CN110781003B - Load balancing method for particle swarm fusion variation control - Google Patents
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Abstract
The invention relates to a load balancing method for particle swarm fusion variation control, and belongs to the field of computer load balancing. The method comprises the following steps of S1: generating an initial population; s2: selecting operation; s3: performing cross operation; s4: performing mutation operation; s5: carrying out iterative updating; this can be terminated by achieving one of the following conditions: (1) finding an optimal solution; and (2) reaching the maximum iteration number. S7: and after the termination condition is reached, stopping the flow, and obtaining the optimal scheme of the processing relation between the tasks and the nodes by the last chromosome which is left according to the logical inverse solution generated by the sequence. The invention combines the ideas of the two methods, adds a control function and is assisted by a proper fitness function, thereby not only avoiding the situation of falling into local optimum in the early stage, but also avoiding the problem of poor convergence precision in the later stage.
Description
Technical Field
The invention belongs to the technical field of computer load balancing, and relates to a load balancing method for particle swarm fusion variation control.
Background
The appearance of a computer is used for assisting people in processing some complex and repeated things, the things are more and more complex as time goes on, under the requirement, people have to improve the processing capacity of the computer, namely the processing capacity of a CPU (central processing unit), however, the data information amount is increased explosively when the internet era comes, network data communication is the most common mode, a server is accessed at high concurrency with large data amount, so that the server cannot work effectively and even generates errors, the problem can not be solved effectively by improving the hardware operation capacity of the server alone at the time, the processing capacity of the computer is wasted, and network congestion is caused.
Under the condition, it is crucial to design a load balancing mechanism to achieve reasonable task allocation, namely to design a task scheduler, the core of which is a load balancing algorithm, and a good load balancing algorithm, so that hardware resources can be utilized to the maximum extent, network tasks are processed efficiently, interaction experience of users is improved, and compared with the level of replacing hardware alone, the method has higher cost performance, and can save resources.
So far, load balancing algorithms can be roughly divided into static and dynamic, the static algorithms allocate requests in a fixed manner, although the implementation is easy, the effect is not good in practice, because the considered conditions are too ideal and simple, such as dead board operation and different performance of servers in a cluster, the dynamic algorithms consider more than the static algorithms, and are more comprehensive, the current research situation is basically based on the improvement or combination of the traditional dynamic algorithms, the improvement point depends on which aspect an author focuses on, and of course, a perfect algorithm cannot exist in reality, and each improved algorithm is prone to different application scenarios and is the point that the author optimizes different scenarios.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a load balancing method for controlling fusion and variation of particle clusters, which can maintain load balancing as much as possible when a server cluster processes tasks by a more effective means, aiming at the problem of poor processing effect of the server due to high concurrent access in the internet, and the method has two advantages that firstly, on the basis of not increasing additional cost, load balancing is realized in a software manner, and higher cost performance is achieved; secondly, hardware resources are utilized more effectively.
In order to achieve the purpose, the invention provides the following technical scheme:
a load balancing method for particle swarm fusion variation control comprises the following steps:
s1: generating an initial population: the population is a set of 'chromosomes', the chromosome sequence comprises two pieces of information, the tasks correspond to the nodes, and an initial population is generated by a random method;
s2: selecting operation: based on the theory of survival of the fittest, selecting chromosome individuals in the population through a fitness function F (i);
s3: and (3) cross operation: the crossing is a means of generating new individuals and enriching a gene library, and the individuals in the population are used as parents, and partial genes between the parents are exchanged according to a designed crossing mode, so that the new individuals are formed;
s4: mutation operation:
variation control is added into the particle swarm algorithm, so that the algorithm avoids excessively fast shrinkage in the early stage, the local optimum is achieved, the convergence precision is ensured, and a variation control function is introduced to control the variation rate of variation operation:
if the predetermined variation rate is t, the variation rate controlled by the variation function is: t is t * ＝tf(d)；
Where d is the number of current iterations, d max Is the total number of pre-iterations, d ∈ [0 max ]α is a control coefficient, k = (d) max +δ) α δ is a relative d max A small number in order to ensure that d is taken to be d max At times f (d) is small but not zero.
S5: iterative updating
And (3) carrying out iterative updating and evolutionary logic according to a particle swarm algorithm and carrying out appropriate modification:
wherein
Indicates that the individual has a history which is optimal>
Indicates that the overall history is optimal>
Represents the value of the d-th generation of the individual,
representing the cumulative optimal difference; the power of updating comes from the history of the individual and the history of the whole body;
s6: and (4) terminating:
terminating the process if one of the following conditions is reached: (1) finding an optimal solution; (2) reaching a maximum number of iterations;
s7: and (3) decoding:
and after the termination condition is reached, stopping the flow, and obtaining the optimal scheme of the processing relation between the tasks and the nodes by the last chromosome which is left according to the logical inverse solution generated by the sequence.
Further, the random method in step S1 generates an initial population, which specifically includes: setting the number of tasks as M and the number of nodes as n, randomly generating M numbers between [1, n ], then arranging the M numbers, taking the number of the arranging method as the population scale, if the number of the arranged combinations is larger than the set population scale number M, selecting M types, and simultaneously determining the iteration number.
Further, in step S2, the fitness function is designed as:
F(i)＝exp{[Rstime min -Rstime(i)]·σ}·
wherein i represents a chromosome number, rstill (i) represents a time consumed by a project processing task corresponding to the ith chromosome, and Rstill (i) represents a time consumed by a project processing task corresponding to the ith chromosome min Represents the minimum value of the time consumed by each chromosome corresponding scheme in the same time period, and sigma represents the load variance, so that the fitness function is the chromosomes which consume less time and are more balanced in load, and the fitness is higher and is easier to keep.
Further, step S4 specifically includes: introducing a variation control function to control the variation rate of the variation operation:
if the predetermined variation rate is t, the variation rate controlled by the variation function is: t is t * ＝tf(d)；
Where d is the number of current iterations, d max Is the total number of pre-iterations, d ∈ [0 max ]α is a control coefficient, k = (d) max +δ) α δ is a relative d max A small number in order to ensure that d is taken to d max At times f (d) is small but not zero.
The invention has the beneficial effects that: the ideas of the two methods are combined, a control function is added, and a proper fitness function is used for assisting, so that the situation that the early stage is trapped in local optimization is avoided, and the problem that the later stage convergence precision is poor is also avoided.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
fig. 1 is a flow chart of a load balancing method for particle swarm fusion variation control according to the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
1 particle swarm algorithm
It is optimized by the mutual transmission competition of particle information in the population, which can be expressed as: randomly initializing a particle swarm with the number of m in an N-dimensional space, and updating the speed and the position of the example through individual optimization and overall optimization:
2GA algorithm
A simple genetic algorithm refers to a rule of biological evolution, namely 'survival of a suitable person', firstly, a realistic problem is randomly coded into chromosomes to form a population, and the chromosomes are continuously evolved towards the optimal state through operations such as evaluation, intersection, variation and the like of a fitness function, so that the optimal solution of the problem is finally obtained.
3 fusion improvement
Firstly, the ideas of the two algorithms are fused together, and then a variation control function is added, so that the variation rate is controlled, enough individuals in the early stage can be ensured, the local optimum is avoided, the convergence precision in the later stage can be accelerated, and whether the individuals are reserved or not can be judged more reasonably by assisting with a proper fitness function.
F(i)＝exp{[Rstime min -Rstime(i)]·σ}
Wherein i represents a chromosome number, rstill (i) represents a time consumed by a project processing task corresponding to the ith chromosome, and Rstill (i) represents a time consumed by a project processing task corresponding to the ith chromosome min The method includes that the minimum value of time consumed by each chromosome corresponding to a scheme in the same time period is represented, sigma represents load variance, theoretically, the capacity of each distributed task amount corresponding to each chromosome is called balance, the total cluster performance on the total task ratio is a fixed value, the performance or the remaining performance of each node distributed task ratio is a variable value, the balance variance is dynamic, the difference between the individual ratio and the total ratio can be measured, and a fitness function can be understood as a chromosome which consumes less time and is more balanced in load, and the chromosome is higher in fitness and easier to survive.
In more detail: suppose the performance of the ith computing node is C i The factors affecting performance include CPU, bandwidth, memory, disk, etc., so the total computing power of the cluster is:
assuming that the calculation amount of the jth task is t corresponding to m tasks to be processed j Then the total calculated amount of m tasks is ≥>
Branch to node p k The number of tasks on is->
Node p k Has the performance of->
Then there should be the following:
in fact, is fluctuating if it is assumed that:
wherein k is [1, n ]]And sigma is the variance of n mu.
Thus, as shown in fig. 1, the present invention provides a load balancing method for controlling fusion variation of particle swarm, which includes the following steps:
s1: generating an initial population: population, i.e. the set of "chromosomes", generally the chromosome sequence contains two pieces of information, the relationship between tasks and nodes, and there are several methods for generating the initial population, and the initial population can be generated by using the most general random method, and assuming that the number of tasks is m and the nodes are n, the chromosome sequence can be generated by:
randomly generating M numbers between [1, n ], and then arranging the M numbers, wherein the number of the arranged methods can be used as the population scale, of course, the situation can also be adopted, the number of the arranged combinations is larger than the set population scale number M, and then M kinds are selected, and the iteration number is determined at the same time.
S2: selecting operation: based on the theory of survival of the fittest, the chromosomal individuals in the population must be selected by a fitness function F (i), and the fittest is left behind.
S3: and (3) cross operation: crossing is a means for generating new individuals and enriching gene libraries, and the individuals in the population are used as parents, and partial genes between the parents are exchanged according to a designed crossing mode, so that the new individuals are formed.
S4: mutation operation:
the particle swarm algorithm of the list obtains the optimal solution through the competition of the particles, but is easy to fall into the local optimal solution, the genetic algorithm of the list seeks optimization through iteration and variation, but the convergence precision of the algorithm is not good, if at the moment, proper variation control is added into the particle swarm algorithm, namely when two algorithms are fused, the two algorithms are improved simultaneously, the algorithm is prevented from shrinking too fast in the early stage, the algorithm falls into the local optimal solution, the convergence precision is ensured, and therefore, a variation control function is introduced to control the variation rate of the variation operation:
if the predetermined variation rate is t, the variation rate controlled by the variation function is: t is t * ＝tf(d)；
Where d is the number of current iterations, d max Is the total number of pre-iterations, d ∈ [0 max ]α is a control coefficient, k = (d) max +δ) α δ is a relative d max A small number in order to ensure that d is taken to be d max At times f (d) is small but not zero.
Through the structure of the function, the method is obtained through analysis, the larger variation rate is kept in the early stage of the hybrid algorithm, the population keeps diversity as much as possible, the search surface is larger, the local optimization is avoided, the variation rate is reduced in the later stage of the hybrid algorithm iteration, and only fine adjustment is needed because the optimization is already approached at the moment, and meanwhile, the convergence precision is guaranteed.
S5: iterative updating
And (3) carrying out iterative updating and evolutionary logic according to a particle swarm algorithm and carrying out appropriate modification:
description of the drawings:
indicates that the individual has an optimum history>
Indicates that the overall history is optimal>
Represents the value of the d-th generation of the individual,
indicating the cumulative optimum difference. In other words, the updated power comes from two aspects: one is the history of the individual, and the other is the history of the whole.
S6: and (4) terminating:
either of the following conditions may be met: (1) finding an optimal solution; and (2) reaching the maximum iteration number.
S7: and (3) decoding:
and after the termination condition is reached, stopping the flow, and obtaining the optimal scheme of the processing relation between the tasks and the nodes by the last chromosome which is left according to the logical inverse solution generated by the sequence.
Optionally, the fitness function design emphasizes two factors, i.e., the load of the cluster and the time consumed by the processing task, generally, the design requirement of the fitness function is non-negative, and the chromosome is more adaptive to the environment as the fitness value is larger, so we design the fitness function as:
F(i)＝exp{[Rstime min -Rstime(i)]·σ}
wherein i represents a chromosome number, rstill (i) represents a scenario corresponding to the ith chromosome, time consumed for processing a task, rstill (i) represents a scenario corresponding to the ith chromosome min Represents the minimum value of time consumed by each chromosome correspondence scheme in the same time period, and σ representsThe load variance, and therefore the fitness function, can be understood as a chromosome that is less time consuming and more evenly loaded, and is more highly adapted and easier to retain.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (2)
1. A load balancing method for particle swarm fusion variation control is characterized in that: the method comprises the following steps:
s1: generating an initial population: the population is a set of 'chromosomes', the chromosome sequence comprises two pieces of information, the tasks correspond to the nodes, and an initial population is generated by a random method;
s2: selecting operation: based on the theory of survival of the fittest, selecting chromosome individuals in the population through a fitness function F (i); the fitness function is designed to:
F(i)＝exp{[Rstime min -Rstime(i)]·σ}
wherein i represents a chromosome number, rstime (i) represents a time consumed for a project processing task corresponding to the ith chromosome, and Rstime min The system represents the minimum value of time consumed by each chromosome corresponding scheme in the same time period, and sigma represents load variance, so that the fitness function is a chromosome which consumes less time and has more balanced load, and the higher the fitness is, the easier the fitness is to keep;
s3: and (3) cross operation: the crossing is a means of generating new individuals and enriching a gene library, and the individuals in the population are used as parents, and partial genes between the parents are exchanged according to a designed crossing mode, so that the new individuals are formed;
s4: mutation operation:
variation control is added into the particle swarm algorithm, so that the algorithm avoids too fast shrinkage in the early stage, falls into local optimum, and ensures convergence accuracy; introducing a variation control function to control the variation rate of the variation operation:
if the predetermined variation rate is t, the variation rate controlled by the variation function is: t is t * ＝tf(d)；
Where d is the number of current iterations, d max Is the total number of pre-iterations, d ∈ [0 max ]α is a control coefficient, k = (d) max +δ) α δ is a relative d max A small number in order to ensure that d is taken to d max F (d) is small but not zero;
s5: iterative updating
Performing iterative updating and evolutionary logic according to a particle swarm algorithm and performing appropriate modification:
wherein
Indicates that the individual has a history which is optimal>
Indicates that the overall history is optimal>
Represents the value of the d-th generation of the individual,
representing the cumulative optimum difference value; the updated power comes from the individual calendarHistory and global history;
s6: and (4) terminating:
terminating the process if one of the following conditions is reached: (1) finding an optimal solution; (2) reaching a maximum number of iterations;
s7: and (3) decoding:
and after the termination condition is reached, stopping the flow, and finally obtaining the optimal scheme of the processing relation between the tasks and the nodes by the left chromosome according to the logical inverse solution generated by the sequence.
2. The load balancing method for particle swarm fusion variation control according to claim 1, wherein: the random method in the step S1 generates an initial population, and specifically includes: setting the number of tasks as M and the number of nodes as n, randomly generating M numbers between [1, n ], then arranging the M numbers, taking the number of the arranging method as the population scale, if the number of the arranged combinations is larger than the set population scale number M, selecting M types, and simultaneously determining the iteration number.
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