CN110119316A - A kind of associated task scheduling strategy based on slackness and Ant ColonySystem - Google Patents
A kind of associated task scheduling strategy based on slackness and Ant ColonySystem Download PDFInfo
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- CN110119316A CN110119316A CN201910412931.5A CN201910412931A CN110119316A CN 110119316 A CN110119316 A CN 110119316A CN 201910412931 A CN201910412931 A CN 201910412931A CN 110119316 A CN110119316 A CN 110119316A
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- G06F9/00—Arrangements for program control, e.g. control units
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Abstract
The invention belongs to cloud and mist calculating field, specifically a kind of that task scheduling strategy is associated under scenes of internet of things, target is under the premise of priority constraint relationship between not destroying task, it is contemplated that in terms of comprehensive task deadline and the total energy consumption of task processing.Be broadly divided into two steps: A. obtains priority sequence based on the priority algorithm of slackness；B. the constrained optimization method based on Ant ColonySystem realizes task distribution.The LBP-ACS dispatching algorithm that the present invention is proposed for cloud and mist calculating field is solving to have in the associated task scheduling problem for mixing deadline, can effectively reduce energy consumption while improving task schedule success rate.
Description
Technical field
The invention belongs to cloud and mist calculating fields, specifically under scenes of internet of things be associated with task schedule target do not destroy appoint
Between business under the premise of priority constraint relationship, in terms of the total energy consumption for considering comprehensive task deadline and task processing, formulation is based on
The associated task scheduling strategy of slackness and Ant ColonySystem.
Background technique
It include the application program of large amount of complex in the scenes of internet of things such as intelligent transportation, smart home, intelligent medical, many is answered
It is made of with program multiple modules for executing different task, and there are close connections between a large amount of task.As it can be seen that in cloud
Under mist Computational frame, there is only mutually independent tasks, and there is also the tasks with priority restrictions relationship.There are priority
The task of the constraint relationship only can be just performed in the result information for obtaining its all predecessor task, and it is all to be unable to parallel processing
Task, it can be seen that have associated task schedule face difficulty and challenge.The scheduling strategy of associated task will not only consider
To relation of interdependence, communication cost and the priority between task, but also the demand of user is fully taken into account, such as appoint
The deadline limitation for being engaged in mixed, reduces energy consumption etc..
Using intelligent transportation system as in the scenes of internet of things of representative, wherein traffic lights, mobile phone, sensor, CCTV are supervised
The networked devices such as device for shooting are controlled by wireless network connection to mist equipment, mist equipment is connected in cloud computing data by optical fiber
The heart.Mist node monitors the data in device for shooting reception traffic environment by sensor or CCTV, to detect close pedestrian
It with the speed of vehicle, further being interacted with neighbouring signal lamp, is based on information above, mist equipment is sent a warning message to vehicle,
It avoids colliding by adjusting the neighbouring green light period or congestion[59], while by the rush hour of collection, emergency situations place etc.
Traffic information is sent to for statistical analysis on Cloud Server, and detailed road information is finally returned to user.Whole process
In, vehicle receives warning message, intelligent signal lamp regulating cycle and user's smart machine and receives the application such as traffic information
The deadline of task is different, and there are priority restrictions relationships between task.In above-mentioned scene, by mist node and
The cooperation of cloud node, collaboration processing have the application program for the task composition for interdepending and mixing deadline, meet low prolong
Slow processing and analysis traffic information guarantee traffic safety and smooth.Simultaneously in view of the main trend that global energy consumption rises violently, low energy
Consumption calculates urgently to be resolved.Reduce cloud and mist resource energy consumption, on the one hand can reduce production cost, on the other hand can with energy-saving and emission-reduction,
Environment is protected, realizes that green calculates.It is calculated by cloud and mist resource coordinating, efficent use of resources, guarantees the mixing for meeting DAG task
The demand of deadline.Therefore it will study under cloud and mist Computational frame herein, there is the task that interdepends of mixing deadline
Mission Scheduling.
Summary of the invention
1. a kind of associated task scheduling strategy based on slackness and Ant ColonySystem mainly includes following two step:
A. the priority algorithm based on slackness (LBPA) obtains priority sequence: LBPA algorithm is intended to through recursive side
Method calculates the slackness of each task, and then the priority of each association subtask is calculated according to slackness, will finally be based on
The task image of DAG is converted into orderly task sequence.The slackness of task is ductile before indicating task deadline the latest
Maximum duration, the urgency level or time-sensitive degree of Lai Hengliang task.
B. the constrained optimization method based on Ant ColonySystem (COA-ACS) realizes task distribution: being calculated using LBPA above
Method obtains the priority sequence TaskList of task in DAG task image, next, by associated task scheduling problem and ant colony system
System combines, and proposes the constrained optimization task scheduling algorithm based on Ant ColonySystem, thus for the task v in TaskList sequenceiChoosing
Select appropriate cloud or mist node.Initialization information element is first had in suboptimization algorithm, circular is as follows:
Wherein, FzIt is total processing energy consumption of greedy algorithm allocation plan.
Then selection resource and calculating heuristic information, circular are as follows:
Wherein, τij(t) it is pheromones in the t times iteration on path edge (i, j), α, β are respectively control parameter,
allowedkIndicate the cloud or mist resource that kth ant can choose,Indicate the rule of probability of roulette selection resource.
Then the update of local information element is carried out, circular is as follows:
τij(t)=(1- ξ) × τij(t)+ξ×τ0
Wherein, ξ (0≤ξ≤1) is pheromones volatilization factor.
Then global information update is carried out, circular is as follows:
τij(t+1)=(1- ρ) × τij(t)+ρ×Δτij k
The present invention, which compares prior art, has following remarkable advantage:
1. considering the relevance between task during processing task, thus use based on the excellent of slackness
First grade task scheduling algorithm, so as to so that the strong task of time sensitivity can preferentially be scheduled.
2. combining associated task scheduling problem and Ant ColonySystem in task assignment procedure, propose to be based on Ant ColonySystem
Constrained optimization task scheduling algorithm, to reduce energy consumption under conditions of considering task priority.
Detailed description of the invention
Fig. 1 is overview flow chart of the invention.
Fig. 2 is a DAG task image input process figure.
Fig. 3 is an algorithmic dispatching length vs figure.
Fig. 4 is an algorithm energy consumption comparison figure.
Fig. 5 is an algorithm failure rate comparison diagram.
Specific embodiment
With reference to the accompanying drawing, illustrate embodiments of the present invention.An intelligent transportation system scene is assumed in embodiment, is appointed
Business figure, which describes vehicle and receives warning message, intelligent signal lamp regulating cycle and user's smart machine, receives traffic information
Dependence between equal application tasks, since the property of task is different, each task is different to the sensitivity of delay,
The deadline of i.e. each task is different.
Fig. 1 is overview flow chart of the invention, the scheduling strategy specific implementation of associated task are as follows:
A. the priority algorithm based on slackness (LBPA) obtains priority sequence: LBPA algorithm is intended to through recursive side
Method calculates the slackness of each task, and then the priority of each association subtask is calculated according to slackness, will finally be based on
The task image of DAG is converted into orderly task sequence.The slackness of task is ductile before indicating task deadline the latest
Maximum duration, the urgency level or time-sensitive degree of Lai Hengliang task.
B. the constrained optimization method based on Ant ColonySystem (COA-ACS) realizes task distribution: being calculated using LBPA above
Method obtains the priority sequence TaskList of task in DAG task image, next, by associated task scheduling problem and ant colony system
System combines, and proposes the constrained optimization task scheduling algorithm based on Ant ColonySystem, thus for the task v in TaskList sequenceiChoosing
Select appropriate cloud or mist node.Initialization information element is first had in suboptimization algorithm, circular is as follows:
Wherein, FzIt is total processing energy consumption of greedy algorithm allocation plan.
Then selection resource and calculating heuristic information, circular are as follows:
Wherein, τij(t) it is pheromones in the t times iteration on path edge (i, j), α, β are respectively control parameter,
allowedkIndicate the cloud or mist resource that kth ant can choose,Indicate the rule of probability of roulette selection resource.
Then the update of local information element is carried out, circular is as follows:
τij(t)=(1- ξ) × τij(t)+ξ×τ0
Wherein, ξ (0≤ξ≤1) is pheromones volatilization factor.
Then global information update is carried out, circular is as follows:
τij(t+1)=(1- ρ) × τij(t)+ρ×Δτij k。
Claims (1)
1. a kind of associated task scheduling strategy based on slackness and Ant ColonySystem mainly includes following two step:
A. obtain priority sequence by the priority algorithm of slackness (LBPA): LBPA algorithm is intended to by based on recursive method
The slackness of each task is calculated, and then calculates the priority of each association subtask according to slackness, it finally will be based on DAG's
Task image is converted into orderly task sequence.Before the slackness expression task deadline the latest of task when ductile longest
Between, the urgency level or time-sensitive degree of Lai Hengliang task.
B. the constrained optimization method based on Ant ColonySystem (COA-ACS) realizes task distribution: using LBPA algorithm above, obtaining
The priority sequence TaskList for obtaining task in DAG task image, next, by associated task scheduling problem and Ant ColonySystem knot
It closes, proposes the constrained optimization task scheduling algorithm based on Ant ColonySystem, thus for the task v in TaskList sequenceiSelection is proper
When cloud or mist node.Initialization information element is first had in suboptimization algorithm, circular is as follows:
Wherein, FzIt is total processing energy consumption of greedy algorithm allocation plan.
Then selection resource and calculating heuristic information, circular are as follows:
Wherein, τij(t) it is pheromones in the t times iteration on path edge (i, j), α, β are respectively control parameter,
allowedkIndicate the cloud or mist resource that kth ant can choose,Indicate the rule of probability of roulette selection resource.
Then the update of local information element is carried out, circular is as follows:
τij(t)=(1- ξ) × τij(t)+ξ×τ0
Wherein, ξ (0≤ξ≤1) is pheromones volatilization factor.
Then global information update is carried out, circular is as follows:
τij(t+1)=(1- ρ) × τij(t)+ρ×Δτij k。
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Cited By (2)
| 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 |
| CN111565375A (en) * | 2020-05-26 | 2020-08-21 | 多伦科技股份有限公司 | Ant colony optimization method based on vehicle-mounted fog calculation in vehicle-mounted ad hoc network |
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