CN108287666B - Data storage method and device for cloud storage environment - Google Patents
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Abstract
The invention provides a data storage method and a data storage device for a cloud storage environment, and relates to the technical field of cloud storage, wherein the method comprises the following steps: when data to be stored is received, determining a numerical value corresponding to each storage influence factor according to the data to be stored, the current multiple storage nodes and the network environment; storage influencing factors include: the data storage method comprises the following steps of (1) data volume of data to be stored, transmission distance between the data to be stored and a storage node, fault rate of the storage node, residual space of the storage node, memory occupancy rate of the storage node, network bandwidth corresponding to the storage node or network delay corresponding to the storage node; and determining an optimal storage node matched with the data to be stored according to the numerical values corresponding to the storage influence factors so as to correspondingly store the data to be stored in the optimal storage node. The invention can reasonably match the storage nodes for the data to be stored based on multi-factor consideration, and provides a better data storage scheme.
Description
Technical Field
The invention relates to the technical field of cloud storage, in particular to a data storage method and device for a cloud storage environment.
Background
A resource scheduling scheme in a cloud storage environment mainly provides an optimal data storage scheme, namely data to be stored can be reasonably stored in a storage node, so that the minimum storage time is obtained. In a traditional resource scheduling method in a cloud storage environment, a suitable storage node is selected for data mainly based on the remaining space of the storage node (such as Openstack render block storage), that is, reasonable storage node scheduling is realized for the data to be stored. However, this method of performing data storage only by considering the remaining space of the storage node has a single consideration, so that the resulting data storage scheme is not good.
Disclosure of Invention
In view of this, the present invention provides a data storage method and apparatus for a cloud storage environment, which can provide a better data storage scheme for reasonably matching storage nodes for data to be stored based on multi-factor consideration.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, an embodiment of the present invention provides a data storage method for a cloud storage environment, including:
when data to be stored is received, determining a numerical value corresponding to each storage influence factor according to the data to be stored, the current multiple storage nodes and a network environment; the storage influencing factors include: the data storage method comprises the following steps of (1) data volume of data to be stored, transmission distance between the data to be stored and a storage node, fault rate of the storage node, residual space of the storage node, memory occupancy rate of the storage node, network bandwidth corresponding to the storage node or network delay corresponding to the storage node;
and determining an optimal storage node matched with the data to be stored according to the numerical value corresponding to each storage influence factor so as to correspondingly store the data to be stored in the optimal storage node.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the step of determining, according to a numerical value corresponding to each storage influence factor, an optimal storage node that matches the data to be stored includes:
determining the weight corresponding to each storage influence factor by a triangular fuzzy analytic hierarchy process;
and determining the optimal storage node matched with the data to be stored based on a genetic algorithm according to the numerical value and the weight corresponding to each storage influence factor.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the step of determining, based on a genetic algorithm, an optimal storage node that matches the data to be stored according to the value and the weight corresponding to each storage influence factor includes:
normalizing the numerical value corresponding to each storage influence factor;
and substituting the numerical values corresponding to the storage influence factors and the weights corresponding to the storage influence factors after normalization processing into a pre-established genetic objective function, and solving to obtain the corresponding relation between the data to be stored and the optimal storage node.
With reference to the second possible implementation manner of the first aspect, the present invention provides a third possible implementation manner of the first aspect, wherein the establishing of the genetic objective function includes:
setting a sequence of data to be stored as X ═ X1,x2,…,xmY ═ Y, the sequence of currently available storage nodes1,y2,...,ymFourthly, establishing a storage scheme matrix MkThe following were used:
and MkNeed to satisfy
Wherein,
when x isiyjWhen 1, represents xiIs stored in yjPerforming the following steps; when x isiyjWhen 0, x is representediIs not stored in yjPerforming the following steps;
memory scheme matrix MkConstructing the following genetic objective function Zk：
And Z iskNeed to satisfy
Wherein the matrix P is a cost matrix corresponding to each of the storage nodes, DjIs the transmission distance, Size, between the data center root node where the data to be stored is located and the jth storage nodeiFor the data volume of the ith data to be stored after normalization processing，CapacityjSpace, the initial Space of the j storage node after normalization processingjTo normalize the processed residual space of the j-th storage node, MemjIs the memory utilization rate of the j storage node after normalization processing, NetBjNetwork bandwidth, NetD, corresponding to the j-th storage node after normalization processingjFail to normalize the processed network delay corresponding to the jth storage nodejTo normalize the processed failure rate, w, of the jth storage node1Weight corresponding to failure rate of storage node, w2Weight corresponding to the remaining space of the storage node, w3Weight, w, corresponding to memory usage of a storage node4Weight corresponding to the transmission distance of the data to be stored, w5Weight corresponding to the data size of the data to be stored, w6Weight corresponding to network bandwidth of storage node, w7A corresponding weight for the network latency of the storage node.
With reference to the third possible implementation manner of the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the step of solving to obtain the correspondence between the data to be stored and the optimal storage node includes:
in the process of solving the genetic objective function, M is addedkPerforming variation processing to realize population evolution in the genetic algorithm; wherein, M is setkIs a chromosome in the genetic algorithm;
in the population evolution process, calculating by adopting a multi-iteration mode to obtain an optimal storage scheme matrix MAG(ii) a Wherein, each iteration selects high-quality chromosomes with fitness ranking within a preset range
As M is calculated during the next iterationkAnd eliminate lethal chromosomes with fitness ranking outside a preset range
According to the optimal storage scheme matrix MAGAnd determining the corresponding relation between the data to be stored and the optimal storage node.
With reference to the fourth possible implementation manner of the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the manner of performing mutation processing includes:
random slave storage scheme matrix MkIn the selection
For the selected
In satisfying
On the basis of (1), if
Is adjusted to
If it is not
Is adjusted to
With reference to the fourth possible implementation manner of the first aspect, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect, where the matrix M according to the optimal storage scheme isAGThe step of determining the corresponding relation between the data to be stored and the optimal storage node comprises the following steps:
according to the optimal storage scheme matrix MAGThe value of each element in the data storage device, and the sequence of the data to be stored is determined as X ═ { X ═ X1,x2,…,xmThe sequence with the currently available storage node is Y ═ Y1,y2,...,ymOfStoring the matching relationship K ═ K1,k2,…,km) (ii) a In the storage matching relation K, m is the serial number of the data to be stored, and KmThe sequence number of the best storage node corresponding to the data to be stored with the sequence number m.
With reference to the fourth possible implementation manner of the first aspect, the embodiment of the present invention provides a seventh possible implementation manner of the first aspect, wherein the sequence X of the data to be stored is set to { X ═ X1,x2,…,xmIn (v), xi＝{si,mi,ni}，siThe storage space m occupied by the ith data to be storediThe amount of memory n required for the ith data to be storediNetwork resources required to be occupied by the ith data to be stored;
setting the sequence of the storage nodes as Y ═ Y1,y2,...,ymIn (v), yi＝{Si,Mi,Ni}，SiStorage space for the ith storage node, MiIs the memory resource of the ith storage node, NiA network resource that is the ith storage node;
assuming that the ith storage node correspondingly stores the a-th data to be stored, the b-th data to be stored and the c-th data to be stored, the storage scheme matrix MkThe following constraints need to be satisfied:
Si≥sa+sb+…sc
Mi≥ma+mb+…mc
Ni≥na+nb+…nc
and solving the obtained storage scheme matrix MkSatisfy the requirement of
Wherein t isi(s) time taken by data to be stored during data storage, ti(m) time spent by the storage node during data storage, ti(n) is in data storageThe time consuming process is caused by the network environment.
In a second aspect, an embodiment of the present invention further provides a data storage apparatus for a cloud storage environment, including:
the factor determining module is used for determining a numerical value corresponding to each storage influence factor according to the data to be stored, the current plurality of storage nodes and the network environment when the data to be stored is received; the storage influencing factors include: the data storage method comprises the following steps of (1) data volume of data to be stored, transmission distance between the data to be stored and a storage node, fault rate of the storage node, residual space of the storage node, memory occupancy rate of the storage node, network bandwidth corresponding to the storage node or network delay corresponding to the storage node;
and the optimal node determining module is used for determining an optimal storage node matched with the data to be stored according to the numerical value corresponding to each storage influence factor so as to correspondingly store the data to be stored in the optimal storage node.
In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method in any one of the first aspect.
The embodiment of the invention provides a data storage method and device for a cloud storage environment, which can determine values corresponding to storage influence factors according to data to be stored, a plurality of current storage nodes and a network environment when the data to be stored is received, and further determine an optimal storage node matched with the data to be stored according to the values corresponding to the storage influence factors. The mode of selecting the appropriate storage node based on the comprehensive consideration of various storage influence factors is comprehensive in consideration, and can be considered based on the whole situation to provide a better data storage scheme.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part may be learned by the practice of the above-described techniques of the disclosure, or may be learned by practice of the disclosure.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart illustrating a data storage method for a cloud storage environment according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating storage influencing factors of a cloud storage environment according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating another data storage method for a cloud storage environment according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a data storage device for a cloud storage environment according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. 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.
The problem of minimum storage time consumption of a resource scheduling scheme in a cloud storage environment mainly refers to that data to be stored which arrive at a data center at the same time are stored in a storage node by adopting a proper strategy so as to obtain the minimum storage time consumption. The process of resource scheduling may be understood as a process of selecting an existing resource (storage node) to store data. In a traditional resource scheduling method under a cloud storage environment, storage nodes are matched for data to be stored mainly based on the residual space of the storage nodes, and the consideration factor of a resource scheduling decision is too single, so that the finally obtained data storage scheme can only fall into a local optimal solution, comprehensive analysis cannot be performed in a global angle, and a global optimal solution cannot be obtained. In addition, since resource scheduling in a cloud storage environment is also a complex discrete combination optimization problem, in a solution space with high complexity, it is difficult to find a polynomial time algorithm to find an optimal solution by using the existing resource scheduling method based on the residual space of the storage node, and therefore, the method for performing resource scheduling based on the residual space is also unreasonable.
In order to solve the above problems, embodiments of the present invention provide a data storage method and apparatus for a cloud storage environment, and it can also be understood that embodiments of the present invention provide a data storage method and apparatus for a cloud storage environment.
The first embodiment is as follows:
referring to a flowchart of a data storage method for a cloud storage environment shown in fig. 1, the method may be executed by a terminal, and the terminal may be an intelligent terminal such as a computer, an upper computer, a server, and the like, and includes the following steps:
step S102, when data to be stored is received, determining a numerical value corresponding to each storage influence factor according to the data to be stored, the current plurality of storage nodes and the network environment; storage influencing factors include: the data amount of the data to be stored, the transmission distance between the data to be stored and the storage node, the fault rate of the storage node, the residual space of the storage node, the memory occupancy rate of the storage node, the network bandwidth corresponding to the storage node or the network delay corresponding to the storage node. The storage influencing factor may also be referred to as a resource scheduling influencing factor, the data size is also the data size, and the remaining space is also the remaining storage space.
In this embodiment, three major indexes, namely, a storage node, data to be stored, and a network environment, are mainly considered, where, referring to a schematic diagram of storage influence factors of a cloud storage environment shown in fig. 2, it is indicated that three major indexes, namely, the storage node, the data to be stored, and the network environment, need to be considered when selecting an optimal storage node, and storage influence factors corresponding to storage node indexes are a failure rate, a remaining space, and a memory occupancy rate, storage influence factors corresponding to data indexes to be stored are a data size and a data distance, and storage influence factors corresponding to network environment indexes are a network bandwidth and a network delay.
In a specific application, multiple of the storage influencing factors can be flexibly selected as reference factors in data storage according to actual needs, and details are not repeated here.
And step S104, determining an optimal storage node matched with the data to be stored according to the numerical values corresponding to the storage influence factors, so as to correspondingly store the data to be stored in the optimal storage node.
In one embodiment, the weight corresponding to each storage influence factor can be determined by triangular fuzzy analytic hierarchy process; and determining the optimal storage node matched with the data to be stored based on a genetic algorithm according to the numerical value and the weight corresponding to each storage influence factor.
The data to be stored may be multiple, that is, when multiple data to be stored arrive at the data center at the same time, the data storage method may be used to schedule the corresponding optimal storage node for the multiple data to be stored for storage.
In the method of this embodiment, when the data to be stored is received, the value corresponding to each storage influence factor can be determined according to the data to be stored, the current plurality of storage nodes and the network environment, and then the optimal storage node matched with the data to be stored is determined according to the value corresponding to each storage influence factor. The method for selecting the appropriate storage node based on the comprehensive consideration of various storage influence factors has the advantages that the consideration factors are comprehensive, the consideration can be carried out based on the overall situation, the overall optimal solution is obtained, the storage time consumption is less, the obtained data storage scheme is more reliable, the better load balance is realized, and the cloud resource storage system can stably operate.
The triangular fuzzy analytic hierarchy process is a qualitative and quantitative combined system analytic process, can quantify the evaluation index, provides basis for the selection of the optimal scheme, compares every two elements of the same level (grade), determines the relative importance degree according to the evaluation scale, and finally establishes a fuzzy judgment matrix according to the relative importance degree, thereby determining the relative importance degree of each element. By adopting a triangular fuzzy analytic hierarchy process, the weight corresponding to each storage influence factor can be determined. In specific implementation, the importance of the index can be measured by adopting a scale of 1-9, three groups of triangular fuzzy data can be selected for comparison of storage influence factors between every two groups by analyzing data of the cloud storage environment, and finally the weight corresponding to each storage influence factor is determined so as to construct a genetic algorithm target function. Wherein the sum of the weights corresponding to all the storage influencing factors is 1.
In specific implementation, the step of determining the optimal storage node matched with the data to be stored based on a genetic algorithm according to the numerical value and the weight corresponding to each storage influence factor comprises the following steps:
(1) and carrying out normalization processing on the numerical values corresponding to the storage influence factors.
Taking seven storage influence factors including the data volume of the data to be stored, the transmission distance between the data to be stored and the storage node, the fault rate of the storage node, the residual space of the storage node, the memory occupancy rate of the storage node, the network bandwidth corresponding to the storage node and the network delay corresponding to the storage node as examples, the residual space of the storage node and the network bandwidth corresponding to the storage node are made to be positive indexes (namely, the larger the numerical value is, the better the other storage influence factors are), and the dimension effect can be eliminated by normalizing each numerical value.
(2) And substituting the numerical values corresponding to the storage influence factors and the weights corresponding to the storage influence factors after the normalization processing into a pre-established genetic objective function, and solving to obtain the corresponding relation between the data to be stored and the optimal storage node.
In specific implementation, the resources to be scheduled are mainly storage nodes, and the remaining space, memory occupancy rate, and network delay of the storage nodes are specifically considered. The memory occupancy rate and the network delay will increase with the increase of the data amount stored simultaneously or continuously, and the data transmission will fail after reaching a certain degree. In addition, other storage influencing factors may be embodied in the genetic objective function in the form of a penalty function as constraints of the genetic objective function.
In the data storage process, the data volume of the data to be stored, the transmission distance between the data to be stored and the storage nodes, the fault rate of the storage nodes, the residual space of the storage nodes, the memory occupancy rate of the storage nodes, the network bandwidth corresponding to the storage nodes and the network delay corresponding to the storage nodes are considered, and the establishing process of the genetic objective function comprises the following steps:
setting a sequence of data to be stored as X ═ X1,x2,…,xmY ═ Y, the sequence of currently available storage nodes1,y2,…,ymSetting n to be less than or equal to M, namely setting the number of data to be stored to be less than the number of storage nodes, and establishing a storage scheme matrix MkThe following were used:
and MkNeed to satisfy
Wherein,
when x isiyjWhen 1, represents xiIs stored in yjPerforming the following steps; when x isiyjWhen 0, x is representediIs not stored in yjPerforming the following steps;
memory scheme matrix MkConstructing the following genetic objective function Zk：
And Z iskNeed to satisfy
Wherein, the matrix P is a cost matrix corresponding to each storage node, DjIs the transmission distance (which can be the route hop number) between the data center root node where the data to be stored is located and the jth storage node, and is SizeiThe Capacity is the data volume of the ith data to be stored after normalization processingjSpace, the initial Space of the j storage node after normalization processingjTo normalize the processed residual space of the j-th storage node, MemjIs the memory utilization rate of the j storage node after normalization processing, NetBjNetwork bandwidth, NetD, corresponding to the j-th storage node after normalization processingjFail to normalize the processed network delay corresponding to the jth storage nodejTo normalize the processed failure rate, w, of the jth storage node1Weight corresponding to failure rate of storage node, w2Weight corresponding to the remaining space of the storage node, w3Weight, w, corresponding to memory usage of a storage node4Weight corresponding to the transmission distance of the data to be stored, w5Weight corresponding to the data size of the data to be stored, w6Weight corresponding to network bandwidth of storage node, w7A corresponding weight for the network latency of the storage node. Wherein, w1To w7The sum of the seven weights of (a) is 1. In order to make the solution of the genetic objective function more concise, all storage nodes are assumed to have the same I/O frequency, and the data volume of each data to be stored can be uniformly set to be constant Size in consideration of low influence of the data volume on the selection of the storage nodes on the whole.
Setting data to be storedX ═ X1,x2,…,xmIn (v), xi＝{si,mi,ni}，siThe storage space m occupied by the ith data to be storediThe amount of memory n required for the ith data to be storediNetwork resources required to be occupied by the ith data to be stored;
setting the sequence of the storage nodes as Y ═ Y1,y2,…,ymIn (v), yi＝{Si,Mi,Ni}，SiStorage space for the ith storage node, MiIs the memory resource of the ith storage node, NiA network resource that is the ith storage node;
assuming that the ith storage node correspondingly stores the a-th data to be stored, the b-th data to be stored and the c-th data to be stored, and storing a scheme matrix MkThe following constraints need to be satisfied:
Si≥sa+sb+…sc
Mi≥ma+mb+…mc
Ni≥na+nb+…nc
and solving the obtained storage scheme matrix MkSatisfy the requirement of
Wherein t isi(s) time taken by data to be stored during data storage, ti(m) time spent by the storage node during data storage, ti(n) is a time consuming generation by the network environment during the data storage process.
In the step of obtaining the corresponding relationship between the data to be stored and the optimal storage node, the following steps may be specifically referred to:
(1) in the process of solving the genetic objective function, M is addedkPerforming variation processing to realize population evolution in a genetic algorithm; wherein, M is setkIs a chromosome in a genetic algorithm.
Wherein, the mutation processing mode comprises the following steps: random slave storage scheme matrix MkIn the selection
For the selected
In satisfying
On the basis of (1), if
Is adjusted to
If it is not
Is adjusted to
In a specific implementation, the following definitions may be given: when the existence matrix T is an n x M-order primary matrix, the result of the individual swapping in the population is M'k＝MkT; chromosome MkThe "1" in several rows is adjusted to "0", and one "0" is randomly selected to be adjusted to "1" in the row, which is called mutation. In ensuring that
On the basis of establishment, the population evolution can be completed.
(2) In the population evolution process, calculating by adopting a multi-iteration mode to obtain an optimal storage scheme matrix MAG(ii) a Wherein, each iteration selects high-quality chromosomes with fitness ranking within a preset range
As calculated during the next iterationM of (A)kAnd eliminate lethal chromosomes with fitness ranking outside a preset range
In specific implementation, for the lethal chromosome generated during the evolution process, the present embodiment provides a self-improvement algorithm for the lethal chromosome based on learning, which is first defined as follows:
the individual chromosomes with fitness ranking of top 10 percent in the population are high-quality chromosomes
Rank 10% after fitness in the population and not fit
The chromosome of (A) is called a lethal chromosome
Specifically, the algorithm firstly selects one of the high-quality chromosome sets as a learning template for each lethal chromosome, if the values of the two genes at the same gene position are both 1, the two genes are retained, then a plurality of genes with the value of 1 are randomly selected from the learning template according to the learning rate, the genes with the value of 1 are replaced to the corresponding positions of the lethal chromosomes, the value of the gene with the original value of 1 in the row is replaced by 0, finally, the genes of all the high-quality chromosomes are accumulated, the genes with the accumulated value equal to the number of the high-quality chromosomes are retained and set as 1, and a comprehensive high-quality chromosome M of the population is obtainedAGAnd M isAGThe lethal chromosome gene value corresponding to the gene position with the median value of 1 is set as 1, and correspondingly, the gene with the line original 1 is replaced by 0. The algorithm improves the gene quality on the basis of ensuring the diversity of the population genes.
(3) According to the optimal storage scheme matrix MAGAnd determining the corresponding relation between the data to be stored and the optimal storage node. In specific implementation, the matrix M is based on the optimal storage schemeAGThe value of each element in the data storage device, and the sequence of the data to be stored is determined as X ═ { X ═ X1,x2,…,xmThe sequence with the currently available storage node is Y ═ Y1,y2,...,ymStore matching relationship of (K) ═ K1,k2,…,km) (ii) a In the storage matching relation K, m is the serial number of the data to be stored, and KmThe sequence number of the best storage node corresponding to the data to be stored with the sequence number m.
On the basis of the foregoing, reference may be made to a schematic diagram of another data storage method for a cloud storage environment shown in fig. 3, and for ease of understanding, fig. 3 is explained here:
and 1, analyzing the scheduling influence factors (namely the storage influence factors) according to the current storage node, and determining the scheduling influence factors. In one embodiment, the determining of the scheduling impact factor may include: the data amount of the data to be stored, the transmission distance between the data to be stored and the storage node, the fault rate of the storage node, the residual space of the storage node, the memory occupancy rate of the storage node, the network bandwidth corresponding to the storage node or the network delay corresponding to the storage node.
And 2, performing triangular fuzzy hierarchical analysis according to the scheduling influence factors and the user data sequence (namely, the sequence of the data to be stored) obtained by analysis to obtain a hierarchical analysis result, namely determining the weight corresponding to each scheduling influence factor.
And 3, constructing an improved genetic algorithm according to the hierarchical analysis result and the user data sequence. The improved genetic algorithm refers to solving based on the genetic objective function proposed in this embodiment.
And 4, obtaining a preferred storage scheme according to the user data sequence and the constructed improved genetic algorithm.
And 5, distributing storage nodes for the data to be stored according to the preferred storage scheme, namely realizing the scheduling of the storage nodes.
The specific implementation manner of each step in fig. 3 can refer to the foregoing, and each number in fig. 3 corresponds to the above step, which indicates the execution sequence and is not described herein again.
In summary, the data storage method for the cloud storage environment provided by this embodiment can be considered based on the global situation to obtain a global optimal solution, the storage time is less, and the obtained data storage scheme is more reliable, so as to achieve better load balance, and enable the cloud resource storage system to operate stably.
Example two:
corresponding to the data storage method provided by the foregoing embodiment, the present embodiment provides a data storage apparatus, referring to a schematic structural diagram of a data storage apparatus for a cloud storage environment shown in fig. 4, including:
a factor determining module 402, configured to determine, when data to be stored is received, a numerical value corresponding to each storage influencing factor according to the data to be stored, the current multiple storage nodes, and a network environment; storage influencing factors include: the data storage method comprises the following steps of (1) data volume of data to be stored, transmission distance between the data to be stored and a storage node, fault rate of the storage node, residual space of the storage node, memory occupancy rate of the storage node, network bandwidth corresponding to the storage node or network delay corresponding to the storage node;
an optimal node determining module 404, configured to determine, according to the value corresponding to each storage influencing factor, an optimal storage node matched with the data to be stored, so as to correspondingly store the data to be stored in the optimal storage node.
When the data storage device for the cloud storage environment provided by the embodiment of the invention receives the data to be stored, the current plurality of storage nodes and the network environment can be used for determining the values corresponding to the storage influence factors, and further, the optimal storage node matched with the data to be stored is determined according to the values corresponding to the storage influence factors. The mode of selecting the appropriate storage node based on the comprehensive consideration of various storage influence factors is more comprehensive in consideration, and a better data storage scheme can be provided.
In a specific implementation, the factor determining module 302 includes:
the weight determining unit is used for determining the weight corresponding to each storage influence factor through a triangular fuzzy analytic hierarchy process;
and the optimal node determining unit is used for determining an optimal storage node matched with the data to be stored based on a genetic algorithm according to the numerical value and the weight corresponding to each storage influence factor.
The best node determining unit is further configured to: normalizing the numerical values corresponding to the storage influence factors; and substituting the numerical values corresponding to the storage influence factors and the weights corresponding to the storage influence factors after the normalization processing into a pre-established genetic objective function, and solving to obtain the corresponding relation between the data to be stored and the optimal storage node.
The establishment process of the genetic objective function comprises the following steps:
setting a sequence of data to be stored as X ═ X1,x2,…,xmY ═ Y, the sequence of currently available storage nodes1,y2,...,ymFourthly, establishing a storage scheme matrix MkThe following were used:
and MkNeed to satisfy
Wherein,
when x isiyjWhen 1, represents xiIs stored in yjPerforming the following steps; when x isiyjWhen 0, x is representediIs not stored in yjPerforming the following steps;
memory scheme matrix MkConstructing the following genetic objective function Zk：
And Z iskNeed to satisfy
Wherein, the matrix P is a cost matrix corresponding to each storage node, DjIs the transmission distance, Size, between the data center root node where the data to be stored is located and the jth storage nodeiThe Capacity is the data volume of the ith data to be stored after normalization processingjSpace, the initial Space of the j storage node after normalization processingjTo normalize the processed residual space of the j-th storage node, MemjIs the memory utilization rate of the j storage node after normalization processing, NetBjNetwork bandwidth, NetD, corresponding to the j-th storage node after normalization processingjFail to normalize the processed network delay corresponding to the jth storage nodejTo normalize the processed failure rate, w, of the jth storage node1Weight corresponding to failure rate of storage node, w2Weight corresponding to the remaining space of the storage node, w3Weight, w, corresponding to memory usage of a storage node4Weight corresponding to the transmission distance of the data to be stored, w5Weight corresponding to the data size of the data to be stored, w6Weight corresponding to network bandwidth of storage node, w7A corresponding weight for the network latency of the storage node.
The best node determining unit is further configured to:
in the process of solving the genetic objective function, M is addedkPerforming variation processing to realize population evolution in a genetic algorithm; wherein, M is setkIs a chromosome in a genetic algorithm; the mutation treatment mode comprises the following steps: random slave storage scheme matrix MkIn the selection
For the selected
In satisfying
On the basis of (1), if
Is adjusted to
If it is not
Is adjusted to
In the population evolution process, calculating by adopting a multi-iteration mode to obtain an optimal storage scheme matrix MAG(ii) a Wherein, each iteration selects high-quality chromosomes with fitness ranking within a preset range
As M is calculated during the next iterationkAnd eliminate lethal chromosomes with fitness ranking outside a preset range
According to the optimal storage scheme matrix MAGAnd determining the corresponding relation between the data to be stored and the optimal storage node.
The best node determining unit is further configured to: according to the optimal storage scheme matrix MAGThe value of each element in the data storage device, and the sequence of the data to be stored is determined as X ═ { X ═ X1,x2,…,xmThe sequence with the currently available storage node is Y ═ Y1,y2,...,ymStore matching relationship of (K) ═ K1,k2,…,km) (ii) a In the storage matching relation K, m is the serial number of the data to be stored, and KmThe sequence number of the best storage node corresponding to the data to be stored with the sequence number m.
Setting a sequence X of data to be stored as { X ═ X1,x2,…,xmIn (v), xi＝{si,mi,ni}，siThe storage space m occupied by the ith data to be storediThe amount of memory n required for the ith data to be storediNetwork resources required to be occupied by the ith data to be stored;
setting the sequence of the storage nodes as Y ═ Y1,y2,...,ymIn (v), yi＝{Si,Mi,Ni}，SiStorage space for the ith storage node, MiIs the memory resource of the ith storage node, NiA network resource that is the ith storage node;
assuming that the ith storage node correspondingly stores the a-th data to be stored, the b-th data to be stored and the c-th data to be stored, and storing a scheme matrix MkThe following constraints need to be satisfied:
Si≥sa+sb+…sc
Mi≥ma+mb+…mc
Ni≥na+nb+…nc
and solving the obtained storage scheme matrix MkSatisfy the requirement of
Wherein t isi(s) time taken by data to be stored during data storage, ti(m) time spent by the storage node during data storage, ti(n) is a time consuming generation by the network environment during the data storage process.
The device provided by the embodiment has the same implementation principle and technical effect as the foregoing embodiment, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiment for the portion of the embodiment of the device that is not mentioned.
Example three:
corresponding to the foregoing embodiments, the present embodiment provides a terminal including a memory for storing a program that supports a processor to execute the data storage method for a cloud storage environment provided in the first embodiment, and a processor configured to execute the program stored in the memory. Specifically, the terminal can be a computer, an upper computer, a server and the like.
Further, the present embodiment also provides a computer storage medium for storing computer software instructions for the data storage method for a cloud storage environment provided in the first embodiment.
Fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present invention, including: the processor 50, the memory 51, the bus 52 and the communication interface 53, wherein the processor 50, the communication interface 53 and the memory 51 are connected through the bus 52; the processor 50 is arranged to execute executable modules, such as computer programs, stored in the memory 51.
The Memory 51 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 53 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 52 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 5, but this does not indicate only one bus or one type of bus.
The memory 51 is used for storing a program, the processor 50 executes the program 501 after receiving an execution instruction, and the method executed by the apparatus defined by the flow process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 50, or implemented by the processor 50.
The processor 50 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 50. The Processor 50 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 51, and the processor 50 reads the information in the memory 51 and completes the steps of the method in combination with the hardware thereof.
The computer program product of the data storage method and apparatus for a cloud storage environment provided by the embodiments of the present invention includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiments, and specific implementation may refer to the method embodiments, and is not described herein again.
The functions, if implemented in the form of software functional units 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 removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (9)
1. A data storage method for a cloud storage environment, comprising:
when data to be stored is received, determining a numerical value corresponding to each storage influence factor according to the data to be stored, the current multiple storage nodes and a network environment; the storage influencing factors include: the data storage method comprises the following steps of (1) data volume of data to be stored, transmission distance between the data to be stored and a storage node, fault rate of the storage node, residual space of the storage node, memory occupancy rate of the storage node, network bandwidth corresponding to the storage node or network delay corresponding to the storage node;
determining the weight corresponding to each storage influence factor by a triangular fuzzy analytic hierarchy process;
and determining an optimal storage node matched with the data to be stored based on a genetic algorithm according to the numerical value and the weight corresponding to each storage influence factor so as to correspondingly store the data to be stored in the optimal storage node.
2. The method according to claim 1, wherein the step of determining an optimal storage node matching the data to be stored based on a genetic algorithm according to the numerical value and the weight corresponding to each storage influence factor comprises:
normalizing the numerical value corresponding to each storage influence factor;
and substituting the numerical values corresponding to the storage influence factors and the weights corresponding to the storage influence factors after normalization processing into a pre-established genetic objective function, and solving to obtain the corresponding relation between the data to be stored and the optimal storage node.
3. The method of claim 2, wherein the genetic objective function is established by:
setting a sequence of data to be stored as X ═ X1,x2,…,xmY ═ Y, the sequence of currently available storage nodes1,y2,...,ymFourthly, establishing a storage scheme matrix MkThe following were used:
and MkNeed to satisfy
Wherein,
when x isiyjWhen 1, represents xiIs stored in yjPerforming the following steps; when x isiyjWhen 0, x is representediIs not stored in yjPerforming the following steps;
memory scheme matrix MkConstructing the following genetic objective function Zk：
And Z iskNeed to satisfy
Wherein the matrix P is a cost matrix corresponding to each of the storage nodes, DjIs the transmission distance, Size, between the data center root node where the data to be stored is located and the jth storage nodeiThe Capacity is the data volume of the ith data to be stored after normalization processingjSpace, the initial Space of the j storage node after normalization processingjTo normalize the processed residual space of the j-th storage node, MemjIs the memory utilization rate of the j storage node after normalization processing, NetBjNetwork bandwidth, NetD, corresponding to the j-th storage node after normalization processingjFail to normalize the processed network delay corresponding to the jth storage nodejTo normalize the processed failure rate, w, of the jth storage node1Weight corresponding to failure rate of storage node, w2Weight corresponding to the remaining space of the storage node, w3Weight, w, corresponding to memory usage of a storage node4Weight corresponding to the transmission distance of the data to be stored, w5Weight corresponding to the data size of the data to be stored, w6Weight corresponding to network bandwidth of storage node, w7A corresponding weight for the network latency of the storage node.
4. The method according to claim 3, wherein the step of solving to obtain the correspondence between the data to be stored and the optimal storage node comprises:
in the process of solving the genetic objective function, M is addedkPerforming variation processing to realize population evolution in the genetic algorithm; wherein, M is setkIs a chromosome in the genetic algorithm;
in the population evolution process, calculating by adopting a multi-iteration mode to obtain an optimal storage scheme matrix MAG(ii) a Wherein, each iteration selects high-quality chromosomes with fitness ranking within a preset range
As M is calculated during the next iterationkAnd eliminate lethal chromosomes with fitness ranking outside a preset range
According to the optimal storage scheme matrix MAGAnd determining the corresponding relation between the data to be stored and the optimal storage node.
5. The method of claim 4, wherein the mutation comprises:
random slave storage scheme matrix MkIn the selection
For the selected
In satisfying
On the basis of (1), if
Is adjusted to
If it is not
Is adjusted to
6. The method of claim 4, wherein the matrix M is based on the optimal storage schemeAGThe step of determining the corresponding relation between the data to be stored and the optimal storage node comprises the following steps:
according to the optimal storage scheme matrix MAGThe value of each element in the data storage device, and the sequence of the data to be stored is determined as X ═ { X ═ X1,x2,…,xmThe sequence with the currently available storage node is Y ═ Y1,y2,...,ymStore matching relationship of (K) ═ K1,k2,…,km) (ii) a In the storage matching relation K, m is the serial number of the data to be stored, and KmThe sequence number of the best storage node corresponding to the data to be stored with the sequence number m.
7. The method of claim 3,
setting a sequence X of data to be stored as { X ═ X1,x2,…,xmIn (v), xi＝{si,mi,ni}，siThe storage space m occupied by the ith data to be storediThe amount of memory n required for the ith data to be storediNetwork resources required to be occupied by the ith data to be stored;
setting the sequence of the storage nodes as Y ═ Y1,y2,...,ymIn (v), yi＝{Si,Mi,Ni}，SiStorage space for the ith storage node, MiIs the memory resource of the ith storage node, NiA network resource that is the ith storage node;
assuming that the ith storage node correspondingly stores the a-th data to be stored, the b-th data to be stored and the c-th data to be stored, the storage scheme matrix MkThe following constraints need to be satisfied:
Si≥sa+sb+…sc
Mi≥ma+mb+…mc
Ni≥na+nb+…nc
and solving the obtained storage scheme matrix MkSatisfy the requirement of
Wherein t isi(s) time taken by data to be stored during data storage, ti(m) time spent by the storage node during data storage, ti(n) is a time consuming generation by the network environment during the data storage process.
8. A data storage device for a cloud storage environment, comprising:
the factor determining module is used for determining a numerical value corresponding to each storage influence factor according to the data to be stored, the current plurality of storage nodes and the network environment when the data to be stored is received; the storage influencing factors include: the data storage method comprises the following steps of (1) data volume of data to be stored, transmission distance between the data to be stored and a storage node, fault rate of the storage node, residual space of the storage node, memory occupancy rate of the storage node, network bandwidth corresponding to the storage node or network delay corresponding to the storage node;
and the optimal node determining module is used for determining the weight corresponding to each storage influence factor through a triangular fuzzy analytic hierarchy process, and determining the optimal storage node matched with the data to be stored based on a genetic algorithm according to the numerical value and the weight corresponding to each storage influence factor so as to correspondingly store the data to be stored in the optimal storage node.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1 to 7.
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| CN108900626B (en) * | 2018-07-18 | 2021-11-19 | 中国联合网络通信集团有限公司 | Data storage method, device and system in cloud environment |
| CN109063121B (en) * | 2018-08-01 | 2024-04-05 | 平安科技（深圳）有限公司 | Data storage method, device, computer equipment and computer storage medium |
| CN109542352B (en) * | 2018-11-22 | 2020-05-08 | 北京百度网讯科技有限公司 | Method and apparatus for storing data |
| CN109831508B (en) * | 2019-02-15 | 2021-11-19 | 网宿科技股份有限公司 | Caching method and equipment and storage medium |
| CN109960587A (en) * | 2019-02-27 | 2019-07-02 | 厦门市世纪网通网络服务有限公司 | The storage resource distribution method and device of super fusion cloud computing system |
| CN111240900B (en) * | 2019-05-23 | 2020-11-24 | 北京天华星航科技有限公司 | Data backup method based on virtual tape library |
| CN110442449A (en) * | 2019-07-09 | 2019-11-12 | 北京云和时空科技有限公司 | A kind of resource regulating method and device |
| CN113688062B (en) * | 2020-05-18 | 2022-08-26 | 北京市商汤科技开发有限公司 | Method for storing data and related product |
| CN112118323B (en) * | 2020-11-18 | 2021-02-19 | 北京滕华软件有限公司 | Data encryption modularization cloud storage system based on artificial intelligence |
| CN112463673B (en) * | 2020-11-25 | 2023-03-21 | 海光信息技术股份有限公司 | On-chip bus, and service quality arbitration method and device for on-chip bus |
| CN113268376B (en) * | 2021-04-16 | 2023-01-31 | 济南轨道交通集团有限公司 | Data center object storage method and system based on genetic algorithm |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1450493A (en) * | 2003-04-25 | 2003-10-22 | 北京工业大学 | Nerve network system for realizing genetic algorithm |
| CN104899100A (en) * | 2015-05-28 | 2015-09-09 | 北京邮电大学 | Resource scheduling method for cloud system |
| CN106161552A (en) * | 2015-04-16 | 2016-11-23 | 国家电网公司 | Load-balancing method and system under a kind of mass data environment |
| CN106484512A (en) * | 2016-10-08 | 2017-03-08 | 电子科技大学 | The dispatching method of computing unit |
| CN106611277A (en) * | 2016-05-13 | 2017-05-03 | 四川用联信息技术有限公司 | Cloud resource scheduling method in mixed cloud model |
| CN107239336A (en) * | 2016-03-28 | 2017-10-10 | 中兴通讯股份有限公司 | A kind of method and device for realizing task scheduling |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170344900A1 (en) * | 2016-05-24 | 2017-11-30 | Sultan Saad ALZAHRANI | Method and apparatus for automated organization of visual-content media files according to preferences of a user |
-
2018
- 2018-01-16 CN CN201810042737.8A patent/CN108287666B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1450493A (en) * | 2003-04-25 | 2003-10-22 | 北京工业大学 | Nerve network system for realizing genetic algorithm |
| CN106161552A (en) * | 2015-04-16 | 2016-11-23 | 国家电网公司 | Load-balancing method and system under a kind of mass data environment |
| CN104899100A (en) * | 2015-05-28 | 2015-09-09 | 北京邮电大学 | Resource scheduling method for cloud system |
| CN107239336A (en) * | 2016-03-28 | 2017-10-10 | 中兴通讯股份有限公司 | A kind of method and device for realizing task scheduling |
| CN106611277A (en) * | 2016-05-13 | 2017-05-03 | 四川用联信息技术有限公司 | Cloud resource scheduling method in mixed cloud model |
| CN106484512A (en) * | 2016-10-08 | 2017-03-08 | 电子科技大学 | The dispatching method of computing unit |
Non-Patent Citations (1)
| Title |
|---|
| 云存储调度关键技术研究与实现;肖博;《中国优秀硕士学位论文全文数据库》;20170215;全文 * |
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