CN102710508B - Virtual network resource allocation method - Google Patents

Virtual network resource allocation method Download PDF

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CN102710508B
CN102710508B CN201210154452.6A CN201210154452A CN102710508B CN 102710508 B CN102710508 B CN 102710508B CN 201210154452 A CN201210154452 A CN 201210154452A CN 102710508 B CN102710508 B CN 102710508B
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service provider
resource
state
service
resource allocation
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CN102710508A (en
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邱雪松
熊翱
吕霞
王智立
孟洛明
李文璟
高志鹏
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Beijing University of Posts and Telecommunications
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Beijing University of Posts and Telecommunications
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Abstract

The invention relates to the technical field of computer networks, and provides a virtual network resource allocation method. The method comprises the steps of: building a resource allocation model; submitting competitive demands to an InP (Internal Network Processor) through SPs (Service Providers); calculating the quantity and consumption of the resource through the InP; acquiring the resource, assuming the consumption and calculating the income through each SP; adjusting the competitive strategies through each SP; and waiting for competing at the next time based on the adjusted competitive strategies. In the scheme of the invention, a novel virtual network resource allocation method is provided; the resource is periodically allocated to a plurality of service providers based on the trend of the demands on the virtual network resource, so that the resource allocation can be carried out as demands, and the performance of the whole network can be improved. Moreover, a scheme for effectively selecting the competitive strategies is provided at the same time; by using the scheme, the service providers can be guided to select the competitive strategies; the real network demands can be quickly obtained; the network performance can be improved; and the complexity of the algorithm can be effectively reduced.

Description

Virtual network resource allocation methods
Technical field
The present invention relates to technical field of the computer network, particularly a kind of virtual network resource allocation methods.
Background technology
In network virtualization environment, traditional Internet Service Provider is divided into two parts: infrastructure provider (InP) and service provider (SP).InP is responsible for disposing and management bottom-layer network, i.e. bottom physical resource; SP is by renting the virtual network of resource construction oneself, for terminal use (EU) provides service to InP.In order fully effectively to utilize the resource of physical network, for the user of virtual network provides convenience and has the service of quality assurance, the resource of virtual network has been distributed into an inevitable major issue.
In prior art, virtual network resource allocation methods is mainly divided into two types, static allocation and dynamic assignment: static allocation is before its life cycle finishes, not allow distributed change in resources after virtual network has been distributed resource; Common static allocation mode can be shone upon when initialization, such as taking nearby principle to select bottom physical network nodes.Dynamic assignment can dynamically be adjusted institute's Resources allocation according to running status and changes in demand in virtual network life cycle, common dynamic assignment mode can be according to dynamic resource demand, and heavily distribution or the fairness doctrine etc. are carried out resource distribution to further consider dynamic network model, periodicity.
But, dumb performance and the efficiency that has obviously had influence on network of static resource allocation, it does not consider the resource requirement possibility dynamic change of each virtual network conventionally, cause initial virtual network mapping result cannot meet the dynamic need of virtual network, may cause virtual network normally to move.And that the algorithm of dynamic assignment is compared static allocation is more flexible and efficient, but conventionally more complicated and be difficult to dispose to realize, such as, the topological structure that needs virtual network in frequent updating model of model Network Based, that periodically heavily distributes does not consider the harm that the greedy behavior of selfish virtual network brings to whole network environment, and the fairness doctrine cannot be applicable to the more complex environment of service type.
Summary of the invention
(1) technical problem that will solve
For the shortcoming of prior art, the present invention is in order to solve the problem that virtual network resource distribution mode in prior art is dumb or be difficult to realize, and a kind of novel virtual network resource allocation methods that dynamically carries out is provided.
(2) technical scheme
Solve the problems of the technologies described above, the present invention specifically adopts following scheme to carry out for this reason:
First, the invention provides a kind of virtual network resource allocation methods, described method comprises step: 101) Resource Allocation in Networks participant is carried out to modeling, build Dynamic Resource Allocation for Multimedia model;
102) a plurality of service providers that participate in resource contention submit the rival demand information of one dimension to infrastructure provider, the demand of this service provider to resource when this rival demand represents this competition;
103) infrastructure provider receives the service provider's of all participation resource contentions demand information, calculation services provider obtainable stock number and the required loss of bearing;
104) each service provider obtains the Internet resources that infrastructure provider distributes, and bears loss and calculates the income that self effectiveness is improved;
105) each service provider adjusts respectively its competitive strategy according to loss and income;
106) each service provider adopts the pending next round resource contentions such as competitive strategy after adjustment.
Preferably, in step 103, the account form of described stock number is:
max x Σ i = 1 m F i ( x i )
s.t.Ax≤C
Wherein, vector x represents the network bandwidth that all service providers are assigned with, x irepresent to provide the service provider SP of service i ithe network bandwidth being assigned with, m represents service provider's number, vectorial C represents the capacity of all links, the elements A in matrix A lirepresent SP iwhether the path at place comprises link l, F i(x i) be SP iutility function, wherein, b irepresent SP irival demand information during this competition, K irepresent user's set of subscribed services i, α kthe weight of user k, u kthe extent function that represents user k.
Preferably, in step 103, SP ithe required loss of bearing is:
τ i = Σ j = 1 , j ≠ i m F j ( x j , - i * ) - Σ j = 1 , j ≠ i m F j ( x j * )
Wherein, for equation the optimal solution of s.t.Ax≤C, while having neither part nor lot in allocated bandwidth for service i, the optimal solution of this equation.
Preferably, in step 104, SP ithe income that self effectiveness is improved is:
ψ i = F i ( x i * ) - τ i = θ i U i ( x i * ) - Σ j = 1 , j ≠ i m F j ( x j , - i * ) + Σ j = 1 , j ≠ i m F j ( x j * )
Wherein, θ ifor SP ireal demand, function
Preferably, in step 105, the adjustment algorithm of competitive strategy is:
201) init state-action schedule SA, arranges algorithm parameter;
202) perception current network environment, the situation of subscribing to this service according to the resource provision amount perceiving, the situation that self takies resource and terminal use represents the current status s of service provider;
203), based on status, service provider adopts ε-greedy algorithm to concentrate and choose the action a that a strategy forms service provider from competitive strategy;
204) the immediately award of calculation services provider from obtaining state s execution a;
205) more current state-action in new state-action schedule (s, a) to corresponding value SA (s, a);
206) a that performs an action enters NextState s';
207) whether evaluation algorithm restrains, if algorithmic statement obtains optimal policy value, to infrastructure provider, submits its optimum competitive strategy to; If algorithm is not restrained, proceed Action Selection next time.
Preferably, in step 204, what described award was immediately service provider's profit under state s and the lower service provider's profit of state s ' is poor.
Preferably, in step 205, described value SA (s, is a):
SA(s,a)＝SA(s,a)+β·(R(s,a)+γ·E(s')-SA(s,a))
Wherein, s' is that service provider executes the state after action a at state s; E (s ') is the effectiveness of state s', E (s')=max asA (s', a); γ is the discount rate of time delay return E (s'), γ ∈ [0,1]; β is learning rate, has indicated the degree of belief of giving to the more new portion improving, β ∈ [0,1].
Preferably, in step 207, according to difference before and after the renewal of definite state-action schedule, whether be less than certain threshold value and come evaluation algorithm whether to restrain.
Preferably, in step 207, described threshold value is 10 -4.
Preferably, in step 106, if having adjustment to jump to step 102, competitive strategy participates in competition next time; Otherwise continue to wait for.
(3) beneficial effect
In the solution of the present invention, a kind of novel virtual network resource allocation methods is provided, for the dynamic of virtual network resource requirement, periodically Resources allocation is given a plurality of service providers, resource is distributed and carry out as required, improves overall performance of network.Owing to also having proposed a kind of scheme of effective Selection of Competition Strategy in the present invention simultaneously, by this scheme, guide service provider Selection of Competition Strategy, obtains live network demand fast, improves network performance, effectively reduces algorithm complex.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of virtual network resource allocation methods in embodiments of the invention;
Fig. 2 is the schematic flow sheet of Competitive Tactics'Choice algorithm in the preferred embodiments of the present invention;
Fig. 3 is the structural representation of network model in a specific embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is a part of embodiment of the present invention, rather than whole embodiment.Embodiment based in the present invention, the every other embodiment that those of ordinary skills obtain under the prerequisite of not making creative work, belongs to the scope of protection of the invention.
The present invention, in order to solve the defect of static state in prior art or Dynamic Resource Allocation for Multimedia mode, provides a kind of novel virtual network resource allocation methods, and for the dynamic of virtual network resource requirement, periodically Resources allocation is given a plurality of service providers.Owing to also having proposed a kind of scheme of effective Selection of Competition Strategy in the present invention simultaneously, by this scheme, guide service provider Selection of Competition Strategy, obtains live network demand fast, effectively reduces algorithm complex.
First, referring to Fig. 1, the general flow of virtual network resource allocation methods of the present invention is:
101) set up resource allocator model: Resource Allocation in Networks participant is carried out to modeling, build Dynamic Resource Allocation for Multimedia model.
In resource allocator model, there are three class roles, are respectively the InP of infrastructure provider, service provider SP and terminal use EU.In model, InP is unique, is responsible for disposing and managing physical Internet resources, for a plurality of service providers (representing a plurality of service providers with abbreviation SPs below) provide resource; SPs is responsible for disposing and managing virtual network, and each virtual network is managed by unique SP, to a plurality of terminal uses, provides service.Suppose to have m SP, each SP provides a kind of service, is expressed as SP={1 ..., m}, the service of ordering according to terminal use, user is assigned to m group, and every group corresponding to a kind of service (being provided by service provider SP).Subscribed services i(is by service provider SP iprovide) user set by K irepresent, suppose that each user only orders a service the same time period, the total number of users of system is
102) SPs submits competition information to InP: the SPs that participates in resource contention submits the demand information of one dimension to InP, this demand information represents the demand of SP to resource.
SP in this mechanism iall Policies form set of strategies B i, consider that set of strategies is the situation of discrete set, suppose SP ithere is N iindividual different strategy, definition SP ireal demand be θ i, represent SP ifor the real demand amount of the network bandwidth, B iin comprise SP ireal demand θ i, i.e. θ i∈ B i.SP iduring each competition, demand strategy is b i, i.e. b i∈ B i, every, to take turns in competition process, the demand information of all SP represents by m dimensional vector b, i.e. b={b 1..., b m.
103) InP calculating SPs gained resource and required loss: InP receive the demand information of the SPs of all participation resource contentions, calculate the obtainable stock number of SPs.
Wherein, stock number computational methods are
max x Σ i = 1 m F i ( x i )
s.t.Ax≤C
Wherein, vector x represents the network bandwidth that all SPs are assigned with, x irepresent SP ithe network bandwidth being assigned with, vectorial C represents the capacity of all links, the elements A in matrix A lirepresent SP iwhether the path at place comprises link l, F i(x i) be SP iutility function, by SP idemand information and user satisfaction function jointly determine, wherein, α kthe weight of user k, u krepresent the extent function of user k, establish f i(x i)=b iu i(x i).
InP calculates according to the stock number of SPs gained the loss that SPs brings to system, and its value is SP iwhile not adding network in network the utility function of all SPs and, deduct and use SP iadd other SPs after network utility function and, computational methods are
τ i = Σ j = 1 , j ≠ i m F j ( x j , - i * ) - Σ j = 1 , j ≠ i m F j ( x j * )
Wherein, for equation the optimal solution of s.t.Ax≤C, while having neither part nor lot in allocated bandwidth for i, the optimal solution of this equation.
104) SP iobtain the Internet resources that InP distributes, bear loss and calculate the income that self effectiveness is improved, computational methods are
ψ i = F i ( x i * ) - τ i = θ i U i ( x i * ) - Σ j = 1 , j ≠ i m F j ( x j , - i * ) + Σ j = 1 , j ≠ i m F j ( x j * )
105) SPs adjusts respectively its competitive strategy according to loss and income.
106) SPs adopts the pending next round resource contentions such as demand information after adjusting.Wherein, if having adjustment to jump to step 102, competitive strategy participates in competition next time; Otherwise continue to wait for.
With further reference to Fig. 2, in step 105, the adjustment of competitive strategy preferably adopts Competitive Tactics'Choice algorithm, and the general flow of this algorithm is:
201) init state-action schedule, SA table, arranges parameters.According to policy selection algorithm, (s, a) corresponding to a SA value, (s a), represents that state s executes and wishes the accumulation return (depending on the current time delay of returning immediately and expecting return) that obtains after a SA for the every pair of state-action.SA table is initialized as to 0; Algorithm parameter β and γ are set, and γ (γ ∈ [0,1]) is the discount rate of time delay return E (s'); β (β ∈ [0,1]) is learning rate, and having indicated will to how many degree of beliefs of more new portion of improving.
202) observe current environment state s.SP perception current network environment, the situation that the situation that takies resource according to the InP resource provision amount perceiving, SP self and terminal use subscribe to this service represents the current status s of SP.By all state s, building the state space forming is the state space of Discrete Finite, substantially meets the requirement of policy selection algorithm to state space, also can in native system, reflect current network conditions fully simultaneously.
203) select action a.Based on status s, SP is from competitive strategy collection B iin choose a tactful b jbehavior corresponding to one of SP action a, the set of a of moving can be expressed as adopt ε-greedy algorithm (ε-greedy), under current state s, with probability ε, select at random to move a, have the action of maximum SA value with 1-ε probability selection, system of selection is a=argmax asA (s, a).
204) calculate and return immediately R.The foundation of Reward-Penalty Functions R is very crucial, is determining the right SA value of each state-move.In this algorithm, Reward-Penalty Functions adopts actively return, and the income of action is larger, and the return R obtaining (s, a) larger, namely reward larger.SP from the R of award immediately that obtains state s execution a (s, a), i.e. SP under state s iin timeslice t and next timeslice t+1 poor (what, award was immediately service provider's profit under state s and the lower service provider's profit of state s ' is poor, and income calculation mode is shown in step 104 explanation) of profit, computational methods are
R ( s , a ) ψ i t + 1 ( b i , x i ) - ψ i t ( b i , x i )
205) upgrade SA table.Upgrade in SA table, (s, a), to corresponding SA value, update method is in current state-action
SA(s,a)＝SA(s,a)+β·(R(s,a)+γ·E(s′)-SA(s,a))
Wherein, s' is that SP executes the state after action a at state s; E (s ') is the effectiveness of state s', i.e. E (s')=max asA (s', a); γ (γ ∈ [0,1]) is the discount rate of time delay return E (s'); β (β ∈ [0,1]) is learning rate, and having indicated will to how many degree of beliefs of more new portion of improving.
206) a that performs an action enters NextState s'.
207) whether evaluation algorithm restrains.SP implementation strategy selection algorithm, if algorithmic statement obtains optimal policy value, submits its optimum competitive strategy to InP; If algorithm is not restrained, proceed Action Selection next time.Judgement convergence method is that difference is less than certain threshold value (as 10 before and after SA table upgrades -4).
The part of links structure of a real network of take is below shown resource allocation methods of the present invention as example.As shown in Figure 3, network environment is set to SP 1with SP 2the Internet resources of share I nP, SP 1and SP 2all pass through link N an b, the bandwidth of this link is 20Mbps, node N c, N dfor providing service to user.The total number of users of system is 10, and the random user who subscribes in a certain service and same SP of each user has equal weight.SP 1bidding strategies integrate as B 1={ 6,8,10,12,14}, wherein 10 is SP 1true bandwidth demand; SP 2bidding strategies integrate as B 2={ 7,9,11,13,15}, wherein 11 is SP 2true bandwidth demand.
Based on above-mentioned network example, the detailed implementation step of the virtual network resource allocation methods that the present invention proposes is as follows:
Set up resource allocator model.The InP of supplying party is unique, resource provision amount 20Mbps; The SP of party in request number is 2, the demand information that resources requirement is its submission.
SPs submits demand information to InP respectively, and InP Resources allocation, informs the required loss of SPs, and SPs obtains resource, bears loss, calculates effectiveness and improves income.Wherein, the system situation after first competition is as shown in table 1:
SP Demand Bandwidth Loss Income
1 12 9.6000 8.5010 14.1166
2 13 10.4000 8.8077 16.9522
System status after the first competition of table 1
SPs adjusts demand.SPs selects demand by SA learning compete policy selection algorithm.With SP 1for example, demand selects concrete steps as follows:
1. according to SP 1strategy set B 1={ 6,8,10,12,14}, division state space is S={s 1, s 2, s 3, s 4, s 5, motion space is A 1={ 6,8,10,12,14}.Initialization SA table is SA 5 * 5=0, parameters β is that 0.8, γ is 0.3.Random initializtion state is s2.
2. observe current environment state s.
3. select action a.According to ε-greedy algorithm from behavior aggregate A 1middle selection action 8.
4. calculate and return immediately R.R(s 1,a)=0.0872。
5. upgrade SA table.Upgrade current state-move corresponding SA value, i.e. SA (s 1, a)=0.2758.
6. a that performs an action enters NextState s'.
7. whether evaluation algorithm restrains.Next stage competition is waited in convergence, otherwise, forward to and 2. proceed to learn next time.Judge that convergence method for thinking that SA shows to restrain when SA table renewal front and back difference is less than threshold value (10-4).
SP 1after 1300 study, its SA table convergence, as shown in table 2:
Table 2SP 1sA table convergence situation after study
SA table by convergence is known, SP 1competitive strategy 10 is optimal policy.By competition selection algorithm, the requirement vector that participates in the SPs of resource distribution is b={10,11}.Resource allocation conditions, its result is as shown in table 3:
SP Demand Bandwidth Loss Income
1 10 9.5238 7.1129 15.4251
2 11 10.4762 7.4194 18.4208
Table 3 is competed rear system status again
Contrast demand is respectively { 12,13} and { during 10,11}, resource allocation result is known, and when SPs demand be its real demand, resource allocation result is optimum, the total revenue maximum of SPs.Above process has been shown a kind of resource distribution overall process of virtual network resource allocation methods.Under the prerequisite of fair and reasonable allocation of network resources, improved the total revenue of SPs.
In sum, the invention provides a kind of virtual network resource allocation methods, beneficial effect of the present invention is:
1) effectively suppressed the selfishness of SP.By the loss function, calculate the required loss of bearing of SP Gains resources, based on this function, when SP lies about demand, cause loss to increase considerably, the honest reflection demand of excitation SP.The SP of all participations is all willing to report real demand, thereby resource is distributed, carries out as required, improves overall performance of network.
2) resource distributes the improvement of participant's overall utility to be improved.Because the competition mechanism of using in this method itself meets dominant strategy, encourage compatible characteristic, this method excitation SP reports real demand, in fair and reasonable Resources allocation, SPs gained total utility is improved.And the rival demand information that adopts one dimension represents and the resource requirement of SPs has further alleviated the flow load in network.
3) Competitive Tactics'Choice algorithmic statement is fast, and SPs selects dominant strategy fast, effectively reduces resource and distributes consuming time.
Above execution mode is only for illustrating the present invention; and be not limitation of the present invention; the those of ordinary skill in relevant technologies field; without departing from the spirit and scope of the present invention; can also make a variety of changes and modification; therefore all technical schemes that are equal to also belong to category of the present invention, and real protection scope of the present invention should be defined by the claims.

Claims (10)

1. a virtual network resource allocation methods, is characterized in that, described method comprises step:
101) Resource Allocation in Networks participant is carried out to modeling, build Dynamic Resource Allocation for Multimedia model, described Dynamic Resource Allocation for Multimedia model comprises the InP of infrastructure provider, service provider SP and terminal use EU, in described Dynamic Resource Allocation for Multimedia model, InP is unique, for disposing and managing physical Internet resources, for a plurality of service providers provide resource; Service provider is for disposing and managing virtual network, and each virtual network is managed by unique service provider, to a plurality of terminal uses, provides service;
102) a plurality of service providers that participate in resource contention submit the rival demand information of one dimension to infrastructure provider, the demand of this service provider to resource when this rival demand represents this competition;
103) infrastructure provider receives the service provider's of all participation resource contentions demand information, calculation services provider obtainable stock number and the required loss of bearing;
104) each service provider obtains the Internet resources that infrastructure provider distributes, and bears loss and calculates the income that self effectiveness is improved;
105) each service provider adjusts respectively its competitive strategy according to loss and income;
106) each service provider adopts the pending next round resource contentions such as competitive strategy after adjustment.
2. method according to claim 1, is characterized in that, in step 103, the account form of described stock number is:
max x Σ i = 1 m F i ( x i )
s.t.Ax≤C
Wherein, vector x represents the network bandwidth that all service providers are assigned with, x irepresent to provide the service provider SP of service i ithe network bandwidth being assigned with, m represents service provider's number, vectorial C represents the capacity of all links, the elements A in matrix A lirepresent SP iwhether the path at place comprises link l, F i(x i) be SP iutility function, wherein, b irepresent SP ithis competes resistance to rival demand information, k irepresent user's set of subscribed services i, α kthe weight of user k, u kthe extent function that represents user k.
3. method according to claim 2, is characterized in that, in step 103, and SP ithe required loss of bearing is:
τ i = Σ j = 1 , j ≠ i m F j ( x j , - i * ) - Σ j = 1 , j ≠ i m F j ( x j * )
Wherein, for equation optimal solution, while having neither part nor lot in allocated bandwidth for service i, the optimal solution of this equation.
4. method according to claim 3, is characterized in that, in step 104, and SP ithe income that self effectiveness is improved is:
ψ i = F i ( x i * ) - τ i = θ i U i ( x i * ) - Σ j = 1 , j ≠ i m F j ( x j , - i * ) + Σ j = 1 , j ≠ i m F j ( x j * )
Wherein, θ ifor SP ireal demand, function
5. method according to claim 1, is characterized in that, in step 105, the adjustment algorithm of competitive strategy is:
201) init state one action schedule SA, arranges algorithm parameter;
202) perception current network environment, according to the resource provision amount perceiving, self take
The situation that the situation of resource and terminal use subscribe to this service represents the current status s of service provider;
203), based on status, service provider adopts ε-greedy algorithm to concentrate and choose the action α that a strategy forms service provider from competitive strategy; ε-greedy algorithm is, under current state s, with probability ε, selects at random to move a, has the action of maximum SA value with 1-ε probability selection, and system of selection is a=argmax asA (s, a);
204) the immediately award of calculation services provider from obtaining state s execution α;
205) more in new state-action schedule current state-action (s, α) to corresponding value SA (s, α);
206) α that performs an action enters NextState s';
207) whether evaluation algorithm restrains, if algorithmic statement obtains optimal policy value, to infrastructure, provides high its optimum competitive strategy of submitting to; If algorithm is not restrained, proceed Action Selection next time.
6. method according to claim 5, is characterized in that, in step 204, what described award was immediately service provider's profit under service provider's profit under state s and state s' is poor.
7. method according to claim 5, is characterized in that, in step 205, described value SA (s, α) is:
SA(s,a)＝SA(s,a)+β·(R(s,a)+γ·E(s')-SA(s,a))
Wherein, s' is the state of service provider after state s executes action; E (s') is the effectiveness of state s', E (s')=max asA (s', a); γ is the discount rate of time delay return E (s'), γ ∈ [0,1]; β is learning rate, has indicated the degree of belief of giving to the more new portion improving, β ∈ [0,1].
8. whether method according to claim 5, is characterized in that, in step 207, whether be less than certain threshold value come evaluation algorithm to restrain according to difference before and after definite state one action schedule renewal.
9. method according to claim 8, is characterized in that, in step 207, described threshold value is 10 -4.
10. method according to claim 1, is characterized in that, in step 106, if competitive strategy has adjustment to jump to step 102, participates in competition next time; Otherwise continue to wait for.
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