CN113031522B - Low-power-consumption scheduling method suitable for periodically dependent tasks of open type numerical control system - Google Patents

Low-power-consumption scheduling method suitable for periodically dependent tasks of open type numerical control system Download PDF

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CN113031522B
CN113031522B CN201911354473.0A CN201911354473A CN113031522B CN 113031522 B CN113031522 B CN 113031522B CN 201911354473 A CN201911354473 A CN 201911354473A CN 113031522 B CN113031522 B CN 113031522B
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郭锐锋
彭阿珍
胡毅
吴昊天
王楚婷
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Shenyang Zhongke Cnc Technology Co ltd
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Abstract

The invention discloses a low-power-consumption scheduling method suitable for periodically dependent tasks of an open numerical control system, which comprises the following steps of: step 1: initializing genetic algorithm parameters; step 2: modeling the periodic dependence task by adopting a directed acyclic graph; and step 3: establishing an objective function of a scheduling task, and setting constraint conditions to obtain an optimized model; and 4, step 4: and solving the optimized model by using an improved genetic algorithm to obtain a task scheduling sequence and a power supply voltage required to be configured by the processor. The invention considers the periodic dependency relationship of tasks, designs an initial population generation algorithm and cross operation which can keep the topological structure of the tasks, and has faster searching speed and lower energy consumption of a scheduling scheme corresponding to the optimal solution compared with other algorithms. The invention carries out variable neighborhood search on the optimal individual generated by the genetic algorithm so as to improve the local search capability of the algorithm, and the algorithm can effectively reduce the energy consumption of the system on the premise of ensuring the schedulability of the system.

Description

Low-power-consumption scheduling method suitable for periodically dependent tasks of open type numerical control system
Technical Field
The invention relates to real-time scheduling of tasks in the field of multi-core platform real-time systems, in particular to a low-power-consumption scheduling method suitable for periodically dependent tasks of an open numerical control system.
Background
The open numerical control system is a typical real-time system, and the key functions of the open numerical control system are realized by real-time tasks. Numerical control systems not only require that tasks be completed within a deadline, but also ensure that tasks are properly executed. With the increase of various functional requirements in a numerical control system, the application of a multi-core processor is more and more extensive. Compared with a single processor, the scheduling of tasks on the multi-core platform needs to consider the behaviors of migration, communication and the like among processors, so the energy consumption of the system is correspondingly higher and higher. The high heat generated by high energy consumption affects the service life of the processor, and also causes resource waste, thereby affecting the environment. This puts new requirements on the scheduling algorithm: on the premise of ensuring that the task is completed before the deadline, the energy consumption of the system is reduced as much as possible.
Because the scheduling problem on the multi-core platform is NP-hard, many scholars solve the low-energy-consumption scheduling problem on the multi-core platform by adopting an intelligent heuristic algorithm, and the essence of the scheduling problem is to formally describe the task scheduling problem as an optimization problem with some priority constraints and find an approximately optimal solution of the problem by a heuristic search algorithm.
The paper "Communication-aware task scheduling and voltage selection for total energy generation in a multiprocessor system using an Ant Colony Optimization" published by Kim H et al proposes a low energy consumption scheduling algorithm based on an Ant Colony algorithm that employs global heuristic information with total energy consumption and local heuristic information with inter-processor traffic to perform a random decision search on the solution space and update pheromone trajectories by normalizing the total energy consumption. The method is not suitable for the multi-core platform numerical control system because the dependency relationship among tasks is not considered.
Disclosure of Invention
The invention provides an energy-saving scheduling method suitable for a periodic dependent task in an open type numerical control system, aiming at the defects of a low-power-consumption scheduling algorithm on the existing multi-core platform.
The low-power-consumption scheduling method suitable for the periodical dependent tasks of the open type numerical control system comprises the following steps of:
step 1: initializing genetic algorithm parameters;
step 2: modeling the periodic dependence task by adopting a directed acyclic graph;
and step 3: establishing an objective function of a scheduling task, and setting constraint conditions to obtain an optimized model;
and 4, step 4: and solving the optimized model by using an improved genetic algorithm to obtain a task scheduling sequence and a power supply voltage required to be configured by the processor.
The genetic algorithm parameters comprise: total number of iterations isMax _ gen, population size PsizeCross probability of PCThe mutation probability is PMAnd the iteration number is R.
The optimization model includes: when the system meets the constraint condition, finding out the optimal task scheduling sequence and the processor voltage to ensure that the total energy consumption E of the system is minimum;
and (3) minimizing:
Figure BDA0002335525660000021
constraint conditions are as follows:
Figure BDA0002335525660000022
Figure BDA0002335525660000023
Figure BDA0002335525660000024
wherein e isiklRepresenting a task tiAt a voltage vlProcessor p ofkEnergy consumption during run-up; n is the number of tasks in the system, M is the number of processors, vkSet of supply voltages, EST, for processorsiAs task tiAt the earliest start time, STiAs task tiActual start time of (FT)iAs task tiEnd time of diAs task tiBy a deadline of xiklIs defined as follows:
Figure BDA0002335525660000031
the method for solving the optimization problem by using the improved genetic algorithm to obtain the task scheduling sequence and the power supply voltage required to be configured by the processor comprises the following steps:
step 4.1: adopting an improved genetic algorithm to search an approximate optimal solution of the optimization model in a circular iteration mode, and then carrying out variable neighborhood search to obtain an optimal solution;
step 4-2: and scheduling the tasks according to the optimal solution.
The method comprises the following steps of adopting an improved genetic algorithm to search an approximate optimal solution of an optimization model in a circular iteration mode, and then carrying out variable neighborhood search to obtain an optimal solution, wherein the method comprises the following steps:
a. determining the chromosome structure;
b. generating an initial population: generating a containment P from a directed acyclic graph of taskssizeAn initial population of individual chromosomes;
c. calculating population fitness
Figure BDA0002335525660000032
d. Selecting, crossing and mutating the initialized population;
e. combining the offspring chromosome and parent chromosome obtained by mutation to generate 2PsizeCalculating the fitness value of each chromosome of the new population, sorting the fitness values from big to small, and selecting the top PsizeGenerating a next generation population by each chromosome;
f. performing variable neighborhood search on the current population optimal chromosome BS to obtain a new current generation optimal chromosome S in a neighborhood, and selecting a chromosome with a large fitness value in S and BS as an optimal solution of the current population; wherein, the optimal individual BS is the chromosome with the highest fitness value;
g. judging whether a cutoff condition is met: if yes, outputting the optimal solution, otherwise, returning to d.
A 3 x N two-dimensional matrix is selected to represent the chromosome, where N is the number of tasks, the first row of the matrix representing the task number, the second row representing the processor number assigned to the respective task, and the third row representing the supply voltage provided by the respective processor.
The selection is a roulette selection method based on probabilities.
And the crossing is to select the parent chromosomes for the topological structures of the offspring chromosomes according to the crossing probability and generate the offspring chromosomes in a section crossing mode.
And the mutation is to select a chromosome according to the mutation probability to carry out single-point mutation operation to obtain a progeny chromosome.
The invention has the following beneficial effects and advantages:
1. the invention considers the periodic dependency relationship of tasks, designs an initial population generation algorithm and cross operation which can keep the topological structure of the tasks, and has faster searching speed and lower energy consumption of a scheduling scheme corresponding to the optimal solution compared with other algorithms.
2. The method carries out variable neighborhood search on the optimal individual generated by the genetic algorithm so as to improve the local search capability of the algorithm, and the algorithm can effectively reduce the energy consumption of the system on the premise of ensuring the schedulability of the system.
Drawings
Fig. 1 is a flow chart of a scheduling method of the present invention.
FIG. 2 is a flow chart of the operation of the improved genetic algorithm.
FIG. 3 is a variable neighborhood search flow diagram.
Fig. 4(a) is a graph showing the results of a simulation experiment in which the number of tasks is 50.
Fig. 4(b) is a graph showing the results of a simulation experiment when the number of tasks is 100.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention provides a low-power-consumption scheduling method of an open numerical control system based on an improved genetic algorithm. Aiming at a topological structure depending on tasks, an initial solution generation method capable of keeping the topological structure of the tasks and cross operation are provided, then an approximate optimal solution is generated through a genetic algorithm, a variable neighborhood search algorithm is adopted to expand a search range, a local optimal solution is found, and the aims of quick task allocation and low power consumption on a multi-core processor system are achieved.
As shown in fig. 1, a low power consumption scheduling method suitable for periodically dependent tasks of an open numerical control system includes the following steps:
initializing genetic algorithm parameters;
modeling the periodic dependence task by adopting a directed acyclic graph;
establishing an objective function of a scheduling task, and setting constraint conditions to obtain an optimized model;
and solving the optimized model by using an improved genetic algorithm to obtain a task scheduling sequence and a power supply voltage required to be configured by the processor.
As shown in FIG. 2, the improved genetic algorithm of the present invention is implemented by the following steps:
step 1: coding
Determining a chromosome structure by adopting an operation-based coding method, determining the chromosome structure, and selecting a 3 XN two-dimensional matrix to represent the chromosome, wherein N is the number of tasks, the first row in the matrix represents the task number, the second row represents the processor number allocated to the corresponding task, and the third row represents the voltage level provided by the corresponding processor. Wherein, the chromosome can be expressed as an individual.
Step 2: generating an initial population
(1) And adding all tasks with the former task set as an empty set in the system task set T into the task set sTasks.
(2) And randomly distributing processors and power supply voltage for the tasks in the sTasks, removing the distributed tasks from the sTasks and the T, and updating the task sets sTasks and the T.
(3) And when the sTasks is an empty set, repeating the steps 1 and 2 until the task set T is the empty set, and ending the operation.
(4) If the individual (scheduling sequence) obtained in the above step is able to pass the schedulability test, the individual is added to the initial population.
(5) The above steps are repeated until an initial population of population size Psize is generated.
And step 3: calculating fitness
The fitness function of the invention is:
Figure BDA0002335525660000051
Figure BDA0002335525660000052
wherein e isiklRepresenting a task tiAt a voltage vlProcessor p ofkEnergy consumption during runtime; n is the number of tasks in the system, M is the number of processors, VkA set of supply voltages for the processor,
and xiklIs defined as follows:
Figure BDA0002335525660000061
and 4, step 4: selection operation
The selection operation is to select high-quality individuals from the population according to the fitness and eliminate poor-quality individuals. The invention adopts a roulette selection method based on probability, and defines the selection probability of the chromosome as the proportion of the fitness of the chromosome in the population fitness in the whole population. Thus, the probability of individual selection is positively correlated with its fitness. The greater the fitness of the chromosome, the higher the likelihood of selection and vice versa. The method can ensure that individuals with stronger adaptability can evolve to the next generation, and the influence of randomness of genetic operation is weakened, so that the convergence of the algorithm is ensured.
And 5: crossover operation
Firstly, segment labels are added to chromosomes according to a DAG graph of tasks, and tasks in the same segment do not have predecessor and successor relations and can be scheduled according to any sequence. A segment label is then randomly generated, and the segments of the two parent chromosomes within the selected segment are swapped. Such a crossover operation can ensure that the topological order of tasks in the child chromosomes is not disturbed.
Step 6: mutation operation
The function of the mutation operation is to improve the local searching capability of the algorithm, maintain the diversity of the population and prevent the phenomenon of local convergence. A gene location is randomly selected and the processor or corresponding voltage level at that location is altered.
And 7: variable neighborhood operation
As shown in fig. 3, the step of the neighborhood change operation is as follows:
(1) determining four neighborhood structures NkAnd k is 1,2,3,4, and initialization parameters are input; number of cycles P and initial solution S0Let i equal to 0 and the optimal individual BS equal to S0。
(2) If the loop termination condition i < P is met, outputting the optimal individual BS; otherwise, let k equal to 1.
(3) According to the neighborhood structure NkA new solution S 'is randomly generated, and the fitness value of the new solution S' is compared with the fitness value of the initial solution S.
(4) If f (S ') > f (S), k, BS ═ S', a new solution with a larger fitness value is output instead of the initial solution,
proceed to the neighborhood structure NkSearching in; otherwise, k is k + 1.
(5) If k is greater than 4, returning to the step 2; otherwise, returning to the step (3), and entering the next neighborhood structure for searching.
And 8: outputting an optimal solution
And performing variable neighborhood search on the current population optimal individual BS to obtain a new current generation optimal individual S, and selecting an individual with higher fitness between S and BS as the optimal solution of the current population.
Application example: in order to verify the performance of the improved genetic algorithm on low-power-consumption real-time scheduling in the open numerical control system, the model is tested in a Matlab programming environment. Inputs to the experiments herein include: DAGs of different sizes, workload, task deadlines, number of processors, operating frequency, and voltage level. The task graph generator TGFF is adopted to synthesize application task graphs of different scales, and deadlines are allocated to tasks according to an algorithm proposed by Balbastre et al. To verify the effectiveness of the HGA-VNS algorithm, the selection was compared to the Genetic Algorithm (GA) and the ant colony Algorithm (ACO). In order to obtain a fair experimental result, the population scale selected in the comparative experiment is consistent with the number of ant colonies.
Fig. 4(a) and (b) show the operation results of the three algorithms when the number of tasks is 50 and 100, respectively, where the abscissa is the operation time of the algorithm, and the ordinate is the average energy consumption when the scheduling sequence corresponding to the optimal solution generated by the algorithm is operated. From fig. 4, it can be derived: compared with the ant colony algorithm and the genetic algorithm of unmixed variable neighborhood search, the algorithm provided by the invention not only has higher search speed, but also has lower energy consumption of the scheduling scheme corresponding to the optimal solution. The method considers the periodic dependency relationship of tasks, designs a special initial population generation algorithm and cross operation, and further improves the quality of the solution by combining the search space of the variable neighborhood search expansion solution. A comprehensive comparison of FIGS. 4(a) and (b) can yield: when the number of tasks in the system is increased, the time required for searching the optimal solution by the three algorithms is increased, but compared with the other two algorithms, the algorithm disclosed by the invention can better adapt to the influence of the increase of the number of tasks on a system processor, and can search the optimal solution with lower energy consumption more quickly.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (6)

1. The low-power-consumption scheduling method suitable for the periodically dependent tasks of the open type numerical control system is characterized by comprising the following steps of:
step 1: initializing genetic algorithm parameters;
step 2: modeling the periodic dependence task by adopting a directed acyclic graph;
and step 3: establishing an objective function of a scheduling task, and setting constraint conditions to obtain an optimized model;
and 4, step 4: solving the optimized model by using an improved genetic algorithm to obtain a task scheduling sequence and a power supply voltage required to be configured by the processor;
the method for solving the optimization model by using the improved genetic algorithm to obtain the task scheduling sequence and the power supply voltage required to be configured by the processor comprises the following steps:
step 4.1: adopting an improved genetic algorithm to search an approximate optimal solution of the optimization model in a circular iteration mode, and then carrying out variable neighborhood search to obtain an optimal solution;
step 4-2: scheduling the tasks according to the optimal solution;
the optimization model includes: when the system meets the constraint condition, finding out the optimal task scheduling sequence and the processor voltage to ensure that the total energy consumption E of the system is minimum;
and (3) minimizing:
Figure FDA0003609991100000011
constraint conditions are as follows:
Figure FDA0003609991100000012
Figure FDA0003609991100000013
Figure FDA0003609991100000014
wherein e isiklRepresenting a task tiAt a voltage vlProcessor p ofkEnergy consumption during running, N is the number of tasks in the system, M is the number of processors, vkSet of supply voltages, EST, for processorsiAs task tiAt the earliest start time, STiAs task tiActual start time of (FT)iAs task tiEnd time of diAs task tiBy a deadline of xiklIs defined as follows:
Figure FDA0003609991100000021
the method comprises the following steps of adopting an improved genetic algorithm to search an approximate optimal solution of an optimization model in a circular iteration mode, and then carrying out variable neighborhood search to obtain an optimal solution, wherein the method comprises the following steps:
a. determining the chromosome structure;
b. generating an initial population: generating a containment P from a directed acyclic graph of taskssizeAn initial population of individual chromosomes;
c. calculating population fitness
Figure FDA0003609991100000022
d. Selecting, crossing and mutating the initialized population;
e. combining the offspring chromosome and parent chromosome obtained by mutation to generate 2PsizeCalculating the fitness value of each chromosome of the new population, sorting the fitness values from big to small, and selecting the top PsizeGenerating a next generation population by each chromosome;
f. performing variable neighborhood search on the current population optimal chromosome BS to obtain a new current generation optimal chromosome S in a neighborhood, and selecting a chromosome with a large fitness value in S and BS as an optimal solution of the current population; wherein the optimal individual BS' is the chromosome with the highest fitness value;
g. judging whether a cutoff condition is met: if yes, outputting the optimal solution, otherwise, returning to d.
2. The low-power-consumption scheduling method suitable for the periodically dependent tasks of the open numerical control system according to claim 1, wherein the genetic algorithm parameters comprise: the total iteration number is Max _ gen, the population size is PsizeCross probability of PCThe mutation probability is PMAnd the iteration number is R.
3. The method as claimed in claim 1, wherein a 3 × N two-dimensional matrix is selected to represent the chromosome, where N is the number of tasks, a first row in the matrix represents a task number, a second row represents a processor number allocated to a corresponding task, and a third row represents a supply voltage provided by a corresponding processor.
4. The method for scheduling low power consumption tasks of the open numerical control system according to claim 1, wherein the selection is a roulette selection method based on probability.
5. The low-power-consumption scheduling method suitable for the periodically dependent tasks of the open numerical control system according to claim 1, wherein the crossing is a topological structure that parent chromosomes are selected for offspring chromosomes according to a crossing probability, and offspring chromosomes are generated in an intra-segment crossing mode.
6. The low-power-consumption scheduling method suitable for the periodically dependent tasks of the open numerical control system according to claim 1, wherein the mutation is to select a chromosome according to a mutation probability to perform a single-point mutation operation to obtain a child chromosome.
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