CN118233468A

CN118233468A - Resource downloading management method and system based on cloud computing

Info

Publication number: CN118233468A
Application number: CN202410349201.6A
Authority: CN
Inventors: 李志�
Original assignee: Hunan Youchuang Technology Co ltd
Current assignee: Hunan Youchuang Technology Co ltd
Priority date: 2024-03-26
Filing date: 2024-03-26
Publication date: 2024-06-21

Abstract

The present invention relates to the field of cloud computing technologies, and in particular, to a resource download management method and system based on cloud computing. The method comprises the following steps: acquiring user downloading request data and obtaining edge storage condition data according to the user downloading request data; when the edge storage condition data are determined to comprise storage resource condition data, carrying out collaborative cache downloading operation according to the edge storage condition data; when the edge storage condition data comprise storage resource condition data, the user download request data obtain bandwidth allocation data and server resource allocation data so as to perform concurrent download operation and obtain concurrent download record data; and when the concurrent download record data is determined to comprise failed download record data or interrupt download record, performing high-efficiency breakpoint continuous transmission operation according to the concurrent download record data. The method and the device can provide more efficient and reliable resource downloading service and reduce the load cost of cloud storage resources.

Description

Resource downloading management method and system based on cloud computing

Technical Field

The present invention relates to the field of cloud computing technologies, and in particular, to a resource download management method and system based on cloud computing.

Background

The resource download management method refers to a method of planning, organizing, coordinating and monitoring the process of downloading and acquiring various resources, including acquiring files, data, applications or other resources from the internet or an internal network, and ensuring availability, security and efficiency of the resources. The resource downloading management method based on cloud computing utilizes a cloud computing platform and service to download and manage resources, generally stores the resources in cloud storage, downloads, distributes and monitors the resources by using cloud computing service, and provides high scalability, elasticity and usability for cloud computing, so that the resource downloading and management are more flexible and efficient. The resource download management method based on cloud computing relies on network connection, if the network is unstable or interrupted, the ability to download and manage resources is affected, and meanwhile, cloud computing services usually require payment, and the cost increases with the increase of the usage amount.

Disclosure of Invention

The invention provides a resource downloading management method and system based on cloud computing for solving at least one technical problem.

The application provides a resource downloading management method based on cloud computing, which comprises the following steps:

S1, responding to a user request downloading operation, obtaining user downloading request data, and carrying out edge storage scanning according to the user downloading request data to obtain edge storage condition data, wherein the user downloading request data comprises request resource identifier data, user identification data and request timestamp data;

S2, when the edge storage condition data comprise storage resource condition data, carrying out collaborative cache downloading operation according to the edge storage condition data;

s3, when the edge storage condition data comprise storage resource condition data, bandwidth allocation processing and server resource allocation processing are carried out according to the user downloading request data, so that bandwidth allocation data and server resource allocation data are obtained;

S4, carrying out concurrent downloading operation according to the bandwidth allocation data and the server resource allocation data, and recording the concurrent downloading operation in real time to obtain concurrent downloading record data;

And S5, when the concurrent download record data is determined to comprise failed download record data or the download record is interrupted, performing high-efficiency breakpoint continuous transmission operation according to the concurrent download record data.

According to the invention, through edge storage scanning and collaborative cache downloading, the system can more effectively utilize the edge storage resources, and reduce the dependence on cloud storage, so that the utilization rate of the resources is improved. Through the processing of bandwidth allocation and server resource allocation, the system can dynamically allocate resources according to the requirements of the user download request, so as to ensure that the download task can run with optimal performance. The system supports concurrent downloading, allows a plurality of downloading tasks to be carried out simultaneously, improves the downloading efficiency, and in addition, the high-efficiency breakpoint continuous transmission mechanism ensures that the downloading can be quickly recovered when interrupted or failed, thereby saving the time and bandwidth resources of users. Through resource importance evaluation and collaborative downloading decision-making, the system can provide faster and more reliable downloading service according to the user demands, thereby improving the satisfaction and experience of the user. According to the method and the device, more efficient and reliable resource downloading service is realized through edge storage, dynamic resource allocation and efficient downloading management, better experience is provided for users, and meanwhile, the load and cost of cloud storage resources can be reduced.

Optionally, the responding to the user request downloading operation obtains user downloading request data, and performs edge storage scanning according to the user downloading request data to obtain edge storage condition data, including:

S11, responding to a user request downloading operation, and acquiring user downloading request data;

s12, generating edge storage scanning data according to the user downloading request data;

s13, performing multi-node scanning task allocation according to the edge storage scanning data to obtain multi-node scanning task allocation data;

S14, generating edge storage condition data according to the multi-node scanning task allocation data to obtain the edge storage condition data.

According to the invention, through scanning and analyzing the user downloading request data, the system can better know the distribution and availability of the resources, so that the utilization of the edge storage is optimized, the dependence on cloud storage is reduced, and the delay and cost of resource acquisition are reduced. By adopting the multi-node scanning task allocation strategy, resources can be obtained from a plurality of nodes in parallel, the obtaining speed and efficiency of the resources are improved, the waiting time of users is reduced, and the downloading experience is improved. Through bandwidth allocation and server resource allocation, the system can better meet the downloading demands of users, ensure that the downloading task can run with the best performance, and improve the downloading speed and stability. Through edge storage scanning and multi-node scanning, the system can more accurately determine the availability of resources, and the risk of resource deficiency is reduced, so that the success rate of downloading is improved.

Optionally, the performing multi-node scan task allocation according to the edge storage scan data to obtain multi-node scan task allocation data includes:

S131, acquiring edge node data;

s132, selecting scanning task nodes according to the edge node data to obtain scanning task node selection data;

S133, performing task allocation processing on the scanning task node selection data according to the edge storage scanning code data to obtain multi-node scanning task allocation data.

According to the invention, by acquiring the edge node data, the system can know the distribution and performance characteristics of the edge nodes and select the edge node most suitable for the task, so that the execution efficiency of the task is improved. The scanning task node selection is carried out according to the edge node data, so that more intelligent and reasonable task allocation can be ensured. The system can select nodes with better performance so as to meet the downloading requirement and fully utilize available resources. Through task allocation processing, the system can allocate scanning tasks to a plurality of nodes for parallel execution, so that the speed of resource scanning is improved, the load can be balanced, and efficient acquisition of resources is ensured. The invention improves the efficiency of resource acquisition, and a user can acquire the required resources more quickly, reduce waiting time and improve downloading experience. Through intelligent task allocation and node selection, the system can better utilize edge nodes and resources, and the risk of resource waste is reduced.

Optionally, when the determining that the edge storage condition data includes storage resource condition data, performing a collaborative cache downloading operation according to the edge storage condition data includes:

S21, carrying out data aggregation on the edge storage condition data set to obtain global edge storage condition data;

s22, carrying out resource importance assessment on the user downloading request data and the global edge storage condition data to obtain resource importance assessment data;

s23, generating a collaborative downloading decision according to the global edge storage condition data and the resource importance evaluation data to obtain collaborative downloading decision data;

s24, when the collaborative download decision data corresponding to the request resource identifier data is determined to be the edge storage download decision data, performing edge storage download operation according to the user download request data and the collaborative download decision data;

and S25, when the collaborative download decision data corresponding to the request resource identifier data is cloud storage download decision data, cloud storage download operation is carried out according to the user download request data and the collaborative download decision data.

According to the invention, through aggregation and global analysis of the edge storage condition data, the system can obtain the state and the resource distribution of the edge storage, so that the utilization of the edge storage resources can be optimized, and the availability and the access speed of the resources can be improved. Through resource importance evaluation, the system can determine which resources are suitable for being acquired from the edge storage according to the demands and the resource attributes of the users, intelligently selects a downloading mode, and provides better user experience. Based on the global edge storage condition data and the resource importance evaluation data, the system can generate a collaborative download decision to decide whether to acquire resources from the edge storage or the cloud storage, and the resource acquisition efficiency can be improved to the greatest extent. Through intelligent selection of the edge storage downloading job and the cloud storage downloading job, the system can accelerate the acquisition speed of resources, reduce waiting time and improve availability. By optimizing the utilization of the edge storage, the system can reduce the dependence on cloud storage, reduce the cost of cloud storage, and provide faster download experience.

Optionally, the data aggregation of the edge storage case data set to obtain global edge storage case data includes:

Carrying out real-time data aggregation on the edge storage condition data set to obtain global edge real-time storage condition data;

acquiring historical edge storage condition data, and performing incremental data aggregation on the historical edge storage condition data according to the edge storage condition data set to obtain global edge incremental storage condition data;

And generating global edge storage condition data according to the global edge real-time storage condition data and the global edge increment storage condition data.

According to the invention, through real-time aggregation of the edge storage condition data, the system can timely know the state and availability of the current edge storage resource, so that the downloading request of a user can be responded more quickly, and the user experience is improved. Incremental data aggregation is carried out on the historical edge storage condition data, so that the system is facilitated to manage the historical data more effectively, the complexity and resource consumption of data processing are reduced, and the performance and efficiency of the system are improved. By comprehensively considering real-time and historical data, global edge storage condition data is generated, and the system can acquire the overall state of edge storage, so that resource management and downloading decision can be better carried out. The real-time data aggregation and the incremental data aggregation are beneficial to the system to acquire and process the edge storage condition data more quickly and intelligently, so that the efficiency of resource management is improved, and the complexity of the system is reduced.

Optionally, the performing resource importance assessment on the user download request data and the global edge storage condition data to obtain resource importance assessment data includes:

carrying out user downloading demand analysis on the user downloading request data and the user identification data to obtain user downloading demand data, wherein the user downloading demand data comprises resource type data, user role data and user authority data;

Extracting resource attribute of the global edge storage condition data according to the request resource identifier data to obtain resource attribute data, wherein the resource attribute data comprises resource capacity data, resource type data and resource relevance data;

extracting user characteristics according to the user identification data to obtain user characteristic data, wherein the user characteristic data comprises user history downloading behavior characteristic data, user preference data and user role characteristic data;

carrying out static weight identification on the global edge storage condition data according to the user characteristic data, the user downloading demand data and the resource attribute data to obtain first resource importance evaluation data;

Acquiring current position data of a user, and performing resource usage prediction on the current position data of the user to obtain resource usage data;

Continuously monitoring the user network connection state and the equipment state to respectively obtain user network connection state data and equipment state data;

Carrying out dynamic weight identification on the global edge storage condition data according to the resource use data, the user network connection state data and the equipment state data to obtain second resource importance assessment data;

and sorting and screening the first resource importance evaluation data and the second resource importance evaluation data to obtain resource importance evaluation data.

The method can evaluate the importance of the resource more accurately, only some static factors are considered in the traditional method, and dynamic factors are introduced in the method, so that the evaluation accuracy is improved. Based on static and dynamic weight identification, the system can intelligently adjust the priority of the resources to meet the requirements of different users and environments, so that the resources are more effectively utilized, and the performance and the resource utilization rate of the system are improved. By dynamically adjusting the importance of the resources according to the needs and environment of the user, the system can better meet the downloading needs of the user, and provide better user experience, including faster downloading speed, fewer downloading interruptions and higher success rate. Because of the adoption of dynamic weight identification, the system is more adaptive to the changing user requirements and environmental changes, so that the system can cope with various complex situations and provide more flexible resource management.

Optionally, the generating the collaborative download decision according to the global edge storage condition data and the resource importance evaluation data to obtain collaborative download decision data includes:

Performing resource positioning according to the global edge storage condition data and the resource importance evaluation data to obtain resource positioning data;

Performing resource availability check on the resource positioning data to obtain resource availability data, wherein the resource availability check comprises resource existence check, resource occupation check and resource transmission minimum requirement check, and the resource availability data comprises resource availability data and resource deletion data;

When the resource availability data is determined to be the resource availability data, generating edge storage downloading decision data according to the resource availability data;

When the resource availability data is determined to be the resource missing data, cloud storage downloading decision data is generated according to the resource missing data;

And integrating the edge storage downloading decision data and the cloud storage downloading decision data to obtain collaborative downloading decision data.

According to the invention, the system can more intelligently select proper resource sources by carrying out resource positioning according to the global edge storage condition data and the resource importance evaluation data, so that the downloading efficiency and success rate are improved, and the resource waste is reduced. The invention comprises the resource existence check, the resource occupation check and the minimum requirement check of the resource transmission, is helpful for determining whether the resource can meet the downloading requirement of the user, and improves the availability and the applicability of the resource. Different download decision data are generated according to the resource availability data, and edge storage and cloud storage can be considered simultaneously, so that different resource sources are better utilized, and the resource utilization rate is improved. And integrating the edge storage downloading decision data and the cloud storage downloading decision data to generate collaborative downloading decision data, thereby being beneficial to comprehensively considering the advantages of different resource sources and providing better downloading service for users.

Optionally, when the determining that the concurrent download record data includes failed download record data or interrupts download record, performing efficient breakpoint continuous transmission operation according to the concurrent download record data, including:

When the concurrent download record data is determined to comprise failed download record data or interrupt download record, breakpoint data acquisition is carried out according to the concurrent download record data to obtain breakpoint data, wherein the breakpoint data comprise downloaded data, download progress data, download file capacity data and download resource identifier data;

performing request resource range processing according to the breakpoint data to obtain request resource range data;

and carrying out recovery downloading operation according to the request resource range data.

According to the invention, through collecting breakpoint data, including downloaded data, downloading progress data, downloaded file capacity data and downloaded resource identifier data, efficient breakpoint continuous transmission is realized, and resource waste and user inconvenience caused by downloading interruption or failure are greatly reduced. By generating the request resource range data according to the breakpoint data, the method can ensure that only partial resources which are not downloaded are requested during breakpoint continuous transmission, and the whole file does not need to be downloaded again, so that the bandwidth and time are saved, and the downloading efficiency is improved. By implementing efficient breakpoint resume, user experience is improved and users can more smoothly continue with previously interrupted or failed download tasks without restarting.

Optionally, the processing the request resource scope according to the breakpoint data to obtain request resource scope data includes:

continuously monitoring the current network condition, the current server load condition and the current user equipment performance condition to respectively obtain current network condition data, current server load condition data and current user equipment performance condition data;

Generating an initial request resource range according to the breakpoint data to obtain initial request resource range data;

and carrying out dynamic range adjustment on the initial request resource range data according to the current network condition data, the current server load condition data and the current user equipment performance condition data to obtain request resource range data.

The invention can realize the dynamic adjustment of the resource request range by continuously monitoring the current network condition, the server load and the user equipment performance, and can utilize the available bandwidth and the server resources to the greatest extent according to the actual situation, thereby improving the downloading speed and the downloading efficiency. Through dynamic range adjustment, the method can better adapt to continuously changing network conditions and equipment performance, provide more stable and efficient downloading experience, reduce possibility of downloading failure and interruption, and improve user satisfaction. And the resource request range is adjusted according to the current situation, only the needed resource part is requested instead of the whole file, unnecessary data transmission and resource waste are reduced, and the bandwidth cost is reduced.

Optionally, the present application further provides a resource download management system based on cloud computing, configured to execute the resource download management method based on cloud computing, where the resource download management system based on cloud computing includes:

the edge storage scanning module is used for responding to the user request downloading operation, obtaining user downloading request data, and carrying out edge storage scanning according to the user downloading request data to obtain edge storage condition data;

The collaborative cache downloading module is used for carrying out collaborative cache downloading operation according to the edge storage condition data when the edge storage condition data comprise storage resource condition data;

The allocation processing module is used for carrying out bandwidth allocation processing and server resource allocation processing according to the user download request data when the edge storage condition data comprise storage resource condition data, so as to obtain bandwidth allocation data and server resource allocation data;

the concurrent downloading module is used for carrying out concurrent downloading operation according to the bandwidth allocation data and the server resource allocation data and recording the concurrent downloading operation in real time so as to obtain concurrent downloading record data;

and the high-efficiency breakpoint continuous transmission module is used for determining that the concurrent download record data comprises failed download record data or interrupting download record, and carrying out high-efficiency breakpoint continuous transmission operation according to the concurrent download record data.

The invention aims to quickly identify available storage resources through edge storage scanning and collaborative caching downloading operation, thereby reducing resource searching and downloading delay. Through the efficient breakpoint continuous transmission operation, the file can be quickly recovered when the downloading is interrupted or failed, the whole file does not need to be downloaded again, the waiting time of a user is greatly reduced, and the satisfaction degree of the user is improved. The method can effectively manage and optimize the resource allocation through the bandwidth allocation and the server resource allocation processing, ensures the optimal utilization of the resources, reduces unnecessary resource waste and reduces the cost. Due to the adoption of edge storage scanning and collaborative cache downloading, resources are distributed on the edge nodes more, so that direct requests to cloud servers are reduced, the possibility of network congestion is reduced, and the network performance is improved. Through the high-efficiency breakpoint continuous transmission operation, only the interrupted part is needed to be downloaded instead of the whole file, so that the bandwidth consumption can be reduced, and the data transmission cost is reduced.

Drawings

Other features, objects and advantages of the application will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:

FIG. 1 is a flow chart illustrating steps of a method for cloud computing-based resource download management of an embodiment;

FIG. 2 is a flow chart illustrating steps of an edge storage scanning method of an embodiment;

FIG. 3 is a flow chart illustrating steps of a multi-node scan task allocation method of an embodiment;

FIG. 4 is a flow chart illustrating steps of a data aggregation method of an embodiment;

the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

Referring to fig. 1 to 4, the present application provides a resource download management method based on cloud computing, which includes:

Specifically, when a user requests to download a file, the system obtains user download request data, including an identifier of the requested resource, a user identification, and a request timestamp. The system then performs an edge storage scan based on the data, queries the edge storage device to determine if storage resources are present, and gathers relevant information.

Specifically, if the edge store scan results indicate that the required resources are present in the edge store, the system will perform a collaborative cache download job, such as transmitting the resources from the edge store to the user device, to speed up the download while reducing reliance on the cloud server.

Specifically, if resources exist in the edge storage, the system will perform bandwidth allocation and server resource allocation according to the user download request data and the edge storage status data, including deciding from which edge node the resources are downloaded and allocating bandwidth and computing resources to support the download task.

specifically, the system executes concurrent download operation according to the bandwidth allocation data and the server resource allocation data, allows downloading a plurality of resources simultaneously, and records the state and progress of the download task in real time, including information such as the downloaded data amount, the download progress, the size of the downloaded file, and the downloaded resource identifier.

Specifically, if a download failure or download interruption occurs during the concurrent download process, the system will perform an efficient breakpoint resume operation according to the concurrent download record data, including collecting the downloaded data, the download progress, the download file capacity, the download resource identifier, and other breakpoint data, and determining the scope of resuming the download based on these data, so as to avoid re-downloading the entire file.

Specifically, the system waits for a user to request a download of a certain resource. Once the user makes a download request, the system obtains user download request data, which typically includes: identifier of the requested resource: specific resources, such as file names, URLs, etc., to be downloaded by the user are identified. User identification: the identification of the user requesting the download may be an account number or a unique identifier of the user. Request timestamp: the time when the user makes the download request is recorded for recording the time-dependent operations.

Specifically, the user download request data is parsed: the user download request data includes information such as the requested resource identifier, the user identifier, and the request timestamp, and the like, and the data needs to be parsed first to obtain detailed information therein. Determining resources to be downloaded: based on the resource identifier data, the particular resource requested by the user, such as a file, an object, a web page, or other type of resource, is determined, such as by parsing the resource identifier data to extract a unique identifier of the resource for further processing. Checking edge storage conditions: for the determined resource, a case in the edge storage system needs to be queried or checked, including checking whether the resource is cached on the edge node, a cache time stamp of the resource, version information of the resource, etc., such as sending a query request to the edge storage system to obtain relevant information. Generating scan data: based on the information, edge store scan data is generated, wherein the edge store scan data includes which resources need to be retrieved from the edge store and how to retrieve the resources.

For example, if a resource is already cached at an edge node, and the cache is up-to-date, then an instruction may be generated to retrieve the resource from the corresponding edge node.

If the resource is not cached at the edge node, or the cache is not up-to-date, then an instruction needs to be generated to retrieve the resource from the central store or cloud storage and cache it at the edge node.

Specifically, the system uses edge store scan data to determine an allocation policy for a multi-node scan task, including: the location of the resource is determined based on the resource identifier to determine which edge nodes possess the resource. And deciding which edge nodes to allocate the tasks to according to the user identification or the role information of the user, and optimizing according to the geographic position and the network condition of the user. The request timestamp is considered to determine the urgency or timeliness of the download request, affecting the task's allocation priority.

Specifically, the system uses the multi-node scan tasking data to generate edge storage case data, which includes: it is determined which edge nodes are responsible for storing the required resources. And recording the storage condition of each edge node, including the information of availability, capacity, load and the like of the resources. Edge storage case data is generated for use in subsequent download decisions and task allocation.

Specifically, when a user issues a download request through an application or network interface, the system listens to the request. Once the system detects a download request, it will obtain user download request data, including: the identifier of the requested resource, e.g., file name, URL, resource ID, etc., the user identification may be a user name, user ID, or other unique identifier, request timestamp, record the time of the request, typically expressed in UNIX timestamp format. At this step, the system uses the user download request data to create edge store scan data, the generated data comprising: the resource identifier is parsed to determine attributes of the desired resource, such as the type of resource, size, etc. Based on the user identification and the request timestamp, a priority of acquisition of the resource or urgency of the request is determined. Scan data is generated including a resource identifier, a user identification, a request timestamp, and other necessary information. The system allocates multi-node scan tasks based on the edge store scan data. Based on the resource identifier, it is determined on which edge nodes the resource is available or should be stored. Based on the user identification or role information, tasks are assigned to the appropriate edge nodes, taking into account the geographical location of the user and the network conditions to optimize task assignment, e.g., assigning tasks to nodes closer to the user and having better network connectivity. Determining the urgency or timeliness of the task according to the request timestamp can affect the priority of the task. The system uses the multi-node scan tasking data to generate edge storage instance data comprising: it is determined which edge nodes are responsible for storing the required resources and their identifiers or locations are recorded. And recording the storage condition of each edge node, including information such as availability, capacity, load and the like of the resources. A data structure is created containing the edge node storage case for subsequent download decisions and task allocation use.

S131, acquiring edge node data;

In particular, the system needs to acquire data about the available edge nodes, which typically include: location information of the edge node, such as latitude and longitude coordinates or physical location. Performance information of the edge node, such as processing power, storage capacity, bandwidth, etc. Network connection quality information of the edge nodes such as delay, bandwidth and stability. Such data may be collected and maintained from an edge node management system or monitoring tool.

Specifically, the system uses the acquired edge node data to select a node suitable for the scanning task, including: the nearest edge node is selected according to the location of the resource and the geographical location of the user to reduce delay and increase download speed. The performance and load conditions of the edge nodes are considered, and the nodes capable of processing the scanning task are selected. A node that is stable and has sufficient bandwidth is selected in consideration of network connection quality.

Specifically, the system uses edge store scan data and scan task node selection data for task allocation, including: it is determined which resources need to be scanned, and their location information. And distributing the scanning task to the selected edge node to ensure the coverage and downloading efficiency of the resource. Each edge node is assigned a corresponding task including which resources to download, when to download, and how to optimize the download order.

Specifically, storage condition data of each edge node is collected, including information such as available storage capacity, resource type, resource relevance, and the like. The collected data is summarized and aggregated to generate global edge storage case data, including calculating the total storage capacity of each resource type, determining the number of copies of each resource, and the like. And maintaining global edge storage condition data updated in real time to reflect the actual state of the edge network.

Specifically, according to the user downloading request data, the requirements of the user are determined, including the resource type, the user role, the user authority and the like. Global edge storage case data is used to evaluate availability and applicability of resources. For example, depending on the type of resource and the storage, it is determined which resources are available and which are most appropriate. Based on the user demand and the availability of resources, an importance score for the resources is calculated, including using weights or rules to measure the importance of the resources.

Specifically, in combination with global edge storage case data and resource importance assessment data, collaborative download decisions are generated, including determining which resources should be downloaded from an edge storage node and which resources should be downloaded from a cloud storage node. For edge storage download decision data, edge nodes are determined and download tasks, such as analyzing the location and availability of resources, are allocated to minimize latency and bandwidth consumption. And (3) for the cloud storage downloading decision data, distributing the downloading task to a cloud server, and considering the service level and pricing strategy of the cloud storage provider.

specifically, the system checks the type of collaborative download decision data according to the request resource identifier data in the user download request data, and confirms that the collaborative download decision data is edge storage download decision data. And determining which edge nodes are responsible for downloading the required resources according to the task allocation information in the collaborative downloading decision data. And selecting the edge node which is closest to the user and has highest availability to execute the downloading task according to the resource position information in the collaborative downloading decision data by the system. The system configures the edge node to perform the download job using the download parameters (download speed, priority, etc.) specified by the user download request data. The edge node starts downloading the required resources and monitors and manages the download tasks during the download process.

Specifically, the system checks the type of the collaborative download decision data according to the request resource identifier data in the user download request data, and confirms that the collaborative download decision data is cloud storage download decision data. And determining that the downloading task is to be executed by a server of the cloud storage service provider according to the task allocation information in the collaborative downloading decision data. And the system sends a downloading request to a server of the cloud storage server according to the resource position information in the collaborative downloading decision data. The system will specify download parameters in the user download request data to convey specific requirements regarding the download to the cloud storage server. And the server of the cloud storage service provider starts to execute the downloading task and transmits the required resources to the user equipment.

Specifically, the real-time storage condition data of each edge node is collected, including the current available storage capacity, the resource type, the resource relevance and other information. The real-time data is summarized and aggregated to generate global edge real-time storage condition data, such as calculating total available storage capacity, tracking the distribution condition of various current resource types, and the like. The global edge real-time storage case data is updated to reflect the latest state of the edge network.

specifically, historical edge storage case data previously collected and stored is accessed, including information of past storage capacity, evolution of resource types, and the like. The historical data and the real-time data are compared to identify portions where changes occur, such as increased or decreased storage capacity, newly added resource types, and the like. Incremental aggregation is performed based on the change data to generate global edge incremental storage case data. This may help determine trends and changes in resource distribution.

Specifically, the global edge real-time storage case data and the global edge delta storage case data are combined to generate global edge storage case data. The method comprises the steps of merging real-time data and incremental data, ensuring that global edge storage condition data contains latest storage information, and simultaneously considering historical trend and change.

specifically, the user download request data is analyzed, including a resource identifier, a user identification, a request timestamp, and the like. Information such as roles, rights, etc. of the user, for example, whether the user is an administrator, a general user, etc., is extracted from the user identification data. Based on the request data and the user identification data, the download needs of the user are determined, including the type of the required resource (e.g., document, image, video, etc.) and the user rights (e.g., read, write, etc.).

Specifically, the global edge storage case data is accessed according to the request resource identifier data to extract attribute information about the required resources, including capacity, type (e.g., picture, video, document, etc.) of the resources and association information between the resources.

Specifically, based on the user identification data, historical download behavior of the user is analyzed, including the type of resources downloaded, the download frequency, and the like. The preference information of the user is collected, such as whether the user prefers to download at a specific time, prefers to download a specific type of resource, etc. User character feature data is extracted, such as whether the user is a new user, an experienced user, etc.

Specifically, the system performs static weight identification according to the user download requirement data, the resource attribute data and the user characteristic data, for example, establishes a plurality of weight models or rules through preset rules so as to determine the relative importance of the resources. For example, the system may assign different weights to each resource taking into account the resource type, user role, and user rights.

Specifically, there is a picture download application, and the user download request data includes a request for a picture, the user is identified as a normal user, and the request time stamp is 2024-01-20. The global edge storage condition data comprises a picture resource with the capacity of 5MB, a type of picture and no association with other resources. Step 1: user download demand analysis, resource type data: picture, user role data: general user, user authority data: read rights. Step 2: extracting resource attributes and resource capacity data: 5MB, resource type data: picture, resource association data: there are no associated resources. Step 3: extracting user characteristics, and downloading behavior characteristic data in a user history mode: the type of resources downloaded by the user in the past, the download frequency, etc. are analyzed. User preference data: it is known whether the user downloads the resource for a specific period of time, and whether the picture type resource is preferred. User character feature data: the user role is a normal user. Step 4: static weight identification, using weight models or rules, in combination with resource attributes, user requirements and user characteristics (0.4, 0.4 and 0.2), calculates importance scores for picture resources. For example, the system assigns different weights according to the resource capacity, type and user role, and the score is a value, such as 0.75 (high importance) or 0.4 (low importance), and the calculated result is compared with a preset threshold (such as 0.53).

Specifically, the user downloads the demand data: user demand resource type data: the user needs to download the picture resources. User role data: the role of the user is a normal user. User rights data: the user has read rights. Resource attribute data: resource capacity data: the capacity of the picture resources is 5MB. Resource type data: the resource type is a picture. Resource association data: the resource is not associated with the user history download record. User characteristic data: user history downloads behavioral profile data: users have downloaded a large amount of picture resources in the past. User preference data: the user prefers to download the resource at night. User character feature data: the user is a normal user, not an administrator or a special role. Resource type weight: the weight of the picture resource is 0.7, indicating that the picture resource is relatively important. User role weight: the weight of the ordinary user is 0.5, which indicates that the needs of the ordinary user are generally important. User rights weight: the weight of the read right is 0.6, which indicates that the requirement that the user can only read the resource is important. Resource capacity weight: a resource capacity weight of 0.3 for 5MB indicates that the resource is smaller and the demand is relatively less important. User history download behavioral characteristics weights: the user history downloads a large amount of picture resources, the weight is 0.8, and the requirement of the user on the picture resources is important. User preference weights: the user prefers to download resources at night, the weight is 0.7, and the requirement of downloading at night is important. User character feature weights: the user is a common user, the weight is 0.5, and the general requirement is indicated.

Specifically, user current location data is obtained and the resource usage involved in the current location is analyzed, e.g., the user needs to access resources related to his current location.

specifically, the user's network connection status and device status, such as bandwidth, latency, device performance, etc., are continuously monitored.

Specifically, the system continuously monitors the network connection status and the device status of the user, and the actual use condition of the resources. According to the resource use data, the user network connection state data and the equipment state data, the system carries out dynamic weight identification, and adjusts the importance assessment of the resources according to the real-time condition, for example, if the network connection of the user becomes unstable, the system can reduce the dependence on cloud storage and improve the importance of edge storage.

Specifically, based on the collected resource usage data, user network connection status data, and device status data, the system may use a weight adjustment algorithm or rule to re-evaluate the importance of the resource, and make adjustments according to the actual situation. The following is an example weight adjustment algorithm: bandwidth Weight (Bandwidth Weight): the importance of the resources is adjusted according to the bandwidth availability of the user, and if the user has a high-speed bandwidth connection, the bandwidth weight of the resources can be increased, because the high-speed connection can download the resources faster, the greater the bandwidth, the higher the weight. Delay weight (LATENCY WEIGHT): adjusting the importance of the resource according to the user's delay, a lower delay may provide a faster response time, and thus the delay weight may be increased for resources that require real-time. Device performance weight (Device Performance Weight): the resource importance is adjusted according to the performance level of the user equipment, and the higher performance equipment can process and display the resources faster, so that the performance weight of the equipment can be increased for the resources with high performance requirements. Storage space weight (Storage SPACE WEIGHT): the importance of the resources is adjusted according to the available memory space on the user device, and if the device memory space is limited, the memory space weight of the resources can be reduced to avoid taking up too much space.

Bandwidth Weight (Bandwidth Weight): the resource importance is adjusted according to the bandwidth availability of the user, and the weight of the bandwidth is set as follows: high-speed bandwidth (> 50 Mbps): weight is 0.9, medium bandwidth (20-50 Mbps): weight is 0.7, low speed bandwidth (< 20 Mbps): the weight is 0.5. Delay weight (LATENCY WEIGHT): the resource importance is adjusted according to the delay of the user, and the weight of the delay is set as follows: low latency (< 50 ms): weight 0.9, medium delay (50-100 ms): weight 0.7, high latency (> 100 ms): the weight is 0.5. Device performance weight (Device Performance Weight): resource importance is adjusted according to the performance level of the user equipment, and the weight of the equipment performance is set as follows: high performance devices (fast processor, large memory): weight 0.9, medium performance device (general processor, medium memory): weight 0.7, low performance device (slower processor, limited memory): the weight is 0.5. Storage space weight (Storage SPACE WEIGHT): the resource importance is adjusted according to the available memory space on the user equipment, and the weight of the memory space is set as follows: a large amount of available memory: weight 0.9, medium available memory: weight 0.7, limited available storage space: the weight is 0.5.

Specifically, resource usage data: the user is playing an online video. Network connection status data: the current network connection state of the user is 4G, but the network stability is poor. Device status data: the user's equipment is a old smart phone with low processing power. The system uses preset rules for dynamic weight identification. The system considers the following factors: resource usage weight: the user is playing an online video, a higher bandwidth and a more stable connection are required, and the weight of the resource usage can be set to 0.8, which indicates that the requirement of the online video is important. User network connection state weight: the current network connection state of the user is 4G, but is unstable, so that the dependence on cloud storage can be reduced, and the weight of the network connection state can be set to 0.6, which means that the network stability is poor. Device state weights: the user's device is a legacy smart phone with less processing power and more time to buffer and process the video, the device status weight can be set to 0.7, indicating a lower device performance.

Specifically, the first resource importance assessment data and the second resource importance assessment data are combined together to determine the final importance of the resource. The resources are ranked according to their importance using ranking and screening algorithms for subsequent collaborative download decisions. Combining the static weight identification with the dynamic weight identification, the system can obtain final resource importance assessment data of each resource. The system sorts and filters the data to determine which resources are to be downloaded by the edge storage node and which resources are to be downloaded by the cloud storage node. Resources with high first resource importance assessment data are more suitable for downloading from the edge storage nodes, while resources with low first resource importance assessment data and low second resource importance assessment data are more suitable for downloading from the cloud storage nodes.

In particular, global edge storage case data is used to determine the location and availability of resources, such as resource location through multiple edge nodes or cloud servers. Based on the resource importance assessment data, a resource location is determined that is most suitable to meet the user's needs, which may be an edge node or a cloud server.

In particular, resource availability checks are performed on the resource location data to ensure resource availability. The resource availability check includes the following aspects: resource presence check: ensuring that the selected resource is actually present and accessible. And (3) checking the occupation of resources: it is checked whether the resource is occupied or locked by other users to prevent conflicts. Resource transmission minimum requirement checking: and checking whether the transmission speed and the bandwidth of the resource meet the user requirements.

Specifically, if the resource availability data indicates that the selected resource is available, then edge storage download decision data is generated to indicate that the resource is downloaded from the edge storage node.

Specifically, if the resource availability data indicates that the resource is not available, cloud storage download decision data is generated to indicate that the resource is downloaded from a cloud server.

Specifically, the edge storage download decision data and the cloud storage download decision data are integrated together to form final collaborative download decision data. In some cases, resources may be downloaded from an edge store, while in other cases, it is desirable to download from a cloud store to meet user needs and resource availability.

specifically, when the system detects that the concurrent download record data includes failed download record data or interrupts download records, it initiates a breakpoint data collection process. Breakpoint data acquisition includes: downloaded data: and recording the part of the file data which has been successfully downloaded. Downloading progress data: and recording the position of the last download interruption or the download progress. Downloading file capacity data: indicating the size of the entire download file. Downloading resource identifier data: a unique identification identifying the download resource.

In particular, based on the collected breakpoint data, particularly the downloaded data and the download progress data, the system can determine from which location to continue downloading the file. The request resource scope data specifies the scope of the file segments that need to be continuously downloaded, typically in byte-scope. This range is dynamically generated based on information in the breakpoint data to minimize the data that needs to be re-downloaded.

Specifically, based on the request resource scope data, the system initiates a resume download job, downloading only the data blocks after the break point. This allows the system to continue downloading efficiently without having to re-download the entire file. The downloaded data blocks will gradually fill in the downloaded data until the complete file is available again.

Specifically, the system will continuously monitor data for three key aspects: current network condition data: including bandwidth availability, delay, packet loss rate, etc. Current server load status data: including information on the load, response time, number of connections, etc. of the server. Current user equipment performance status data: including information on the processing power, memory usage, battery status, etc. of the device.

Specifically, the system monitors network conditions, server load, and user device performance every second and records the data. Current network condition data: the bandwidth availability is 10Mbps, the average delay is 30 ms, and the packet loss rate is 2%. Current server load status data: the server load was 60%, the average response time was 50 ms, and the number of connections was 200. Current user equipment performance status data: the device processor speed was 2GHz, the available memory was 2GB, and the battery power was 60%.

specifically, based on the information in the breakpoint data, the system may calculate initial request resource scope data. The initial request resource scope data is typically a file scope following the breakpoint location in order to continue downloading from the breakpoint.

Specifically, the breakpoint data shows that the file total size is 100MB, and the download has reached 30MB.

Specifically, the system dynamically adjusts the initial request resource range data based on continuously monitored current environmental data, particularly network conditions, server load and user equipment performance. The purpose of this dynamic adjustment is to optimize the download performance to better adapt to the current environment. For example, if the network conditions are poor, the system may reduce the request resource range to reduce the packet loss rate and increase the download success rate. Conversely, if the network quality improves, the system will expand the range of requested resources to increase the download speed.

Specifically, the system dynamically adjusts the request resource range according to the current environment data: based on network conditions: because of the low network bandwidth availability (10 Mbps), the system decides to reduce the request resource range to avoid excessive packet loss and download failure. The requested resource range is reduced to the last 10MB (from 30MB to 40 MB) of the currently downloaded data. Based on server load: the server load is 60%, which means that the server load is higher and the response time is longer. The system decides to leave the request resource scope unchanged to reduce the requests to the server. Based on user equipment performance: the user equipment performance is enough, the processor speed and the memory are large enough, and the request resource range does not need to be adjusted. Thus, the final requested resource range data is from 30MB to 40MB to accommodate the current network conditions.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A resource download management method based on cloud computing, the method comprising:

2. The method according to claim 1, wherein the obtaining the user download request data in response to the user request download operation, and performing the edge storage scan according to the user download request data, to obtain the edge storage condition data, includes:

responding to a user request downloading operation, and acquiring user downloading request data;

Generating edge storage scanning data according to the user downloading request data;

Performing multi-node scanning task allocation according to the edge storage scanning data to obtain multi-node scanning task allocation data;

and generating edge storage condition data according to the multi-node scanning task allocation data to obtain the edge storage condition data.

3. The method according to claim 2, wherein the performing multi-node scan task allocation according to the edge storage scan data to obtain multi-node scan task allocation data includes:

Acquiring edge node data;

selecting scanning task nodes according to the edge node data to obtain scanning task node selection data;

and performing task allocation processing on the scanning task node selection data according to the edge storage code scanning data to obtain multi-node scanning task allocation data.

4. The method of claim 1, wherein when the determining that the edge storage case data includes storage resource case data, performing a collaborative cache download job according to the edge storage case data includes:

Data aggregation is carried out on the edge storage condition data set to obtain global edge storage condition data;

Carrying out resource importance assessment on the user downloading request data and the global edge storage condition data to obtain resource importance assessment data;

performing collaborative downloading decision generation according to the global edge storage condition data and the resource importance evaluation data to obtain collaborative downloading decision data;

When the collaborative download decision data corresponding to the request resource identifier data is determined to be the edge storage download decision data, carrying out edge storage download operation according to the user download request data and the collaborative download decision data;

And when the collaborative download decision data corresponding to the request resource identifier data is cloud storage download decision data, carrying out cloud storage download operation according to the user download request data and the collaborative download decision data.

5. The method of claim 4, wherein the aggregating the edge storage case data set to obtain global edge storage case data comprises:

6. The method of claim 4, wherein performing the resource importance assessment on the user download request data and the global edge storage status data to obtain resource importance assessment data comprises:

7. The method of claim 6, wherein the performing collaborative download decision generation according to the global edge storage condition data and the resource importance assessment data to obtain collaborative download decision data comprises:

8. The method of claim 1, wherein when the determining that the concurrent download record data includes failed download record data or interrupting download record, performing an efficient breakpoint resume operation according to the concurrent download record data comprises:

9. The method of claim 8, wherein said performing request resource scope processing according to the breakpoint data to obtain request resource scope data comprises:

10. A cloud computing-based resource download management system for executing the cloud computing-based resource download management method as claimed in claim 1, the cloud computing-based resource download management system comprising: