CN108173958A - Data-optimized storage method based on ant group algorithm under a kind of cloudy environment - Google Patents
Data-optimized storage method based on ant group algorithm under a kind of cloudy environment Download PDFInfo
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- CN108173958A CN108173958A CN201810011239.7A CN201810011239A CN108173958A CN 108173958 A CN108173958 A CN 108173958A CN 201810011239 A CN201810011239 A CN 201810011239A CN 108173958 A CN108173958 A CN 108173958A
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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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
- H04L41/5003—Managing SLA; Interaction between SLA and QoS
- H04L41/5019—Ensuring fulfilment of SLA
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
- H04L41/5041—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
- H04L41/5051—Service on demand, e.g. definition and deployment of services in real time
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/142—Network analysis or design using statistical or mathematical methods
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
Abstract
The present invention relates to the data-optimized storage methods based on ant group algorithm under a kind of cloudy environment, include the following steps：Obtain the correlation attribute information of cloud storage service；According to the carrying cost and availability of cloud storage, the qos value of each cloud storage service is calculated；User provides the demand that oneself stores data, and founding mathematical models according to the demand of user；Optimization algorithm carries out the solution of mathematical model with the intelligent optimization algorithm based on ant group algorithm, provides the data storage scheme of an inexpensive High Availabitity to the user according to the qos value of cloud service.The present invention can be the data storage scheme that user selects inexpensive high availability.
Description
Technical field
The present invention relates to cloud storage technical fields, excellent more particularly to the data based on ant group algorithm under a kind of cloudy environment
Change storage method.
Background technology
The early 21st century, the Web2.0 of rapid development cause the development of Global Internet to enter the new epoch.User relies on
The portfolio that network is carried out quickly increases, such as needs to carry out information exchange and processing mass data by internet.Quick hair
Also given while exhibition we have proposed it is important the problem of, the growth of user demand necessarily brings the requirement of expansion system, at the same with
The universal of the internet devices such as mobile terminal, existing network will undertake more loads.The system of activating business is meant that
New database hub is opened up, various costs related to this also show the trend of rising.With to computing capability, resource profit
With efficiency and the active demand of resource centralization, " cloud computing is come into being.With the development of generation information technology, " cloud
Calculate " a large amount of computer resources are managed collectively and dispatched using network, computing resource pond is formed, each user may be by
Network carries out data storage and service operation on " cloud "." cloud computing, which has provided one kind to the user, can dynamically distribute calculating
Resource meets the effective way of various requirement.
At present, in order to reduce IT maintenance costs, the reliability of data is improved, more and more enterprises and tissue deposit data
It stores up in cloud.However, in face of numerous cloud suppliers and their pricing strategy, which cloud service provider user may be to selecting
And which kind of storage strategy consumes lower feel confused.Usually, under the preferential temptation of short period price, mostly
Enterprise or individual can select will be in their all data storages to single " cloud ".Can cause in this way leaking data risk,
Supplier locks risk and the low available risk of data.
Invention content
What the technical problems to be solved by the invention were to provide under a kind of cloudy environment based on ant group algorithm data-optimized deposits
Method for storing can be the data storage scheme that user selects inexpensive high availability.
The technical solution adopted by the present invention to solve the technical problems is：It provides and ant group algorithm is based under a kind of cloudy environment
Data-optimized storage method, include the following steps：
(1) correlation attribute information of cloud storage service is obtained；
(2) according to the carrying cost of cloud storage and availability, the qos value of each cloud storage service is calculated；
(3) user provides the demand that oneself stores data, and founding mathematical models according to the demand of user；
(4) optimization algorithm carries out mathematical modulo according to the qos value of cloud service with the intelligent optimization algorithm based on ant group algorithm
The solution of type provides the data storage scheme of an inexpensive High Availabitity to the user.
In the step (1) correlation attribute information of cloud storage service include carrying cost, availability, bandwidth cost and
The various costs to data manipulation.
The step (2) includes following sub-step：
(21) carrying cost of each cloud service provider is calculated；
(22) availability of the carrying cost to each cloud service provider and each cloud service provider is normalized；
(23) value after the two attributes are normalized is weighted summation, the qos value as cloud storage service.
In the step (22) carrying cost of cloud service provider be storage price under the size of data provided in user and
Bandwidth price summation caused by retrieval.
In the step (3) user provide the demand oneself data stored include the size of data, data it is whole can
The cost requirement that data are stored with property and user.
The mathematical model established in the step (3) is that cost is minimum, the highest multiple target of availability of data for solving
Optimized model.
The mathematical model isWherein, ω1、
ω2For weight coefficient, f1(P) for after normalized carrying cost, f2(A) it is the availability after normalized, n is represented
The number of cloud service provider, m represent the number of cloud service provider at low cost, and j is representedThe jth kind of kind situation,It indicatesAvailable situation, k represent that available number, c' represent available n cloud to cloud service provider simultaneously to k cloud service provider of kind simultaneously
Service quotient set, aiRepresent i-th of cloud service provider availability,Expression belongs toIn cloud service provider simultaneously may be used
Probability,It representsThe cloud service provider set of middle jth kind situation,It represents to be not belonging in c'
In the not available probability of cloud service provider,In be not belonging toIn cloud service provider set, AacquiredRepresent user couple
The requirement of availability of data.
It is next mainly to improve ant selection for improved ant group algorithm for the ant group algorithm used in the step (4)
The qos value of the method for node, i.e. cloud storage service is bigger, and the probability that next node is chosen by ant is bigger.
The calculation formula for the probability that the next node is chosen by ant is：Wherein, viTable
Show pheromone concentration, the θ of i-th of cloud service provideriRepresent that the qos value of i-th of cloud service provider, α represent the information in ant group algorithm
Heuristic greedy method, β represent expected heuristic value.
Advantageous effect
As a result of above-mentioned technical solution, compared with prior art, the present invention having the following advantages that and actively imitating
Fruit：For the present invention using ant group algorithm as intelligent optimization algorithm, the QoS attributes of service are the references of ant selection, are using this
It needs first to calculate the QoS attributes of each cloud service before algorithm, QoS attributes herein are mainly according to carrying cost and cloud
What the availability calculations of service obtained；After data processing is completed, the demand specified according to user, such as size of data, availability
It is required that selecting the data storage scheme of inexpensive high availability for user with the ant group algorithm improved, that is, store data institute
The cloud service provider used.
Description of the drawings
Fig. 1 is the principle of the present invention figure；
Fig. 2 is the flow chart for calculating data storage scheme in the present invention using ant group algorithm.
Specific embodiment
With reference to specific embodiment, the present invention is further explained.It should be understood that these embodiments are merely to illustrate the present invention
Rather than it limits the scope of the invention.In addition, it should also be understood that, after reading the content taught by the present invention, people in the art
Member can make various changes or modifications the present invention, and such equivalent forms equally fall within the application the appended claims and limited
Range.
Embodiments of the present invention are related to the data-optimized storage method based on ant group algorithm under a kind of cloudy environment, including
Following steps：
First, the attribute of various cloud services is obtained from the official website of third party website and major cloud service provider, is calculated each
The QoS attributes of cloud service provider, wherein, the correlation attribute information of cloud storage service include carrying cost, availability, bandwidth cost with
And the various costs to data manipulation.
Secondly, user provides the requirement templet of oneself, the size including data, the availability of data entirety and user couple
The requirement of data carrying cost.
Then it according to ant group algorithm, with reference to user demand and the information of cloud service provider, carries out inexpensive, high availability
Combinatorial Optimization solves.
Finally, the optimal data storage scheme of user is provided, including the used cloud service provider of data storage and user
Retrieve the cloud service provider used in data.
(1) calculating of QoS attributes
It is solved the problems, such as in ant group algorithm in traditional TSP, next node and present node distance are shorter, ant selection
Probability is bigger.In the present embodiment, the QoS property values of QoS the attributes alternatively foundation, i.e. cloud service provider of next node
It is bigger, it is easier to be chosen.It is therefore desirable to calculate each cloud service provider for QoS attributes.
Here QoS attributes are the weighted sums for storing price and availability, and calculating process is as follows：
1. bandwidth caused by the storage price and retrieval under calculating the size of data that each cloud service provider is provided in user
Price, referred to as carrying cost；
2. the availability of the carrying cost and each cloud service provider to each cloud service provider is normalized；
3. the value after the two attributes are normalized is weighted summation, the QoS attributes as cloud service provider.
(2) definition of Optimized model
The main problem that the present invention solves is：When user provides the demand of oneself, including size of data, cost, availability
Limitation meets that user requires and entire storing process cost is minimum, availability of data is highest by ant group algorithm to acquire
Storage scheme.
Therefore as shown in Figure 1, the data partitioning scheme used is ErasureCoding, principle is：One data file
The data block that m block sizes can be divided into equal, then this m data library can be with redundancy encoding into n block data blocks (n>M), it is whole
The database that a storage can receive in arbitrary 0~(n-m) a cloud service provider is unavailable；When user is needed using data, only
It needs to find out the m minimum cloud service provider of retrieval cost from this n cloud service provider.It, can be with using ErasureCoding
Improve the availability of data in cloudy environment.The calculation formula of availability of data is as follows：
Wherein, j is representedThe jth kind of kind situation,It indicatesK cloud service provider of kind while available feelings
Condition, k represent that available number, c' represent available n cloud service provider set, a to cloud service provider simultaneouslyiRepresent i-th of cloud service
The availability of quotient,Expression belongs toIn cloud service provider simultaneously available probability,It representsMiddle jth kind
The cloud service provider set of situation,It represents to be not belonging in c'In the not available probability of cloud service provider,In be not belonging toIn cloud service provider set.
The cost formula of entire storing process is as follows：
Wherein, s represents the size of data, PsiRepresent paying price of i-th of cloud service provider to storage, τtUser accesses number
According to frequency, PbiPaying price of i-th of cloud service provider to bandwidth, PoiRepresent charge of i-th of cloud service provider to various operations
Price.
Since the measurement unit of availability of data, storing process cost is different, need to be normalized, it is public
Formula is as follows：
Wherein, PmaxFor maximum storage process cost, PminFor minimum memory process cost, PiFor i-th cloud service provider
Storing process cost, AmaxFor maximum data availability, AminFor minimum data availability, AiData for i-th of cloud service provider
Availability.
Last Optimized model can be defined as below：
MaxQ=ω1f1(P)+ω2f2(A) (5)
ω1+ω2=1 (7)
Wherein, ω1、ω2For weight coefficient, AacquiredRepresent requirement of the user to availability of data.
(3) ant colony optimization for solving Optimized model is utilized
It is solved the problems, such as in ant group algorithm in traditional TSP, next node and present node distance are shorter, ant selection
Probability is bigger.In the present embodiment, the QoS property values of QoS the attributes alternatively foundation, i.e. cloud service provider of next node
It is bigger, it is easier to be chosen.So the probability calculation formula of ant selection next node is improved, it is as follows：
Wherein, viRepresent pheromone concentration, the θ of i-th of cloud service provideriRepresent that the qos value of i-th of cloud service provider, α represent
Information heuristic greedy method, β in ant group algorithm represent expected heuristic value.
The entire flow chart that data storage scheme is calculated using ant group algorithm is as shown in Figure 2.
It is not difficult to find that the present invention uses ant group algorithm, as intelligent optimization algorithm, the QoS attributes of service are ant selections
With reference to needing first to calculate the QoS attributes of each cloud service before the algorithm is used, QoS attributes herein are mainly basis
What carrying cost and the availability calculations of cloud service obtained；After data processing is completed, the demand specified according to user, in full
According to size, availability requirement is the data storage scheme that user selects inexpensive high availability with the ant group algorithm improved,
Store the cloud service provider used in data.
Claims (9)
1. the data-optimized storage method based on ant group algorithm under a kind of cloudy environment, which is characterized in that include the following steps：
(1) correlation attribute information of cloud storage service is obtained；
(2) according to the carrying cost of cloud storage and availability, the qos value of each cloud storage service is calculated；
(3) user provides the demand that oneself stores data, and founding mathematical models according to the demand of user；
(4) optimization algorithm carries out mathematical model according to the qos value of cloud service with the intelligent optimization algorithm based on ant group algorithm
It solves, provides the data storage scheme of an inexpensive High Availabitity to the user.
2. the data-optimized storage method based on ant group algorithm under cloudy environment according to claim 1, which is characterized in that
The correlation attribute information of cloud storage service includes carrying cost, availability, bandwidth cost and various logarithms in the step (1)
According to the cost of operation.
3. the data-optimized storage method based on ant group algorithm under cloudy environment according to claim 1, which is characterized in that
The step (2) includes following sub-step：
(21) carrying cost of each cloud service provider is calculated；
(22) availability of the carrying cost to each cloud service provider and each cloud service provider is normalized；
(23) value after the two attributes are normalized is weighted summation, the qos value as cloud storage service.
4. the data-optimized storage method based on ant group algorithm under cloudy environment according to claim 3, which is characterized in that
The carrying cost of cloud service provider is that the storage price under the size of data provided in user is produced with retrieval in the step (22)
Raw bandwidth price summation.
5. the data-optimized storage method based on ant group algorithm under cloudy environment according to claim 1, which is characterized in that
In the step (3) user provide the demand oneself data stored include the sizes of data, the availability of data entirety and
The cost requirement that user stores data.
6. the data-optimized storage method based on ant group algorithm under cloudy environment according to claim 1, which is characterized in that
The mathematical model established in the step (3) is that cost is minimum, the highest Model for Multi-Objective Optimization of availability of data for solving.
7. the data-optimized storage method based on ant group algorithm under cloudy environment according to claim 6, which is characterized in that
The mathematical model isWherein, ω1、ω2For weight
Coefficient, f1(P) for after normalized carrying cost, f2(A) it is the availability after normalized, n represents cloud service provider
Number, m represent the number of cloud service provider at low cost, and j is representedThe jth kind of kind situation,It indicatesK cloud of kind
Service provider simultaneously available situation, k represent cloud service provider simultaneously available number, c' represent available n cloud service provider set,
aiRepresent i-th of cloud service provider availability,Expression belongs toIn cloud service provider simultaneously available probability,
It representsThe cloud service provider set of middle jth kind situation,It represents to be not belonging in c'In cloud service provider
Not available probability,In be not belonging toIn cloud service provider set, AacquiredRepresent user to availability of data
Requirement.
8. the data-optimized storage method based on ant group algorithm under cloudy environment according to claim 1, which is characterized in that
The ant group algorithm used in the step (4) mainly improves the side of ant selection next node for improved ant group algorithm
The qos value of method, i.e. cloud storage service is bigger, and the probability that next node is chosen by ant is bigger.
9. the data-optimized storage method based on ant group algorithm under cloudy environment according to claim 8, which is characterized in that
The calculation formula for the probability that the next node is chosen by ant is：Wherein, viRepresent i-th of cloud
Pheromone concentration, the θ of service provideriRepresent the qos value of i-th of cloud service provider, α represent the information in ant group algorithm it is heuristic because
Son, β represent expected heuristic value.
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Application publication date: 20180615 |