CN110111006A - Scientific workflow Cost Optimization dispatching method in a kind of cloud based on chaos Ant ColonySystem - Google Patents

Scientific workflow Cost Optimization dispatching method in a kind of cloud based on chaos Ant ColonySystem Download PDF

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CN110111006A
CN110111006A CN201910383575.9A CN201910383575A CN110111006A CN 110111006 A CN110111006 A CN 110111006A CN 201910383575 A CN201910383575 A CN 201910383575A CN 110111006 A CN110111006 A CN 110111006A
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庞善臣
王淑玉
王珣
李艳青
徐克祥
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China University of Petroleum East China
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Abstract

Herein for the lease expenses problem under deadline constraint, the Cost Optimization dispatching algorithm R-CACS of the upward weight of task probability is proposed based on chaos Ant ColonySystem and combined.Under deadline constraint, algorithm considers influence of the key task for the scientific workflow deadline, task priority is ranked up using traditional probability upward weight, virtual machine selection then is carried out as target to minimize lease expenses to the task in scientific workflow using chaos Ant ColonySystem.The total cost leasing virtual machine and spending can be effectively reduced in method proposed by the present invention.

Description

Scientific workflow Cost Optimization dispatching method in a kind of cloud based on chaos Ant ColonySystem
Technical field
The invention belongs to cloud computing and the big field of dispatching algorithm two more particularly to a kind of clouds based on chaos Ant ColonySystem Middle scientific workflow Cost Optimization dispatching method.
Background technique
Modern science runs increasingly many and diverse extensive in the different research field such as biological information, astronomy, physics Scientific application, to simulate and analyze the activity of real world, and scientific workflow has been found to be to model and manage these complexity The most effective means of problem.With the continuous complication of scientific system, scientific workflow feature is mainly shown as that data are close Collection type and computation-intensive.Usually scientific workflow is deployed in distributed computing environment, is reached with this and is held within the reasonable time The purpose of row workflow.However, the deployment in traditional compartment system platform (supercomputer, cluster and grid computing platform etc.) Scientific workflow not only somewhat expensive, and resource scalability is poor.Nowadays, cloud computing because demand Resource supply and payable at sight be With the outstanding advantage of payment mode, so that more and more research-on-research stream applications are transferred in cloud computing, it is research-on-research Stream executes and provides preferable solution.
The process of scientific workflow scheduling to be in the case where meeting some performance indicators be each task distribution computing resource, wherein In the performance indicator of dispatching algorithm, user is most concerned with the total cost of lease computing resource.Nowadays cloud computing service supplier Charging mode be generally minimum charge unit (such as Amazon EC2) with hour, it is meant that even if charge unit is used only in user Sub-fraction, it is still necessary to pay the expense of entire charge unit, that is, no matter lease resource 59 minutes or one second Clock all carried out collecting for lease expenses with one hour for unit.Therefore, how interior with lower in the regular hour (deadline) Expense carry out scheduler task and become a main problem of scientific workflow in cloud, and good a dispatching method or plan Slightly, it can efficiently complete task schedule and the total cost of lease resource is effectively reduced.
Summary of the invention
It is an object of the invention to: for scientific workflow scheduling lease virtual machine institute in the cloud based on chaos Ant ColonySystem Cost issues are needed, propose scientific workflow Cost Optimization dispatching method in a kind of cloud based on chaos Ant ColonySystem, it is intended to subtract The expense of workflow task lease virtual machine in few entire cloud.
In order to achieve the above object, the technical solution adopted in the present invention includes following part:
1. scientific workflow Cost Optimization dispatching method in a kind of cloud based on chaos Ant ColonySystem, specific implementation step is such as Under:
Step 1: user submits demand: user submits scientific workflow and relevant resource requirement and entire scientific workflow Dispatch deadline；
Step 2: related notion being defined: including between the transmission data this paper objective function, constraint condition, task Communication overhead, task operation starting time, the definition for terminating runing time etc.；
Step 3: scientific workflow task ranking in cloud: the upward weight of the probability of calculating task and according to upward weight descending Arrangement obtains task sequence SortedT；
Step 4: selecting virtual machine using chaos Ant ColonySystem: being existed using chaos Ant ColonySystem to the task in SortedT Meet under deadline using cost minimization as the most suitable virtual machine of target selection.
2. further, according to patent requirements 1, scientific workflow in the cloud proposed by the present invention based on chaos Ant ColonySystem Lower Cost Optimization dispatching method, the workflow that the user is submitted are described with oriented no circulation figure G={ T, E }, wherein All vertex T={ t in figure1,t2,...,tnIndicate the finite aggregate containing n task node, all directed edge E={ e in figurei,j |ti,tj∈ T } priority restrictions finite aggregate between expression task, each single item ei,jIt indicates only to work as task tiAfter execution Task t can be executedj.Further, data is seti,jTo be attached to ei,jOn transmission data, indicate only work as task tjIt connects completely Receipts task tiIt could be executed after the data of transmission.
3. further, objective function defined in the step 2 according to patent requirements 1 is all virtual machines of lease Total cost, constraint condition are that the deadline of its workflow need to meet user-defined deadline.
s.t.WFT≤D (2)
4. the present invention is based on scientific workflow Cost Optimization dispatching methods in the cloud of chaos Ant ColonySystem, according to patent requirements Step 4 described in 1 is the process of an iteration using the process that chaos Ant ColonySystem is scheduled sorted task, specifically Iterative step is as follows:
Step 4.1: initialization with Ant colony algorithm relevant parameter, wherein for initial ant colony pheromones τ0It is mixed using Logistic Ignorant function generates；
Step 4.2: iteration starts: judging whether to have reached maximum number of iterations ItmaxIf not reaching, then follow the steps 4.3, otherwise iteration terminates, and goes to step 4.9；
Step 4.3: node transition rule: ant antkTask and calculating task t are successively taken from task sequence SortedTi It distributes to virtual machine vmlProbability；
Step 4.4: virtual machine selection virtual machine: being selected according to greedy algorithm or roulette mode；
Step 4.5: judging ant antkWhether completion is searched for, and is gone to step 4.7 if search is completed, is otherwise gone to step Rapid 4.3；
Step 4.6: local updating pheromones；
Step 4.7: judge whether that all ants are complete search, goes to step 4.9 if being fully completed search, it is no Then go to step 4.3；
Step 4.8: updating pheromones using the chaos operator overall situation and record local optimum scheduling scheme；
Step 4.9: output global optimum's scheduling scheme.
5. the present invention is based on scientific workflow expense in the cloud of chaos Ant ColonySystem is excellent further, according to claim 4 Change dispatching method, in order to improve convergence speed of the algorithm in the step 4.8, rule is updated to global information element and increases chaos Disturbance, it is as follows that modified global information element updates rule:
τi,l(t+1)=(1- ρ) τi,l(t)+Δτi,l(t)+R (3)
Wherein, NC indicates current iteration number, NC0Indicate iteration threshold, xnIt indicates to be generated according to Logistic chaotic function Chaos amount.
Optimization Scheduling provided by the invention has the following advantages and beneficial effects: the present invention in view of key task pair In the influence of scientific workflow deadline, task priority is ranked up using traditional probability upward weight, is then made It is carried out carrying out virtual machine selection as target to minimize lease expenses with chaos Ant ColonySystem, the method can effectively reduce task Total lease expenses.
Detailed description of the invention
Fig. 1 is that research-on-research flows down Cost Optimization dispatching method flow chart in cloud provided by the invention；
Fig. 2 is cloud workflow instance structure chart；
Specific embodiment
In order to make those skilled in the art more fully understand the technical problem in the application, technical solution and technical effect, Expense is flowed down to research-on-research in a kind of cloud based on chaos Ant ColonySystem of the present invention with reference to the accompanying drawings and detailed description Optimization Scheduling is described in further detail.
If Fig. 1 is method specific implementation step of the invention:
Step 1: user submits workflow G={ T, E } and entire workflow schedule deadline D；Specific example such as Fig. 2 institute Show, one task of each vertex representation in figure, shares 13 tasks in this example；Directed edge represents priority restrictions between task Relationship.Such as from task t2Direction task t8Directed edge just indicate only task t2Execution ends task t8It can just execute, this When data2,8It is 3, indicates task t2With task t8Transmitted data amount is 3.
Step 2: the present invention sets the shared m kind virtual machine of virtual machine and all in same data center, all due to virtual machine In the same data center, then virtual machine bandwidth (namely bw) is all identical.
Step 3: task tiThe upward weight R of probabilityiIt is from itself to texitCritical path depth, defined formula is such as public Shown in formula 5, wherein ETiExpression task tiAverage performance times, defined formula is as shown in formula 6.According to the upward weight R of probabilityi Size to tasks all in workflow carry out the big minispread of priority, obtain task ranking queue SortedT.
Step 4.1: initialization with Ant colony algorithm relevant parameter, wherein using Logistic chaotic function for initial information element The chaos amount of generation namely carries out pheromones initialization to each path, so that the pheromones in each path are different from, The time of search optimal path can be effectively reduced；
Step 4.2: iteration starts: judging whether to have reached maximum number of iterations ItmaxIf not reaching, then follow the steps 4.3, otherwise iteration terminates, and goes to step 4.10；
Step 4.3: calculating transition probability: ant antkTask and calculating task t are successively taken from task sequence SortedTi Select the probability of virtual machine vml；For in task tiAnt antkSelect virtual machine vmlRandom chance formula it is such as public Formula 7, if q≤q0, ant antkUsing the maximum value of greedy algorithm selection pheromones value and heuristic value product, otherwise use Roulette selection virtual machine carries out when roulette used state transition probability formula as depicted in figure 8.
Wherein, τi,lIndicate the pheromone concentration on path (i, l)；α is pheromones heuristic factor, indicates ant in path On the importance of pheromones that leaves, the bigger influence for illustrating pheromones to the selection of other ants below path is bigger；β is expectation Heuristic factor, reflects the importance of heuristic function, β is bigger illustrate ant in moving process heuristic information by attention degree more It is high.
For heuristic function η mentioned abovei,l, it is defined on virtual machine vmlUpper newly-increased task tiIncreased lease expenses afterwards AddCi,lInverse as heuristic function, that is, increased lease expenses AddCi,lIt is fewer, heuristic function ηi,lValue is bigger, appoints Be engaged in tiIt is assigned to virtual machine vmlProbability it is higher.
AddCi,l=NewCl-OldCl (10)
Wherein, NewClFor in virtual machine vmlUpper increase task tiNew total lease expenses afterwards, OldClFor in virtual machine vml Upper increase task tiPreceding original total lease expenses.It is worth noting that, in virtual machine vmlUpper newly-increased task tiThe expense spent With may be 0, therefore the present invention increases a constant c in the denominator of heuristic function, and avoiding denominator with this is going out for 0 situation It is existing.
Step 5.4: selecting virtual machine: shifting formula and meet sub- deadline selecting suitably according to probability for task Virtual machine.
Step 5.5: judging ant antkWhether completion is searched for, and is gone to step 5.7 if search is completed, is otherwise gone to step Rapid 5.3.
Step 5.6: local updating pheromones: if an ant completes the scheduling to task, carrying out local updating information Element goes to step 5.3, otherwise goes to step 5.4.Local information element more new formula are as follows:
Wherein Δ τk i,lFor the sum of the pheromones Increment Matrix that kth ant on path (i, l) is left, Q indicates pheromones Intensity constant, TLCkAll expenses of virtual machine are used for kth ant.
Step 5.7: judge whether that all ants are complete search, goes to step 5.9 if being fully completed search, it is no Then go to step 5.3.
Step 5.8: the overall situation updates pheromones and records local optimum scheduling scheme；
In order to improve convergence speed of the algorithm, chaos operator is added to global information element in iteration early period and updated by us In rule.Modified global information element more new formula is as shown in 12:
All information element more new formula are as follows:
τi,l(t+1)=(1- ρ) τi,l(t)+Δτi,l(t)+R (12)
Wherein ρ indicates pheromones volatility coefficient；(1- ρ) indicates the residual factor of pheromones, and pheromones are unlimited in order to prevent Accumulation, value range are limited between 0~1；Δτi,lIndicate the pheromones Increment Matrix that all ants are left on path (i, l) The sum of, it can be calculated by formula 14.
Step 5.9: output global optimum's scheduling scheme.
Above example is only to illustrate the present invention rather than limits technical solution described in the invention, for those skilled in the art Member it should be understood that research-on-research in cloud disclosed in foregoing invention flow down deadline constraint Cost Optimization dispatching method, Do not depart from big name far under the premise of, improvement can also be made on this basis, these improvement are also considered as protection of the invention Range.

Claims (5)

1. scientific workflow Cost Optimization dispatching method, specific implementation step are as follows in a kind of cloud based on chaos Ant ColonySystem:
Step 1: user submits demand: user submits scientific workflow and relevant resource requirement and the scheduling of entire scientific workflow Deadline；
Step 2: related notion being defined: including between the logical of the transmission data this paper objective function, constraint condition, task Believe the definition of expense, task operation starting time, end runing time etc.；
Step 3: scientific workflow task ranking in cloud: the upward weight of the probability of calculating task is simultaneously arranged according to upward weight descending Obtain task sequence SortedT；
Step 4: selecting virtual machine using chaos Ant ColonySystem: the task in SortedT being met using chaos Ant ColonySystem Using cost minimization as the most suitable virtual machine of target selection under deadline.
2. research-on-research flows down expense in the cloud proposed by the present invention based on chaos Ant ColonySystem further, according to patent requirements 1 With Optimization Scheduling, the workflow that the user is submitted is described with oriented no circulation figure G={ T, E }, wherein in figure All vertex T={ t1,t2,...,tnIndicate the finite aggregate containing n task node, all directed edge E={ e in figurei,j|ti, tj∈ T } priority restrictions finite aggregate between expression task, each single item ei,jIt indicates only to work as task tiIt can just be held after execution Row task tj.Further, data is seti,jTo be attached to ei,jOn transmission data, indicate only work as task tjIt receives and appoints completely Be engaged in tiIt could be executed after the data of transmission.
3. further, objective function defined in the step 2 according to patent requirements 1 is that all the total of virtual machine of lease are taken With constraint condition is that the deadline of its workflow need to meet user-defined deadline.
s.t.WFT≤D (2)
4. the present invention is based on scientific workflow Cost Optimization dispatching method in the cloud of chaos Ant ColonySystem, according to 1 institute of patent requirements The step 4 stated is the process of an iteration using the process that chaos Ant ColonySystem is scheduled sorted task, it is specific repeatedly In generation, steps are as follows:
Step 4.1: initialization with Ant colony algorithm relevant parameter, wherein for initial ant colony pheromones τ0Use Logistic chaos letter Number generates；
Step 4.2: iteration starts: judging whether to have reached maximum number of iterations ItmaxIf not reaching, 4.3 are thened follow the steps, Otherwise iteration terminates, and goes to step 4.9；
Step 4.3: node transition rule: ant antkTask and calculating task t are successively taken from task sequence SortedTiDistribution To virtual machine vmlProbability；
Step 4.4: virtual machine selection virtual machine: being selected according to greedy algorithm or roulette mode；
Step 4.5: judging ant antkWhether completion is searched for, and is gone to step 4.7 if search is completed, is otherwise gone to step 4.3；
Step 4.6: local updating pheromones；
Step 4.7: judging whether that all ants are complete search, go to step 4.9 if being fully completed search, otherwise turn To step 4.3；
Step 4.8: updating pheromones using the chaos operator overall situation and record local optimum scheduling scheme；
Step 4.9: output global optimum's scheduling scheme.
5. the present invention is based on scientific workflow Cost Optimization tune in the cloud of chaos Ant ColonySystem further, according to claim 4 Degree method updates rule to global information element and increases chaos and disturb in order to improve convergence speed of the algorithm in the step 4.8 Dynamic, it is as follows that modified global information element updates rule:
τi,l(t+1)=(1- ρ) τi,l(t)+Δτi,l(t)+R (3)
Wherein, NC indicates current iteration number, NC0Indicate iteration threshold, xnIt indicates to be generated according to Logistic chaotic function mixed Ignorant amount.
CN201910383575.9A 2019-05-08 2019-05-08 Scientific workflow Cost Optimization dispatching method in a kind of cloud based on chaos Ant ColonySystem Pending CN110111006A (en)

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CN110825527A (en) * 2019-11-08 2020-02-21 北京理工大学 Deadline-budget driven scientific workflow scheduling method in cloud environment
CN111240804A (en) * 2020-01-12 2020-06-05 桂林理工大学 Cloud data center cost optimization method based on resource management
CN114781950A (en) * 2022-06-22 2022-07-22 中国人民解放军32035部队 Ant colony algorithm-based radar resource scheduling strategy, device and electronic equipment

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Publication number Priority date Publication date Assignee Title
CN110825527A (en) * 2019-11-08 2020-02-21 北京理工大学 Deadline-budget driven scientific workflow scheduling method in cloud environment
CN110825527B (en) * 2019-11-08 2022-01-04 北京理工大学 Deadline-budget driven scientific workflow scheduling method in cloud environment
CN111240804A (en) * 2020-01-12 2020-06-05 桂林理工大学 Cloud data center cost optimization method based on resource management
CN114781950A (en) * 2022-06-22 2022-07-22 中国人民解放军32035部队 Ant colony algorithm-based radar resource scheduling strategy, device and electronic equipment

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Application publication date: 20190809