, using the forgetting curve to calculate the weight of the historical access cycle;

[0048] Step 203: using an incremental method to describe the process in which the node's demand for service content changes along with the node's service traffic;

[0049] Step 204 : Based on the weight of the historical access cycle and the change process of the node's demand for service content along with the node's service traffic, a dynamic demand model for the node's service content is established.

[0050] In this embodiment, the demand prediction of the node's service content by the dynamic demand model of the node's service content changes dynamically over time, which can improve the accuracy of the demand prediction of the node's service content.

[0051] In one embodiment, a content prefetching method based on a node traffic profile includes the following steps:

[0052] (1) Build a node business traffic profile.

[0053] Node network traffic is collected on a daily basis, and access statistics are collected for node services based on access cycles. Node business rules are summarized and a node business traffic profile is constructed.

[0054] (2) Modeling node business dynamic demand based on node business traffic profile

[0055] The node's business traffic profile is used to map the node's business demand, mainly considering the following factors: 1) If a certain business traffic of a node does not appear again for a long time after it appears, or is intermittent and has a long period, it means that the demand for this business content is low (that is, lower than a certain set demand threshold) or the demand is decreasing rapidly; if a certain business traffic of a node is large for a long time, or the traffic is small but lasts for a long time (that is, the duration is greater than a certain set duration threshold), it means that the demand for this business content is high (that is, higher than the set demand threshold); 2) The node's demand for business content is the superposition of the influence of multiple periodic access situations; 3) The node's access time to business content is a continuously declining process.

[0056] The forgetting curve is used to calculate the weight of the historical access cycle, and the incremental method is used to describe the change of the node's demand for business content as the traffic changes. The dynamic demand vector of the node for business content at a certain moment in the next access cycle is predicted. The demand vector reflects the overall demand situation of the node's business.

[0057] (3) Generate a node business content pre-fetch list

[0058] 1) Define the priority of content based on the node's demand for business content: calculate the priority of the content with the highest demand in the node demand vector;

[0059] 2) Adding content to the prefetch list based on its priority: The highest-priority content is added to the prefetch list. To prevent overloading of high-demand content, the priority calculation method is adjusted based on the proportion of added content. For example, when the number of service content items in the list reaches the upper limit, the demand level of the corresponding service content in the demand vector used for priority calculation is set to 0, and the priority calculation is then repeated for the remaining service content.

[0060] 3) Repeat the above process until the demand levels of the service contents in the demand vector used to calculate the priority are all 0, or the length of the pre-fetch list reaches the maximum, thereby forming a final node service content pre-fetch list.

[0061] (4) Pre-fetching node business content

[0062] The node cache server pre-fetches content based on the node network traffic situation and the node service content pre-fetch list when the traffic is low or low. The detailed process is as follows:

[0063] 1) The node cache server collects statistics on the node user access records every cycle to obtain the demand level of each business content, and calculates the node's dynamic demand vector for the business content;

[0064] 2) The node cache server calculates the priority of the content based on the dynamic demand vector and obtains a node service content pre-fetch list based on the priority;

[0065] 3) The node cache server pre-fetches content according to the node business content pre-fetch list, and defines the demand value of the content by predicting the frequency of node content access in the next cycle. During the pre-fetch process, if the node has sufficient storage space, the content will be pre-fetched directly according to the node business content pre-fetch list. If the node has insufficient storage space, but the demand value of the cached content is less than the demand value of the content to be pre-fetched, the cached content will be deleted and the content to be pre-fetched will be pre-fetched from the node business content pre-fetch list. Otherwise, the pre-fetch operation will be stopped.

[0066] 4) When a node cache server performs a prefetch operation, if the intended content exists on multiple nodes, it will make a comprehensive assessment based on the number of node requests, node storage capacity, and node traffic, and select the node with the largest (i.e., optimal) prefetch coefficient to perform the prefetch. The prefetched content may come from multiple nodes, and the prefetch coefficient measures the performance of the prefetch operation across multiple nodes. A larger prefetch coefficient indicates a higher success rate for the prefetch operation.

[0067] After testing, the content pre-fetching method based on the node business traffic portrait provided by this embodiment has an accuracy rate of predicting the demand for node business content of not less than 80%, and the node business content is automatically pre-fetched to avoid manual operation, and the content pre-fetching efficiency is improved by not less than 50%.

[0068] The present application also provides a computer device, the internal structure of which can be as follows: Figure 3As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a content prefetching method based on a node business traffic profile is implemented. The display unit of the computer device is used to form a visually visible image, and can be a display screen, a projection device or a virtual reality imaging device. The input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse, etc.

[0069] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0070] like Figure 3 As shown, the present application also provides a computer device, which includes a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to implement the steps in the above-mentioned method embodiments.

[0071] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive, a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0072] The present application also provides a computer program product, including a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0073] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0074] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0075] The above description is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0076] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0077] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A content pre-fetching method based on node traffic profile, characterized in that: include: Collect node network traffic and extract node service information, including traffic source IP address, sink IP address, source port number, sink port number, and transport layer protocol type; Based on the node service information, the node service is accessed and counted according to the access cycle to obtain the service content and node service patterns that the node users are most interested in. Based on the service content and node service patterns that the node users are most interested in, a node service traffic profile is constructed. The node service traffic is the traffic generated by a specific service of the node. Based on the node business traffic distribution and duration in the node business traffic profile, the node's demand for business content is determined; the weight of the historical access cycle is calculated using the forgetting curve; an incremental method is used to describe the change process of the node's demand for business content as the node business traffic changes; based on the weight of the historical access cycle and the change process of the node's demand for business content as the node business traffic changes, a dynamic demand model for the node's business content is established. The dynamic demand model for the node's business content is used to predict the node's dynamic demand vector for business content at any time in the next access cycle. The demand vector reflects the overall demand situation of the node's business; Generate a node service content pre-fetch list based on the dynamic demand vector of the node for service content at each moment in the next access cycle; Content is pre-fetched according to the node service content pre-fetch list.

2. The method according to claim 1, wherein Determining the node's demand for service content based on the node's service traffic distribution and duration in the node's service traffic profile includes: If a service flow on a node does not reappear for a long time after it appears, or appears intermittently with a long period, it means that the node's demand for the service content is lower than the demand threshold or the demand is decreasing and the rate of decrease is accelerating; If a service flow of a node lasts longer than a set duration threshold, it means that the node's demand for the service content is higher than the demand threshold.

3. The method according to claim 1, wherein The generating of a node service content pre-fetch list according to the dynamic demand vector of the node for service content at each moment in the next access cycle includes: Define the priority of service content based on the dynamic demand vector of service content at each node in the next access cycle; According to the priority level, each business content is added to the initial pre-fetch list to generate a node business content pre-fetch list.

4. The method according to claim 3, wherein The process of adding each service content to the initial pre-fetch list according to the priority level to generate a node service content pre-fetch list includes: The maximum value of the computing node's demand for business content is given the highest priority; Add the highest priority content to the initial prefetch list; When the number of a certain service content in the initial pre-fetch list reaches the upper limit, the demand degree of the node represented by the dynamic demand vector for the said service content is set to 0, and the priority of the remaining service contents is calculated; The above process is repeated until the demand degree of each service content of the node represented by the dynamic demand vector is 0, or the length of the initial pre-fetch list reaches the maximum, and the node service content pre-fetch list is generated.

5. The method according to claim 3, wherein The performing content prefetching according to the node service content prefetching list includes: Define the demand value of content by predicting the frequency of access to node business content in the next cycle; If the node has sufficient storage space, content is pre-fetched in sequence according to the node service content pre-fetch list; If the storage space of the node is insufficient, then if the demand value of the cached content is less than the demand value of the content to be pre-fetched, the cached content is deleted and the content to be pre-fetched is pre-fetched from the node service content pre-fetching list; otherwise, the content to be pre-fetched is not pre-fetched; When performing a prefetch operation, if the business content to be prefetched exists on multiple nodes, a comprehensive judgment is made based on the number of node business requests for the business content to be prefetched, the node storage capacity, and the node traffic situation, and the node with the largest prefetch coefficient is selected to perform the prefetch operation.

6. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer program product, characterized in that The invention comprises a computer program, which implements the steps of the method according to any one of claims 1 to 5 when the computer program is executed by a processor.

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