A dynamic feedback weighted cloud storage resource scheduling method, device and equipment

Abstract

The present invention relates to the technical field of scheduling cross-regional cloud storage resources, and discloses a dynamic feedback weighted cloud storage resource scheduling method, device and equipment. The present invention pre-applies for corresponding storage resources from each cloud storage service node, stores storage resource information including the service response data of the node in a preset cache pool, and when receiving a storage request sent by an intelligent device, calculates the service performance weight of each node based on the geographical location weight information, load threshold and service response data of each node to determine the optimal cloud storage service node, and schedules the storage request to the optimal cloud storage service node. The present invention calculates the service performance weight of each node on the basis of the weighted minimum connection scheduling algorithm as a basis for resource scheduling. It can realize the measurement of downstream service load conditions without deploying relevant performance collection programs, improves the rationality and reliability of cloud storage resource scheduling, and can be applied to scenarios using third-party cloud storage services.

Description

Technical Field

[0001] The present invention relates to the technical field of scheduling cross-region cloud storage resources, and in particular to a dynamic feedback weighted cloud storage resource scheduling method, device and equipment. Background Art

[0002] Currently, cloud storage technology is used to store data collected by smart devices (such as smart probes, smart doorbells, etc.).

[0003] Cloud storage resource allocation typically uses a proximity strategy, allocating service resources based on geographic location. However, this static allocation strategy often fails to balance factors such as storage service quality, service outbound bandwidth, service price, and service concurrency, thus failing to effectively maximize the development of storage service services.

[0004] Prior art optimizes cloud storage resource allocation by employing a cloud storage resource scheduling optimization scheme based on weighted least connections. For example, Chinese invention patent publication number CN106790381A provides a dynamic feedback load balancing method based on weighted least connections. This method, in a high-traffic multi-server node parallel network, combines a reverse Nginx reverse proxy server to improve and optimize the weighted least connection algorithm. This algorithm collects upstream server load information without increasing communication overhead, and dynamically modifies upstream server weights on the Nginx reverse proxy server, providing a basis for optimizing load distribution. The Chinese invention patent with publication number CN103338228A provides a cloud computing load balancing scheduling algorithm based on a dual-weighted least-connection algorithm. This algorithm uses real-time information such as the CPU idle rate and memory idle rate on the server to dynamically represent the weight of the server performance on the basis of weighting the performance of the resource server by the weighted least-connection scheduling algorithm, so as to fully evaluate and utilize the remaining processing capacity of each node server; assign corresponding weights to tasks according to the complexity of the task type; to ensure that the load of each node does not tilt significantly when the device is running for a long time, before each task assignment, the scheduler will calculate the ratio of the sum of the weights of all tasks on each server to the weight of the server performance, and assign the new task to the server with the smallest ratio.

[0005] However, existing cloud storage resource scheduling schemes require the installation of additional collection programs on the storage infrastructure to collect dynamic feedback information. When using third-party cloud storage services, the difficulty of deploying these collection programs on the storage nodes prevents the collection of relevant performance metrics, rendering these methods unusable. Summary of the Invention

[0006] The present invention provides a dynamic feedback weighted cloud storage resource scheduling method, device and equipment, which solves the technical problem that existing cloud storage resource scheduling optimization solutions cannot be used in scenarios where third-party cloud storage services are used due to the difficulty in deploying collection programs for relevant performance indicators.

[0007] A first aspect of the present invention provides a dynamic feedback weighted cloud storage resource scheduling method, comprising:

[0008] Obtain cloud storage pre-configuration information, including geographical location weight information, load threshold, and pre-requested resource quantity of each registered cloud storage service node;

[0009] Pre-apply for corresponding storage resources from each of the cloud storage service nodes according to the cloud storage pre-configuration information, and store the corresponding storage resource information in a pre-set cache pool; the storage resource information includes service response data of each of the cloud storage service nodes for the corresponding storage resources;

[0010] When receiving a storage request sent by a smart device, calculating the service performance weight of each cloud storage service node based on the geographical location weight information, the load threshold and the service response data in the preset cache pool;

[0011] An optimal cloud storage service node is determined from each of the cloud storage service nodes according to the calculated service performance weight, and the storage request is dispatched to the optimal cloud storage service node.

[0012] According to one implementation of the first aspect of the present invention, the calculating the service performance weight of each cloud storage service node based on the geographic location weight information, the load threshold, and the service response data in the preset cache pool includes:

[0013] Determine the current number of concurrent connections of each cloud storage service node and the average response time in each preset continuous time period based on the service response data in the preset cache pool;

[0014] The service performance weight of each cloud storage service node is calculated according to the following formula:

[0015]

[0016] Where, W(S i ) represents the cloud storage service node S i The service performance weight, T(S i ) is the cloud storage service node S i The load threshold, C(S i ) is the cloud storage service node S i The current number of concurrent connections, R1(S i ) is the cloud storage service node Si The average response time of the service within 1 minute, R j (S i ) is the cloud storage service node S i The average response time of the service in j minutes, n is the preset service time, L(S i ) is the cloud storage service node S i The geographical location weight, k1 is the weight coefficient of the service performance weight of the concurrent load, k2 is the weight coefficient corresponding to the service response, and k3 is the weight coefficient corresponding to the geographical location attribute, k1+k2+k3=1.

[0017] According to one implementation of the first aspect of the present invention, the calculating the service performance weight of each cloud storage service node based on the geographic location weight information, the load threshold, the current number of concurrent connections, and the service response data further includes:

[0018] Set k1 = 0.6, k2 = 0.35, and k3 = 0.05.

[0019] According to one implementation of the first aspect of the present invention, the storage request includes a local storage requirement, and determining the optimal cloud storage service node from each of the cloud storage service nodes based on the calculated service performance weight includes:

[0020] Selecting a cloud storage service node that meets the local storage requirement from each of the cloud storage service nodes as a candidate node;

[0021] The node with the largest service performance weight is selected from the candidate nodes as the optimal cloud storage service node.

[0022] According to another implementation of the first aspect of the present invention, determining the optimal cloud storage service node from each of the cloud storage service nodes based on the calculated service performance weight includes:

[0023] The optimal cloud storage service node is selected from the cloud storage service nodes with the largest service performance weight.

[0024] According to one implementation of the first aspect of the present invention, the method further includes:

[0025] After the storage request is dispatched to the optimal cloud storage service node, corresponding dispatch result information is recorded and stored in the preset cache pool.

[0026] According to an implementation of the first aspect of the present invention, the cloud storage pre-configuration information further includes a cache time threshold, and the method further includes:

[0027] If the storage time of the storage resource information in the preset cache pool reaches the cache time threshold, new storage resources are pre-applied from each of the cloud storage service nodes to update the current storage resource information.

[0028] A second aspect of the present invention provides a dynamic feedback weighted cloud storage resource scheduling device, comprising:

[0029] An acquisition module is used to acquire cloud storage pre-configuration information, wherein the cloud storage pre-configuration information includes geographical location weight information, load threshold, and resource pre-application quantity of each registered cloud storage service node;

[0030] A pre-application module is configured to pre-apply for corresponding storage resources from each of the cloud storage service nodes according to the cloud storage pre-configuration information, and store the corresponding storage resource information in a pre-set cache pool; the storage resource information includes service response data of each of the cloud storage service nodes for the corresponding storage resources;

[0031] a calculation module, configured to calculate, upon receiving a storage request sent by a smart device, a service performance weight of each of the cloud storage service nodes based on the geographic location weight information, the load threshold, and the service response data in the preset cache pool;

[0032] The scheduling module is used to determine the optimal cloud storage service node from each of the cloud storage service nodes according to the calculated service performance weight, and schedule the storage request to the optimal cloud storage service node.

[0033] According to an implementation of the second aspect of the present invention, the calculation module includes:

[0034] A determining unit, configured to determine the current number of concurrent connections of each of the cloud storage service nodes and the average response time in each preset continuous time period based on the service response data in the preset cache pool;

[0035] The computing unit is configured to calculate the service performance weight of each of the cloud storage service nodes according to the following formula:

[0036]

[0037] Where, W(S i ) represents the cloud storage service node S i The service performance weight, T(S i ) is the cloud storage service node S i The load threshold, C(S i ) is the cloud storage service node S i The current number of concurrent connections, R1(S i ) is the cloud storage service node S i The average response time of the service within 1 minute, Rj (S i ) is the cloud storage service node S i The average response time of the service in j minutes, n is the preset service time, L(S i ) is the cloud storage service node S i The geographical location weight, k1 is the weight coefficient of the service performance weight of the concurrent load, k2 is the weight coefficient corresponding to the service response, and k3 is the weight coefficient corresponding to the geographical location attribute, k1+k2+k3=1.

[0038] According to an implementation of the second aspect of the present invention, the calculation module further includes:

[0039] The setting unit is used to set k1=0.6, k2=0.35, and k3=0.05.

[0040] According to an implementation of the second aspect of the present invention, the storage request includes a local storage requirement, and the scheduling module includes:

[0041] A first selection unit is configured to select a cloud storage service node that meets the local storage requirement from each of the cloud storage service nodes as a candidate node;

[0042] The second selection unit is used to select the node with the largest service performance weight from the candidate nodes as the optimal cloud storage service node.

[0043] According to another implementation of the second aspect of the present invention, the scheduling module includes:

[0044] The third selection unit is used to select the cloud storage service node with the largest service performance weight from each of the cloud storage service nodes as the optimal cloud storage service node.

[0045] In one possible implementation, the device further includes:

[0046] The scheduling recording module is used to record corresponding scheduling result information after scheduling the storage request to the optimal cloud storage service node, and store the scheduling result information in the preset cache pool.

[0047] According to an achievable manner of the second aspect of the present invention, the cloud storage pre-configuration information further includes a cache time threshold, and the apparatus further includes:

[0048] The update module is used to pre-apply for new storage resources from each of the cloud storage service nodes to update the current storage resource information if the storage time of the storage resource information in the preset cache pool reaches a cache time threshold.

[0049] A third aspect of the present invention provides a dynamic feedback weighted cloud storage resource scheduling device, comprising:

[0050] A memory for storing instructions; wherein the instructions are used to implement the dynamic feedback weighted cloud storage resource scheduling method as described in any one of the above implementation methods;

[0051] A processor is configured to execute instructions in the memory.

[0052] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the dynamic feedback weighted cloud storage resource scheduling method as described in any of the above implementation methods.

[0053] It can be seen from the above technical solutions that the present invention has the following advantages:

[0054] The present invention pre-applies for corresponding storage resources from each cloud storage service node based on cloud storage pre-configuration information, stores storage resource information including the node's service response data in a preset cache pool, and when receiving a storage request sent by an intelligent device, calculates the service performance weight of each cloud storage service node based on the geographical location weight information, load threshold and service response data of each cloud storage service node, determines the optimal cloud storage service node from each cloud storage service node based on the calculated service performance weight, and schedules the storage request to the optimal cloud storage service node; based on the weighted minimum connection scheduling algorithm, the present invention obtains dynamic factors such as the number of concurrent connections and the average service response time of each node through the service response data that can be obtained by itself, calculates the service performance weight of each node in combination with the pre-obtained node location information as a basis for resource scheduling, and can realize the measurement of downstream service load conditions without deploying related performance collection programs, thereby improving the rationality and reliability of cloud storage resource scheduling, and can be applicable to scenarios using third-party cloud storage services. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 A flowchart of a dynamic feedback weighted cloud storage resource scheduling method provided by an optional embodiment of the present invention;

[0057] Figure 2A schematic diagram illustrating the principles of a dynamic feedback weighted cloud storage resource scheduling method provided by an optional embodiment of the present invention;

[0058] Figure 3 This is a structural connection block diagram of a dynamic feedback weighted cloud storage resource scheduling device provided by an optional embodiment of the present invention.

[0059] Reference numerals:

[0060] 1-Acquisition module; 2-Pre-application module; 3-Calculation module; 4-Scheduling module. DETAILED DESCRIPTION

[0061] Embodiments of the present invention provide a dynamic feedback weighted cloud storage resource scheduling method, apparatus, and device for resolving the technical problem that existing cloud storage resource scheduling optimization solutions cannot be used in scenarios where third-party cloud storage services are used due to the difficulty in deploying programs for collecting relevant performance indicators.

[0062] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0063] The present invention provides a dynamic feedback weighted cloud storage resource scheduling method.

[0064] Figure 1 A flow chart of a dynamic feedback weighted cloud storage resource scheduling method provided by an embodiment of the present invention is shown; Figure 2 A schematic diagram illustrating the principles of a dynamic feedback weighted cloud storage resource scheduling method provided by an optional embodiment of the present invention is shown.

[0065] See also Figure 1 、 Figure 2 , an embodiment of the present invention provides a dynamic feedback weighted cloud storage resource scheduling method, including steps S1-S4.

[0066] Step S1: Obtain cloud storage pre-configuration information, which includes geographical location weight information, load threshold, and resource pre-application quantity of each registered cloud storage service node.

[0067] The cloud storage pre-configuration information may be obtained when the device / equipment executing the method is initialized.

[0068] Among them, the geographical location weight information can be obtained by setting the weight corresponding to each geographical location according to the actual situation according to the geographical location of the cloud storage service node. For example, each target area range can be specified according to actual business needs, and a corresponding location weight can be set for each target area range, and the corresponding geographical location weight can be determined according to the target area range to which the location of each cloud storage service node belongs. For another example, the geographical location weight can be set according to the distance between the node and the device / equipment that executes the method of the present application. Specifically, a larger geographical location weight is set for the cloud storage service node that is closer to the device / equipment that executes the method of the present application, so as to facilitate localized storage.

[0069] As a specific implementation method, the geographical location weight ranges from 0 to 1, so as to facilitate the calculation of subsequent service performance weights.

[0070] It should be noted that the size range of the geographical territorial weight can also be adjusted according to actual needs, for example, set between 0.2 and 0.8, or between 0.2 and 1.2.

[0071] The resource pre-application quantity is used to guide the subsequent pre-application quantity of corresponding storage resources.

[0072] Step S2: pre-apply for corresponding storage resources from each of the cloud storage service nodes according to the cloud storage pre-configuration information, and store the corresponding storage resource information in a preset cache pool; the storage resource information includes service response data of each of the cloud storage service nodes to the corresponding storage resources.

[0073] As a specific implementation method, Figure 2 As shown, the preset cache pool is a Redis cache pool. Of course, the preset cache pool can also adopt other applicable existing database forms according to actual conditions.

[0074] The storage resource information also includes the identifier of the cloud storage service node that provides the pre-applied storage resource and corresponding information of the corresponding storage resource, such as the storage type, capacity, interface type and interface address of the storage resource.

[0075] When storing the corresponding storage resource information in the pre-configured cache pool, it can be partitioned and stored according to different cloud storage service nodes, so that the corresponding storage resource information can be stored in different containers to facilitate subsequent storage resource scheduling.

[0076] Step S3, when receiving a storage request sent by a smart device, calculate the service performance weight of each cloud storage service node based on the geographical location weight information, the load threshold and the service response data in the preset cache pool.

[0077] When calculating the service performance weight of each cloud storage service node, first obtain the current number of concurrent connections of each cloud storage service node and the average response time in each preset continuous time period based on the service response data in the preset cache pool, and then calculate the service performance weight of each cloud storage service node based on the obtained data.

[0078] In one achievable manner, the service performance weight of each cloud storage service node is calculated according to the following formula:

[0079]

[0080] Where, W(S i ) represents the cloud storage service node S i The service performance weight, T(S i ) is the cloud storage service node S i The load threshold, C(S i ) is the cloud storage service node S i The current number of concurrent connections, R1(S i ) is the cloud storage service node S i The average response time of the service within 1 minute, R j (S i ) is the cloud storage service node S i The average response time of the service in j minutes, n is the preset service time, L(S i ) is the cloud storage service node S i The geographical location weight, k1 is the weight coefficient of the service performance weight of the concurrent load, k2 is the weight coefficient corresponding to the service response, and k3 is the weight coefficient corresponding to the geographical location attribute, k1+k2+k3=1.

[0081] In a specific implementation manner, n is set to be ≤ 30 min.

[0082] In this embodiment, the service performance weight of the cloud storage service node is and For the convenience of calculation, L(S i ) is set to a value between 0 and 1.

[0083] As can be seen from the formula in the first part, the higher the load, the smaller the corresponding formula value, and the smaller the corresponding service performance weight calculation result; as can be seen from the formula in the second part, the performance of the cloud hard disk is close to linear. The larger the change in the response amplitude, the larger the formula value obtained, or even a negative value, resulting in a smaller overall service performance weight. In the initial stage of the method execution, because the formula value of the first part is close to k1 and the formula value of the second part is close to 0, the service performance weight of the cloud storage service node is mainly affected by the formula in the third part. Based on this, in order to improve the rationality of the service performance weight calculation, as a specific implementation method, k1 = 0.6, k2 = 0.35 and k3 = 0.05 are set.

[0084] It should be noted that other values ​​may be set for k1, k2, and k3 according to actual conditions.

[0085] In this embodiment, the second part of the formula is calculated based on the average response time of the service corresponding to 1 to n minutes of the cloud storage service node. In other achievable methods, the average response time of the service within several minutes can be used for calculation according to actual needs. For example, the cloud storage service node S i Average response time R1(S) of the service within 1 minute i ), the average response time of the service within 5 minutes R5(S i ), the average response time of the service within 10 minutes R 10 (S i ) and the average response time R of the service within 30 minutes 30 (S i ) to perform the second part of the calculation, the formula for the second part is replaced by:

[0086]

[0087] The above-described embodiment of the present invention utilizes readily available service response data to determine dynamic factors such as the number of concurrent connections and average service response time for each node. This takes into account service carrying capacity, concurrency performance, real-time response, and locality information. Combined with rapid computation of cached data, this method weights key factors and calculates the service performance weight for each node, providing a basis for subsequent resource scheduling. This embodiment enables the measurement of downstream service load without the need to deploy relevant performance collection programs.

[0088] Step S4: Determine the optimal cloud storage service node from each of the cloud storage service nodes according to the calculated service performance weight, and schedule the storage request to the optimal cloud storage service node.

[0089] When the storage request is dispatched to the optimal cloud storage service node, specifically, according to the cache request of the smart device, the corresponding resource information of the optimal cloud storage service node is obtained from the preset cache pool, assembled into a response message and fed back to the smart device.

[0090] In one practicable manner, the storage request includes a local storage requirement, and determining the optimal cloud storage service node from among the cloud storage service nodes based on the calculated service performance weight includes:

[0091] Selecting a cloud storage service node that meets the local storage requirement from each of the cloud storage service nodes as a candidate node;

[0092] The node with the largest service performance weight is selected from the candidate nodes as the optimal cloud storage service node.

[0093] As a specific implementation, the local storage requirement may include the local area range of the requested storage resource. Furthermore, when executing the method, storage service nodes belonging to the local area range may be selected as candidate nodes based on the geographical location of the storage service node.

[0094] In other embodiments, the local storage requirement may also be a distance threshold from the target cloud storage service node. Furthermore, when executing this method, the distance between the smart device and each storage service node can be determined based on the storage service node's geographic location, and the storage service node corresponding to the distance not exceeding the distance threshold can be selected as a candidate node.

[0095] In this embodiment, the optimal cloud storage service node is determined in combination with the local storage requirements, so that resource scheduling can better meet the storage needs of smart devices. It is suitable for resource scheduling scenarios with storage locality requirements.

[0096] In another achievable manner, determining the optimal cloud storage service node from the cloud storage service nodes according to the calculated service performance weight includes:

[0097] The optimal cloud storage service node is selected from the cloud storage service nodes with the largest service performance weight.

[0098] In this embodiment, the node with the largest service performance weight is selected as the optimal cloud storage service node, which can be applied to scenarios where the storage request has no storage location requirement.

[0099] In one possible implementation, the method further includes:

[0100] After the storage request is dispatched to the optimal cloud storage service node, corresponding dispatch result information is recorded and stored in the preset cache pool.

[0101] In one possible implementation, the cloud storage pre-configuration information further includes a cache time threshold, and the method further includes:

[0102] If the storage time of the storage resource information in the preset cache pool reaches the cache time threshold, new storage resources are pre-applied from each of the cloud storage service nodes to update the current storage resource information.

[0103] In this embodiment, a data expiration policy is set using a cache time threshold. This policy can automatically eliminate expired data and implement the updating of pre-applied storage resource information in the cache pool.

[0104] The present invention also provides a dynamic feedback weighted cloud storage resource scheduling device, which is used to execute the dynamic feedback weighted cloud storage resource scheduling method described in any of the above embodiments of the present invention. As a specific implementation, the dynamic feedback weighted cloud storage resource scheduling device is a cloud storage gateway.

[0105] See also Figure 3 , Figure 3 A structural connection block diagram of a dynamic feedback weighted cloud storage resource scheduling device provided by an embodiment of the present invention is shown.

[0106] An embodiment of the present invention provides a dynamic feedback weighted cloud storage resource scheduling device, comprising:

[0107] Acquisition module 1 is used to obtain cloud storage pre-configuration information, which includes geographical location weight information, load threshold and resource pre-application quantity of each registered cloud storage service node;

[0108] Pre-application module 2, configured to pre-application corresponding storage resources from each cloud storage service node according to the cloud storage pre-configuration information, and store the corresponding storage resource information in a pre-set cache pool; the storage resource information includes service response data of each cloud storage service node for the corresponding storage resource;

[0109] Calculation module 3, configured to calculate the service performance weight of each cloud storage service node based on the geographic location weight information, the load threshold, and the service response data in the preset cache pool when receiving a storage request sent by a smart device;

[0110] The scheduling module 4 is used to determine the optimal cloud storage service node from each of the cloud storage service nodes according to the calculated service performance weight, and schedule the storage request to the optimal cloud storage service node.

[0111] In one possible implementation, the calculation module 3 includes:

[0112] A determining unit, configured to determine the current number of concurrent connections of each of the cloud storage service nodes and the average response time in each preset continuous time period based on the service response data in the preset cache pool;

[0113] The computing unit is configured to calculate the service performance weight of each of the cloud storage service nodes according to the following formula:

[0114]

[0115] Where, W(S i ) represents the cloud storage service node S i The service performance weight, T(S i ) is the cloud storage service node S i The load threshold, C(S i ) is the cloud storage service node S i The current number of concurrent connections, R1(S i ) is the cloud storage service node S i The average response time of the service within 1 minute, R j (S i ) is the cloud storage service node S i The average response time of the service in j minutes, n is the preset service time, L(S i ) is the cloud storage service node S i The geographical location weight, k1 is the weight coefficient of the service performance weight of the concurrent load, k2 is the weight coefficient corresponding to the service response, and k3 is the weight coefficient corresponding to the geographical location attribute, k1+k2+k3=1.

[0116] In one possible implementation, the calculation module 3 further includes:

[0117] The setting unit is used to set k1=0.6, k2=0.35, and k3=0.05.

[0118] In one possible implementation, the storage request includes a local storage requirement, and the scheduling module 4 includes:

[0119] A first selection unit is configured to select a cloud storage service node that meets the local storage requirement from each of the cloud storage service nodes as a candidate node;

[0120] The second selection unit is used to select the node with the largest service performance weight from the candidate nodes as the optimal cloud storage service node.

[0121] In another possible implementation, the scheduling module 4 includes:

[0122] The third selection unit is used to select the cloud storage service node with the largest service performance weight from each of the cloud storage service nodes as the optimal cloud storage service node.

[0123] In one possible implementation, the device further includes:

[0124] The scheduling recording module is used to record corresponding scheduling result information after scheduling the storage request to the optimal cloud storage service node, and store the scheduling result information in the preset cache pool.

[0125] In one achievable manner, the cloud storage pre-configuration information further includes a cache time threshold, and the apparatus further includes:

[0126] The update module is used to pre-apply for new storage resources from each of the cloud storage service nodes to update the current storage resource information if the storage time of the storage resource information in the preset cache pool reaches a cache time threshold.

[0127] The present invention also provides a dynamic feedback weighted cloud storage resource scheduling device, comprising:

[0128] A memory for storing instructions; wherein the instructions are used to implement the dynamic feedback weighted cloud storage resource scheduling method as described in any one of the above embodiments;

[0129] A processor is configured to execute instructions in the memory.

[0130] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the dynamic feedback weighted cloud storage resource scheduling method as described in any one of the above embodiments is implemented.

[0131] The above-mentioned embodiment of the present invention is based on the weighted minimum connection scheduling algorithm. When it is impossible to collect relevant performance parameters of the downstream cloud storage service, it utilizes service response information, takes into account the service carrying capacity, concurrency performance and local information, and combines cached data to weight key factors, thereby realizing rapid calculation of the weight of the downstream cloud storage service performance. Based on the service performance weight, the storage task is allocated to the appropriate downstream server node, which provides a basis for service scheduling and can effectively improve the rationality and reliability of cloud storage resource scheduling.

[0132] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working processes of the above-described devices, equipment and modules can refer to the corresponding processes in the aforementioned method embodiments, and the specific beneficial effects of the above-described devices, equipment and modules can refer to the corresponding beneficial effects in the aforementioned method embodiments, which will not be repeated here.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed devices, equipment, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division. In actual implementation, other division methods may be used. For example, multiple modules or components may be combined or integrated into another device, or some features may be ignored or not performed.

[0134] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of the present embodiment according to actual needs.

[0135] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0136] If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0137] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic feedback weighted cloud storage resource scheduling method, characterized in that: include: Obtain cloud storage pre-configuration information, including geographical location weight information, load threshold, and pre-requested resource quantity of each registered cloud storage service node; Pre-apply for corresponding storage resources from each of the cloud storage service nodes according to the cloud storage pre-configuration information, and store the corresponding storage resource information in a pre-configured cache pool; The storage resource information includes service response data of each cloud storage service node to the corresponding storage resource; When receiving a storage request sent by a smart device, calculating the service performance weight of each cloud storage service node based on the geographical location weight information, the load threshold and the service response data in the preset cache pool; Determining an optimal cloud storage service node from among the cloud storage service nodes according to the calculated service performance weights, and dispatching the storage request to the optimal cloud storage service node; The calculating the service performance weight of each cloud storage service node based on the geographical location weight information, the load threshold, and the service response data in the preset cache pool includes: Determine the current number of concurrent connections of each cloud storage service node and the average response time in each preset continuous time period based on the service response data in the preset cache pool; The service performance weight of each cloud storage service node is calculated according to the following formula: Where, W(S i ) represents the cloud storage service node S i The service performance weight, T(S i ) is the cloud storage service node S i The load threshold, C(S i ) is the cloud storage service node S i The current number of concurrent connections, R1(S i ) is the cloud storage service node S i The average response time of the service within 1 minute, R j (S i ) is the cloud storage service node S i The average response time of the service in j minutes, n is the preset service time, L(S i ) is the cloud storage service node S i The geographical location weight, k1 is the weight coefficient of the service performance weight of the concurrent load, k2 is the weight coefficient corresponding to the service response, and k3 is the weight coefficient corresponding to the geographical location attribute, k1+k2+k3=1.

2. The dynamic feedback weighted cloud storage resource scheduling method according to claim 1, characterized in that: The calculating of the service performance weight of each cloud storage service node based on the geographical location weight information, the load threshold, the current number of concurrent connections, and the service response data further includes: Set k1 = 0.6, k2 = 0.35, and k3 = 0.

05.

3. The dynamic feedback weighted cloud storage resource scheduling method according to claim 1, characterized in that: The storage request includes a local storage requirement, and determining the optimal cloud storage service node from each of the cloud storage service nodes based on the calculated service performance weight includes: Selecting a cloud storage service node that meets the local storage requirement from each of the cloud storage service nodes as a candidate node; The node with the largest service performance weight is selected from the candidate nodes as the optimal cloud storage service node.

4. The dynamic feedback weighted cloud storage resource scheduling method according to claim 1, characterized in that: The determining the optimal cloud storage service node from each of the cloud storage service nodes according to the calculated service performance weights includes: The optimal cloud storage service node is selected from the cloud storage service nodes with the largest service performance weight.

5. The dynamic feedback weighted cloud storage resource scheduling method according to claim 1, characterized in that: The method further comprises: After the storage request is dispatched to the optimal cloud storage service node, corresponding dispatch result information is recorded and stored in the preset cache pool.

6. The dynamic feedback weighted cloud storage resource scheduling method according to claim 1, characterized in that: The cloud storage pre-configuration information further includes a cache time threshold, and the method further includes: If the storage time of the storage resource information in the preset cache pool reaches the cache time threshold, new storage resources are pre-applied from each of the cloud storage service nodes to update the current storage resource information.

7. A dynamic feedback weighted cloud storage resource scheduling device, characterized in that: include: An acquisition module is used to acquire cloud storage pre-configuration information, wherein the cloud storage pre-configuration information includes geographical location weight information, load threshold, and resource pre-application quantity of each registered cloud storage service node; A pre-application module is used to pre-apply for corresponding storage resources from each of the cloud storage service nodes according to the cloud storage pre-configuration information, and store the corresponding storage resource information in a preset cache pool; The storage resource information includes service response data of each cloud storage service node to the corresponding storage resource; a calculation module, configured to calculate, upon receiving a storage request sent by a smart device, a service performance weight of each of the cloud storage service nodes based on the geographic location weight information, the load threshold, and the service response data in the preset cache pool; a scheduling module, configured to determine an optimal cloud storage service node from among the cloud storage service nodes based on the calculated service performance weights, and schedule the storage request to the optimal cloud storage service node; The calculation module includes: A determining unit, configured to determine the current number of concurrent connections of each of the cloud storage service nodes and the average response time in each preset continuous time period based on the service response data in the preset cache pool; The computing unit is configured to calculate the service performance weight of each of the cloud storage service nodes according to the following formula: Where, W(S i ) represents the cloud storage service node S i The service performance weight, T(S i ) is the cloud storage service node S i The load threshold, C(S i ) is the cloud storage service node S i The current number of concurrent connections, R1(S i ) is the cloud storage service node S i The average response time of the service within 1 minute, R j (S i ) is the cloud storage service node S i The average response time of the service in j minutes, n is the preset service time, L(S i ) is the cloud storage service node S i The geographical location weight, k1 is the weight coefficient of the service performance weight of the concurrent load, k2 is the weight coefficient corresponding to the service response, and k3 is the weight coefficient corresponding to the geographical location attribute, k1+k2+k3=1.

8. A dynamic feedback weighted cloud storage resource scheduling device, characterized in that: include: A memory for storing instructions; wherein the instructions are used to implement the dynamic feedback weighted cloud storage resource scheduling method according to any one of claims 1 to 6; A processor is configured to execute instructions in the memory.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the dynamic feedback weighted cloud storage resource scheduling method according to any one of claims 1 to 6 is implemented.

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