CN115525230A - Storage resource allocation method and device, storage medium and electronic equipment - Google Patents
Storage resource allocation method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The invention discloses a method and a device for allocating storage resources, a storage medium and electronic equipment, and relates to the technical field of data management. The method comprises the following steps: acquiring service demand information of a current application server on storage resources in a storage server; determining a service type corresponding to the service demand information; determining target influence characteristics of the storage server according to the service type, wherein the target influence characteristics are influence factors influencing the current application server to access storage resources in the storage server; analyzing the target influence characteristics based on a resource planning model, and determining a resource allocation strategy for allocating resources to the current application server, wherein the resource planning model is obtained based on historical service demand information of the application server and influence characteristics corresponding to the historical service demand information through training; and allocating storage resources for the current application server based on the resource allocation strategy. The invention solves the technical problem of low resource planning efficiency in manual planning of storage resources in the related technology.
Description
Technical Field
The present invention relates to the field of data management technologies, and in particular, to a method and an apparatus for allocating storage resources, a storage medium, and an electronic device.
Background
With the advent of the big data era, the scale of the storage resource requirements of a data center has reached the PB and even BE level, and therefore, storage resource planning is very important. In addition, more and more applications put forward online and storage requirements, and even though cloud computing is introduced to reduce the operation and maintenance management capacity of the storage server on the storage resources, the factors such as the security, stability and performance of the storage resources still need to be considered. Moreover, because a large number of traditional environments cannot enter the cloud, the traditional artificial storage resource planning method cannot meet the existing huge and complicated demand. Moreover, the traditional method for artificially planning the storage resources has low efficiency, high labor cost caused by excessive input of manpower, and low artificial planning speed, and the performance of the storage server cannot be effectively and comprehensively considered, so that the waste of the storage resources is caused, and therefore, the method has important significance for improving the planning efficiency of the storage resources.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a method, a device, a storage medium and electronic equipment for allocating storage resources, which are used for at least solving the technical problem of low resource planning efficiency in manual planning of storage resources in the related technology.
According to an aspect of the embodiments of the present invention, there is provided a method for allocating storage resources, including: acquiring service demand information of a current application server on storage resources in a storage server; determining a service type corresponding to the service demand information; determining at least one target influence characteristic of the storage server according to the service type, wherein the at least one target influence characteristic is an influence factor influencing the current application server to access storage resources in the storage server; analyzing at least one target influence characteristic based on a resource planning model, and determining a resource allocation strategy for allocating resources for the current application server, wherein the resource planning model is obtained based on historical service demand information of at least one application server and influence characteristics corresponding to the historical service demand information through training; and allocating storage resources for the current application server based on the resource allocation strategy.
Further, a resource planning model is generated by: acquiring historical service demand information corresponding to at least one application server and historical influence characteristics corresponding to a storage server, wherein the historical influence characteristics are influence factors corresponding to the at least one application server accessing storage resources in the storage server within a historical time period; carrying out data preprocessing on the historical influence characteristics to obtain preprocessed historical influence characteristics; carrying out staged analysis processing on the preprocessed historical influence characteristics to obtain data streams of at least one stage; and training a preset dynamic planning model based on the data stream of at least one stage and the historical service demand information to obtain a resource planning model.
Further, the method for allocating storage resources further comprises: screening the historical influence characteristics based on the historical service demand information corresponding to at least one application server to obtain target historical influence characteristics, wherein the target historical influence characteristics comprise at least one of the following characteristics: the method comprises the steps of storing the residual storage capacity of a disk of a server, the port response time of the server and the bandwidth information corresponding to the server; grouping the target historical influence characteristics based on the performance parameters of the storage server to obtain grouped characteristic data, wherein each group of characteristic data corresponds to one performance parameter, and the performance parameters at least comprise storage capacity, a controller, a front end port, a rear end port and a storage disk; clustering the grouped feature data to obtain a performance state corresponding to each group of feature data, wherein the performance state represents whether the group of feature data can enable the storage server to be in a stable performance state; and scaling the characteristic data corresponding to the performance state based on the performance state to obtain the preprocessed historical influence characteristics.
Further, the method for allocating storage resources further comprises: step 1, determining first initial characteristic data and second initial characteristic data from characteristic data in a current group; step 2, calculating the distance between other characteristic data in the current group and the first initial characteristic data and the second initial characteristic data, and clustering the other characteristic data based on the distance to obtain a first data set and a second data set; step 3, calculating the distance sum between the feature data in the first data set and the first initial feature data to obtain a first distance sum; calculating the distance sum between the feature data in the second data set and the second initial feature data to obtain a second distance sum; step 4, when the first distance sum and/or the second distance sum do not meet the preset criterion function, updating the first initial characteristic data and/or the second initial characteristic data, and repeatedly executing the steps 1 to 4 until the first distance sum and the second distance sum meet the preset criterion function; step 5, calculating the ratio of a first data volume to a second data volume, wherein the first data volume is the number of data contained in the first data set, and the second data volume is the number of data contained in the second data set; step 6, when the ratio is larger than or equal to a preset ratio, determining the characteristic data of the current group to enable the storage server to be in a stable performance state; and when the ratio is smaller than the preset ratio, determining the characteristic data of the current group to enable the storage server to be in a non-performance stable state.
Further, the method for allocating storage resources further comprises: determining attribute information corresponding to the preprocessed historical influence characteristics; and dividing the preprocessed historical influence characteristics into at least one stage according to the attribute information to obtain the data stream of the at least one stage.
Further, the method for allocating storage resources further comprises: acquiring characteristic demand information corresponding to at least one stage from data streams of the at least one stage, wherein the characteristic demand information represents the demand of at least one application server on storage resources in the current stage; determining a resource allocation decision of a corresponding stage according to the characteristic demand information; combining resource allocation decisions corresponding to at least one stage to obtain a plurality of strategy sequence groups; determining a target policy sequence group from a plurality of policy sequence groups; and training a preset dynamic planning model based on the target strategy sequence group and the historical service demand information to obtain a resource planning model.
Further, the method for allocating storage resources further comprises: analyzing at least one target influence characteristic based on a resource planning model, and acquiring a target resource allocation strategy obtained by analyzing a target object based on service demand information after determining a resource allocation strategy for allocating resources for a current application server; comparing the target resource allocation strategy with the resource allocation strategy to obtain a comparison result; and determining whether to update the resource planning model according to the comparison result.
According to another aspect of the embodiments of the present invention, there is also provided an apparatus for allocating storage resources, including: the acquisition module is used for acquiring the service demand information of the current application server on the storage resources in the storage server; the service determining module is used for determining the service type corresponding to the service demand information; the system comprises a characteristic determining module, a storage server and a service type determining module, wherein the characteristic determining module is used for determining at least one target influence characteristic of the storage server according to the service type, and the at least one target influence characteristic is an influence factor influencing the current application server to access storage resources in the storage server; the analysis module is used for analyzing at least one target influence characteristic based on a resource planning model and determining a resource allocation strategy for allocating resources for the current application server, wherein the resource planning model is obtained based on historical service demand information of at least one application server and influence characteristics corresponding to the historical service demand information through training; and the resource allocation module is used for allocating storage resources for the current application server based on the resource allocation strategy.
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, in which a computer program is stored, where the computer program is configured to execute the above-mentioned method for allocating storage resources when running.
According to another aspect of embodiments of the present invention, there is also provided an electronic device, including one or more processors; memory for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out a method for executing a program, wherein the program is arranged to carry out the above-mentioned method of allocating storage resources when executed.
In the embodiment of the invention, a mode of analyzing the service requirement of the application server and automatically allocating storage resources based on the analysis result is adopted, after the service requirement information of the current application server for the storage resources in the storage server is obtained, the service type corresponding to the service requirement information is determined, at least one target influence characteristic influencing the access of the current application server to the storage resources in the storage server is determined according to the service type, then, at least one target influence characteristic is analyzed based on a resource planning model, a resource allocation strategy for allocating resources to the current application server is determined, and finally, the storage resources are allocated to the current application server based on the resource allocation strategy. The resource planning model is obtained by training based on historical service demand information of at least one application server and influence characteristics corresponding to the historical service demand information.
In the process, the storage server does not need to participate in the process of allocating the storage resources, so that the cost of manually planning the storage resources is reduced, and the planning efficiency of the storage resources is improved. In addition, the allocation of the storage resources is related to the service requirements of the application server, that is, the storage server takes the service requirements of the application server into consideration when allocating the storage resources, thereby improving the utilization rate of the storage resources. In addition, in the process of allocating the storage resources, the influence characteristics corresponding to different service types are considered, and the resource allocation strategy is determined based on the influence characteristics, so that the reasonable allocation of the storage resources is ensured, and the high efficiency and the reliability of the storage resources are met.
Therefore, the scheme provided by the application achieves the purpose of allocating the storage resources in the storage server, so that the technical effect of improving the planning efficiency of the storage resources is achieved, and the technical problem of low resource planning efficiency in manual planning of the storage resources in the related technology is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method for allocating storage resources according to an embodiment of the present invention;
FIG. 2 is a flow chart diagram of an alternative method for allocating storage resources according to an embodiment of the present invention;
FIG. 3 is a stage diagram of an alternative data flow in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating the generation of an alternative policy sequence set according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an alternative storage resource allocation apparatus according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an alternative electronic device according to an embodiment of the invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the related information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for presentation, analyzed data, etc.) related to the present invention are information and data authorized by the user or sufficiently authorized by each party. For example, an interface is provided between the system and the relevant user or organization, before obtaining the relevant information, an obtaining request needs to be sent to the user or organization through the interface, and after receiving the consent information fed back by the user or organization, the relevant information is obtained.
Example 1
In accordance with an embodiment of the present invention, there is provided a method embodiment of a method for allocation of storage resources, it being noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
In addition, it should be further noted that the storage server storing the storage resource may serve as an execution main body of the method provided in this embodiment, where the storage server may serve as an external storage of the application server and is used to provide the storage resource for the application server.
Fig. 1 is a flowchart of an alternative allocation method of storage resources according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
step S102, obtaining the service demand information of the current application server to the storage resource in the storage server.
In step S102, the current application server is any one of a plurality of application servers that access storage resources in the storage server. The corresponding business requirements of different application servers may be different, for example, the application server transacts a deposit business, and the corresponding business requirements are that the storage resource of the storage server can be quickly read or data can be quickly written into the storage server; for another example, an application server transacting loan transaction can acquire, from a storage server, client information of a client transacting the loan transaction, and analyze the client information in response to a service request.
In an alternative embodiment, when the current application server needs to access a storage resource in the storage server, the current application server sends an access request to the storage server. After receiving the access request, the storage server determines a server identifier corresponding to the current application server according to the access request, and the service requirement information of the current application server can be determined by identifying the server identifier.
And step S104, determining the service type corresponding to the service requirement information.
In step S104, the service types may include, but are not limited to, an On-Line Transaction Processing (OLTP) service and an online Analytical Processing (OLAP) service, where for the OLTP service, when the application server accesses the storage server, the requirement On the real-time performance of the storage server is high, and the storage server may determine whether to allocate storage resources to the application server by analyzing an average I/O (Input/Output) response time. For the OLAP service, when an application server accesses a storage server, the storage server needs to execute complex statistical query operation, and the storage server can determine whether to allocate storage resources to the application server by analyzing network bandwidth.
It should be noted that the average I/O response time represents an average response delay of the I/O port processing of the storage server, and is used to measure the I/O processing capability of the storage server; the network bandwidth characterizes the data amount that the storage server can process every second, and is used for measuring the throughput of the storage server.
In an alternative embodiment, the storage server may analyze the service requirement information to determine the service type, for example, an application server transacting a deposit service, where the service requirement is that the storage resource of the storage server can be quickly read or data can be quickly written into the storage server, and then the storage server may determine that the service type is an OLTP service; for another example, if the application server transacts loan transaction and the corresponding service requirement is to obtain the client information of the client transacting the loan transaction from the storage server and analyze the client information, the storage server may determine that the service type is OLAP service.
And step S106, determining at least one target influence characteristic of the storage server according to the service type, wherein the at least one target influence characteristic is an influence factor influencing the current application server to access the storage resources in the storage server.
In step S106, the influencing factors influencing the current application server to access the storage resources in the storage server may include, but are not limited to, the remaining storage capacity of the disk of the storage server, the port response time of the storage server, and the bandwidth information corresponding to the storage server. For example, for an OLTP service, the corresponding impact characteristic is a port response time of the storage server; for the OLAP service, the corresponding influence characteristic is bandwidth information corresponding to the storage server. Therefore, after the storage server determines the service type corresponding to the application server, the target influence characteristic corresponding to the service type can be determined.
It should be noted that, in practical applications, a user may also customize an impact characteristic of a service, for example, a target impact characteristic corresponding to the OLAP service that the user can use is bandwidth information corresponding to the storage server and a remaining storage capacity of a disk of the storage server.
And S108, analyzing at least one target influence characteristic based on a resource planning model, and determining a resource allocation strategy for allocating resources to the current application server, wherein the resource planning model is obtained based on historical service demand information of at least one application server and influence characteristics corresponding to the historical service demand information through training.
In step S108, the storage server inputs the target impact characteristics obtained by analysis in step S106 into the resource planning model, so as to obtain a resource allocation policy for allocating resources to the current application server, where the resource allocation policy is an optimal storage resource planning policy, and the policy not only can ensure that the current application server can access storage resources in the storage server, but also can avoid the problem of resource waste caused by unreasonable allocation of the storage resources.
Optionally, after the target impact characteristics are input into the resource planning model, the resource planning model may perform data analysis in stages according to a default data stream, or perform data analysis in stages according to a custom rule, so as to ensure the rationality of the resource allocation policy.
Step S110, distributing storage resources for the current application server based on the resource distribution strategy.
In step S110, after determining the resource allocation policy, the storage server may allocate a storage resource to the current application server according to the resource allocation policy, so that the application server can access the storage resource.
Based on the schemes defined in steps S102 to S110, it can be known that, in the embodiment, a manner of analyzing the service demand of the application server and automatically allocating storage resources based on an analysis result is adopted, after the service demand information of the current application server for the storage resources in the storage server is obtained, a service type corresponding to the service demand information is determined, at least one target influence feature influencing the current application server to access the storage resources in the storage server is determined according to the service type, then, at least one target influence feature is analyzed based on the resource planning model, a resource allocation policy for allocating resources to the current application server is determined, and finally, the storage resources are allocated to the current application server based on the resource allocation policy. The resource planning model is obtained by training based on historical service demand information of at least one application server and influence characteristics corresponding to the historical service demand information.
It is easy to note that, in the above process, the storage server does not need to participate in the process of allocating the storage resource, so that the cost of manually planning the storage resource is reduced, and the planning efficiency of the storage resource is improved. In addition, the allocation of the storage resources is related to the service requirements of the application server, that is, the storage server takes the service requirements of the application server into consideration when allocating the storage resources, thereby improving the utilization rate of the storage resources. Moreover, in the process of allocating the storage resources, the influence characteristics corresponding to different service types are considered, and the resource allocation strategy is determined based on the influence characteristics, so that the reasonable allocation of the storage resources is ensured, and the high efficiency and the reliability of the storage resources are met.
Therefore, the scheme provided by the application achieves the purpose of allocating the storage resources in the storage server, so that the technical effect of improving the planning efficiency of the storage resources is achieved, and the technical problem of low resource planning efficiency in manual planning of the storage resources in the related technology is solved.
In an alternative embodiment, fig. 2 shows a flow chart of the method provided in this embodiment, and as can be seen from fig. 2, the flow chart mainly includes a modeling layer, a planning layer, and an evaluation layer, wherein a resource planning model can be constructed through the modeling layer; the storage server can distribute storage resources for the current application server through a planning layer; adjustment of the resource planning model may be achieved by the evaluation layer.
It should be noted that the schemes defined in the above steps S102 to S110 are deployed in the planning layer.
As shown in fig. 2, the process of constructing the resource planning model mainly includes: the method comprises four steps of data acquisition, data preprocessing, data analysis and model construction. Specifically, the storage server firstly obtains historical service demand information corresponding to at least one application server and historical influence characteristics corresponding to the storage server, then performs data preprocessing on the historical influence characteristics to obtain preprocessed historical influence characteristics, performs staged analysis processing on the preprocessed historical influence characteristics to obtain data streams of at least one stage, and finally trains a preset dynamic planning model based on the data streams of at least one stage and the historical service demand information to obtain a resource planning model. The historical influence characteristics are influence factors corresponding to the access of the at least one application server to the storage resources in the storage server in the historical time period.
Optionally, as shown in fig. 2, the storage server may obtain historical impact features related to performance capacity of the storage server from the centralized management platform, and collect historical service requirement information of the application server to generate sample data. And then the storage server can perform operations such as feature specification, data grouping, cluster division, data standardization and the like on the sample data so as to realize the pretreatment of the sample data. And then, the storage server performs staged analysis on the preprocessed sample data according to the direction of the data stream, so as to obtain the data stream of each stage. And finally, the storage server trains a preset dynamic planning model according to the data stream of each stage and the historical service demand information, and then the resource planning model can be obtained.
In an optional embodiment, in the process of performing data preprocessing on the history influence characteristic to obtain a preprocessed history influence characteristic, the storage server performs the following steps:
step S11, a characteristic specification, namely, historical influence characteristics are screened based on historical service demand information corresponding to at least one application server, and target historical influence characteristics are obtained. Wherein the target history influence characteristics comprise at least one of the following: the method comprises the steps of storing the residual storage capacity of a disk of a server, the port response time of the server and the bandwidth information corresponding to the server.
In step S11, the storage server divides the impact features corresponding to the storage server into relevant features and irrelevant features according to the historical service requirements, deletes the records of the irrelevant features, and only retains the records of the relevant features, that is, only retains the impact features such as the residual storage capacity of the disk of the storage server, the port response time of the storage server, and the bandwidth information corresponding to the storage server. In addition, the user can also perform feature specification on the influence features according to the custom service.
And S12, grouping the data, namely grouping the target historical influence characteristics by the storage server based on the performance parameters of the storage server to obtain grouped characteristic data. Each group of characteristic data corresponds to a performance parameter, and the performance parameter at least comprises a storage capacity, a controller, a front end port, a rear end port and a storage disk.
Optionally, the storage server may group the single variable values (i.e., the performance parameters) and group the variables according to a certain planning rule, and default the single variable as a group. For example, a single storage capacity of a storage server is used as a group, and a single front-end port is used as a group. In addition, the user can also bind two or four ports into a group according to a preset planning rule. The specific binding mode is not specifically limited in this application.
And S13, clustering, namely clustering the grouped characteristic data by the storage server to obtain a performance state corresponding to each group of characteristic data, wherein the performance state represents whether the group of characteristic data can enable the storage server to be in a stable performance state.
And S14, standardizing data, namely, the storage server scales the characteristic data corresponding to the performance state based on the performance state to obtain the preprocessed historical influence characteristics.
Optionally, the storage server may scale the feature data such that the scaled feature data falls within a specific interval (e.g., [0,1] interval), wherein the scaling may be determined by the following equation:
in the above formula, f (x) represents a scaling ratio, x i Representing feature data, va representing an attribute threshold corresponding to the feature data, wherein the attribute threshold may be determined by the performance state.
In an alternative embodiment, the storage server may implement the clustering of the feature data by:
step 1, determining first initial characteristic data and second initial characteristic data from characteristic data in a current group. For example, the storage server randomly selects two feature data O1 and O2 from the feature data in the current group as initial center points of two data sets.
And 2, calculating the distance between other feature data in the current group and the first initial feature data and the second initial feature data, and clustering the other feature data based on the distance to obtain a first data set and a second data set. That is, the storage server clusters other feature data into a first data set and a second data set respectively according to the principle that the distance between the other feature data and two initial center points is the closest, wherein the center point of the first data set is O1, and the center point of the second data set is O2.
Step 3, calculating the distance sum between the feature data in the first data set and the first initial feature data to obtain a first distance sum; and calculating the distance sum between the feature data in the second data set and the second initial feature data to obtain a second distance sum. That is, the feature data corresponding to the minimum sum of the distances from the feature data to the initial feature data in the current data set is the new center point of the current data set.
And 4, when the first distance sum and/or the second distance sum do not meet the preset criterion function, updating the first initial characteristic data and/or the second initial characteristic data, and repeatedly executing the steps 1 to 4 until the first distance sum and the second distance sum meet the preset criterion function, namely until the central point corresponding to each data set does not change any more.
And 5, calculating the ratio of a first data volume to a second data volume, wherein the first data volume is the number of data contained in the first data set, and the second data volume is the number of data contained in the second data set.
Step 6, when the ratio is larger than or equal to a preset ratio, determining the characteristic data of the current group to enable the storage server to be in a stable performance state; and when the ratio is smaller than the preset ratio, determining the characteristic data of the current group to enable the storage server to be in a non-performance stable state.
It should be noted that, the clustering algorithm can perform K-medoid clustering division on sample data of a small data set of performance parameters of a storage server with noise and independent points, and divide historical data into performance stationary section data and performance steep-rise section data according to the classification condition of the performance data.
In an alternative embodiment, as shown in fig. 2, after the historical influence feature is subjected to data preprocessing to obtain a preprocessed historical influence feature, the storage server continues to perform staged analysis processing on the preprocessed historical influence feature to obtain a data stream of at least one stage. Specifically, the storage server determines attribute information corresponding to the preprocessed historical influence features, divides the preprocessed historical influence features into at least one stage according to the attribute information, and obtains data streams of the at least one stage.
Optionally, the storage server may determine the correlation of data between different performance modules of the storage server according to the data characteristics in the data set, perform statistical analysis on the preprocessed data, divide the data into several stages according to certain rules and the properties and characteristics of the data by using a staged analysis method, and analyze the internal structure and the mutual relationship of the data. Fig. 3 shows an optional data flow stage schematic diagram, and as can be seen from fig. 3, the data flow stage at least includes: the storage capacity, the front-end port performance, the controller performance, the back-end port performance, the storage pool performance and the like of the storage server. And, the above-mentioned several stages are executed according to the order, namely, analyze the storage capacity, controller performance, front end port performance, back end port performance, storage disk performance of the storage server sequentially through the dataflow. In addition, as can be seen from fig. 3, the application server communicates with the switch through the network link, and the switch communicates with the storage server through the network link, so that the communication between the application server and the storage server is realized.
In an alternative embodiment, as shown in fig. 2, after performing a staged analysis on the preprocessed historical impact features, the storage server trains a preset dynamic planning model based on data streams of at least one stage and historical service demand information to obtain a resource planning model. Specifically, the storage server obtains characteristic demand information corresponding to at least one stage from data streams of the at least one stage, determines a resource allocation decision of the corresponding stage according to the characteristic demand information, then combines the resource allocation decisions corresponding to the at least one stage to obtain a plurality of strategy sequence groups, determines a target strategy sequence group from the plurality of strategy sequence groups, and finally trains a preset dynamic planning model based on the target strategy sequence group and historical service demand information to obtain a resource planning model. Wherein the characteristic demand information characterizes a demand of the at least one application server for storage resources at a current stage.
It should be noted that, as can be seen from fig. 3, the data flow is determined in the analysis process of the storage server, and in the actual decision process, multiple stages may be combined with each other, so as to obtain multiple resource allocation policies, for example, the stage of the data flow corresponding to the resource allocation policy one is the storage capacity and front-end port performance of the storage server, and the stage of the data flow corresponding to the resource allocation policy two is the storage capacity and, controller performance and back-end port performance of the storage server.
Optionally, fig. 4 shows a schematic diagram of generating an optional policy sequence group. In fig. 4, k =1,2.. N denotes a phase variable, for example, k =1 denotes a phase corresponding to a storage capacity of a storage server; k =2 represents the phase corresponding to the controller performance; k =3 represents a stage corresponding to the performance of the front-end port; k =4 represents a stage corresponding to the performance of the back-end port; k =5 represents the phase corresponding to the performance of the storage disk. In addition, in FIG. 4, d i Is the characteristic demand (i.e., characteristic demand information) of the i-th stage, vb i Is a characteristic threshold value, x, of the ith stage i Is a state variable of the i-th stage, x i Satisfies the following formula:
wherein x is i Default value is 0.05.
In addition, in FIG. 4, x k,p Set of allowed states representing the p-th resource decision variable of the kth stage, where x k,p Can describe the state of the process and meet the requirement of no aftereffect.
In addition, the storage server further determines an allowable state set (i.e., a range of allowable values of the state variable), for example, if the state variable is a characteristic demand/characteristic threshold, and the resource decision variable is a disk drive of a different storage server, the allowable state set is a disk drive with a disk drive capacity utilization rate of < 90%.
In addition, in FIG. 4, decision variables
For example, the decision variable may be a disk drive that satisfies 1T storage capacity; the state transition equation is:
determining a stage index of
The stage index function is a minimum value of the stage index, namely the stage index function satisfies the following formula:
V k,p (x k,1 ,u k,1 ,x k,2 …x k,n )＝min(V k,p (x k,p ,u k,p ))
set of optimum functions f k (x k ) F is the idle percentage from the starting point to the completion of the predetermined target k (x k )＝V k,p (x k,1 ,u k,1 ,x k,2 …x k,n )+f k+1 (x k+1 ) K =1, … n; the free termination conditions were: f. of k+1 (x k+1 ) And =0. Calculating f k (x k ) Then, the optimal target strategy sequence set { u } can be obtained by utilizing the stage index function k,p (x k )}。
In an optional embodiment, as shown in fig. 2, after analyzing at least one target influence characteristic based on a resource planning model and determining a resource allocation policy for allocating resources to a current application server, a storage server obtains a target resource allocation policy obtained by analyzing a target object based on service demand information, and compares the target resource allocation policy with the resource allocation policy to obtain a comparison result, and then determines whether to update the resource planning model according to the comparison result.
Optionally, as shown in fig. 2, the target object analyzes the service requirement of the application server in a manual planning manner to obtain a target resource allocation policy. Then, the storage server compares the target resource allocation strategy with the resource allocation strategy, and judges whether the actual environment meets the actual requirement, that is, whether the resource allocation strategy determined by the resource planning model is reasonable. If the resource allocation strategy determined by the resource planning model is not reasonable, the storage server updates the resource planning model according to the target resource allocation strategy; and if the resource allocation strategy determined by the resource planning model is reasonable, the storage server allocates the storage resources for the current application server based on the resource allocation strategy.
Based on the problem of low planning efficiency of the existing storage resources, the method for planning the storage resources is simple to implement, low in implementation cost and capable of effectively planning the storage resources. The method can effectively and reasonably distribute the storage resources according to the requirements. The method takes the performance, capacity and demand of the storage resources as reference values, not only reasonably distributes the storage resources and avoids causing continuous high utilization of single resources, but also combines the demand characteristics and meets the requirements of high efficiency and reliability of the service storage resources. In addition, the method can balance the storage resources, improve the utilization rate of the storage resources and reduce the failure rate of hardware. The method can also reduce the cost of artificial planning, realize the resource planning for different service requirements and different storage systems, and has strong adaptability and universality.
Example 2
According to an embodiment of the present invention, there is further provided an embodiment of an apparatus for allocating storage resources, where fig. 5 is a schematic diagram of an optional apparatus for allocating storage resources according to an embodiment of the present invention, and as shown in fig. 5, the apparatus includes: an acquisition module 501, a service determination module 503, a feature determination module 505, an analysis module 507, and a resource allocation module 509.
The acquiring module 501 is configured to acquire service demand information of a current application server for storage resources in a storage server; a service determining module 503, configured to determine a service type corresponding to the service requirement information; a characteristic determining module 505, configured to determine at least one target impact characteristic of the storage server according to the service type, where the at least one target impact characteristic is an impact factor that affects access of the current application server to a storage resource in the storage server; an analysis module 507, configured to analyze at least one target impact feature based on a resource planning model, and determine a resource allocation policy for allocating resources to a current application server, where the resource planning model is obtained based on historical service demand information of at least one application server and impact features corresponding to the historical service demand information through training; a resource allocation module 509, configured to allocate storage resources for the current application server based on the resource allocation policy.
Optionally, the apparatus for allocating storage resources further includes a model generation module, where the model generation module includes: the device comprises a first acquisition module, a preprocessing module, an analysis module and a model training module. The system comprises a first acquisition module, a second acquisition module and a storage server, wherein the first acquisition module is used for acquiring historical service demand information corresponding to at least one application server and historical influence characteristics corresponding to the storage server, and the historical influence characteristics are influence factors corresponding to the at least one application server when accessing storage resources in the storage server in a historical time period; the preprocessing module is used for preprocessing the data of the historical influence characteristics to obtain preprocessed historical influence characteristics; the analysis module is used for carrying out staged analysis processing on the preprocessed historical influence characteristics to obtain data streams of at least one stage; and the model training module is used for training a preset dynamic planning model based on the data stream of at least one stage and the historical service demand information to obtain a resource planning model.
Optionally, the preprocessing module includes: the device comprises a characteristic screening module, a characteristic grouping module, a characteristic clustering module and a data scaling module. The feature screening module is configured to screen historical impact features based on historical service demand information corresponding to at least one application server to obtain target historical impact features, where the target historical impact features include at least one of the following: the method comprises the steps of storing the residual storage capacity of a disk of a server, the port response time of the server and the bandwidth information corresponding to the server; the characteristic grouping module is used for grouping the target historical influence characteristics based on the performance parameters of the storage server to obtain grouped characteristic data, wherein each group of characteristic data corresponds to one performance parameter, and the performance parameters at least comprise storage capacity, a controller, a front end port, a rear end port and a storage disk; the characteristic clustering module is used for clustering the grouped characteristic data to obtain a performance state corresponding to each group of characteristic data, wherein the performance state represents whether the group of characteristic data can enable the storage server to be in a stable performance state; and the data scaling module is used for scaling the characteristic data corresponding to the performance state based on the performance state to obtain the preprocessed historical influence characteristics.
Optionally, the feature clustering module is configured to perform the following method: step 1, determining first initial characteristic data and second initial characteristic data from characteristic data in a current group; step 2, calculating the distance between other characteristic data in the current group and the first initial characteristic data and the second initial characteristic data, and clustering the other characteristic data based on the distance to obtain a first data set and a second data set; step 3, calculating the distance sum between the feature data in the first data set and the first initial feature data to obtain a first distance sum; calculating the sum of the distances between the feature data in the second data set and the second initial feature data to obtain a second sum of the distances; step 4, when the first distance sum and/or the second distance sum do not meet the preset criterion function, updating the first initial characteristic data and/or the second initial characteristic data, and repeatedly executing the steps 1 to 4 until the first distance sum and the second distance sum meet the preset criterion function; step 5, calculating a ratio of a first data volume to a second data volume, wherein the first data volume is the number of data contained in the first data set, and the second data volume is the number of data contained in the second data set; step 6, when the ratio is larger than or equal to a preset ratio, determining the characteristic data of the current group to enable the storage server to be in a stable performance state; and when the ratio is smaller than the preset ratio, determining the characteristic data of the current group to enable the storage server to be in a non-performance stable state.
Optionally, the analysis module includes: the device comprises a first determining module and a stage dividing module. The first determining module is used for determining attribute information corresponding to the preprocessed historical influence characteristics; and the stage division module is used for dividing the preprocessed historical influence characteristics into at least one stage according to the attribute information to obtain the data stream of the at least one stage.
Optionally, the model training module includes: the system comprises a second acquisition module, a second determination module, a decision combination module, a third determination module and a target training module. The second obtaining module is used for obtaining the characteristic demand information corresponding to the at least one stage from the data stream of the at least one stage, wherein the characteristic demand information represents the demand of the at least one application server on the storage resource at the current stage; the second determining module is used for determining a resource allocation decision of a corresponding stage according to the characteristic demand information; the decision combination module is used for combining the resource allocation decisions corresponding to at least one stage to obtain a plurality of strategy sequence groups; a third determining module, configured to determine a target policy sequence group from the plurality of policy sequence groups; and the target training module is used for training a preset dynamic planning model based on the target strategy sequence group and the historical service demand information to obtain a resource planning model.
Optionally, the apparatus for allocating storage resources further includes: the device comprises a third acquisition module, a comparison module and a fourth determination module. The third obtaining module is used for obtaining a target resource allocation strategy obtained by analyzing the target object based on the service demand information after analyzing at least one target influence characteristic based on the resource planning model and determining the resource allocation strategy for allocating resources for the current application server; the comparison module is used for comparing the target resource allocation strategy with the resource allocation strategy to obtain a comparison result; and the fourth determining module is used for determining whether to update the resource planning model according to the comparison result.
Example 3
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, in which a computer program is stored, where the computer program is configured to execute the above-mentioned allocation method of storage resources when running.
Example 4
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, wherein fig. 6 is a schematic diagram of an alternative electronic device according to the embodiments of the present invention, as shown in fig. 6, the electronic device includes one or more processors; memory for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out a method for executing the program, wherein the program is arranged to perform the above-mentioned method of allocating storage resources when executed.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit may be a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be considered as the protection scope of the present invention.
Claims (10)
1. A method for allocating storage resources, comprising:
acquiring service demand information of a current application server on storage resources in a storage server;
determining a service type corresponding to the service demand information;
determining at least one target influence characteristic of the storage server according to the service type, wherein the at least one target influence characteristic is an influence factor influencing the current application server to access storage resources in the storage server;
analyzing the at least one target influence characteristic based on a resource planning model, and determining a resource allocation strategy for allocating resources to the current application server, wherein the resource planning model is obtained based on historical service demand information of the at least one application server and influence characteristics corresponding to the historical service demand information through training;
and allocating the storage resources for the current application server based on the resource allocation strategy.
2. The method of claim 1, wherein the resource planning model is generated by:
acquiring historical service demand information corresponding to the at least one application server and historical influence characteristics corresponding to the storage server, wherein the historical influence characteristics are influence factors corresponding to the at least one application server when accessing storage resources in the storage server within a historical time period;
carrying out data preprocessing on the historical influence characteristics to obtain preprocessed historical influence characteristics;
carrying out staged analysis processing on the preprocessed historical influence characteristics to obtain data streams of at least one stage;
and training a preset dynamic planning model based on the data stream of the at least one stage and the historical service demand information to obtain the resource planning model.
3. The method of claim 2, wherein the pre-processing the historical impact signature to obtain a pre-processed historical impact signature comprises:
screening the historical influence characteristics based on the historical service demand information corresponding to the at least one application server to obtain target historical influence characteristics, wherein the target historical influence characteristics comprise at least one of the following characteristics: the residual storage capacity of a magnetic disk of the storage server, the port response time of the storage server and the bandwidth information corresponding to the storage server;
grouping the target historical influence characteristics based on the performance parameters of the storage server to obtain grouped characteristic data, wherein each group of characteristic data corresponds to one performance parameter, and the performance parameters at least comprise storage capacity, a controller, a front end port, a rear end port and a storage disk;
clustering the grouped feature data to obtain a performance state corresponding to each group of feature data, wherein the performance state represents whether the group of feature data can enable the storage server to be in a stable performance state;
and scaling the characteristic data corresponding to the performance state based on the performance state to obtain the preprocessed historical influence characteristic.
4. The method of claim 3, wherein clustering the grouped feature data to obtain the performance status corresponding to each group of feature data comprises:
step 1, determining first initial characteristic data and second initial characteristic data from characteristic data in a current group;
step 2, calculating the distance between other feature data in the current group and the first initial feature data and the second initial feature data, and clustering the other feature data based on the distance to obtain a first data set and a second data set;
step 3, calculating the distance sum between the feature data in the first data set and the first initial feature data to obtain a first distance sum; calculating the distance sum between the feature data in the second data set and the second initial feature data to obtain a second distance sum;
step 4, when the first distance sum and/or the second distance sum do not meet a preset criterion function, updating the first initial characteristic data and/or the second initial characteristic data, and repeatedly executing the steps 1 to 4 until the first distance sum and the second distance sum meet the preset criterion function;
step 5, calculating a ratio of a first data volume to a second data volume, wherein the first data volume is the number of data contained in the first data set, and the second data volume is the number of data contained in the second data set;
step 6, when the ratio is larger than or equal to a preset ratio, determining the characteristic data of the current group to enable the storage server to be in a stable performance state; and when the ratio is smaller than the preset ratio, determining the characteristic data of the current group to enable the storage server to be in a non-performance stable state.
5. The method of claim 2, wherein performing a staged analysis process on the preprocessed historical impact signatures to obtain at least one staged data stream, comprises:
determining attribute information corresponding to the preprocessed historical influence characteristics;
and dividing the preprocessed historical influence characteristics into the at least one stage according to the attribute information to obtain the data stream of the at least one stage.
6. The method of claim 2, wherein training a preset dynamic planning model based on the data flow of the at least one phase and the historical service demand information to obtain the resource planning model comprises:
acquiring characteristic demand information corresponding to the at least one stage from the data stream of the at least one stage, wherein the characteristic demand information represents the demand of the at least one application server on the storage resource at the current stage;
determining a resource allocation decision of a corresponding stage according to the characteristic demand information;
combining the resource allocation decisions corresponding to the at least one stage to obtain a plurality of strategy sequence groups;
determining a target policy sequence group from the plurality of policy sequence groups;
and training the preset dynamic planning model based on the target strategy sequence group and the historical service demand information to obtain the resource planning model.
7. The method of claim 1, wherein after analyzing the at least one target impact characteristic based on a resource planning model to determine a resource allocation policy for allocating resources for the current application server, the method further comprises:
acquiring a target resource allocation strategy obtained by analyzing a target object based on the service demand information;
comparing the target resource allocation strategy with the resource allocation strategy to obtain a comparison result;
and determining whether to update the resource planning model according to the comparison result.
8. An apparatus for allocating storage resources, comprising:
the acquisition module is used for acquiring the service demand information of the current application server on the storage resources in the storage server;
the service determining module is used for determining the service type corresponding to the service demand information;
the characteristic determining module is used for determining at least one target influence characteristic of the storage server according to the service type, wherein the at least one target influence characteristic is an influence factor influencing the current application server to access storage resources in the storage server;
the analysis module is used for analyzing the at least one target influence characteristic based on a resource planning model and determining a resource allocation strategy for allocating resources to the current application server, wherein the resource planning model is obtained based on historical service demand information of the at least one application server and influence characteristics corresponding to the historical service demand information through training;
and the resource allocation module is used for allocating the storage resources to the current application server based on the resource allocation strategy.
9. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is arranged to execute the allocation method of storage resources of any one of claims 1 to 7 when running.
10. An electronic device, characterized in that the electronic device comprises one or more processors; memory for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out a method for executing a program, wherein the program is arranged to, when executed, perform the method of allocating storage resources of any of claims 1 to 7.
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