CN105338064B - Task cooperative based on Stackelberg game performs method - Google Patents
Task cooperative based on Stackelberg game performs method Download PDFInfo
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- CN105338064B CN105338064B CN201510640814.6A CN201510640814A CN105338064B CN 105338064 B CN105338064 B CN 105338064B CN 201510640814 A CN201510640814 A CN 201510640814A CN 105338064 B CN105338064 B CN 105338064B
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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
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Abstract
The invention discloses the task cooperatives based on Stackelberg game in a kind of mobile cloud computing to perform method, it is characterized in that carrying out as follows：The utility function of the mobile equipment of 1 definition and the cost function of mobile subscriber；2 mobile subscribers provide the price paid of itself task resource to mobile equipment；3 movement equipment provide the number of task resource to mobile subscriber；4 mobile subscribers provide newer price paid to mobile equipment；5 find unique equilibrium point between the optimal price paid of mobile subscriber and the task resource of mobile equipment using triple searching algorithms.The present invention can promote mobile equipment to share their not used mobile computing resources, so as to improve resource utilization.
Description
Technical field
The present invention relates to won in mobile field of cloud calculation more particularly to a kind of movement cloud computing of base based on Stackelberg
The method that the task cooperative played chess performs.
Background technology
Recently, mobile equipment (such as smart mobile phone and platform etc.) uses more and more common in our daily lifes.But
It is the use that its slight computing capability and battery capacity limit following mobile equipment.It is fortunately that cloud computing is as a kind of
Advanced technology is performed by will move in equipment on task immigration to cloud more than complicated or resource requirement, to alleviate movement
The rare situation of device resource.Traditionally, mobile cloud computing is that mobile subscriber runs the basis that they are applied using cloud system
Facility.
Research recently extends the concept of mobile cloud computing, i.e., by using the unused resources of equipment mobile in system.
Other than the storage of enhancing cloud system and computing capability, task is migrated to local mobile equipment by WLAN, system can be improved
Performance, for example communication delay and network flow are reduced at access point.At present, only a small amount of mobile equipment of literature research excitation is it
The problem of his equipment provides service.Document " Energy-traffic tradeoff cooperative offloading for
In mobile cloud computing " an affairs bill is disposed to each equipment.If the affairs bill of mobile equipment surpasses
A threshold value is crossed, it cannot be serviced at others, but it can be by replacing others' execution task come payment bill.Document
“Cellcould:a novel cost effective formation ofmobile cloud based onbidding
The equipment for providing service for other equipment in incentives " gives certain reward.By safeguard each mobile subscriber etc.
Grade index, the equipment that the mobile equipment of higher level index is lower than those class indexes are considered more reliable.Once it successfully completes
The execution of one task, reward are to increase the class index of mobile equipment.It correspondingly, will if it does not complete task
It pays for.
Although above scheme can promote the cooperation of mobile equipment and avoid the unjustness between them, movement is set
Standby always passively to provide service to other people, this is ignored to negative effect caused by mobile equipment, the residue electricity of such as mobile equipment
Pond, memory space and performance influence on own services etc..
Invention content
The present invention is to avoid above-mentioned the deficiencies in the prior art, providing a kind of task association based on Stackelberg game
Make execution method, to which mobile equipment can be promoted to share their not used mobile computing resources, so as to improve the utilization of resources
Rate.
The present invention is adopted the following technical scheme that solve technical problem：
A kind of task cooperative based on Stackelberg betting model of the present invention performs method, its main feature is that applied to moving
In dynamic cloud computing environment, the mobile cloud computing environment includes：The mobile equipment of Cloud Server S, m, is denoted as D={ d1,d2,...,
dj,...,dm, djRepresent j-th of movement equipment and a mobile subscriber；1≤j≤m；The mobile subscriber u includes n and appoints
Business；J-th of the movement equipment djInclude EjA task resource；The Cloud Server S also includes several task resources；
The method carries out as follows：
Step 1 defines j-th of movement equipment djUtility function UjWith the cost function U of the mobile subscriber uc；
Step 2, the mobile subscriber u provide the price paid p of itself task resource to m mobile equipment Dnc；
Step 3, j-th of the movement equipment djAccording to its utility function UjWith price paid pnc, to the mobile subscriber u
The number for providing task resource is ej；So that m mobile equipment D is to the total of the mobile subscriber u task resources provided
Number isej≤Ej；
Step 4, the mobile subscriber u are according to its cost function UcWith the total number of task resourceIt is set to m movement
Standby D provides newer price paid pnc′；
Step 5, unique equilibrium characteristic according to the Stackelberg betting model are found using triple searching algorithms
The optimal price paid p of the mobile subscriber unc" unique equilibrium point between the task resource of m mobile equipment D.
The spy for the method that task cooperative based on Stackelberg game performs in mobile cloud computing of the present invention
Point is lain also in, in the step 1,
J-th of the movement equipment d is defined using formula (1)jUtility function Uj：
In formula (1), βjRepresent j-th of movement equipment djGinseng is lost caused by mobile subscriber u offer task resources
Number；
The cost function U of mobile subscriber u is defined using formula (2)c：
In formula (2), pcRepresent that the Cloud Server S provides the price paid of itself task resource to the mobile subscriber u,
And meet pnc≤pc；gcRepresent that individual task is moved to the performance of the Cloud Server S by the mobile subscriber u；
gjRepresent that individual task is moved to j-th of the movement equipment d by the mobile subscriber ujPerformance；Represent the price paid summation of the mobile subscriber u；Represent the movement
User u is migratedA task is to the gain of m mobile equipment D overall performance；α represents the price paid summation and the gain
Between proportion；
In the step 3, by maximizing formula (1) and carrying out derivation, j-th of the movement equipment as shown in formula (3) is obtained
djThe number e of task resource is provided to the mobile subscriber uj：
It is formula (4) to be obtained by minimum formula (2), and formula (3) is substituted into formula (4) and obtains formula (5) in the step 4, then
Derivation is carried out to formula (5), so as to obtain newer price paid pnc′：
Unique equilibrium state is found in the step 5 as follows：
Step 5.1, initialization
Definition cycle-index is i, and initializes i=1；
Step 5.2, the price paid of hypothesis ith are midi, calculate j-th of movement equipment djIt is carried to the mobile subscriber u
For the number of task resourceSo as to obtain m mobile equipment D to the mobile subscriber u ith task resources provided
Number is
Step 5.3, basisWith ith price paid midi, obtain mobile subscriber u's
Ith cost function
Step 5.4 assumes that ith price paid is midmidi, j-th of movement equipment djIt is provided to the mobile subscriber u
The number of ith task resourceSo as to obtain the ith task that m mobile equipment D is provided to the mobile subscriber u
The number of resource is
Step 5.5, basisWith price paid midmidi, obtain mobile use
The ith cost function of family u
If step 5.6,L is then seti+1=midi；Ri+1=Ri；Otherwise, R is seti+1=midmidi；
Li+1=Li；
Step 5.7 enablesWithSo as to update the price of ith
Lattice mid and midmid；
Step 5.8 judges Ri+1-Li+1Whether >=ε is true, if so, then, i+1 is assigned to i, and return to step 5.2 is held
Row；Otherwise it stops search, and obtains optimal price paid pnc"=midiAndAs only
One equilibrium point.
Compared with prior art, beneficial effects of the present invention are embodied in：
1st, the present invention is by considering the price paid of mobile subscriber and migrating several tasks to the performance of other movement equipment
Gain sets cost function for it, while considers that mobile equipment provides several task resources and the income obtained and loss, is it
Utility function is set, in Stackelberg game, mobile subscriber provides according to the task that cost function and mobile equipment provide
Source number determines price paid, and moves the price paid that equipment gives according to itself utility function and mobile subscriber to determine
The task resource number of mobile subscriber is provided；It is uniquely equal by establishing Stackelberg betting model searching Stackelberg
Weighing apparatus point solves the problems, such as task cooperative between mobile equipment, and mobile equipment is encouraged to share their not used mobile computing resources, from
And it improves resource utilization.
2nd, the present invention is in the utility function for setting the cost function of mobile subscriber and mobile equipment itself, in addition to considering to move
It employs outside the remuneration that the expense that family is paid due to task resource is obtained with mobile equipment due to task resource, also specially highlights shifting
Family is employed because the performance gain and movement equipment of migration task to mobile equipment belt are because of the loss brought when providing task resource；
Thus it is possible, on the one hand, so that cost function and utility function are more representative and comprehensive；On the other hand so that the solution of problem
As a result a kind of equilibrium is tended to, is more advantageous to describing the problem with Stackelberg Model.
3rd, the present invention has the characteristic of unique maximum according to mobile equipment itself utility function, is given in mobile subscriber
On the basis of price paid, obtained by the mode to mobile equipment itself utility function derivation and provide unique appoint for mobile subscriber
It is maximum can to ensure that mobile equipment itself obtains value of utility in this way for business number of resources.
4th, the present invention has the characteristic of unique minimum according to mobile subscriber itself cost function, is given in all mobile equipment
On the basis of the task resource number given, obtained by the mode to mobile subscriber itself cost function derivation as all mobile equipment
Unique price paid is provided, can ensure mobile subscriber itself cost value minimum in this way.
5th, the present invention finds the unique equilibrium point of Stackelberg by designing the triple searching algorithms being easily understood, this three
Weight searching algorithm equilibrium point location is gradually reduced using iterative manner so that finding unique equilibrium point；The equilibrium point meets
Mobile subscriber and the target call of mobile equipment, task cooperative performs between realizing mobile equipment, promotes mobile device resource
Reasonable utilization.
Description of the drawings
Fig. 1 is triple searching algorithm flow charts in the present invention；
Fig. 2 is performance gain in the present inventionMobile subscriber u is carried to m mobile equipment D under α=2 and α=4
The price paid p of confessionncInfluence variation analogous diagram；
Fig. 3 is the maximum number E of task resource in the present inventionj(the i.e. β under three kinds of different scenesjIt is different) total is appointed
Business total number resourceInfluence variation analogous diagram；
Fig. 4 is the price paid p that proportion α provides mobile subscriber u to m mobile equipment D in the present inventionncIt is mobile with m
Total task resource number that equipment D is providedInfluence variation analogous diagram.
Specific embodiment
In the present embodiment, task cooperative of the one kind based on Stackelberg game (Stackelberg Game) model is held
Row method is that an incentive program is proposed in mobile cloud computing environment mobile equipment to be encouraged to share their not used moneys
Source, and Stackelberg betting model is designed to, designing one for mobile subscriber u in this game considers its price
Lattice summation and several tasks of migration consider to the cost function of the performance gain of mobile equipment and for mobile equipment design one
It obtains income and the utility function being lost caused by providing task resource, while the mobile subscriber u roots in gambling process
The task resource number that is there is provided according to cost function and mobile equipment determines price paid, and move equipment according to utility function and
Price paid determines to provide the task resource number of mobile subscriber u.In addition, in this game existence anduniquess equilibrium point, can make
Both sides benefit, so as to reach Stackelberg game equilibrium.
Mobile cloud computing environment includes：The mobile equipment of Cloud Server S, m is also known as the seller of mobile computing resource or follows
Person is denoted as D={ d1,d2,...,dj,...,dm, djRepresent that j-th of movement equipment and a mobile subscriber u are also known as moved
The buyer or leader of computing resource；1≤j≤m；Mobile subscriber u includes n task；J-th of movement equipment djInclude EjIt is a
Task resource；Cloud Server S also includes several task resources；
A kind of task cooperative based on Stackelberg betting model performs method, as shown in Figure 1, being as follows
It carries out：
Step 1 establishes Stackelberg betting model, defines j-th of movement equipment djUtility function UjIt is used with mobile
The cost function U of family uc；
Step 1.1 defines j-th of movement equipment djUtility function Uj, as shown in formula (1)：
In formula (1), βjRepresent j-th of movement equipment djParameter is lost caused by mobile subscriber u offer task resources；
Step 1.2, the cost function U for defining mobile subscriber uc, as shown in formula (2)：
In formula (2), pcRepresent that Cloud Server S provides the price paid of itself task resource to mobile subscriber u, and meet pnc
≤pc；gcRepresent that individual task is moved to the performance of Cloud Server S by mobile subscriber u；gjRepresent mobile subscriber u by individual task
Move to j-th of movement equipment djPerformance；Represent the price paid summation of mobile subscriber u；Represent mobile subscriber u migrationsA task is to the gain of m mobile equipment D overall performance；α represents payment
Proportion between price summation and gain, such as α=2；
Step 2, mobile subscriber u provide the price paid p of itself task resource to m mobile equipment Dnc；
Step 3, j-th of movement equipment djAccording to its utility function UjWith price paid pnc, task is provided to mobile subscriber u
The number of resource is ej；So that the total number of m mobile equipment D to the mobile subscriber u task resources provided isej
≤Ej；
J-th of movement equipment d as shown in formula (3) can be obtained by maximizing formula (1) and carrying out derivationjIt is used to mobile
Family u provides the number e of task resourcej：
Detailed process is as follows：
Step 3.1 judges that formula (1) is about ejStrictly concave function, process is as follows：Secondary derivation is carried out to formula (1),
It obtains as shown in formula (4)
βj＞ 0
Step 3.2 carries out formula (1) derivation and equal to 0, i.e., such as formula (5), can obtain formula (6)；
Step 3.3, due to ej≤Ej, convolution (5) can obtain formula (3).
Step 4, mobile subscriber u are according to its cost function UcWith the total number of task resourceTo m mobile equipment D
Newer price paid p is providednc′；
Formula (7) is obtained, and formula (3) is substituted into formula (7) and obtains formula (8), then formula (8) is asked by minimizing formula (2)
It leads, so as to obtain newer price paid pnc′：
Detailed process is as follows：
Step 4.1 judges that formula (8) is about pncStrictly convex function, process is as follows：Secondary derivation is carried out to formula (8),
It obtains as shown in formula (9)
Step 4.2 carries out formula (8) derivation and equal to 0, you can obtains newer price paid pnc′。
Step 5, unique equilibrium characteristic according to Stackelberg betting model are found using triple searching algorithms and moved
The optimal price paid p of user unc" unique equilibrium point between the task resource of m mobile equipment D；
Step 5.1, initialization
Definition cycle-index is i, and initializes i=1；
Step 5.3, basisWith ith price paid midi, obtain mobile subscriber u's
Ith cost function
Step 5.5, basisWith price paid midmidi, obtain mobile use
The ith cost function of family u
If step 5.6,L is then seti+1=midi；Ri+1=Ri；Otherwise, R is seti+1=midmidi；
Li+1=Li；
Step 5.7 enablesWithSo as to update the price of ith
Lattice mid and midmid；
Step 5.8 judges Ri+1-Li+1Whether >=ε is true, if so, then, i+1 is assigned to i, and return to step 5.2 is held
Row；Otherwise it stops search, and obtains optimal price paid pnc"=midiAndAs only
One equilibrium point.
It elaborates with reference to the accompanying drawings and examples to the present invention.
Total parameter setting of emulation experiment：
Mobile subscriber u total task numbers for performing of needs are n=200, Cloud Server S provides to mobile subscriber u oneself
The price paid of body task resource is pc=5.0, simulation accuracy is ε=0.00001.
One parameter setting of emulation experiment：
Mobile number of devices is set as m=10, randomly generates loss parameter betaj∈ [1.0,3.0], j-th of movement equipment dj
The maximum number E of task resource is provided to mobile subscriber uj∈[10,25]。
Referring to Fig. 2, compare migration individual task to j-th of movement equipment djPerformance gainIn α=2 and α=4
Under the price paid p that is provided to m mobile equipment D mobile subscriber uncInfluence.Observe higher performance gain to movement
User u brings more incomes.So in order to give m mobile equipment D more multiple activation, mobile subscriber u will improve price paid
pnc.On the other hand, when proportion α increases, it is supplied to the price paid p of m mobile equipment DncAlso increase.This is because when than
During weight α increases, in order to make cost function UcIt is minimum, it is necessary to provide higher price paid p to m mobile equipment DncTo obtain
More performance gains.
Two parameter setting of emulation experiment：
Mobile number of devices is set as m=8, randomly generates migration individual task to j-th of movement equipment djPerformance increase
BenefitParameter beta is lostjRespectively from [1.0,2.0], [2.0,3.0] and [3.0,4.0] random selection.
Referring to Fig. 3, compare j-th of movement equipment djThe maximum number E of task resource is provided to mobile subscriber ujAt three kinds not
With (i.e. β under scenejIt is different) to total task resource sumInfluence situation of change.It is observed that j-th of movement
Equipment djThe maximum number E of the task resource of offerjDuring increase, total task resource number of m mobile equipment D offer
Also increasing.This is because (the i.e. E under more efficient resourcesjBigger), m mobile equipment D is more willing to hold for mobile subscriber u
Row task.On the other hand, parameter beta is lostjTotal task resource number bigger, that m mobile equipment D is providedIt is fewer.
Three parameter setting of emulation experiment：
The mobile number of devices m=10 of setting, randomly generates j-th of movement equipment djTask resource is provided to mobile subscriber u
Maximum number EjParameter beta is lost in ∈ [10,30]j∈[1.0,3.0]。
Referring to Fig. 4, compare the price paid p that proportion α provides mobile subscriber u to m mobile equipment DncIt is set with m movement
Total task resource number that standby D is providedInfluence.It is observed that proportion α is bigger, price paid pncAlso it is bigger.Reason
By being, mobile subscriber u more pays close attention to the performance gain for migrating several tasks to mobile equipment, then higher price paid pncWith regard to energy
M mobile equipment D of excitation shares their not used resources.The reasons why identical, total task resource numberAlso with than
Weight α increases and increases.
Claims (4)
1. a kind of task cooperative based on Stackelberg betting model performs method, it is characterized in that applied to mobile cloud computing
In environment, the mobile cloud computing environment includes：The mobile equipment of Cloud Server S, m, is denoted as D={ d1,d2,...,dj,...,
dm, djRepresent j-th of movement equipment and a mobile subscriber；1≤j≤m；The mobile subscriber u includes n task；It is described
J-th of movement equipment djInclude EjA task resource；The Cloud Server S also includes several task resources；
The method carries out as follows：
Step 1 defines j-th of movement equipment d using formula (1)jUtility function UjThe mobile subscriber u is defined with using formula (2)
Cost function Uc；
In formula (1), βjRepresent j-th of movement equipment djParameter is lost caused by mobile subscriber u offer task resources；
In formula (2), pcRepresent that the Cloud Server S provides the price paid of itself task resource to the mobile subscriber u, and full
Sufficient pnc≤pc；gcRepresent that individual task is moved to the performance of the Cloud Server S by the mobile subscriber u；
gjRepresent that individual task is moved to j-th of the movement equipment d by the mobile subscriber ujPerformance；Represent the price paid summation of the mobile subscriber u；Represent the movement
User u is migratedA task is to the gain of m mobile equipment D overall performance；α represents the price paid summation and the gain
Between proportion；
Step 2, the mobile subscriber u provide the price paid p of itself task resource to m mobile equipment Dnc；
Step 3, j-th of the movement equipment djAccording to its utility function UjWith price paid pnc, provided to the mobile subscriber u
The number of task resource is ej；So that total numbers of the m mobile equipment D to the mobile subscriber u task resources provided
Forej≤Ej；
Step 4, the mobile subscriber u are according to its cost function UcWith the total number of task resourceTo m mobile equipment D
Newer price paid p is providednc′；
Step 5, unique equilibrium characteristic according to the Stackelberg betting model, using described in the searching of triple searching algorithms
The optimal price paid p of mobile subscriber unc" unique equilibrium point between the task resource of m mobile equipment D.
2. the side that the task cooperative based on Stackelberg game performs in mobile cloud computing according to claim 1
Method, which is characterized in that in the step 3, by maximizing formula (1) and carrying out derivation, obtain j-th of shifting as shown in formula (3)
Dynamic equipment djThe number e of task resource is provided to the mobile subscriber uj：
3. the cooperation based on Stackelberg game performs the side of application in mobile cloud computing according to claim 1
Method, which is characterized in that be formula (4) to be obtained by minimum formula (2), and formula (3) is substituted into formula (4) and obtains formula in the step 4
(5), derivation then to formula (5) is carried out, so as to obtain newer price paid pnc′：
4. the side that the task cooperative based on Stackelberg game performs in mobile cloud computing according to claim 1
Method, which is characterized in that find unique equilibrium state in the step 5 as follows：
Step 5.1, initialization
Definition cycle-index is i, and initializes i=1；
In 0 and pcBetween set four initial points, be L respectivelyi=0, Ri=pc、WithIf
Put precision ε ＞ 0；
Step 5.2, the price paid of hypothesis ith are midi, calculate j-th of movement equipment djIt provides and appoints to the mobile subscriber u
The number for resource of being engaged inSo as to obtain numbers of the m mobile equipment D to the mobile subscriber u ith task resources provided
For
Step 5.3, basisWith ith price paid midi, the ith of acquisition mobile subscriber u
Cost function
Step 5.4 assumes that ith price paid is midmidi, j-th of movement equipment djIth is provided to the mobile subscriber u
The number of task resourceSo as to obtain m mobile equipment D to the mobile subscriber u ith task resources provided
Number is
Step 5.5, basisWith price paid midmidi, obtain mobile subscriber u's
Ith cost function
If step 5.6,L is then seti+1=midi；Ri+1=Ri；Otherwise, R is seti+1=midmidi；Li+1=
Li；
Step 5.7 enablesWithSo as to update the price paid mid of ith
And midmid；
Step 5.8 judges Ri+1-Li+1Whether >=ε is true, if so, then, i+1 is assigned to i, and return to step 5.2 performs；
Otherwise it stops search, and obtains optimal price paid pnc"=midiAndIt is as unique equal
Weighing apparatus point.
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| CN107465748B (en) * | 2017-08-18 | 2020-07-31 | 东南大学 | Dynamic resource allocation method based on evolution game in mobile edge cloud computing system |
| CN108319497B (en) * | 2018-01-11 | 2020-11-06 | 上海交通大学 | Distributed node management method and system based on cloud fusion computing |
| CN108664318A (en) * | 2018-05-11 | 2018-10-16 | 北京邮电大学 | Computation migration method and server-side, boundary server-side for computation migration |
| CN109802998B (en) * | 2018-12-28 | 2021-09-17 | 上海无线通信研究中心 | Game-based fog network cooperative scheduling excitation method and system |
| CN112769641B (en) * | 2020-12-24 | 2022-05-03 | 电子科技大学长三角研究院(衢州) | Block chaining computing power optimization scheduling method for intelligent data processing |
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| US9027024B2 (en) * | 2012-05-09 | 2015-05-05 | Rackspace Us, Inc. | Market-based virtual machine allocation |
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