CN106534304A - Cloud storage method and device based on retrievable probability - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
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- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
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- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0668—Interfaces specially adapted for storage systems adopting a particular infrastructure
- G06F3/067—Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]
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Abstract
The invention discloses a cloud storage method and device based on a retrievable probability. The method comprises the steps of establishing a single-time retrievable probability model of each cloud service provider, wherein the single-time retrievable probability model comprises a single-time retrievable probability, namely a success rate of retrieving a file from each cloud service provider at single time; establishing a file storage model, wherein the file storage model comprises a relationship between the single-time retrievable probability of the file and the size of the file; and obtaining a storage scheme with the maximum single-time retrievable probability by employing a genetic algorithm according to the single-time retrievable probability model and the file storage mode. On the basis of competition relations among different cloud service providers, the data confidentiality is ensured by storing the data on different cloud service providers. The data completeness and usability are ensured through data redundancy among the plurality of cloud service providers. Moreover, the storage scheme with the maximum single-time retrievable probability is solved through adoption of the genetic algorithm, thereby facilitating reference and service adjustment of the cloud service providers.
Description
Technical field
The present invention relates to network communication technology field, particularly relates to a kind of cloud storage method based on retrieval probability and dress
Put.
Background technology
It, in the conceptive extension of cloud computing and the new concept that developed, is that a kind of emerging network is deposited that cloud storage is
Storage technology, refers to by functions such as cluster application, network technology or distributed file systems, by a large amount of various inhomogeneities in network
The storage device of type gathers collaborative work by application software, common data storage and the Operational Visit function of externally providing
One system.
Cloud storage has provided the user the storage service of ready access upon use, but just must not when data are given CPS by user
The safety problem of data is not considered.Data safety mainly includes data confidentiality, data integrity, availability of data these three sides
Face.Tradition solves data confidentiality and relies on various AESs, for example：Homomorphic encryption algorithm, act on behalf of re-encryption, based on attribute
Encryption, broadcast enciphering etc.；Solving data integrity then mainly adopts public audit scheme, public audit scheme to be broadly divided into two
Class：POR and PDP；Solving data integrity adopts redundancy strategy, redundancy strategy mainly to include backup and encode mostly.
During the present invention is realized, inventor has found that prior art at least has problems with：First, AES
Have very big algorithm complex with public audit, therefore huge calculation consumption can be brought, it is limited for computing capability and
For the huge user of data storage amount, what this computing cost cannot possibly undertake；Second, based on cryptographic method band
Carry out the problem of key management, once Key Exposure, the privacy of user just do not existed yet, meanwhile, key is lost and be will also result in
User cannot fetch data.
The content of the invention
In view of this, it is an object of the invention to propose a kind of cloud storage method and apparatus based on retrieval probability, use
To provide data access safety guarantee as high as possible under without key state.
Based on above-mentioned purpose the present invention provide a kind of cloud storage method based on retrieval probability, including：
Set up the single retrieval probabilistic model of each cloud service provider；The single retrieval probabilistic model includes list
Secondary retrieval probability, i.e., fetch the success rate of file from each cloud service provider single；
Set up file storage model；The file storage model includes that the single retrieval probability of file is big with the file
Little relation；
Using genetic algorithm, according to the single retrieval probabilistic model and file storage model, single retrieval is obtained
The storage scheme of maximum probability.
Optionally, the single retrieval probabilistic model for setting up each cloud service provider, specifically includes：
According to file size, different capacitance grade is divided documents into；
For single cloud service provider, difference counting user accesses the mortality during file of different capacitance grade；
Mortality when accessing the file of different capacitance grade to each capacitance grade carries out linear fit, obtains the list
The single retrieval probabilistic model of individual cloud service provider.
Optionally, the use genetic algorithm, according to the single retrieval probabilistic model and file storage model, obtains
The storage scheme of single retrieval maximum probability, specifically includes：
Obtain expense upper limit C that user specifiesmaxWith expense lower limit Cmin；Make C=(c1,c2,…,cn), n is more than or equal to 2
Natural number, wherein ci, the service fee of i ∈ [1, n] expression cloud service providers i；Make S=(s1,s2,…,sn)T, wherein sj,
J ∈ [1, n] represent the size of file j；Make F (s1,s2,…,sn) represent 1 file n of file overall retrieval probability；Then constrain
Condition is：Cmin＜ C*S ＜ Cmax, based on above-mentioned constraints, using genetic algorithm for solving F (s1,s2,…,sn) maximum.
Optionally, it is described based on above-mentioned constraints, using genetic algorithm for solving F (s1,s2,…,sn) maximum, tool
Body includes：
The initial value of each file is selected, 0/1 character string is encoded into, as initial chromosome s；
K variation is carried out to initial chromosome s, produces k offspring, as initial population；
Using file single retrieval probability as individual fitness value, calculated all in the initial population respectively
The respective fitness value of body and all individual accumulation probability；
The initial population k offspring is selected with roulette wheel selection, and carries out the sequence of operations such as cross and variation；
Repeat said process until reaching restriction algebraically, then basis obtains coding and obtains optimal placement schemes.
Another aspect of the present invention also provides a kind of cloud storage device based on retrieval probability, including：
Model management unit, for setting up the single retrieval probabilistic model of each cloud service provider；The single can
Fetching probabilistic model includes single retrieval probability, i.e., fetch the success rate of file from each cloud service provider single；Institute
State model management unit to be additionally operable to set up file storage model；The file storage model includes the single retrieval probability of file
With the relation of the file size；
Arithmetic element, for using genetic algorithm, according to the single retrieval probabilistic model and file storage model, obtains
To the storage scheme of single retrieval maximum probability.
Optionally, the model management unit is for according to file size, dividing documents into different capacitance grade；For
Single cloud service provider, difference counting user access the mortality during file of different capacitance grade；To each capacitance grade
The mortality during file of access different capacitance grade carries out linear fit, and the single for obtaining the single cloud service provider can
Fetch probabilistic model.
Optionally, the arithmetic element is used for obtaining expense upper limit C that user specifiesmaxWith expense lower limit Cmin；Make C=
(c1,c2,…,cn), n is the natural number more than or equal to 2, wherein ci, the service fee of i ∈ [1, n] expression cloud service providers i；
Make S=(s1,s2,…,sn)T, wherein sj, the size of j ∈ [1, n] expression file j；Make F (s1,s2,…,sn) represent file 1 to text
The overall retrieval probability of part n；Then constraints is：Cmin＜ C*S ＜ Cmax, based on above-mentioned constraints, using genetic algorithm
Solve F (s1,s2,…,sn) maximum.
Optionally, the arithmetic element is used for selecting the initial value of each file, is encoded into 0/1 character string, as
Initial chromosome s；K variation is carried out to initial chromosome s, produces k offspring, as initial population；File single can use
Probability is returned as individual fitness value, all individual respective fitness values and described are calculated in the initial population respectively
All individual accumulation probability；K offspring of the initial population is selected with roulette wheel selection, and carries out cross and variation etc.
Sequence of operations；Repeat said process until reaching restriction algebraically, then basis obtains coding and obtains optimal placement schemes.
From the above it can be seen that the method and apparatus that the present invention is provided is based on competing between different cloud service providers
Strive relation, by store data in it is different cloud service provider on, it is ensured that data confidentiality；Carried using multiple cloud services
Ensure integrity and the availability of data for the data redundancy between business；Genetic algorithm for solving single retrieval probability is adopted simultaneously
Maximum storage scheme, so as to be referred to and be serviced adjustment for cloud service operator, to improve service quality, ensures user's
Data safety.
Description of the drawings
A kind of schematic flow sheet of the embodiment of cloud storage method based on retrieval probability that Fig. 1 is provided for the present invention；
A kind of flow process of the alternative embodiment of cloud storage method based on retrieval probability that Fig. 2 is provided for the present invention is illustrated
Figure；
Genetic algorithm in a kind of another embodiment based on the cloud storage method of retrieval probability that Fig. 3 is provided for the present invention
Schematic flow sheet；
A kind of module diagram of the embodiment of cloud storage device based on retrieval probability that Fig. 4 is provided for the present invention.
Specific embodiment
For making the object, technical solutions and advantages of the present invention become more apparent, below in conjunction with specific embodiment, and reference
Accompanying drawing, the present invention is described in more detail.
It should be noted that the statement of all use " first " and " second " is for differentiation two in the embodiment of the present invention
The parameter of the entity of individual same names non-equal or non-equal, it is seen that the convenience of " first " " second " only for statement, should not
The restriction to the embodiment of the present invention is interpreted as, subsequent embodiment is no longer illustrated one by one to this.
A kind of schematic flow sheet of the embodiment of cloud storage method based on retrieval probability that Fig. 1 is provided for the present invention.
As illustrated, one aspect of the present invention provides a kind of embodiment of the cloud storage method based on retrieval probability, including：
S10, sets up the single retrieval probabilistic model of each cloud service provider；The single retrieval probabilistic model bag
Single retrieval probability is included, i.e., fetches the success rate of file from each cloud service provider single.Specifically, step S10
In single retrieval probabilistic model, refer to user carry out from a certain specified cloud service provider files passe, download when
Average success rate and mortality, this probability be comprehensive difference file sizes retrieval probability after the synthesis result that obtains.
S11, sets up file storage model；The file storage model includes the single retrieval probability and the text of file
The relation of part size.
S12, using genetic algorithm, according to the single retrieval probabilistic model and file storage model, obtaining single can
Fetch the storage scheme of maximum probability.
The present embodiment provide method based on the competitive relation between different cloud service providers, by storing data in
It is different cloud service provider on, it is ensured that data confidentiality；Ensured using the data redundancy between multiple cloud service providers
The integrity of data and availability；Simultaneously using the storage scheme of genetic algorithm for solving single retrieval maximum probability, so as to supply
Cloud service operator is referred to and is serviced adjustment, to improve service quality, ensures the data safety of user.
A kind of flow process of the alternative embodiment of cloud storage method based on retrieval probability that Fig. 2 is provided for the present invention is illustrated
Figure.As illustrated, in an optional embodiment, S10 sets up the single retrieval probabilistic model of each cloud service provider,
Specifically include：
S20, according to file size, divides documents into different capacitance grade.For example, file can be divided according to size
For capacitance grades such as 0～10M, 10～100M, 100M～1G, 1G～∞.Certainly this dividing mode is one as reference
Example, be not construed as unique dividing mode, other rational dividing modes should also be as being included in the protection of the present invention
Within the scope of.
S21, for single cloud service provider, difference counting user accesses the mortality during file of different capacitance grade
(or success rate).Here statistics is specific to each capacitance grade, such as a certain cloud service provider, user
In the file that access capability is 10～100M, average success rate can reach 99.9%, and be 100～1G's in access capability
During file, average success rate may can only achieve 99.5% etc., merely just provide an example, not represent actual or simulation
Test result.
S22, mortality when accessing the file of different capacitance grade to each capacitance grade carry out linear fit, obtain institute
State the single retrieval probabilistic model of single cloud service provider.By counting the access failure rate of different capacitance grade file
(or success rate), by linear fit, can obtain the overall single retrieval probabilistic model of a certain cloud service provider.
The present embodiment passes through to count access failure rate of the cloud service provider to different capacitance grade file, by Linear Quasi
The mode of conjunction obtains the single retrieval probabilistic model of cloud service provider, is that aforementioned use genetic algorithm carries out storage mode
Condition has been established in optimization.
In another optional embodiment, S12, using genetic algorithm, according to the single retrieval probabilistic model and text
Part storage model, obtains the storage scheme of single retrieval maximum probability, specifically includes：
Obtain expense upper limit C that user specifiesmaxWith expense lower limit Cmin；Make C=(c1,c2,…,cn), n is more than or equal to 2
Natural number, wherein ci, the service fee of i ∈ [1, n] expression cloud service providers i；Make S=(s1,s2,…,sn)T, wherein sj,
J ∈ [1, n] represent the size of file j；Make F (s1,s2,…,sn) represent 1 file n of file overall retrieval probability；Then constrain
Condition is：Cmin＜ C*S ＜ Cmax, based on above-mentioned constraints, using genetic algorithm for solving F (s1,s2,…,sn) maximum.
Specifically, ci, for unit-sized file storage service fee charged, unit can for i ∈ [1, n] expression cloud service providers i
Be unit per GB or M per GB etc.；And sj, the unit of j ∈ [1, n] just corresponds to GB or M.
The optimal storage scheme of i.e. described cloud service provider, can be optimized for a nonlinear programming problem, wherein about
Beam condition includes：
C=(c1,c2,…,cn), S=(s1,s2,…,sn)T, n is the natural number more than or equal to 2
Cmin＜ C*S ＜ Cmax
Need to solve：
F(s1,s2,…,sn) maximum.
In the embodiment of the present invention, above-mentioned nonlinear programming problem is solved using genetic algorithm, so as to obtain as early as possible
Obtain optimal solution.
Genetic algorithm in a kind of another embodiment based on the cloud storage method of retrieval probability that Fig. 3 is provided for the present invention
Schematic flow sheet.As illustrated, in another optional embodiment, it is described based on above-mentioned constraints, using genetic algorithm
Solve F (s1,s2,…,sn) maximum, specifically include：
S30, selects the initial value of each file, is encoded into 0/1 character string, as initial chromosome s.
S31, carries out k variation, produces k offspring, as initial population to initial chromosome s.Arrange and terminate algebraically T, T
Value do not limit, can determine as needed.
S32, using file single retrieval probability as individual fitness value, calculates institute in the initial population respectively
There are individual respective fitness value and all individual accumulation probability.
S33, selects the initial population k offspring with roulette wheel selection, and carries out a series of behaviour such as cross and variation
Make.After the operation such as cross and variation is carried out every time, the offspring for selecting fitness value higher, used as the next generation, the above-mentioned heredity of repetition
Screening process.
S34, repeats said process until reaching restriction algebraically T, and then basis obtains coding and obtains optimal placement schemes.
A kind of module diagram of the embodiment of cloud storage device based on retrieval probability that Fig. 4 is provided for the present invention.
As illustrated, the present invention also provides a kind of cloud storage device based on retrieval probability, including：
Model management unit 40, for setting up the single retrieval probabilistic model of each cloud service provider；The single
Retrieval probabilistic model includes single retrieval probability, i.e., fetch the success rate of file from each cloud service provider single；
The model management unit is additionally operable to set up file storage model；The file storage model includes that the single retrieval of file is general
The relation of rate and the file size.
Arithmetic element 41, for using genetic algorithm, according to the single retrieval probabilistic model and file storage model,
Obtain the storage scheme of single retrieval maximum probability.
The present embodiment provide device based on the competitive relation between different cloud service providers, by storing data in
It is different cloud service provider on, it is ensured that data confidentiality；Ensured using the data redundancy between multiple cloud service providers
The integrity of data and availability；Simultaneously using the storage scheme of genetic algorithm for solving single retrieval maximum probability, so as to supply
Cloud service operator is referred to and is serviced adjustment, to improve service quality, ensures the data safety of user.
Optionally, the model management unit 40 is for according to file size, dividing documents into different capacitance grade；It is right
In single cloud service provider, difference counting user accesses the mortality during file of different capacitance grade；To each capacity etc.
The mortality during file of level access different capacitance grade carries out linear fit, obtains the single of the single cloud service provider
Retrieval probabilistic model.
Optionally, the arithmetic element 41 is used for obtaining expense upper limit C that user specifiesmaxWith expense lower limit Cmin；Make C=
(c1,c2,…,cn), n is the natural number more than or equal to 2, wherein ci, the service fee of i ∈ [1, n] expression cloud service providers i；
Make S=(s1,s2,…,sn)T, wherein sj, the size of j ∈ [1, n] expression file j；Make F (s1,s2,…,sn) represent file 1 to text
The overall retrieval probability of part n；Then constraints is：Cmin＜ C*S ＜ Cmax, based on above-mentioned constraints, using genetic algorithm
Solve F (s1,s2,…,sn) maximum.
Optionally, the arithmetic element 41 is used for selecting the initial value of each file, is encoded into 0/1 character string, makees
For initial chromosome s；Carry out k variation to initial chromosome s, produce k offspring, as initial population, the value of k generally take compared with
Little value；Using file single retrieval probability as individual fitness value, all individualities in the initial population are calculated respectively
Respective fitness value and all individual accumulation probability；K of the initial population is selected with roulette wheel selection
Offspring, and carry out the sequence of operations such as cross and variation；Repeat said process until reaching restriction algebraically, then basis is encoded
Obtain optimal placement schemes.
Those of ordinary skill in the art should be understood：The discussion of any of the above embodiment is exemplary only, not
It is intended to imply that the scope of the present disclosure (including claim) is limited to these examples；Under the thinking of the present invention, above example
Or can also be combined between the technical characteristic in different embodiments, step can be realized with random order, and is existed such as
Many other changes of the different aspect of the upper described present invention, for simple and clear their no offers in details.
The those of ordinary skill in the field is appreciated that to realize all or part of flow process in above-described embodiment method, is
Related hardware can be instructed to complete by computer program, described program can be stored in embodied on computer readable storage
In medium, the program is upon execution, it may include such as the flow process of the embodiment of above-mentioned each method.Wherein, described storage medium can
For magnetic disc, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random
AccessMemory, RAM) etc..
In addition, for simplifying explanation and discussing, and in order to obscure the invention, can in the accompanying drawing for being provided
To illustrate or can not illustrate that the known power ground with integrated circuit (IC) chip and other parts is connected.Furthermore, it is possible to
Device is shown in block diagram form, to avoid obscuring the invention, and this have also contemplated that following facts, i.e., with regard to this
The details of the embodiment of a little block diagram arrangements be depend highly on the platform that will implement the present invention (that is, these details should
It is completely in the range of the understanding of those skilled in the art).Elaborating detail (for example, circuit) to describe the present invention's
In the case of exemplary embodiment, it will be apparent to those skilled in the art that these details can not there is no
In the case of or implement the present invention in the case that these details are changed.Therefore, these descriptions are considered as explanation
It is property rather than restricted.
Although invention has been described to have been incorporated with specific embodiment of the invention, according to retouching above
State, many replacements of these embodiments, modification and modification will be apparent for those of ordinary skills.Example
Such as, other memory architectures (for example, dynamic ram (DRAM)) can use discussed embodiment.
Embodiments of the invention be intended to fall within the broad range of claims it is all such replace,
Modification and modification.Therefore, all any omissions within the spirit and principles in the present invention, made, modification, equivalent, improvement
Deng should be included within the scope of the present invention.
Claims (8)
1. a kind of cloud storage method based on retrieval probability, it is characterised in that include：
Set up the single retrieval probabilistic model of each cloud service provider；The single retrieval probabilistic model includes that single can
Probability is fetched, i.e., fetches the success rate of file from each cloud service provider single；
Set up file storage model；The file storage model includes the single retrieval probability of file and the file size
Relation；
Using genetic algorithm, according to the single retrieval probabilistic model and file storage model, single retrieval probability is obtained
Maximum storage scheme.
2. method according to claim 1, it is characterised in that the single retrieval for setting up each cloud service provider
Probabilistic model, specifically includes：
According to file size, different capacitance grade is divided documents into；
For single cloud service provider, difference counting user accesses the mortality during file of different capacitance grade；
Mortality when accessing the file of different capacitance grade to each capacitance grade carries out linear fit, obtains the single cloud
The single retrieval probabilistic model of service provider.
3. method according to claim 1, it is characterised in that the use genetic algorithm, according to the single retrieval
Probabilistic model and file storage model, obtain the storage scheme of single retrieval maximum probability, specifically include：
Obtain expense upper limit C that user specifiesmaxWith expense lower limit Cmin；Make C=(c1,c2,…,cn), n is oneself more than or equal to 2
So count, wherein ci, the service fee of i ∈ [1, n] expression cloud service providers i；Make S=(s1,s2,…,sn)T, wherein sj,j∈
[1, n] represent the size of file j；Make F (s1,s2,…,sn) represent 1 file n of file overall retrieval probability；Then constraints
For：Cmin＜ C*S ＜ Cmax, based on above-mentioned constraints, using genetic algorithm for solving F (s1,s2,…,sn) maximum.
4. method according to claim 3, it is characterised in that described based on above-mentioned constraints, is asked using genetic algorithm
Solution F (s1,s2,…,sn) maximum, specifically include：
The initial value of each file is selected, 0/1 character string is encoded into, as initial chromosome s；
K variation is carried out to initial chromosome s, produces k offspring, as initial population；
Using file single retrieval probability as individual fitness value, in calculating the initial population respectively, all individualities are each
From fitness value and all individual accumulation probability；
The initial population k offspring is selected with roulette wheel selection, and carries out the sequence of operations such as cross and variation；
Repeat said process until reaching restriction algebraically, then basis obtains coding and obtains optimal placement schemes.
5. a kind of cloud storage device based on retrieval probability, it is characterised in that include：
Model management unit, for setting up the single retrieval probabilistic model of each cloud service provider；The single retrieval
Probabilistic model includes single retrieval probability, i.e., fetch the success rate of file from each cloud service provider single；The mould
Type administrative unit is additionally operable to set up file storage model；The file storage model includes the single retrieval probability of file and institute
State the relation of file size；
Arithmetic element, for using genetic algorithm, according to the single retrieval probabilistic model and file storage model, obtains list
The storage scheme of secondary retrieval maximum probability.
6. device according to claim 5, it is characterised in that the model management unit, will for according to file size
File is divided into different capacitance grade；For single cloud service provider, difference counting user accesses the text of different capacitance grade
Mortality during part；Mortality when accessing the file of different capacitance grade to each capacitance grade carries out linear fit, obtains
The single retrieval probabilistic model of the single cloud service provider.
7. device according to claim 5, it is characterised in that the arithmetic element is used for obtaining the expense that user specifies
Limit CmaxWith expense lower limit Cmin；Make C=(c1,c2,…,cn), n is the natural number more than or equal to 2, wherein ci, i ∈ [1, n] expressions
The service fee of cloud service provider i；Make S=(s1,s2,…,sn)T, wherein sj, the size of j ∈ [1, n] expression file j；Make F
(s1,s2,…,sn) represent file 1 to file n overall retrieval probability；Then constraints is：Cmin＜ C*S ＜ Cmax, it is based on
Above-mentioned constraints, using genetic algorithm for solving F (s1,s2,…,sn) maximum.
8. device according to claim 7, it is characterised in that the arithmetic element is used for selecting the initial of each file
Value, is encoded into 0/1 character string, as initial chromosome s；K variation is carried out to initial chromosome s, produces k offspring,
As initial population；Using file single retrieval probability as individual fitness value, calculated in the initial population respectively
All individual respective fitness values and all individual accumulation probability；The initial kind is selected with roulette wheel selection
The k offspring of group, and carry out the sequence of operations such as cross and variation；Repeat said process until reaching restriction algebraically, then basis
Obtain coding and obtain optimal placement schemes.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111475117A (en) * | 2020-04-09 | 2020-07-31 | 南京信息职业技术学院 | Aggregation cloud storage QoS control method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104902027A (en) * | 2015-06-12 | 2015-09-09 | 电子科技大学 | Cloud storage service-oriented dynamic data integrity auditing program |
| CN105072086A (en) * | 2015-07-06 | 2015-11-18 | 武汉科技大学 | Cloud-storage batch auditing method based on MapReduce |
-
2016
- 2016-11-11 CN CN201610998698.XA patent/CN106534304B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104902027A (en) * | 2015-06-12 | 2015-09-09 | 电子科技大学 | Cloud storage service-oriented dynamic data integrity auditing program |
| CN105072086A (en) * | 2015-07-06 | 2015-11-18 | 武汉科技大学 | Cloud-storage batch auditing method based on MapReduce |
Non-Patent Citations (1)
| Title |
|---|
| ABHISHEK V: ""Cloud Storage and Retrieval - A user perspective "", 《2014 WORLD CONGRESS ON COMPUTING AND COMMUNICATION TECHNOLOGIES》 * |
Cited By (2)
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| CN111475117A (en) * | 2020-04-09 | 2020-07-31 | 南京信息职业技术学院 | Aggregation cloud storage QoS control method and system |
| CN111475117B (en) * | 2020-04-09 | 2024-01-23 | 南京信息职业技术学院 | Aggregated cloud storage QoS control method and system |
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