CN109003104A - Service trust Quantitative Calculation Method based on grey correlation in a kind of cloud computing - Google Patents
Service trust Quantitative Calculation Method based on grey correlation in a kind of cloud computing Download PDFInfo
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Abstract
The invention discloses the service trust quantization methods in a kind of cloud computing based on grey correlation, comprising: Step 1: reading the history credit data of cloud user i, constructs decision matrix A, calculates each cloud user i to the trust weight TW of the service evaluationiWith credit CWiWeight obtains the weight matrix A' of factor of evaluation；Gray scale TV is trusted Step 2: calculating separatelyiWith credit gray scale CVi, gray scale R is obtained, and cloud user is calculated to the objective preference B in each scheme factor of evaluation section by B=A ' ο R；Step 3: calculating cloud user to the subjective preferences θ in each scheme factor of evaluation sectioni；Step 4: calculating the objective preference B of each scheme evaluation interval and subjective preferences θiGrey incidence coefficient ε；Step 5: calculating cloud user i in evaluation interval to the final trust quantization value T of cloud service.Current trust quantification calculating is carried out according to the history credit data of cloud user, the services selection in cloud computing environment is realized, improves safety and the resilience of cloud service system.
Description
Technical field
The present invention relates to the service trust Quantitative Calculation Method based on grey correlation in a kind of cloud computing, belongs to computer network
Security fields.
Background technique
The core technology of cloud computing platform has mass data distributed storage technology, distributed programmed, virtualization etc., and cloud
The core technology for calculating application is Enterprise SOA and calculating.Service discovery, which refers to, under Service-Oriented Architecture Based works as service request
When person is generated to certain demand, available service is inquired from the public directory of service register center first, then center will expire
The service of sufficient user demand returns to service requester.When service requester receive return the result in comprising multiple candidate clothes
When business, service selecting module will be executed.
Huge Service Source in cloud platform provides opportunity to the service provider of malice, causes to user
Greatly puzzlement causes waste to limited resource, and the distinctive service offer mode of cloud computing brings incomparable excellent
Also its distinctive safety problem is brought while user experience, wherein extensive Service Source is provided and used under cloud computing environment
The contradiction of the controllable personalized service demand in family is an important issue.Existing personalization the methods of access control and access filtering are simultaneously
The user controllable personalized service needs of problems of cloud cannot be fully solved.
Summary of the invention
The present invention has designed and developed the service trust Quantitative Calculation Method based on grey correlation in a kind of cloud computing, according to cloud
The history credit data of user carries out current trust quantification calculating, realizes the services selection in cloud computing environment, improves cloud service
The safety of system and resilience.
Technical solution provided by the invention are as follows:
Service trust Quantitative Calculation Method based on grey correlation in a kind of cloud computing, comprising:
Step 1: choosing cloud user, evaluation interval is determined；
Step 2: reading cloud user the last time credit data, decision matrix A is constructed
Step 3: standardization processing is carried out to decision matrix A, the decision matrix B after being standardized；
Step 4: calculating cloud user to the subjective preferences value θ in each scheme factor of evaluation sectioni；
Step 5: calculating the grey incidence coefficient ε of each scheme evaluation interval；
Step 6: calculating cloud user i in evaluation interval to the final trust quantization value T of cloud service.
Preferably, the credit data includes trust value TrustiWith credit value Crediti。
Preferably, the step 2 includes:
If A is decision matrix, there is m option A1,A2,...,Am, t factor F1,F2,...,Ft, n evaluation interval, side
Case AiIn factor of evaluation FjBe evaluated as aij, then A be
Preferably, the step 3 includes:
Each cloud user is calculated to the trust weight TW of the service evaluationiWith credit weight CWi:
CWi=1-TWi；
Each cloud user is calculated to the trust gray scale TV of the service evaluationiWith credit gray scale CVi:
TVi=Trusti×(1-Crediti)；
CVi=Crediti×(1-Trusti)；
Gray matrix R={ (x, y), μR(x,y),vR(x, y)) x ∈ X, y ∈ Y }, wherein μR(x, y) be given space X=
{ x }, the degree of membership of Y={ y } fuzzy relation R, VR(x, y) is gray scale；According to the weight matrix A' table of gray matrix factor of evaluation
It is shown as A'=[(aij, vA′(aij))]mt, wherein aij>=0, i=1,2 ... t, andIf each factor of clear stipulaties
Weight distribution, then A'=[(aij, 0)]mt；
Decision matrix is denoted asWherein bijIt is policymaker to option AiAbout evaluation interval Fj's
Objective preference.
Preferably, the subjective preferences θ in the step 4iAre as follows:
Preferably, the grey incidence coefficient ε are as follows:
Preferably, the step 6 includes, in evaluation intervalIn, cloud user i is calculated to cloud
The final trust quantization value T of service,
It is of the present invention the utility model has the advantages that the present invention is based on the service trust quantization method application cloud users couple of grey correlation
The quantization of service trust, introduce preference trust valuation mechanism combine directly trust and recommendation trust obtain comprehensive trust value obtain to
Service list is selected to make the decision of services selection, and the privacy information that service trust quantization depends on user provides, and with hidden
What personal letter breath provided increases, and service trust quantization is more accurate.
Medium cloud user of the present invention has the Behavioral change of service node quick using the service trust quantization based on grey correlation
The adaptive and ability of regulation and control of sense, the fraud provided service have resilience.
Detailed description of the invention
Fig. 1 is the schematic diagram of cloud user of the present invention and service broker's interaction establishment process.
Fig. 2 provides the associated diagram of record sheet Yu user's evaluation table for service of the present invention.
Specific embodiment
Present invention will be described in further detail below with reference to the accompanying drawings, to enable those skilled in the art referring to specification text
Word can be implemented accordingly.
As shown in Figs. 1-2, the present invention provides the service trust Quantitative Calculation Method based on grey correlation in a kind of cloud computing,
By choosing the n cloud user i for thering is history to interact with service side, the last letter that cloud user i saves service side is read
Appoint value TrustiWith credit value Crediti, carry out gray count and preference calculate, service letter is calculated in final Calculation Estimation section
Appoint quantized value T.Specifically comprise the following steps:
Step 1 chooses the n cloud user i for having history to interact with service side, is successively read cloud user i and saves to service side
The last trust value TrustiWith credit value Crediti, when cloud user node not service history solicited message, then
Trusti=0, Crediti=0；
Step 2, according to the data configuration decision matrix A read in step 1:
If A is decision matrix, there is m option A1,A2,...,Am, t factor F1,F2,...,Ft, n evaluation interval, side
Case AiIn factor of evaluation FjBe evaluated as aij, then A are as follows:
Step 3, specified decision matrix,
Each cloud user is calculated to the trust weight TW of the service evaluationiWith credit weight CWi:
CWi=1-TWi；
Each cloud user is calculated to the trust gray scale TV of the service evaluationiWith credit gray scale CVi:
TVi=Trusti×(1-Crediti)；
CVi=Crediti×(1-Trusti)；
Gray matrix R={ (x, y), μR(x,y),vR(x, y)) x ∈ X, y ∈ Y }, wherein μR(x, y) be given space X=
{ x }, the degree of membership of Y={ y } fuzzy relation R, VR(x, y) is gray scale；According to the weight matrix A' table of gray matrix factor of evaluation
It is shown as A'=[(aij, vA′(aij))]mt, wherein aij>=0, i=1,2 ... t, andIf each factor of clear stipulaties
Weight distribution, then A'=[(aij, 0)]mt；
Decision matrix after being standardized, is denoted asWherein bijIt is policymaker to option AiIt closes
In evaluation interval FjObjective preference.
IfGather for two, compositive relation
Wherein,
Step 4 calculates cloud user to the subjective preferences θ in each scheme factor of evaluation sectioni,
Step 5 calculates each objective preference B of evaluation of programme evaluation interval and each project Director's preference θiGrey incidence coefficient
ε,Wherein εl j/εr jFor the degree of association, reflects the objective preference of j evaluation interval policymaker and subjectivity is inclined
Good similarity, similarity is bigger, illustrates that objective preference and supervisor's preference are closer, so that it is determined that evaluation interval；Value get over
Greatly, illustrate that policymaker more accepts j evaluation interval, seeking final appraisal results is sectionSo that
Step 6, according to evaluation intervalCloud user is T to the final trust quantization value of cloud service,
Embodiment
Assuming that the cloud user of service interaction is C, and cloud service S, trust quantification calculating step is such as in cloud computing environment
Under:
(1) random selection method is used, 4 cloud users for having history to interact with the service is selected, is denoted as respectively: C1,
C2,C3,C4, the C of demand for services is proposed with this0The evaluation " expert " to the service trust is collectively constituted, will trust and credit is made
For evaluation attribute, evaluation interval W are as follows:
w1=[0,0.25], w2=(0.25,0.5], w3=(0.5,0.75], w4=(0.75,1],
That is evaluation of programme m=5, factor of evaluation t=2, evaluation interval number n=4；
(2)C0To read the last trust value and credit value with 4 cloud users of selection to service in itself, knot is read
Fruit is as shown in table 1.
1 trust value of table and credit value
(3) A is that decision matrix has 5 option As0, A1, A2, A3, A4, 2 factor FstAnd Fε, then decision matrix A be
(4) specified decision matrix,
1) each cloud user is calculated to the trust and credit weight of the service evaluation:
CW0=1-0.468=0.532
Calculated result is as shown in table 2:
The trust and credit weight of 2 cloud user of table
2) each evaluation cloud users to trust and credit gray scale are calculated:
TV0=Trust0×(1-Credit0)=0.81 × (1-0.92)=0.0648
CV0=Credit0×(1-Trust0)=0.92 × (1-0.81)=0.1748
Calculated result is as shown in table 3:
Table 3 trusts grayness and credit grayness
(5) subjective preferences of each scheme are calculated
Subjective preferences are as follows:
θ0=[0.224,0], θ1=[0.195,0], θ2=[0.161,0], θ3=[0.215,0], θ4=[0.205,0]
(6) grey incidence coefficient is calculated
ε=[(0.224,0), (0.195,0), (0.161,0), (0.215,0), (0.205,0)].
ε1=0/1, ε2=0/1, ε3=0.776/0.00081, ε4=0.839/0.000007
Then there is ε4Value is maximum, illustrates that the objective preference of each evaluation of programme evaluation interval 4 and each scheme subjective preferences are the most similar.
(7) service trust quantized value T is calculated, according to formula
T=0.75+ (1-0.75) × 0.839=0.9598.
Trust and the acquirement of credit is to provide record sheet, such as Fig. 2 by the service evaluation table of cloud user and cloud service agency
It is shown.Cloud user service evaluation table provides information to each cloud service and carries out evaluation record, and table structure specifically includes that cloud user
ID, service type, service agent identification, time, space, historical behavior trust (credit after this is trusted and feeds back), interaction time
Number etc..Service evaluation table only has this user to have the permission read it and write.When a cloud user proposes service request, Yong Hugen
According to service type, service evaluation table is searched, if it is enough to find record count, according to user preference service category
Property carry out trust quantification；Otherwise, the last item record of agent list is found, the service of lookup carries out respectively as service to be selected
Trust update, and demand for services is submitted by cloud service interface, obtain new optional service.Obtaining the new demand servicing offer first step is
By directly trusting and recommendation trust comprehensive quantification cloud service.Entire service interaction terminates, and updates the service evaluation table of user.Clothes
Business evaluation table only has this user to have the permission write and modified, but can read relevant information on services by other users, is it
Its user provides decision support with to the trust quantification of service.
The main function of service evaluation table is established in each cloud user are as follows:
(1) behavior of cloud service is recorded, the important informations data such as trust and credit to each service carry out structuring pipe
Reason is conducive to the service offer to service and is tracked and analyzed；
(2) information in the agent list of each user can carry out letter to service in other users to other user sharings
When appointing quantization, the data such as necessary trust and credit are provided and are supported, so that the trust quantification to service is more acurrate；
(3) cloud user service evaluation table identifies according to cloud service and carries out the centrally stored management of piecemeal, has faster search to imitate
Rate, and the preservation of service evaluation record, according to the distance of history service provision time, top-down sequential storage, so that most
New demand servicing offer, which is recorded in, is maintained at front end.
After cloud users service needs are issued by service interface (service has premised on automatic acquisition and discovery feature),
Service discovery module is called, for service type demand, the service that cloud service dynamic proxy inquires itself provides record sheet, and (cloud is used
Family mark, users to trust degree), actively submit a request for a military assignment if trusting cloud user, user according to evaluation table with there is what is interacted to convince
Business agency gets in touch.Automated service discovery is the premise of cloud user and service interaction, and (1) cloud user's issuing service first is asked
It asks；Cloud service agency is according to service type query service record sheet, if it find that matching service type and having really with the user
Telecommunications services interactive history then arrives (2) service broker to cloud user and sends own services agent identification, feedback；(3) cloud user connects
The mark of service broker's transmission is received, inquires user's evaluation table if there is Sincere Service interactive history, then (4) establish interaction.
It is as shown in Figure 1 to connect interaction establishment process.
Premised on the history of cloud service and cloud user are trusted, service both sides establish connection, using grey correlation analysis side
Method calculates the synthesis degree of belief of service, if meeting the trust condition of user, service list to be selected is added, and completes service hair
It is existing；When the selection of cloud user service, after trust quantification, if receiving cloud service offer, cloud user is allowed first to look into
It looks for cloud service to provide record sheet, if finding the historical record of oneself, verifies oneself relevant information, and carry out corresponding
It updates；Otherwise, i.e., the cloud service provides service to cloud user for the first time, then cloud user first comments the node identification of oneself and user
The important informations such as the entry address of valence table are appended to cloud service and provide in record, then allow its offer service, finally by the cloud
The evaluation informations such as the trust and credit of service are inserted into the evaluation table of this cloud user.Using the analysis method of grey correlation, cloud
The cloud service trust evaluation element of user couple includes: trust and credit record of the cloud user to service；Evaluate expert's set: { cloud
User, expert 1 ..., expert n }.
Wherein, the selection process of expert are as follows: cloud user and optional service connect after establishing, and cloud user passes through inquiry cloud service
Other clouds user that record was had interactive history therewith is provided, inquires cloud user service evaluation table, is taken according to interaction time
Suitable cloud user is obtained as evaluation expert, evaluates the relevant information in expert service evaluation table as essential elements of evaluation.It is drawn trusting
It is divided into element of each evaluation interval as evaluation attributes set.
By being serviced in the service agents management domain of domain under stratification trust management framework based on agency and trust domain in cloud,
According to the Service Properties of cloud user, matching is serviced and the nominator as service interacts with cloud service completion, according to service broker
Comprehensive Trust Values Asses are made to service, it is the important set for servicing comprehensive quantification that cloud service agency, which provides record sheet service broker,
It is problematic.
Expert is more easily selected when quantifying for cloud user to service trust, building cloud service agency provide record sheet, and
It establishes between cloud user's evaluation table and contacts.Service offer table is stored on each service broker, but service broker does not have to clothes
Business provide record sheet write and modification authority, the cloud service provide user it is serviced provide record sheet have read all note
Record, write or modify the permissions such as record relevant to oneself.Each service provides record sheet, provides for recording the service broker
All cloud user informations, cloud service provide record table structure specifically include that service agent identification, cloud user identifier, cloud user
Degree of belief, service type, service interaction time and address of evaluation table of cloud user etc..It is commented by service offer table with user
Association between valence table can make the history of cloud service service offer behavioural information and sufficiently share in cloud environment, be greatly reduced
The risk of allied cheating.
Although the embodiments of the present invention have been disclosed as above, but its is not only in the description and the implementation listed
With it can be fully applied to various fields suitable for the present invention, for those skilled in the art, can be easily
Realize other modification, therefore without departing from the general concept defined in the claims and the equivalent scope, the present invention is simultaneously unlimited
In specific details and legend shown and described herein.
Claims (7)
1. the service trust Quantitative Calculation Method in a kind of cloud computing based on grey correlation characterized by comprising
Step 1: choosing cloud user, evaluation interval is determined；
Step 2: reading cloud user the last time credit data, decision matrix A is constructed；
Step 3: standardization processing is carried out to decision matrix A, the decision matrix B after being standardized；
Step 4: calculating cloud user to the subjective preferences value θ in each scheme factor of evaluation sectioni；
Step 5: calculating the grey incidence coefficient ε of each scheme evaluation interval；
Step 6: calculating cloud user i in evaluation interval to the final trust quantization value T of cloud service.
2. the service trust Quantitative Calculation Method in cloud computing according to claim 1 based on grey correlation, feature exist
In the credit data includes trust value TrustiWith credit value Crediti。
3. the service trust Quantitative Calculation Method in cloud computing according to claim 2 based on grey correlation, feature exist
In the step 2 includes:
If A is decision matrix, there is m option A1,A2,...,Am, t factor F1,F2,...,Ft, n evaluation interval, option Ai?
Factor of evaluation FjBe evaluated as aij, then A are as follows:
4. the service trust Quantitative Calculation Method in cloud computing according to claim 3 based on grey correlation, feature exist
In the step 3 includes:
Each cloud user is calculated to the trust weight TW of the service evaluationiWith credit weight CWi:
CWi=1-TWi；
Each cloud user is calculated to the trust gray scale TV of the service evaluationiWith credit gray scale CVi:
TVi=Trusti×(1-Crediti)；
CVi=Crediti×(1-Trusti)；
Gray matrix R={ (x, y), μR(x,y),vR(x, y)) x ∈ X, y ∈ Y }, wherein μR(x, y) is given space X={ x }, Y
The degree of membership of={ y } fuzzy relation R, VR(x, y) is gray scale；A' is expressed as according to the weight matrix A' of gray matrix factor of evaluation
=[(aij,vA'(aij))]mt, wherein aij>=0, i=1,2 ... t, andIf the weight of each factor of clear stipulaties point
Match, then A'=[(aij,0)]mt；
Decision matrix is denoted asWherein bijIt is policymaker to option AiAbout evaluation interval FjIt is objective
Preference.
5. the service trust Quantitative Calculation Method in cloud computing according to claim 4 based on grey correlation, feature exist
In subjective preferences θ in the step 4iAre as follows:
6. the service trust Quantitative Calculation Method in cloud computing according to claim 5 based on grey correlation, feature exist
In the grey incidence coefficient ε are as follows:
7. the service trust Quantitative Calculation Method in cloud computing according to claim 6 based on grey correlation, feature exist
In the step 6 includes, in evaluation intervalIn, cloud user i is calculated to the final trust amount of cloud service
Change value T,
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| CN101923615A (en) * | 2010-06-11 | 2010-12-22 | 北京工业大学 | Grey fuzzy comprehensive evaluation-based trust quantization method |
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| CN104094576A (en) * | 2012-02-06 | 2014-10-08 | 国际商业机器公司 | Consolidating disparate cloud service data and behavior based on trust relationships between cloud services |
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2018
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| CN101923615A (en) * | 2010-06-11 | 2010-12-22 | 北京工业大学 | Grey fuzzy comprehensive evaluation-based trust quantization method |
| CN104094576A (en) * | 2012-02-06 | 2014-10-08 | 国际商业机器公司 | Consolidating disparate cloud service data and behavior based on trust relationships between cloud services |
| CN103546471A (en) * | 2013-10-28 | 2014-01-29 | 北京工业大学 | Method for service quantification based on rough set in cloud computing |
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