CN112148213A - Annotated data storage server allocation method based on evolution optimization - Google Patents

Annotated data storage server allocation method based on evolution optimization Download PDF

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CN112148213A
CN112148213A CN202010842865.8A CN202010842865A CN112148213A CN 112148213 A CN112148213 A CN 112148213A CN 202010842865 A CN202010842865 A CN 202010842865A CN 112148213 A CN112148213 A CN 112148213A
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郭肇禄
陈远存
谭力江
张文生
黄文俊
蔡岳城
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Abstract

The invention discloses a labeled data storage server distribution method based on evolution optimization. The invention solves the allocation problem of the storage server of the marked data by adopting an improved sine and cosine algorithm. In the improved sine and cosine algorithm, firstly generating random population and setting survival time of individuals; then comprehensively considering the priority and the survival time of the individual to generate a guiding probability; selecting leading individuals based on the leading probability, executing sine and cosine operation operators based on the leading individuals, selecting excellent individuals to enter a new generation of population, and updating the survival time of the individuals; the search process is repeatedly performed until a termination condition is satisfied. The invention comprehensively considers the individual priority and the survival time to select the search guide direction, reduces the probability of trapping in local optimum and can improve the comprehensive storage efficiency of the distributed label data storage server.

Description

Annotated data storage server allocation method based on evolution optimization
Technical Field
The invention relates to the field of cloud computing, in particular to an annotated data storage server allocation method based on evolution optimization.
Background
Artificial intelligence is a hot technology in modern information society. Artificial intelligence techniques have been widely applied to various industries of society, such as: intelligent video monitoring, fingerprint identification, handwriting identification, voice identification and the like. The support foundation of artificial intelligence is mass marking data. The accuracy and the usability of the artificial intelligence system can be improved well only if the system has massive marking data.
In view of the huge amount of labeled data, technicians often need to construct a cloud computing system to store the huge amount of labeled data. In a cloud computing system storing annotation data, technicians often need to solve an annotation data storage server allocation problem: ND station annotation data storage servers are arranged in the cloud computing system, wherein each annotation data storage server has an external storage space SVdtInternal memory MVdtAnd energy consumption per unit time value PEdtWherein the dimension subscript dt ═ 1, 2.., ND; for a given annotation data storage task, a server needs to be selected from ND annotation data storage servers to store annotation data, the sum of external storage of the selected annotation data storage servers is required to be maximized, the sum of internal storage of the selected annotation data storage servers is required to be greater than the lower limit LM of the internal storage, and the sum of energy consumption values per unit time of the selected annotation data storage servers is required to be less than the upper limit SE of energy consumption. The annotation data storage server allocation problem is an NP-completeness problem. When the scale of the annotation data storage server is large, the traditional method has difficulty in obtaining a better annotation data storage server allocation scheme within an acceptable time. To this end, many researchers have proposed evolutionary optimization algorithms that use natural law simulations to solve.
The sine and cosine algorithm is an evolutionary optimization algorithm proposed in recent years, and achieves certain effects in optimizing a plurality of practical engineering problems [ on Kun, burnt and bright, Liuzi Longa, Jiang Ying, Zhang Qiao Xiang, Liuyufang, spectral characteristic peak positioning method [ J ] based on the improved sine and cosine algorithm, 2019,39(09):411 and 417 ]. However, the sine and cosine algorithm is easy to fall into the local optimum when solving the problem of allocation of the labeled data storage server, so that the comprehensive storage efficiency of the allocated labeled data storage server is not high.
Disclosure of Invention
The invention provides a marked data storage server distribution method based on evolution optimization. The method can overcome the defect that the traditional sine and cosine algorithm is easy to fall into local optimum when solving the problem of allocation of the label data storage server to a certain extent, and can improve the comprehensive storage efficiency of the allocated label data storage server.
The technical scheme of the invention is as follows: a method for allocating an annotation data storage server based on evolution optimization comprises the following steps:
step 1, inputting the number ND of the labeled data storage servers;
step 2, inputting external storage space SV of ND station mark data storage serverdtInternal memory MVdtAnd energy consumption per unit time value PEdtWherein the dimension subscript dt ═ 1, 2.., ND;
step 3, inputting a lower limit LM and an upper limit SE of energy consumption of the internal memory;
step 4, setting a population size NPS, a maximum iteration number MIT and a maximum survival time MSL;
step 5, setting the current iteration time it to be 0;
step 6, randomly generating an NPS individual composition population RP ═ { RA ═ RA }1,RA2,...,RAki,...,RANPSIn which RA iski＝[RAki,1,RAki,2,...,RAki,dt,...,RAki,ND]Is the ki individual in the population, and individual RAkiStoring the responsiveness of the ND station annotation data storage server; RAki,dtRepresenting individual RAkiThe dt th station stored in (1) annotates the responsiveness of the data storage server;wherein the individual subscript ki 1, 2.., NPS, and the dimension subscript dt 1, 2.., ND;
step 7, calculating the adaptive values of the NPS individuals in the population; for individual RAkiAdaptation value PFitkiThe calculation process of (2) is as follows: first, RA is given to the individualkiStored ND tag data storage server responsiveness decodes into a state pool RTkiThen calculating individual RA according to equation (1)kiAdapted value PFit ofki：
Figure BDA0002642071660000021
Figure BDA0002642071660000022
Figure BDA0002642071660000023
Wherein the status pool RTkiStoring the allocation status of the ND tag data storage server, and RTki,dtRepresents RTkiThe dt th station stored in (1) marks the distribution state of the data storage server; aw1 is a memory penalty factor; aw2 is an energy consumption penalty factor; wmv is the memory difference; wpe is excess energy consumption; max is a function of taking the maximum value;
step 8, setting the survival period SL of each individual in the populationki1, wherein the individual subscript ki 1, 2., NPS;
step 9, finding out the individual with the minimum adaptive value from the population and storing the individual with the minimum adaptive value into the optimal individual GRA;
step 10, calculating the integration coefficient kw according to the formula (2)
Figure BDA0002642071660000024
Step 11, calculating the guiding probability LP of each individual in the population according to the formula (3)ki：
Figure BDA0002642071660000025
Among them, APFitkiRepresenting individual RAkiThe individual subscript ki 1, 2., NPS;
step 12, according to the guiding probability LP of each individual in the populationkiSelecting leading individual EA from population by roulette selection methodki；
Step 13, executing sine and cosine operation operators based on the guide individuals according to the formula (4):
Figure BDA0002642071660000031
wherein
Figure BDA0002642071660000032
r2 and r4 are randomly generated, and r2 is [0,2 x pi ]]Random real number in between, and pi is the circumferential ratio; r3 is [0,2 ]]Random real numbers in between; r4 is [0,1 ]]Random real numbers in between; sin is a sine function; cos is a cosine function; RU (RU)kiIs a newborn individual;
step 14, calculating the new individual RU according to the formula (1)kiAdapted value of UFitki；
Step 15, if newborn individual RUkiIs less than the individual RAkiThe adaptation value of (a) is then to use the newly born individual RU in the populationkiReplacement of individual RAkiOtherwise, individual RA is maintained in the populationkiThe change is not changed;
step 16, updating the lifetime SL according to the formula (5)ki：
Figure BDA0002642071660000033
Step 17, if SLkiIf it is larger than MSL, an individual NA is randomly generatedkiThen utilizing individual NA in the populationkiReplacement of individual RAkiAnd is provided with SLkiOtherwise, individual RA is maintained in the populationkiThe change is not changed;
step 18, finding out the individual with the minimum adaptive value from the population and storing the individual with the minimum adaptive value into the optimal individual GRA;
step 19, setting the current iteration time it as it + 1;
step 20, if the current iteration time it is greater than MIT, go to step 21, otherwise go to step 10;
and step 21, decoding the optimal individual GRA into the distribution state of the marked data storage server, and obtaining the distribution result of the marked data storage server.
The invention has the beneficial effects that: when the distribution problem of the storage server of the optimization labeling data is solved by the traditional sine and cosine algorithm, the optimal individuals in the population are directly used as search guide directions in the sine search strategy and the cosine search strategy, so that the optimization search shows larger greedy, the population is often lack of diversity, and the defect of trapping in local optimization easily occurs. In order to improve the defects of the traditional method, the invention adopts an improved sine and cosine algorithm to optimize the allocation problem of the annotation data storage server. In the modified sine-cosine algorithm, the adaptation value of an individual is set as a priority for selecting a search guidance direction, while the survival time of the individual in the population is also set as another criterion for selecting a search guidance direction. The improved sine and cosine algorithm integrates the priority and the survival time of the individual to select the search guide direction of the individual, so that the diversity of the population is kept, the probability of being trapped into local optimum is reduced, and the integrated storage efficiency of the distributed labeled data storage server can be improved.
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FIG. 1 is a flow chart of an annotated data storage server allocation method based on evolutionary optimization.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b):
in this embodiment, with reference to the accompanying drawings, the specific implementation steps of the present invention are as follows:
step 1, inputting the number ND of the labeled data storage servers as 150;
step 2, inputting external storage space SV of ND station mark data storage serverdtInternal memory MVdtAnd energy consumption per unit time value PEdtWherein the dimension subscript dt ═ 1, 2.., ND;
the annotation data storage server refers to a server for storing annotation data; wherein, the marking data refers to data which is washed and labeled; the external storage capacity refers to the size of the free space of the hard disk of the label data storage server; the internal storage refers to the size of the memory of the label data storage server; the energy consumption value in unit time refers to the power consumption of the marking data storage server working for 1 hour;
step 3, inputting a lower limit LM of the internal memory space to be 2600 and an upper limit SE of the energy consumption to be 68000;
step 4, setting the population size NPS to be 100, the maximum iteration number MIT to be 3000 and the maximum survival time MSL to be 160;
step 5, setting the current iteration time it to be 0;
step 6, randomly generating an NPS individual composition population RP ═ { RA ═ RA }1,RA2,...,RAki,...,RANPSIn which RA iski＝[RAki,1,RAki,2,...,RAki,dt,...,RAki,ND]Is the ki individual in the population, and individual RAkiStoring the responsiveness of the ND station annotation data storage server; RAki,dtRepresenting individual RAkiThe dt th station stored in (1) annotates the responsiveness of the data storage server; wherein the individual subscript ki 1, 2.., NPS, and the dimension subscript dt 1, 2.., ND; the responsiveness of the annotation data storage server is [0,1 ]]Real numbers in between;
step 7, calculating the adaptive values of the NPS individuals in the population; for individual RAkiAdaptation value PFitkiThe calculation process of (2) is as follows: first, RA is given to the individualkiStored ND tag data storage server responsiveness decodes into a state pool RTkiThen calculating individual RA according to equation (1)kiAdapted value PFit ofki：
Figure BDA0002642071660000041
Figure BDA0002642071660000042
Figure BDA0002642071660000043
Wherein the status pool RTkiStoring the allocation status of the ND tag data storage server, and RTki,dtRepresents RTkiThe dt th station stored in (1) marks the distribution state of the data storage server; aw1 is a memory penalty factor; aw2 is an energy consumption penalty factor; wmv is the memory difference; wpe is excess energy consumption; max is a function of taking the maximum value;
the individual RAkiStored ND tag data storage server responsiveness decodes into a state pool RTkiThe specific process is as follows: first RA is performed on an individualkiThe responsiveness of the stored ND station annotation data storage server is subjected to four-round-five-in integer processing to obtain an integer with the ND value of 0 or 1, and then the obtained integer with the ND value of 0 or 1 is sequentially stored in a state pool RTkiPerforming the following steps; state pool RTkiThe integer with the ND value of 0 or 1 stored in the ND table is the distribution state of the ND station marking data storage server; wherein the status pool RTkiThe value of 1 in the index table indicates that the corresponding label data storage server is allocated to complete the given storage task; state pool RTkiThe value of 0 indicates that the corresponding label data storage server is not allocated to complete the given storage task;
step 8, setting the survival period SL of each individual in the populationki1, wherein the individual subscript ki 1, 2., NPS;
step 9, finding out the individual with the minimum adaptive value from the population and storing the individual with the minimum adaptive value into the optimal individual GRA;
step 10, calculating the integration coefficient kw according to the formula (2)
Figure BDA0002642071660000051
Step 11, calculating the guiding probability LP of each individual in the population according to the formula (3)ki：
Figure BDA0002642071660000052
Among them, APFitkiRepresenting individual RAkiThe individual subscript ki 1, 2., NPS;
step 12, according to the guiding probability LP of each individual in the populationkiSelecting leading individual EA from population by roulette selection methodki；
Step 13, executing sine and cosine operation operators based on the guide individuals according to the formula (4):
Figure BDA0002642071660000053
wherein
Figure BDA0002642071660000054
r2 is [0, 2X π]Random real number in between, and pi is the circumferential ratio; r3 is [0,2 ]]Random real numbers in between; r4 is [0,1 ]]Random real numbers in between; sin is a sine function; cos is a cosine function; RU (RU)kiIs a newborn individual;
step 14, calculating the new individual RU according to the formula (1)kiAdapted value of UFitki；
Step 15, if newborn individual RUkiIs less than the individual RAkiThe adaptation value of (a) is then to use the newly born individual RU in the populationkiReplacement of individual RAkiOtherwise, individual RA is maintained in the populationkiThe change is not changed;
step 16, according to the formula(5) Renewal survival SLki：
Figure BDA0002642071660000061
Step 17, if SLkiIf it is larger than MSL, an individual NA is randomly generatedkiThen utilizing individual NA in the populationkiReplacement of individual RAkiAnd is provided with SLkiOtherwise, individual RA is maintained in the populationkiThe change is not changed;
step 18, finding out the individual with the minimum adaptive value from the population and storing the individual with the minimum adaptive value into the optimal individual GRA;
step 19, setting the current iteration time it as it + 1;
step 20, if the current iteration time it is greater than MIT, go to step 21, otherwise go to step 10;
and step 21, decoding the optimal individual GRA into the distribution state of the marked data storage server, and obtaining the distribution result of the marked data storage server.

Claims (1)

1. The method for allocating the annotated data storage servers based on evolution optimization is characterized by comprising the following steps:
step 1, inputting the number ND of the labeled data storage servers;
step 2, inputting external storage space SV of ND station mark data storage serverdtInternal memory MVdtAnd energy consumption per unit time value PEdtWherein the dimension subscript dt ═ 1, 2.., ND;
step 3, inputting a lower limit LM and an upper limit SE of energy consumption of the internal memory;
step 4, setting a population size NPS, a maximum iteration number MIT and a maximum survival time MSL;
step 5, setting the current iteration time it to be 0;
step 6, randomly generating an NPS individual composition population RP ═ { RA ═ RA }1,RA2,...,RAki,...,RANPSIn which RA iski＝[RAki,1,RAki,2,...,RAki,dt,...,RAki,ND]Is the ki individual in the population, and individual RAkiStoring the responsiveness of the ND station annotation data storage server; RAki,dtRepresenting individual RAkiThe dt th station stored in (1) annotates the responsiveness of the data storage server; wherein the individual subscript ki 1, 2.., NPS;
step 7, calculating the adaptive values of the NPS individuals in the population; for individual RAkiAdaptation value PFitkiThe calculation process of (2) is as follows: first, RA is given to the individualkiStored ND tag data storage server responsiveness decodes into a state pool RTkiThen calculating individual RA according to equation (1)kiAdapted value PFit ofki：
Figure FDA0002642071650000011
Wherein the status pool RTkiStoring the allocation status of the ND tag data storage server, and RTki,dtRepresents RTkiThe dt th station stored in (1) marks the distribution state of the data storage server; aw1 is a memory penalty factor; aw2 is an energy consumption penalty factor; wmv is the memory difference; wpe is excess energy consumption; max is a function of taking the maximum value;
step 8, setting the survival period SL of each individual in the populationki1, wherein the individual subscript ki 1, 2., NPS;
step 9, finding out the individual with the minimum adaptive value from the population and storing the individual with the minimum adaptive value into the optimal individual GRA;
step 10, calculating an integration coefficient kw according to a formula (2):
Figure FDA0002642071650000012
step 11, calculating the guiding probability LP of each individual in the population according to the formula (3)ki：
Figure FDA0002642071650000021
Among them, APFitkiRepresenting individual RAkiThe individual subscript ki 1, 2., NPS;
step 12, according to the guiding probability LP of each individual in the populationkiSelecting leading individual EA from population by roulette selection methodki；
Step 13, executing sine and cosine operation operators based on the guide individuals according to the formula (4):
Figure FDA0002642071650000022
wherein
Figure FDA0002642071650000023
r2 is [0, 2X π]Random real number in between, and pi is the circumferential ratio; r3 is [0,2 ]]Random real numbers in between; r4 is [0,1 ]]Random real numbers in between; sin is a sine function; cos is a cosine function; RU (RU)kiIs a newborn individual;
step 14, calculating the new individual RUkiAdapted value of UFitki；
Step 15, if newborn individual RUkiIs less than the individual RAkiThe adaptation value of (a) is then to use the newly born individual RU in the populationkiReplacement of individual RAkiOtherwise, individual RA is maintained in the populationkiThe change is not changed;
step 16, updating the lifetime SL according to the formula (5)ki：
Figure FDA0002642071650000024
Step 17, if SLkiIf it is larger than MSL, an individual NA is randomly generatedkiThen utilizing individual NA in the populationkiReplacement of individual RAkiAnd is provided with SLkiOtherwise, individual RA is maintained in the populationkiThe change is not changed;
step 18, finding out the individual with the minimum adaptive value from the population and storing the individual with the minimum adaptive value into the optimal individual GRA;
step 19, setting the current iteration time it as it + 1;
step 20, if the current iteration time it is greater than MIT, go to step 21, otherwise go to step 10;
and step 21, decoding the optimal individual GRA into the distribution state of the marked data storage server, and obtaining the distribution result of the marked data storage server.
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