CN103970609B - A kind of cloud data center method for scheduling task based on improvement ant group algorithm - Google Patents

Abstract

The present invention proposes a kind of cloud data center method for scheduling task based on improvement ant group algorithm, is related to field of cloud calculation, comprises the following steps：Step 1：What input user submitted to treats traffic control stream set of tasks and the virtual machine set of user's lease；Step 2：The scheduling problem assigning the task to virtual machine execution is expressed as the minima Solve problems of standard；Step 3：With the ant colony optimization for solving cloud computing environment virtual machine Mission Scheduling based on Pheromone update.The present invention can adapt to the dynamic of cloud environment, not only shortens the time overhead of scheduling user task, and the virtual machine load in cloud data center is maintained the state of a relative equilibrium.

Description

A kind of cloud data center method for scheduling task based on improvement ant group algorithm

Technical field

The invention belongs to field of cloud calculation, more particularly, to a kind of cloud data center task scheduling based on improvement ant group algorithm Method.

Background technology

With the sharp transition of business model, bias toward and solve the problems, such as that the grid computing of scientific algorithm has been difficult to solve business Problems present in industry environment, also any business-like product in grid computing up to now.2006, Google company proposes the concept of cloud computing (Cloud Computing) first.Cloud computing utilizes Intel Virtualization Technology by data In in the minds of the resource consolidation such as storage, calculating and communication be one shared, can dynamic configuration IT resource pool.User is no longer Need to purchase the hardware resources such as server it is only necessary to pass through the Internet and pay corresponding expense can be according to the demand of oneself Obtain corresponding service.

One physical host is mapped to multiple stage virtual machine using ripe Intel Virtualization Technology by cloud computing, therefore adjusts in task The task that during degree, user submits to need not be assigned to specific physical node to complete, and each task only need to be appointed according to suitable Business scheduling strategy selects suitable virtual machine can complete whole scheduling process.Additionally due to cloud computing adopts customer-centric " use on demand, according to quantity pay " commerce services pattern, cloud data center task scheduling needs to consider time overhead, meets and use Family to the demand of execution cost, ensure that the load of virtual machine in data center maintains the state of a relative equilibrium.How right Task is reasonably distributed the Important Problems becoming cloud service supplier urgent need to resolve, and unsuitable Task Assigned Policy is not only The execution time of all tasks can be increased, also can affect the stability of virtual machine.

Cloud computing task scheduling is a kind of NP-hard problem.Some approximate datas and heuritic approach is adopted to solve at present Problems.For example：Genetic algorithm, particle cluster algorithm, Immune Evolutionary Algorithm, clonal selection algorithm etc., the essence of this kind of algorithm It is to design a kind of efficient searching algorithm, there is the whole solution space of search and be unlikely to sink into the ability of local optimum.Random calculation Method has the advantages that global search, and adapts to wide, but is a lack of effective Local Search mechanism, and convergence rate is slow.Additionally, Most of algorithms not yet consider the difference of each virtual machine calculating performance in cloud data center at present, cause each void of user's lease Unbalanced situation in the load of plan machine, thus influencing whether the overall performance of virtual machine.

Content of the invention

The technical problem to be solved in the present invention is：A kind of cloud data center task tune based on improvement ant group algorithm is provided Degree method, realizes virtual machine is reasonably distributed, task is efficiently dispatched.

The technical solution adopted for the present invention to solve the technical problems is：In a kind of cloud data based on improvement ant group algorithm Heart method for scheduling task, comprises the steps：

Step 1：What input user submitted to treats traffic control stream set of tasks and the virtual machine set of user's lease；

Step 2：The scheduling problem assigning the task to virtual machine execution is expressed as the minima Solve problems of standard；

Step 3：With the ant colony optimization for solving cloud computing environment virtual machine Mission Scheduling based on Pheromone update.

Further, based on the cloud data center method for scheduling task improving ant group algorithm, described step 2 will for the present invention The scheduling problem that task distributes to virtual machine execution is expressed as the minima Solve problems of standard, and wherein optimization aim is scheduling plan In slightly, all tasks carryings finish minimizing overhead, that is, all tasks carryings finish cost time the shortest；

Constraints be task number be greater than lease virtual machine number, the task in set of tasks be all unit appoint Business, that is, each task can not be split as less subtask again, and each task is carried out using the virtual machine of arbitrary lease Calculate, but each virtual machine can only process a task in the same time, and task does not complete and do not allow to interrupt before calculating.

Further, the present invention based on the cloud data center method for scheduling task improving ant group algorithm, use by described step 3 One iterative process is comprised based on the ant colony optimization for solving cloud computing environment virtual machine Mission Scheduling of Pheromone update, including Following 8 sub-steps：

Step 3.1：Initialization；Basic parameter in this step initialization algorithm includes information heuristic factor α, expectation inspires Factor-beta, pheromone volatilization factor ρ, Formica fusca number m, maximum iteration time NC_max, pheromone τ_i,jAnd transfer expected degree η_i,j；

Step 3.2：Algorithm iteration starts, if iterationses NC is less than maximum iteration time NC_maxWhen, NC=NC+1, enters Enter next step；When iterationses are more than or equal to maximum iteration time, iteration terminates；

Step 3.3：It is the selected probability of every virtual machine of each task computation that every Formica fusca shifts formula according to state；

Described state shifts formula：

Wherein, τ_i,jAnd η_i,jRepresent task T respectively_iDistribute to VM_jWhen pheromone and expected degree, P_i,jExpression will be appointed Business T_iDistribute to virtual machine VM_jProbability, n for user lease virtual machine number；

Step 3.4：By roulette algorithms selection virtual machine；Solve the advance transition probability of Formica fusca by roulette algorithm Problem, when Formica fusca starts as task choosing scheduling virtual machine, makes wheel disc rotate, pointer points to region and corresponds to when wheel disc stops Virtual machine then be kth Formica fusca be task choosing calculate node；Alternative virtual machine corresponding transition probability value is bigger, its The area occupying on wheel disc is bigger, selects it to calculate the probability of this task accordingly bigger；

Step 3.5：When the same layer task in Work flow model all selects same virtual machine, going to step 3.3 is to appoint Virtual machine is redistributed in business, otherwise goes to next step；

Step 3.6：Local information element updates：After a Formica fusca completes all of task distribution, to this Formica fusca dispatching party All virtual machines in case carry out Pheromone update；

Step 3.7：The renewal of global information element：After all Formica fuscas all complete once to travel through, find out in current iteration All virtual machines in this scheme are carried out Pheromone update, then go to step 3.2 by good scheduling scheme；

Step 3.8：Find optimal distributing scheme, the virtual machine in binding scheme and corresponding workflow task.

Further, the present invention is based on the cloud data center method for scheduling task improving ant group algorithm, described pheromone τ_i,jAnd transfer expected degree η_i,jAll represented with the computing capability of calculate node：

τ_i,j=η_i,j=MIPS_j/N

Wherein, MIPS_jRepresent process task T_iVirtual machine VM_jProcessing speed, N is a constant.

Further, the present invention is based on the cloud data center method for scheduling task improving ant group algorithm, described in step 3.6 Local information element updates and specifically includes herein below：

A, residual risk are updated processing, using equation below：

τ_ij(t+1)=(1- ρ) τ_ij(t)+Δτ_ij(t)

Wherein：τ_ij(t+1) task T when representing the t+1 time iteration_iSelect virtual machine VM_jQuantity of information, 1- ρ represents information The element residual factor, in order to prevent the unlimited accumulation of information, the span of ρ is：Δτ_ijT () represents task T_iChoosing Select virtual machine VM_jExecution remains in virtual machine VM_jOn quantity of information；

B, all virtual machines in this Formica fusca scheduling scheme are carried out with the renewal of pheromone：

Δτ_ij(t)=D/clock_ij

Wherein D is a constant, clock_ijRepresenting Formica fusca in this circulation is task T_iSelect virtual machine VM_jExecution when Between；

Further, the present invention is based on the cloud data center method for scheduling task improving ant group algorithm, complete described in step 3.7 The renewal of office's pheromone is according to formula：Δτ_ij(t)=D/bestclock_ijRow information is entered to all virtual machines in the program Element updates, wherein, bestclock_ijRepresent in optimal distributing scheme is task T_iSelect virtual machine VM_jWhen task T_iComplete Time.

Further, the present invention is based on the cloud data center method for scheduling task improving ant group algorithm, described step 3.6 In also include defining a pheromone Dynamic gene PC, according to the virtual machine distribution condition of task, pheromone is adjusted.

Further, the present invention is based on the cloud data center method for scheduling task improving ant group algorithm, described pheromone The computing formula of Dynamic gene PC is：

Wherein, E_jExecute virtual machine VM after all tasks for virtual machine in epicycle iterative process_jThe time being spent.

Further, the present invention based on the cloud data center method for scheduling task improving ant group algorithm, appoint by described basis The virtual machine distribution condition of business is adjusted to pheromone, specially：

After local information element updates and global information element updates, then the pheromone after updating is adjusted according to the following formula,

τ_ij(t+1)=((1- ρ) τ_ij(t)+Δτ_ij(t))*PC；

It is not yet assigned to task T_iOther virtual machines then carry out the adjustment of pheromone according to below equation：

τ_ix(t+1)=τ_ix(t) * PC,

Wherein, τ_ix(t+1) when representing the t+1 time iteration, task T_iSelect to be not yet assigned to the virtual machine VM of task_xInformation Amount.

The technical solution used in the present invention compared with prior art, has following technique effect：

A kind of cloud data center method for scheduling task based on improvement ant group algorithm that the present invention provides, calculates in basic ant colony It is optimized on the basis of method, not only shorten the time overhead of task scheduling but also consider the load condition of each virtual machine Prevent virtual machine from the machine of delaying or light condition occurring, it is to avoid the problems such as the wasting of resources, improve the utilization rate of resource.

Brief description

Fig. 1 is based on the cloud computing environment virtual machine task scheduling algorithm flow chart improving ant group algorithm.

Fig. 2 is Work flow model.

Fig. 3 is roulette algorithm model.

The particular flow sheet that Fig. 4 carries out for roulette algorithm.

Fig. 5 is the deadline of each task in workflow under the scheduling of four kinds of algorithms in the present invention.

Fig. 6 is that in the present invention four kinds of algorithms each virtual machine in completing task scheduling Hou Yun data center is in an experiment Run time account for the ratio of all virtual machine deadline sums.

Specific embodiment

In order that those skilled in the art more fully understand technical problem in the application, technical scheme and technique effect, Cloud data center task scheduling side based on improvement ant group algorithm a kind of to the present invention with reference to the accompanying drawings and detailed description Method is described in further detail.

The present invention proposes a kind of cloud data center Load Balancing Task Scheduling algorithm (Load based on improvement ant group algorithm balancing task scheduling algorithm based on ant colony algorithm for cloud Datacenters, LACO), LACO algorithm not only shortens the time overhead of tasks carrying but also will rent during task scheduling The virtual machine rented maintains the state of load relative equilibrium.Further, it is contemplated that most researchers all focus on independence up till now The scheduling of task, and have ignored that user may submit to priority restrictions relation, be mutually related Work flow model, therefore The present invention carrys out the scheduling of research work stream using DAG (Directed Acyclic Graph, directed acyclic graph).Each by considering The constraint of the sequential between individual task or cause and effect comes for one optimal resource of task choosing, and coordinates the execution of each task To obtain final implementing result.

The present invention is used ClouSim as emulation platform, is simulated emulation experiment by it to LACO algorithm, and with FIFO (First In First Out, FIFO) scheduling strategy and ACO (Ant colony algorithm, basic ant colony Dispatching algorithm) contrasted, the superiority of checking LACO algorithm.

Proposed by the present invention based on improve ant group algorithm cloud data center task scheduling algorithm comprise the steps, flow process As shown in Figure 1：

Step 1：What input user submitted to treats the set of the virtual machine of scheduler task set and user's lease；

Step 2：The scheduling problem assigning the task to resource execution is expressed as the minima Solve problems of standard；

Some tasks submitted to for user there may be complementary relation, and the present invention is right as studying using workflow As to solve the associated task scheduling problem in cloud data center.Generally workflow all can be described as a directed acyclic graph G =(T, E), wherein：T is the set of DAG interior joint, represents n task in workflow, T={ T₁,T₂,T₃,……,T_n}；E It is set the E={ (T of directed edge in Work flow model_i,T_j)|T_i,T_j∈ T }, represent the restricting relation between two tasks.As Fruit task T_iThere is sensing task T_jDirected edge, then T_iIt is referred to as T_jFather's task, T_jIt is referred to as T_iSubtask, this In the case of T_jOnly in T_iAfter the completion of just can execute.Fig. 2 is the basic framework of one group of workflow, and containing ten needs to process Workflow task, label is respectively T₀～T₉, the length of these tasks is different.In fig. 2, T={ T₁,T₂,T₄,T₅, T₆,T₇,T₈, T₉, E={ (T₀,T₁),(T₀,T₂),(T₁,T₃),(T₁,T₄),(T₂,T₅),(T₂,T₆),(T₃,T₇),(T₄,T₇), (T₅,T₈),(T₆,T₈),(T₇,T₉),(T₈,T₉)}.

Present invention VM represents the virtual machine of user's lease, and m represents the number of virtual machine, VM={ VM₁, VM₂... ..., VM_m, VM_iProcessing speed MIPS_iTo represent, MIPS represents million grades of machine language instruction numbers of process per second.

The present invention defines the communication matrix com, com={ c of a n × m_i,j|c_i,j>=0,1≤i≤n, 1≤j≤m }, its In：N represents the number of task, and m represents the virtual machine number of user's lease, c_i,j(as shown in formula (1)) represents task T_iDistribution To virtual machine VM_jThe required call duration time of execution；In addition calculating matrix exe of a n × m, exe={ e are defined_i,j|e_i,j>=0, 1≤i≤n, 1≤j≤m }, wherein e_i,j(as shown in formula (2)) represents task T_iIn virtual machine VM_jThe calculating time of upper execution.

c_ij=outputsize_i/bandwidth (1)

e_ij=Length_i/Mips_j(2)

Wherein：outputsize_iExpression task T_iThe size of output file, bandwidth represents order wire between virtual machine The bandwidth on road；Length_iExpression task T_iSize, Mips_jRepresent process task T_iVirtual machine VM_jProcessing speed；If Former and later two tasks execute all on same virtual machine, there is not data transfer cost.

Virtual machine VM_jThe time overhead of all tasks processing can use E_j(as shown in formula (3)) represents, and whole work Make the total cost time E flowing_totalAs in workflow, last task completes the moment.

Wherein：Task_jRepresent virtual machine VM_jAll tasks of upper execution, Ftask represents virtual machine VM_jUpper execution all (father's task is not in virtual machine VM for father's task of task_jUpper execution).

Step 3：Solve cloud computing environment with the task scheduling algorithm of the improvement ant group algorithm based on Pheromone update virtual Machine Mission Scheduling, is described in detail below：

Step 3.1：Initialization is based on the cloud data center Load Balancing Task Scheduling algorithm improving ant group algorithm；

This step initialization information heuristic factor α, expectation heuristic factor β, pheromone volatilization factor ρ, Formica fusca number m, Big iterationses, pheromone and transfer expected degree.

Ant group algorithm when solving some basic problems the pheromone between two nodes and transfer expected degree generally with away from From waiting, attribute is relevant.But the particularity due to cloud computing environment, the present invention is by pheromone τ_i,jAnd the expected degree of this node η_i,jAll represented with the computing capability of calculate node.

τ_i,j=η_i,j=MIPS_j/N (4)

In formula (4), τ_i,jAnd η_i,jRepresent task T respectively_iDistribute to VM_jWhen pheromone and expected degree, N is One constant (as cooperation index).

Step 3.2：Algorithm iteration starts, if iterationses NC is less than NC_max, NC=NC+1, enter next step；When repeatedly When generation number is more than or equal to maximum iteration time, iteration terminates.

Step 3.3：It is that every virtual machine of each task computation is selected general that every Formica fusca shifts formula (5) according to state Rate.

Formula (5) represents task T_iDistribute to VM_jProbability, n for user lease virtual machine number.

Step 3.4：By roulette algorithms selection virtual machine；

The present invention solves the problems, such as the advance transition probability of Formica fusca by roulette algorithm.Roulette algorithm (Roulette Algorithm) be the process of emulation wheel disc gambling it is assumed that there being a circular wheel disc, and it is different to be divided into m block area Sector region, this m block region represents that for Formica fusca k, one of task-set task to be assigned to every virtual machine corresponding respectively Probit.As shown in figure 3, assuming that alternative virtual machine has 4, respectively VM₁、VM₂、VM₃、VM₄, corresponding probit is respectively For：23%th, 52%, 6% and 19%.

When Formica fusca starts as task choosing scheduling virtual machine, wheel disc is made to rotate, the area that pointer points to when wheel disc stops The corresponding virtual machine in domain then for Formica fusca k be task choosing calculate node.Alternative virtual machine corresponding transition probability value is bigger, its The area occupying on wheel disc is bigger, corresponding select it to execute the probability of this task is bigger, the implementing of this algorithm Journey is as shown in Figure 4.

For each task in workflow, after determining the selected probit of every virtual machine, it will interval in [0,1] Interior random generation one number, this number probability selected with First virtual machine is subtracted each other, if difference is less than zero, then this is empty Plan machine is just selected, is otherwise further continued for deducting the selected probability of next virtual machine, the result after deducting is less than or equal to 0.The virtual machine corresponding to that probit when finally deducting is as the virtual machine of this task choosing.

Step 3.5：When the same layer task in Work flow model all selects same virtual machine, going to step 3.3 is to appoint Virtual machine is redistributed in business, otherwise goes to next step；

Due to the particularity of Work flow model, if the workflow task of same layer all distributes identical virtual machine, now During one task of execution, other tasks of same layer will enter long waiting period, and this results in other and has processed task Virtual machine is in idle condition thus leading to the wasting of resources.In order to solve this problem, this algorithm once detects same layer Workflow task all selects same virtual machine and is absorbed in then to reselect other according to formula (5) for it during waiting period and be in sky The virtual machine of not busy state.

Step 3.6：Local information element updates, after a Formica fusca completes all of task distribution, to this Formica fusca dispatching party All virtual machines in case carry out Pheromone update；

In order to avoid residual risk element excessively floods heuristic information, after therefore every Formica fusca completes all scheduling, need logical Cross formula (6) residual risk to be updated process.

τ_ij(t+1)=(1- ρ) τ_ij(t)+Δτ_ij(t) (6)

Wherein：τ_ij(t+1) task T when representing the t+1 time iteration_iSelect virtual machine VM_jQuantity of information, 1- ρ represents information The element residual factor, in order to prevent the unlimited accumulation of information, the span of ρ is：

The present invention utilizes the Ant-Cycle model in the Basic Ant Group of Algorithm model that M.Dorigo proposes, and this model utilizes Be the overall situation information.Δτ_ijT () represents task T_iSelect virtual machine VM_jExecution remains in virtual machine VM_jOn quantity of information, just Begin moment Δ τ_ij(0)=0, after a Formica fusca completes all of task scheduling, according to formula (7) in this Formica fusca scheduling scheme All virtual machines carry out the renewal of pheromone.

Δτ_ij(t)=D/clock_ij(7)

Wherein D is a constant, clock_ijRepresenting Formica fusca in this circulation is task T_iSelect virtual machine VM_jExecution when Between.

Invention defines a pheromone Dynamic gene PC, according to the virtual machine distribution condition of task, pheromone is carried out Adjustment.

For the task of different layers in workflow, in order to prevent these tasks from all selecting the preferable virtual machine of computing capability, Lead to this virtual machine overload, the present invention defines a pheromone Dynamic gene PC, by formula (8) Suo Shi.Wherein E_jDefinition Execute virtual machine VM after all tasks for virtual machine in epicycle iterative process_jThe time being spent.Update in local information element Again the pheromone after updating is adjusted according to formula (9) after updating with global information element, be not yet assigned to task T_iOther are virtual Machine then carries out the adjustment of pheromone according to formula (10).

τ_ij(t+1)=((1- ρ) τ_ij(t)+Δτ_ij(t))*PC (9)

τ_ix(t+1)=τ_ix(t)*PC (10)

If virtual machine VM_jOverload then E in upper wheel iteration_jRelatively excessive, then the corresponding pheromone of this virtual machine Dynamic gene PC is then relatively small, is task T during next iteration_iSelect virtual machine VM_jProbability relatively low.Many The load relative equilibrium of each virtual machine can be ensured after secondary iteration, improve the execution efficiency of system.

Step 3.7：After all Formica fuscas all complete once to travel through, find out optimal scheduling scheme in current iteration, and press According to formula (11), Pheromone update is carried out to all virtual machines in the program.

Δτ_ij(t)=D/bestclock_ij(11)

Wherein D is a constant, bestclock_ijRepresent in optimal distributing scheme is task T_iSelect virtual machine VM_jWhen Task T_iDeadline.

Step 3.8：Find optimal distributing scheme, the virtual machine in binding scheme and corresponding workflow task.

Apply heuristic load-balancing algorithm proposed by the present invention to obtain the result of cloud data center task scheduling, first will Mission Scheduling is converted to standard and minimizes problem；Next carries out the initialization of method, and what input user submitted to waits to dispatch Set of tasks, the set of the virtual machine of user's lease；Pass through the cloud data center task scheduling based on improving ant group algorithm again to calculate Method carries out the scheduling process shown in step 3.1- step 3.8, finally obtains optimal solution.

In order to check this algorithm with respect to FIFO scheduling strategy, basic ant colony dispatching algorithm (ACO) and ant colony and wheel disc Whether the combination algorithm (RACO) of gambling has more superior scheduling performance and load balance ability, and the present invention is emulated using cloud computing Simulation tool CloudSim simulating the data center of a cloud computing, and rewritten DatacenterBroker therein, The classes such as Cloudlet are it is achieved that analog simulation to above four kinds of task scheduling algorithms.

In addition the present invention devises a kind of Work flow model for checking the effectiveness of LACO algorithm, in this Work flow model The parameter value of ten tasks is：

LACO algorithm parameter value is set to：α=0.7, β=0.7, ρ=0.3, Formica fusca number is 100, iterationses 50 times.

The present invention simulates a data center in CoudSim, and defines four virtual machines wherein, this four void The numbering of plan machine is respectively VM₀、VM₁、VM₂、VM₃It is assumed that the bandwidth of the communication line between all virtual machines all phases in the present invention Deng, the parameter value that this four virtual machines set as：

Virtual machine ID	MIPS	Bandwidth	Memory capacity
				0	420	1000	50GB
1	350	1000	120GB
				2	508	1000	235GB
3	634	1000	450GB

The time that the task allocation result of four groups of algorithms and each tasks carrying corresponding complete is：

Wherein what ACO algorithm, RACO algorithm and LACO algorithm were all taken is the data of the solution of global optimum.Therefrom It can be seen that FIFO scheduling strategy simply distributes for task one by one according to the order of virtual machine, the efficiency of this method is very low , lead to that because it does not account for the process performance of each virtual machine larger bearing also is had on the low virtual machine of disposal ability Carry, thus leading to whole task scheduling process to need the deadline grown very much.ACO algorithm all tasks after successive ignition are all selected Select computing capability virtual machine VM the strongest₃It is scheduling so that virtual machine VM₃Upper overload, time overhead is also excessive.And In RACO algorithm, due to virtual machine VM₂、VM₃For other two virtual machines, process performance is higher, leads to substantial amounts of Business is all assigned on this two virtual machines, virtual machine VM₁On there is no need execution task queue and be in idle condition, Thus causing the wasting of resources of data center.Finally under the scheduling of LACO algorithm, each virtual machine is according to its execution performance Obtain corresponding task, do not cause the waste of resource, improve system execution efficiency.

Fig. 5 shows the deadline of each task in workflow under the scheduling of four kinds of algorithms.As can be seen from the figure The time of FIFO and ACO each task of algorithm process is both greater than two kinds of algorithms of RACO and LACO.Before FIFO execution during several task Deadline is above ACO algorithm, and is executing T₄The deadline of ACO algorithm is higher than gradually FIFO algorithm afterwards；RACO and The time of two kinds of each tasks of algorithm performs of LACO is very close to but being carried out LACO algorithm process time during last three tasks It is significantly less than RACO algorithm.

Fig. 6 is shown that these four algorithms complete the fortune in an experiment of each virtual machine in data center after task scheduling The row time accounts for the ratio of all virtual machine deadline sums.As can be seen from the figure all of virtual machine in FIFO scheduling strategy All it is assigned to task, but because it is that distribution results in virtual machine VM in order₀、VM₁On there is substantial amounts of task, compare and The stronger virtual machine VM of speech disposal ability₂、VM₃The percentage of execution time is less, when leading to whole task processes Between expend excessive.It is that all tasks all select the strong virtual machine VM of computing capability that ACO algorithm leads to Formica fusca after successive ignition₃, Make VM₃Upper overload, other virtual machines are all in idle condition.Virtual machine VM is can be seen that in RACO algorithm₀、VM₁On It is not allocated to task, even if virtual machine VM₂、VM₃Computing capability very strong, but excessive load can be caused thus also can make Runtime is elongated.Finally a kind of all virtual machines of LACO algorithm have been carried out task, and it can be seen that every virtual Percentage ratio shared by machine execution time is directly proportional to its computing capability, and the strong virtual machine of computing capability is used for executing more appointing Business, also makes the load of virtual machine reach equilibrium while guaranteed efficiency.

Obviously, it will be appreciated by those skilled in the art that to disclosed in the invention described above based on improve ant group algorithm cloud Data center's method for scheduling task, can also make various improvement on the basis of without departing from present invention.Therefore, the present invention Protection domain should by appending claims content determine.

Claims (5)

1. a kind of based on improve ant group algorithm cloud data center method for scheduling task it is characterised in that：Comprise the following steps：

Step 1：What input user submitted to treats traffic control stream set of tasks and the virtual machine set of user's lease；

Step 2：The scheduling problem assigning the task to virtual machine execution is expressed as the minima Solve problems of standard；

Step 3：With the ant colony optimization for solving cloud computing environment virtual machine Mission Scheduling based on Pheromone update, wherein comprise One iterative process, including following 8 sub-steps：

Step 3.1：Initialization；Basic parameter in this step initialization algorithm includes information heuristic factor α, expectation heuristic factor β, pheromone volatilization factor ρ, Formica fusca number m, maximum iteration time NC_max, pheromone τ_i,jAnd transfer expected degree η_i,j；

Step 3.2：Algorithm iteration starts, if iterationses NC is less than maximum iteration time NC_maxWhen, NC=NC+1, under entrance One step；When iterationses are more than or equal to maximum iteration time, iteration terminates；

Step 3.3：It is the selected probability of every virtual machine of each task computation that every Formica fusca shifts formula according to state；

Described state shifts formula：

$P_{i,j}=\frac{{\[{\mathrm{\τ}}_{i,j}\]}^{\mathrm{\α}}{\[{\mathrm{\η}}_{i,j}\]}^{\mathrm{\β}}}{\underset{x=1}{\overset{n}{\Σ}}{\[{\mathrm{\τ}}_{i,x}\]}^{\mathrm{\α}}{\[{\mathrm{\η}}_{i,x}\]}^{\mathrm{\β}}}$

Wherein, τ_i,jAnd η_i,jRepresent task T respectively_iDistribute to VM_jWhen pheromone and expected degree, P_i,jRepresent task T_i Distribute to virtual machine VM_jProbability, n for user lease virtual machine number；

Step 3.4：By roulette algorithms selection virtual machine；Asked by the advance transition probability that roulette algorithm solves Formica fusca Topic, when Formica fusca starts as task choosing scheduling virtual machine, makes wheel disc rotate, and when wheel disc stops, pointer sensing region is corresponding Virtual machine is then the calculate node that kth Formica fusca is task choosing；Alternative virtual machine corresponding transition probability value is bigger, and it is in wheel The area occupying on disk is bigger, selects it to calculate the probability of this task accordingly bigger；

Step 3.5：When the same layer task in Work flow model all selects same virtual machine, going to step 3.3 is task weight New distribution virtual machine, otherwise goes to next step；

Step 3.6：Local information element updates：After a Formica fusca completes all of task distribution, in this Formica fusca scheduling scheme All virtual machines carry out Pheromone update；Also include defining a pheromone Dynamic gene PC, divided according to the virtual machine of task Situation of joining is adjusted to pheromone；The computing formula of described pheromone Dynamic gene PC is：

$PC=1-(E_j/\underset{j\⋐VM}{\Σ}{E}_j)$

Wherein, E_jExecute virtual machine VM after all tasks for virtual machine in epicycle iterative process_jThe time being spent；

Wherein said local information element updates and specifically includes herein below：

A, residual risk are updated processing, using equation below：

τ_ij(t+1)=(1- ρ) τ_ij(t)+Δτ_ij(t)

Wherein：τ_ij(t+1) task T when representing the t+1 time iteration_iSelect virtual machine VM_jQuantity of information, 1- ρ represent pheromone remain The factor, in order to prevent the unlimited accumulation of information, the span of ρ is：Δτ_ijT () represents task T_iSelect virtual Machine VM_jExecution remains in virtual machine VM_jOn quantity of information；

B, all virtual machines in this Formica fusca scheduling scheme are carried out with the renewal of pheromone：

Δτ_ij(t)=D/clock_ij

Wherein D is a constant, clock_ijRepresenting Formica fusca in this circulation is task T_iSelect virtual machine VM_jExecution time；

Step 3.7：The renewal of global information element：After all Formica fuscas all complete once to travel through, find out in current iteration and most preferably adjust All virtual machines in this scheme are carried out Pheromone update, then go to step 3.2 by degree scheme；

Step 3.8：Find optimal distributing scheme, the virtual machine in binding scheme and corresponding workflow task.

2. as claimed in claim 1 based on the cloud data center method for scheduling task improving ant group algorithm it is characterised in that：Institute The scheduling problem that the step 2 stated assigns the task to virtual machine execution is expressed as the minima Solve problems of standard, wherein optimizes Target be scheduling strategy in all tasks carryings finish minimizing overhead, that is, all tasks carryings finish cost time the shortest；

Constraints be task number be greater than lease virtual machine number, the task in set of tasks is all Meta task, It is that each task can not be split as less subtask again, and each task is counted using the virtual machine of arbitrary lease Calculate, but each virtual machine can only process a task in the same time, and task does not complete and do not allow to interrupt before calculating.

3. as claimed in claim 1 based on the cloud data center method for scheduling task improving ant group algorithm it is characterised in that：Institute The pheromone τ stating_i,jAnd transfer expected degree η_i,jAll represented with the computing capability of calculate node：

τ_i,j=η_i,j=MIPS_j/N

Wherein, MIPS_jRepresent process task T_iVirtual machine VM_jProcessing speed, N is a constant.

4. as claimed in claim 1 based on the cloud data center method for scheduling task improving ant group algorithm it is characterised in that：Step Described in rapid 3.7, the renewal of global information element is according to formula：Δτ_ij(t)=D/bestclock_ijTo all void in the program Plan machine carries out Pheromone update, wherein, bestclock_ijRepresent in optimal distributing scheme is task T_iSelect virtual machine VM_jWhen Task T_iDeadline.

5. as claimed in claim 1 based on the cloud data center method for scheduling task improving ant group algorithm it is characterised in that：Institute The virtual machine distribution condition according to task stated is adjusted to pheromone, specially：

After local information element updates and global information element updates, then the pheromone after updating is adjusted according to the following formula,

τ_ij(t+1)=((1- ρ) τ_ij(t)+Δτ_ij(t))*PC；

It is not yet assigned to task T_iOther virtual machines then carry out the adjustment of pheromone according to below equation：

τ_ix(t+1)=τ_ix(t) * PC,

Wherein, τ_ix(t+1) when representing the t+1 time iteration, task T_iSelect to be not yet assigned to the virtual machine VM of task_xQuantity of information.