CN114358234B - Resource scheduling method of cloud platform based on improved bat algorithm - Google Patents
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Abstract
A resource scheduling method of a cloud platform based on an improved bat algorithm includes the steps of S1, receiving tasks submitted by users, dividing each task into sub-tasks, generating bat populations for each sub-task, wherein the scale of each sub-task is approximately equal; s2, initializing the position, speed, pulse search frequency and pulse search frequency range of each bat in the bat population generated in the step one; s3, improving a bat algorithm, and solving the bat population initialized in the step two by using the improved bat algorithm; and S4, obtaining a global optimal solution, and carrying out resource scheduling of the cloud platform according to the optimal solution. The speed is updated by an inertia weight method based on a logarithmic decrement strategy, and in order to enable the algorithm not to fall into a local optimal solution too early, the searching performance of the algorithm is improved, the diversity of the population is increased by adding random disturbance to update the current optimal solution, and therefore the random searching capability of the bat algorithm is furthest mined.
Description
Technical Field
The invention relates to the field of cloud platform resource scheduling, in particular to a resource scheduling method of a cloud platform based on an improved bat algorithm.
Background
In recent years, as the scale of cloud computing is gradually enlarged, more and more high-performance computing and big data application depend on the cloud computing, so how to find an efficient and reasonable computing resource scheduling model and a method thereof are important in a cloud environment.
Aiming at the scheduling problem of cloud platform resources, a plurality of students have conducted corresponding researches on the cloud platform resources, a large number of cloud computing resource scheduling algorithms based on intelligent optimization are provided, and the bat algorithm is one of the cloud computing resource scheduling algorithms. The bat algorithm is a heuristic bionic group intelligent algorithm proposed by the Yang professor 2010, and the algorithm finds the optimal solution of the problem by simulating the behavior of the bat for foraging by a small insect through an echo positioning system, has the advantages of less parameters, strong stability, high solving speed, strong searching capability and the like, but the bat algorithm still has the problems of low later convergence speed, low convergence precision and the like.
Disclosure of Invention
In order to overcome the problems, the invention aims to provide a resource scheduling method of a cloud platform based on an improved bat algorithm, which updates the speed based on an inertia weight method of a number decreasing strategy, improves the searching performance of the algorithm in order to prevent the algorithm from falling into a local optimal solution prematurely, and increases the diversity of a population by adding random disturbance to update the current optimal solution, thereby mining the random searching capability of the bat algorithm to the maximum extent.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the resource scheduling method of the cloud platform based on the improved bat algorithm is characterized by comprising the following steps of:
s1, receiving tasks submitted by users, dividing each task into sub-tasks, wherein the scale of each sub-task is approximately equal, generating bat population for each sub-task, assuming that the number of the tasks is n, the number of computing resources is m, dividing each task into a plurality of smaller sub-tasks according to a certain rule, and dividing the tasks into the total number of the sub-tasks which is larger than the total number of the computing resources, wherein the coding sub-task formula is as follows:
wherein: s [ i, j ] represents the j-th subtask number in the i-th task. sn (k) (k=0, 1,2,.,. N) represents the number of h-th tasks divided into smaller sub-tasks, and sn (0) =0 is specified;
s2, initializing the position, speed, pulse search frequency and pulse search frequency range of each bat in the bat population generated in the first step, and assuming that the search space is d-dimensional, the position of the ith bat at the t-th iteration isSpeed is +.>Pulse search frequency f i . Each bat is randomly assigned an initial position within the search spaceAnd an initial speed +.>Pulse search frequency f i ＝f min +(f max -f min ) Beta, pulse search frequency range [ f min ，f max ]。
Wherein: f (f) min For searching the minimum value of the frequency of the pulse, f max For maximum pulse search frequency, β is in interval [0,1 ]]Random numbers uniformly distributed on the base;
s3, improving a bat algorithm, solving the bat population initialized in the second step by using the improved bat algorithm, updating the speed by adding an inertia weight method of a logarithmic decrement strategy, wherein the inertia weight ensures the randomness of each bat speed, and the bat population is distributed in the whole solution space in the initial stage of the algorithm, so that the capability of jumping out of local extremum is enhanced. Later in the algorithm, the speed of the individual bats drops to perform finer searches, thereby improving the performance of the algorithm. Meanwhile, the diversity of the population is increased by adding random disturbance to update the current optimal solution, so that the random searching capability of the bat algorithm is furthest mined, and the speed and position updating formula is as follows:
ω＝ω max -α(ω max -ω min )log MaxIter t+τ*betarnd()
wherein ω is an inertial factor, x best For the current optimal position, alpha is a logarithmic control factor, maxIter is the maximum iteration number, tau is an inertial deviation factor, and tau epsilon [0.1,0.9 ]]Betarnd () is generated following beta distributionA random number;
and S4, obtaining a global optimal solution, carrying out resource scheduling specific positions of the cloud platform according to the optimal solution, and carrying out resource scheduling of the cloud platform according to the partitioning rule corresponding to the step one after obtaining the optimal solution.
For local search, in order to prevent the algorithm from being sunk into a local optimal solution prematurely, the searching performance of the algorithm is improved, the current optimal solution is updated by adding random disturbance, the diversity of the population is increased, and therefore the random searching capability of the bat algorithm is furthest mined.
x new ＝x old +σ∈tA t
Wherein x is new Representing the position of the bat after disturbance, x old Representing the position of the bat before disturbance, e t obeys a gaussian normal distribution N (0, 1), σ is a scaling factor;
pulse loudness A i And pulse frequency R i To update continuously with the iterative process, in general, when the optimal solution is continuously approached, the loudness will gradually decrease, the pulse frequency will gradually increase, and the following is an updated formula of pulse loudness and pulse frequency:
wherein θ and γ are constants, and θ is 0< 1, γ >0;
in the bat algorithm, the pulse frequency increase coefficient gamma and the pulse loudness attenuation coefficient theta have important influence on the algorithm performance. The current space state of the bat subject is changed in such a way as to generate a bat in the interval 0,1]Random number R, if R > R i The i-th bat current new solution is generated by the vicinity of the optimal solution in the current space; if not, then the i-th bat's current new solution is generated from its vicinity.
The beneficial effects of the invention are as follows: the invention can obviously improve the convergence speed and optimizing precision of the original bat algorithm.
Drawings
Fig. 1 is a schematic diagram of an improved bat algorithm provided by an embodiment of the present invention.
Description of the preferred embodiments 2
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, a resource scheduling method of a cloud platform based on an improved bat algorithm is characterized by comprising the following steps:
s1, receiving tasks submitted by users, dividing each task into sub-tasks, wherein the scale of each sub-task is approximately equal, generating bat population for each sub-task, assuming that the number of the tasks is n, the number of computing resources is m, dividing each task into a plurality of smaller sub-tasks according to a certain rule, and dividing the tasks into the total number of the sub-tasks which is larger than the total number of the computing resources, wherein the coding sub-task formula is as follows:
wherein: s [ i, j ] represents the j-th subtask number in the i-th task. sn (k) (k=0, 1,2,.,. N) represents the number of h-th tasks divided into smaller sub-tasks, and sn (0) =0 is specified;
s2, initializing the position, speed, pulse search frequency and pulse search frequency range of each bat in the bat population generated in the first step, and assuming that the search space is d-dimensional, the position of the ith bat at the t-th iteration isSpeed is +.>Pulse search frequency f i . Each bat is randomly assigned an initial position within the search spaceAnd an initial speed +.>Pulse search frequency f i ＝f min +(f max -f min ) Beta, pulse search frequency range [ f min ，f max ]。
Wherein: f (f) min For searching the minimum value of the frequency of the pulse, f max For maximum pulse search frequency, β is in interval [0,1 ]]Random numbers uniformly distributed on the base;
s3, improving a bat algorithm, solving the bat population initialized in the second step by using the improved bat algorithm, updating the speed by adding an inertia weight method of a logarithmic decrement strategy, wherein the inertia weight ensures the randomness of each bat speed, and the bat population is distributed in the whole solution space in the initial stage of the algorithm, so that the capability of jumping out of local extremum is enhanced. Later in the algorithm, the speed of the individual bats drops to perform finer searches, thereby improving the performance of the algorithm. Meanwhile, the diversity of the population is increased by adding random disturbance to update the current optimal solution, so that the random searching capability of the bat algorithm is furthest mined, and the speed and position updating formula is as follows:
ω＝ω max -α(ω max -ω min )log MaxIter t+τ*betarnd()
wherein ω is an inertial factor, x best For the current optimal position, alpha is a logarithmic control factor, maxIter is the maximum iteration number, tau is an inertial deviation factor, and tau epsilon [0.1,0.9 ]]Betarnd () is a random number generated following the beta distribution;
and S4, obtaining a global optimal solution, carrying out resource scheduling specific positions of the cloud platform according to the optimal solution, and carrying out resource scheduling of the cloud platform according to the partitioning rule corresponding to the step one after obtaining the optimal solution.
For local search, in order to prevent the algorithm from being sunk into a local optimal solution prematurely, the searching performance of the algorithm is improved, the current optimal solution is updated by adding random disturbance, the diversity of the population is increased, and therefore the random searching capability of the bat algorithm is furthest mined.
x new ＝x old +σ∈tA t
Wherein x is new Representing the position of the bat after disturbance, x old Representing the position of the bat before disturbance, e t obeys a gaussian normal distribution N (0, 1), σ is a scaling factor;
pulse loudness A i And pulse frequency R i To update continuously with the iterative process, in general, when the optimal solution is continuously approached, the loudness will gradually decrease, the pulse frequency will gradually increase, and the following is an updated formula of pulse loudness and pulse frequency:
wherein θ and γ are constants, and θ is 0< 1, γ >0;
in the bat algorithm, the pulse frequency increase coefficient gamma and the pulse loudness attenuation coefficient theta have important influence on the algorithm performance. The current space state of the bat subject is changed in such a way as to generate a bat in the interval 0,1]Random number R, if R > R i The i-th bat current new solution is generated by the vicinity of the optimal solution in the current space; if not, then the i-th bat's current new solution is generated from its vicinity.
Claims (2)
1. The resource scheduling method of the cloud platform based on the improved bat algorithm is characterized by comprising the following steps of:
s1, receiving tasks submitted by users, dividing each task into sub-tasks, wherein the scale of each sub-task is approximately equal, generating bat population for each sub-task, assuming that the number of the tasks is n, the number of computing resources is m, dividing each task into a plurality of smaller sub-tasks according to a certain rule, and dividing the tasks into the total number of the sub-tasks which is larger than the total number of the computing resources, wherein the coding sub-task formula is as follows:
where s [ h, j ] represents the j-th subtask number in the h-th task, sn (k) (k=0, 1,2, …, n) represents the number of subtasks into which the h-th task is divided, and sn (0) =0 is specified;
s2, initializing the position, the speed, the pulse search frequency and the pulse search frequency range of each bat in the bat population generated in the step S1, and assuming that the search space is d-dimensional, the position of the ith bat at the t-th iteration isSpeed is +.>Pulse search frequency f i Each bat is randomly assigned an initial position within the scope of the search spaceAnd an initial speed +.>Pulse search frequency f i ＝f min +(f max -f min ) Beta, pulse search frequency range [ f min ,f max ]；
Wherein: f (f) min For searching the minimum value of the frequency of the pulse, f max For maximum pulse search frequency, β is in interval [0,1 ]]Random numbers uniformly distributed on the base;
s3, improving a bat algorithm, solving the bat population initialized in the second step by using the improved bat algorithm, updating the speed by adding an inertia weight method of a logarithmic decreasing strategy, wherein the inertia weight ensures the randomness of each bat speed, the bat population is distributed in the whole solution space in the initial stage of the algorithm, the capability of jumping out of a local extremum is enhanced, the speed of a bat individual is reduced in the later stage of the algorithm so as to perform more careful searching, thereby improving the performance of the algorithm, and meanwhile, the diversity of the population is increased by adding random disturbance to update the current optimal solution, so that the random searching capability of the bat algorithm is furthest mined, and the speed and position updating formula is as follows:
ω＝ω max -α(ω max -ω min )log MaxIter t+τ*betarnd()
wherein ω is an inertial factor, x best For the current optimal position, alpha is a logarithmic control factor, maxIter is the maximum iteration number, tau is an inertial deviation factor, and tau epsilon [0.1,0.9 ]]Betarnd () is a random number generated following the beta distribution;
and S4, obtaining a global optimal solution, carrying out resource scheduling specific positions of the cloud platform according to the optimal solution, and carrying out resource scheduling of the cloud platform according to the partitioning rule corresponding to the step S1 after obtaining the optimal solution.
2. The resource scheduling method of a cloud platform based on an improved bat algorithm according to claim 1, wherein for local search, in order to prevent the algorithm from sinking into a local optimal solution prematurely, the search performance of the algorithm is improved, the diversity of the population is increased by adding random disturbance to update the current optimal solution, thereby maximally exploiting the random search capability of the bat algorithm;
x new ＝x old +σεtA t
wherein x is new Representing the position of the bat after disturbance, x old Representing the position of the bat before disturbance, e t obeys a gaussian normal distribution N (0, 1), σ is a scaling factor;
pulse loudness A i And pulse frequency R i To update continuously with the iterative process, in general, when the optimal solution is continuously approached, the loudness will gradually decrease, the pulse frequency will gradually increase, and the following is an updated formula of pulse loudness and pulse frequency:
wherein θ and γ are constants, and 0< θ <1, γ >0;
in the bat algorithm, the pulse frequency increasing coefficient gamma and the pulse loudness attenuating coefficient theta have important influence on the algorithm performance; the current space state of the bat subject is changed in such a way as to generate a bat in the interval 0,1]Random number r, if r>R i The i-th bat current new solution is generated by the vicinity of the optimal solution in the current space; if not, then the i-th bat's current new solution is generated from its vicinity.
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