CN112949195A - Multi-target task scheduling method for power cloud data center - Google Patents
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Abstract
A multi-target task scheduling method for a power cloud data center belongs to the technical field of power informatization. The invention aims to provide a multi-target task scheduling method for an electric power cloud data center, which is used for carrying out multi-target task scheduling on efficiency and energy consumption so as to uniformly coordinate the multi-target task scheduling of the electric power cloud data center. The method comprises the following steps: establishing a model, selecting a variable to be optimized, an optimization target and a determined constraint condition, setting an initial variable and giving a range of the variable to be optimized, taking the variable to be optimized as a population individual in the optimization process, updating corresponding data in task scheduling of the power cloud data center according to the variable value to be optimized contained in each individual in the population, and solving an optimization target function value corresponding to each individual in the population; and (4) carrying out rapid non-dominant sorting on population individuals. The multi-target task scheduling method for the power cloud data center can uniformly coordinate the relation among multiple targets such as efficiency, energy consumption and the like of power cloud data task scheduling, and has the advantages of scientific method, strong applicability, good effect and the like.
Description
Technical Field
The invention belongs to the technical field of electric power informatization.
Background
The scientific and reasonable task scheduling method is an important measure for ensuring energy conservation and consumption reduction of the data center and is an important means for promoting safe and economic operation of power grid enterprises. As a new computing mode, cloud computing can uniformly allocate heterogeneous hardware resources by adopting a virtualization technology; when receiving the user task data, the data center can distribute the tasks to the virtualized hardware resources for processing and return corresponding results. However, when the task is in a peak time, the load of the data center will increase rapidly, and the power consumption also rises rapidly, so that the heat dissipation pressure is large, and therefore how to efficiently and energy-efficiently allocate and schedule the task to the hardware resources is a key problem for the efficiency of cloud computing. The traditional single target scheduling method with highest efficiency or minimum energy consumption can not meet the requirement of task scheduling of the data center. Under the background, the multi-target data center task scheduling has received wide attention from domestic and foreign researchers because of the ability to uniformly coordinate multiple targets with different importance, even conflicting requirements for task scheduling efficiency and energy efficiency.
Aiming at the problem of multi-target task scheduling of the power cloud data center, scholars at home and abroad have conducted some beneficial exploration. Task scheduling under the cloud environment often regards efficiency as the primary objective, and a large amount of electric energy can be consumed in the calculation of a large amount of processing machines of data center simultaneously, and this is very unfavorable to the data center that has distributed a large amount of host computers, not only extravagant energy and increase cost, causes data center heat dissipation problem moreover. However, most of the existing methods only aim at single-target optimization of efficiency or energy consumption, and there are few research reports on multi-target task scheduling of efficiency and energy consumption.
Disclosure of Invention
The invention aims to provide a multi-target task scheduling method for an electric power cloud data center, which is used for carrying out multi-target task scheduling on efficiency and energy consumption so as to uniformly coordinate the multi-target task scheduling of the electric power cloud data center.
The method comprises the following steps:
s1, establishing a model, selecting variables to be optimized, optimizing targets and determining constraint conditions
(a) Selecting a variable to be optimized, wherein the variable to be optimized is a variable for task scheduling of the power cloud data center;
(b) selecting optimization targets, selecting the optimization targets with the efficiency improvement and the energy consumption reduction as the optimization targets, coordinating the relationship among the optimization targets, wherein the expression of an optimization target function is as follows:
in the formula (1), T and E respectively represent two optimization targets of total execution time and total task energy consumption when the virtual machine executes the task; v is the number of virtual machines; VM (v, t) is the time required by the virtual machine v to execute the task t; n is the number of tasks distributed to the virtual machine; powera(v) Power when executing a task for virtual machine v; powerd(v) Is the power of the virtual machine v in the idle state;
s2, setting initial variables and giving variable ranges to be optimized
(c) The upper limit and the lower limit of the variable to be optimized need to specify step length for the discrete variable;
(d) population scale pop, iteration times gen, target function number M, optimized variable number P, selection operation system tour, distribution numbers mu and mum in the process of crossing and mutation operations;
s3, taking the variable to be optimized as the individual population in the optimization process, randomly generating an initial population according to the range of the variable to be optimized in a mixed coding mode, and randomly generating the individual within the range
And forming an initial population
S4, updating corresponding data in the task scheduling of the power cloud data center according to variable values to be optimized contained in each individual in the population, and solving an optimization objective function value corresponding to each individual in the population;
s5, performing rapid non-dominated sorting on population individuals, calculating virtual fitness, and generating sub-populations through selection, crossing and variation treatment in a specific implementation mode:
(g) the fast non-dominated sorting is a sorting method for obtaining a non-inferior set solution of multi-target task scheduling of the power cloud data center through calculation of a related objective function, carrying out layering processing on individuals according to the non-inferior result, and enabling population evolution to approach to the direction of Pareto optimal solution; in the evolution process, in order to keep the diversity of the population, the individual crowding distance is designed, and the individual with larger crowding distance is preferentially selected in the selection process;
(h) when the virtual fitness is calculated, firstly, a chromosome formed by the population and the optimization objective function value is decoded, then the corresponding optimization objective function value of each individual is calculated according to a mathematical model of multi-objective task scheduling of the power cloud data center, and then non-inferior layering is carried out according to the optimization objective function value to calculate the virtual fitness of each layer of the individual;
(i) progeny population DiIn the process, the parent operator is selected by adopting a race system in the selection operation, the simulated binary crossover operator is adopted in the crossover operation, and the normal mutation operator is adopted in the mutation operation;
s6, an elite evolution strategy of the NSGA-II algorithm is described, namely a strategy of directly reserving good individuals in a parent to filial generations to prevent the good individuals in the parent from being discarded in the evolution process;
s7, selecting an optimization termination condition as whether the iteration process reaches a preset maximum algebra;
s8, outputting a calculation result including:
(y) the value corresponding to the variable value to be optimized and the value of the optimization objective function;
(z) an optimal solution set graph that optimizes the objective function values.
The multi-target task scheduling method for the power cloud data center can uniformly coordinate the relation among multiple targets such as efficiency, energy consumption and the like of power cloud data task scheduling, and has the advantages of scientific method, strong applicability, good effect and the like.
Drawings
FIG. 1 is a flow chart of a multi-objective task scheduling method for a power cloud data center;
figure 2 is a CloudSim scheduling architecture diagram.
Detailed Description
The efficiency of the invention is represented by the total time for executing all tasks submitted by the user, i.e. the energy consumption of the data center for executing the tasks. The main idea is as follows: firstly, constructing a data center multi-objective task scheduling model taking efficiency improvement and energy consumption reduction as optimization targets; then, solving a scheduling model by adopting a non-dominated sorting genetic algorithm (NSGA-II) with an elite strategy; and finally, obtaining a series of non-inferior solution sets meeting Pareto optima.
Referring to fig. 1, the multi-target task scheduling method for the power cloud data center of the invention includes the following steps:
1) establishing a model, selecting variables to be optimized, optimizing targets and determining constraint conditions;
2) setting an initial variable and setting a variable range to be optimized;
3) taking variables to be optimized as population individuals in the optimization process, and randomly generating an initial population according to the range of the variables to be optimized by adopting a mixed coding mode;
4) obtaining an optimization objective function value corresponding to each individual in the population;
5) performing rapid non-dominated sorting on population individuals, calculating virtual fitness, and generating sub-populations through selection, crossing and variation treatment;
6) maintaining good individuals in the parents by adopting an elite strategy;
7) judging whether an algorithm termination condition is met, if so, turning to the step 8), and finishing optimization; otherwise, turning to the step 4);
8) and (5) finishing the optimization process and outputting a result.
The modeling process of the step 1) is as follows:
(a) selecting a variable to be optimized, wherein the variable to be optimized is a variable for task scheduling of the power cloud data center;
(b) selecting optimization targets, selecting the optimization targets with the efficiency improvement and the energy consumption reduction as the optimization targets, coordinating the relationship among the optimization targets, wherein the expression of an optimization target function is as follows:
in the formula (1), T and E respectively represent two optimization targets of total execution time and total task energy consumption when the virtual machine executes the task; v is the number of virtual machines; VM (v, t) is the time required by the virtual machine v to execute the task t; n is the number of tasks distributed to the virtual machine; powera(v) Power when executing a task for virtual machine v; powerd(v) Is the power of the virtual machine v in the idle state.
The initial variable and the given variable range to be optimized set in the step 2) comprise:
(c) the upper limit and the lower limit of the variable to be optimized need to specify step length for the discrete variable;
(d) population size pop, iteration times gen, target function number M, optimized variable number P, selection operation system tour, distribution numbers mu and mum in the process of crossing and mutation operations.
In the step 3), the variable to be optimized is used as the population individual in the optimization process, the initial population is randomly generated according to the range of the variable to be optimized, namely, the individual is randomly generated in the range according to the upper and lower limits of the variable to be optimized and the step length of the discrete variable
And an initial population was formed as follows:
in the step 4), the corresponding data in the task scheduling of the power cloud data center is updated according to the variable values to be optimized contained in each individual in the population, and the optimization objective function value corresponding to each individual in the population is obtained.
In the step 5), the population individuals are subjected to rapid non-dominated sorting, the virtual fitness is calculated, and the specific implementation mode of generating the sub-population through selection, crossing and variation processing is as follows:
(g) the fast non-dominated sorting is a sorting method for obtaining a non-inferior set solution of multi-target task scheduling of the power cloud data center through calculation of a related objective function, carrying out layering processing on individuals according to the non-inferior result, and enabling population evolution to approach to the direction of Pareto optimal solution; in the evolution process, in order to keep the diversity of the population, the individual crowding distance is designed, and the individual with larger crowding distance is preferentially selected in the selection process;
(h) when the virtual fitness is calculated, firstly, a chromosome formed by the population and the optimization objective function value is decoded, then the corresponding optimization objective function value of each individual is calculated according to a mathematical model of multi-objective task scheduling of the power cloud data center, and then non-inferior layering is carried out according to the optimization objective function value to calculate the virtual fitness of each layer of the individual;
(i) progeny population DiIn the process, the parent operator is selected by adopting a round-robin system in the selection operation, a simulated binary crossing (SBX) operator is adopted in the crossing operation, and a normal mutation operator is adopted in the mutation operation.
In the step 6), an elite evolution strategy of the NSGA-II algorithm, namely a strategy of directly retaining good individuals in the parent to the offspring, is described, so that the good individuals in the parent are prevented from being discarded in the evolution process.
In the step 7), the optimization termination condition may be whether the iteration process reaches a preset maximum algebra.
In the step 8), the output calculation result includes:
(y) the value corresponding to the variable value to be optimized and the value of the optimization objective function;
(z) an optimal solution set graph that optimizes the objective function values.
The invention is particularly applied to the CloudSim 3.0 platform. The CloudSim 3.0 scheduling architecture is shown in figure 2 below, a framework platform developed by the grid laboratories of melbourne university, which allows seamless modeling, simulation, and experimentation to be conducted on a designed cloud computing facility. In order to embody the heterogeneity of cloud computing resources, the virtual machines with different processing capacities are set in the method, each virtual machine has different processing capacities, the different virtual machines correspond to different utilization rates, the power of the machines is different at the moment, and the parameter setting of an experiment platform is shown in table 1.
Simulation tests are carried out on the platform based on the multi-objective task scheduling method of the power cloud data center, and part of Pareto optimal solutions are shown in a table 2.
Table 1 experimental platform parameter set-up
Table 2 partial Pareto optimal solution
The data in table 2 is one of the feasible solutions obtained when scheduling energy consumption and time, respectively, are the optimization objectives of the main consideration. By the method, a plurality of Pareto optimal solution sets can be obtained through one-time operation, and operators of the power cloud scheduling center can conveniently select the solutions according to actual system requirements.
Claims (1)
1. A multi-target task scheduling method for a power cloud data center is characterized by comprising the following steps: the method comprises the following steps:
s1, establishing a model, selecting variables to be optimized, optimizing targets and determining constraint conditions
(a) Selecting a variable to be optimized, wherein the variable to be optimized is a variable for task scheduling of the power cloud data center;
(b) selecting optimization targets, selecting the optimization targets with the efficiency improvement and the energy consumption reduction as the optimization targets, coordinating the relationship among the optimization targets, wherein the expression of an optimization target function is as follows:
in the formula (1), T and E respectively represent two optimization targets of total execution time and total task energy consumption when the virtual machine executes the task; v is the number of virtual machines; VM (v, t) is the time required by the virtual machine v to execute the task t; n is the number of tasks distributed to the virtual machine; powera(v) Power when executing a task for virtual machine v; powerd(v) Is the power of the virtual machine v in the idle state;
s2, setting initial variables and giving variable ranges to be optimized
(c) The upper limit and the lower limit of the variable to be optimized need to specify step length for the discrete variable;
(d) population scale pop, iteration times gen, target function number M, optimized variable number P, selection operation system tour, distribution numbers mu and mum in the process of crossing and mutation operations;
s3, taking the variable to be optimized as the individual population in the optimization process, randomly generating an initial population according to the range of the variable to be optimized in a mixed coding mode, and randomly generating the individual within the range
And forming an initial population
S4, updating corresponding data in the task scheduling of the power cloud data center according to variable values to be optimized contained in each individual in the population, and solving an optimization objective function value corresponding to each individual in the population;
s5, performing rapid non-dominated sorting on population individuals, calculating virtual fitness, and generating sub-populations through selection, crossing and variation treatment in a specific implementation mode:
(g) the fast non-dominated sorting is a sorting method for obtaining a non-inferior set solution of multi-target task scheduling of the power cloud data center through calculation of a related objective function, carrying out layering processing on individuals according to the non-inferior result, and enabling population evolution to approach to the direction of Pareto optimal solution; in the evolution process, in order to keep the diversity of the population, the individual crowding distance is designed, and the individual with larger crowding distance is preferentially selected in the selection process;
(h) when the virtual fitness is calculated, firstly, a chromosome formed by the population and the optimization objective function value is decoded, then the corresponding optimization objective function value of each individual is calculated according to a mathematical model of multi-objective task scheduling of the power cloud data center, and then non-inferior layering is carried out according to the optimization objective function value to calculate the virtual fitness of each layer of the individual;
(i) progeny population DiIn the process, the parent operator is selected by adopting a race system in the selection operation, the simulated binary crossover operator is adopted in the crossover operation, and the normal mutation operator is adopted in the mutation operation;
s6, an elite evolution strategy of the NSGA-II algorithm is described, namely a strategy of directly reserving good individuals in a parent to filial generations to prevent the good individuals in the parent from being discarded in the evolution process;
s7, selecting an optimization termination condition as whether the iteration process reaches a preset maximum algebra;
s8, outputting a calculation result including:
(y) the value corresponding to the variable value to be optimized and the value of the optimization objective function;
(z) an optimal solution set graph that optimizes the objective function values.
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