CN115794354A - Cloud video transcoding task scheduling based on K-Means clustering and improved AOA algorithm - Google Patents

Cloud video transcoding task scheduling based on K-Means clustering and improved AOA algorithm Download PDF

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CN115794354A
CN115794354A CN202310035364.2A CN202310035364A CN115794354A CN 115794354 A CN115794354 A CN 115794354A CN 202310035364 A CN202310035364 A CN 202310035364A CN 115794354 A CN115794354 A CN 115794354A
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tasks
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詹楷杰
蔡茂国
洪广杰
欧基发
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Shenzhen University
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Abstract

The invention discloses a cloud video transcoding task scheduling method based on K-Means clustering and an improved AOA algorithm, belongs to the technical field of video task scheduling strategies in a cloud computing environment, and comprises the following steps: step S1: clustering the tasks by using a K mean value clustering algorithm according to different video task characteristics; step S2: and based on a task scheduling strategy of an improved Archimedes optimization algorithm, searching an optimal scheduling scheme, and distributing the video task set to the virtual machine resources for transcoding scheduling processing. In the invention, the chaos reverse learning is added in the early stage of the task scheduling algorithm to improve the usability of the initial solution and the convergence speed, and then the nonlinear dynamic density decreasing factor is reconstructed, so that the algorithm can better balance the global search and the local search, and the final convergence result is better.

Description

Cloud video transcoding task scheduling based on K-Means clustering and improved AOA algorithm
Technical Field
The invention belongs to the technical field of video task scheduling strategies in cloud computing environments, and particularly relates to cloud video transcoding task scheduling based on K-Means clustering and an improved AOA algorithm.
Background
With the rapid development of the internet and the arrival of the information era, the data volume required to be processed by a computer is increased in a blowout mode, in order to better meet the requirements and service quality of users, how to improve the computing capacity, network bandwidth and resource utilization rate of the computer and reduce the energy consumption of a system become new research targets, a cloud computing concept is generated from the data volume, a complete solution is provided for the users, the cost can be reduced by cloud computing, the storage is increased, and the resource utilization rate and the adaptability are improved.
The video task is used as one of cloud computing tasks, various requirements of users enable video transcoding directions to be very diversified, and meanwhile, the users and cloud service providers have strict requirements on instantaneity, definition and service quality of the video transcoding task.
How to efficiently and reasonably allocate tasks to virtual machines in a resource pool is one of key technologies of cloud computing, also called task scheduling, a plurality of heuristic intelligent algorithms are applied to task scheduling, such as an artificial bee colony algorithm, a sparrow algorithm, an Archimedes optimization algorithm and the like, the Archimedes Optimization (AOA) algorithm is a novel meta-heuristic algorithm proposed by Hashim and the like in 2020, the algorithm enables individuals to reach a balanced state by simulating the relation of buoyancy force borne by objects completely or partially immersed in fluid when the objects collide, the individual density, the volume and the acceleration are continuously adjusted in an iteration process, so that the population is guided to converge to an optimal position by the individuals with an excellent fitness value, the purpose of optimizing is achieved, but the algorithm is updated around the optimal position in a global search stage because the individuals only depend on a random individual to lead the population to find the optimal solution to an optimal area in the global search stage, when the random individual is a poor solution, the population is easily guided to fall into a local optimal solution, the search stage is incapable of searching the optimal solution, the cloud computing the optimal solution is easy to solve the problem of improving the cloud scheduling in the cloud scheduling process.
Disclosure of Invention
The invention aims to: in order to solve the problems of long task scheduling time, low resource utilization rate, unbalanced load and the like of video task scheduling in a cloud computing environment, the cloud video transcoding task scheduling based on K-Means clustering and an improved AOA algorithm is provided.
In order to achieve the purpose, the invention adopts the following technical scheme:
a cloud video transcoding task scheduling method based on K-Means clustering and an improved AOA algorithm comprises the following steps:
step S1: in cloud computing, a user submits transcoding requirements, a system acquires a video task data set and divides the video task data set into different GOPs through a video divider, the GOPs represent different video tasks, and the video task set is used
Figure 100002_DEST_PATH_IMAGE001
For presentation, for collection of virtual machines
Figure 100002_DEST_PATH_IMAGE002
Is shown in which
Figure 100002_DEST_PATH_IMAGE003
The individual tasks are represented as:
Figure 100002_DEST_PATH_IMAGE004
，
Figure 100002_DEST_PATH_IMAGE005
a number indicating the number of the task,
Figure 100002_DEST_PATH_IMAGE006
which represents the length of the task or tasks,
Figure 100002_DEST_PATH_IMAGE007
representing the amount of computation required for the task,
Figure 100002_DEST_PATH_IMAGE008
the memory required for the task is represented,
Figure 100002_DEST_PATH_IMAGE009
expressing the bandwidth required by the tasks, clustering the tasks by using a K-means clustering algorithm according to different task characteristics, and clustering the tasks with similar requirements in the same cluster;
step S2: the task scheduling strategy based on the improved Archimedes optimization algorithm is characterized in that a chaos mapping reverse learning strategy is added in the early stage of the algorithm, a nonlinear dynamic density decreasing factor is reconstructed, global search and local search capabilities of the algorithm are balanced, an optimal scheduling scheme is searched, and a video task set is distributed to virtual machine resources for transcoding scheduling processing.
As a further description of the above technical solution:
the clustering preprocessing of the video tasks in the step S1 includes:
(1) Determining task samples to be clustered, and randomly selecting k tasks as initial clustering centers;
(2) Calculating the distance from each sample to a clustering center, and classifying the task samples to the closest cluster according to the distance;
sample to cluster centers
Figure 100002_DEST_PATH_IMAGE010
The distance calculation of (2):
Figure 100002_DEST_PATH_IMAGE011
；
determining from the nearest mean vector
Figure 100002_DEST_PATH_IMAGE012
Cluster marking of (2):
Figure 100002_DEST_PATH_IMAGE013
；
dividing tasks into corresponding clusters
Figure 100002_DEST_PATH_IMAGE014
；
(3) The mean of each cluster is recalculated, resulting in a new cluster center:
Figure 100002_DEST_PATH_IMAGE015
；
(4) Repeating the iteration of (2) and (3);
outputting a task clustering calculation result:
Figure 100002_DEST_PATH_IMAGE016
and outputting the mutually different task sets among the k similar clusters.
As a further description of the above technical solution:
the step S2 of allocating the tasks and the virtual machine resources by using the improved archimedes optimization algorithm includes:
mapping distribution between the tasks in each cluster of the cluster preprocessing and the virtual machines is carried out, for example, if one cluster is taken as an example, n subtask sets are distributed to m virtual machines, the size of an object population is NP, the position of each object is represented by a vector X, and the position of the ith object can be coded as
Figure 100002_DEST_PATH_IMAGE017
Wherein
Figure 100002_DEST_PATH_IMAGE018
Each dimension component represents a virtual machine assigned to the task, e.g.If the optimal solution is (1,5,4, …, m, …), it means that virtual machine 1 receives task 1, virtual machine 5 receives task 2, and virtual machine 4 receives task 3;
two n x m matrices Time and S are defined as follows:
Figure 100002_DEST_PATH_IMAGE019
；
Figure 100002_DEST_PATH_IMAGE020
；
wherein
Figure 100002_DEST_PATH_IMAGE021
Representing the time it takes for virtual machine j to process task i,
Figure 100002_DEST_PATH_IMAGE022
indicating whether the task i is executed on the virtual machine, if so
Figure 13402DEST_PATH_IMAGE022
A 1 indicates that task i is executed on virtual machine j, otherwise 0, and the execution time on virtual machine j is the sum of all the task times executed on it:
Figure 100002_DEST_PATH_IMAGE023
。
as a further description of the above technical solution:
in the clustering preprocessing, the time for all the virtual machines to complete all the tasks is set to be FT, FT is the maximum value of the time for all the virtual machines to complete the tasks, and the method comprises the following steps:
Figure 100002_DEST_PATH_IMAGE024
the energy consumption required by different virtual machines is also different, and the loss of the ith virtual machine in each unit operation is set as
Figure 100002_DEST_PATH_IMAGE025
Loss of all virtual machines to complete all tasksConsumption of FC, then
Figure 100002_DEST_PATH_IMAGE026
The load balance can be represented by the ratio of the task running time and the starting time of the virtual machine, when the task is completed, the shorter the idle time of the virtual machine is, the better the load balance capability is, the FZ is set, and then
Figure 100002_DEST_PATH_IMAGE027
。
As a further description of the above technical solution:
in the clustering preprocessing, the fitness function is F,
Figure 100002_DEST_PATH_IMAGE028
the smaller F is, the shorter the comprehensive time and cost for task completion is, the better the load balancing degree is, and the better the algorithm performance is;
each object position is represented by a vector X, then the ith object position can be encoded as
Figure 100002_DEST_PATH_IMAGE029
Wherein
Figure 100002_DEST_PATH_IMAGE030
At the time of initialization
Figure 100002_DEST_PATH_IMAGE031
Is a random integer between 1 and m;
at initialization
Figure 913356DEST_PATH_IMAGE031
And then, adding Logical mapping reverse learning, wherein the formula is as follows:
Figure 100002_DEST_PATH_IMAGE032
；
will be provided with
Figure 387063DEST_PATH_IMAGE031
Each of the dimensions of (0,1) maps to a region of (0,1)In between, a new vector is generated
Figure 100002_DEST_PATH_IMAGE033
The value range of each dimension component in the vector A is (0,1), and then the vector A is used as an initial value to generate a new sequence
Figure 100002_DEST_PATH_IMAGE034
，
Figure 100002_DEST_PATH_IMAGE035
Is a chaotic parameter with the value range of
Figure 100002_DEST_PATH_IMAGE036
，
Figure 100002_DEST_PATH_IMAGE037
The closer to 4, the more evenly the population is distributed and eventually will be
Figure 418473DEST_PATH_IMAGE034
Mapping back to (1,m), and then N of the above formulas
Figure 100002_DEST_PATH_IMAGE038
Generating corresponding N reverse initial solutions
Figure 100002_DEST_PATH_IMAGE039
；
Figure 100002_DEST_PATH_IMAGE040
；
Figure 100002_DEST_PATH_IMAGE041
And
Figure 100002_DEST_PATH_IMAGE042
respectively, the upper and lower limits of the search space, where
Figure 100002_DEST_PATH_IMAGE043
The number of the carbon atoms is 1,
Figure 100002_DEST_PATH_IMAGE044
for m, N will be generated
Figure 307932DEST_PATH_IMAGE038
And N are
Figure 46081DEST_PATH_IMAGE039
Sorting function fitness values of the positions, and taking the first N as initialized populations
Figure 100002_DEST_PATH_IMAGE045
；
Initializing volume (vol) and density (den) of the ith object:
Figure 100002_DEST_PATH_IMAGE046
，
Figure 100002_DEST_PATH_IMAGE047
wherein rand is a number [0,1]N-dimensional vectors randomly generated;
acceleration of the initialization object (acc):
Figure 100002_DEST_PATH_IMAGE048
，
Figure 847946DEST_PATH_IMAGE043
and
Figure 914122DEST_PATH_IMAGE044
respectively, the upper and lower limits of the search space, where
Figure 138430DEST_PATH_IMAGE043
The number of the carbon atoms is 1,
Figure 465506DEST_PATH_IMAGE044
is m, i.e.
Figure 100002_DEST_PATH_IMAGE049
；
In this step, the variables are initialized and the variable with the best fitness value is stored, i.e.
Figure 100002_DEST_PATH_IMAGE050
，
Figure 100002_DEST_PATH_IMAGE051
，
Figure 100002_DEST_PATH_IMAGE052
And
Figure 936807DEST_PATH_IMAGE053
；
density and volume update of ith object for t +1 th iteration:
Figure 100002_DEST_PATH_IMAGE054
；
Figure 100002_DEST_PATH_IMAGE055
；
wherein
Figure 100002_DEST_PATH_IMAGE056
And
Figure 100002_DEST_PATH_IMAGE057
is the best object-associated volume and density found so far, and rand is [0,1 ]]An n-dimensional random number in between.
As a further description of the above technical solution:
in the clustering preprocessing, objects collide with each other, after a period of time, the objects try to reach an equilibrium state, and a transfer operator TF transfers the search from global search to local search:
Figure 100002_DEST_PATH_IMAGE058
；
with followingThe transition operator is gradually increased with the lapse of time from
Figure 100002_DEST_PATH_IMAGE059
The number of the steps is increased to 1,
Figure 100002_DEST_PATH_IMAGE060
and
Figure 100002_DEST_PATH_IMAGE061
respectively representing the current iteration times and the maximum iteration times;
the density decrement factor also facilitates the search of the AOA from global to local, which decreases over time using the formula:
Figure 100002_DEST_PATH_IMAGE062
；
the decreasing density factor of the standard AOA is key to coordinating global and local searches, as can be seen from the equation,
Figure 100002_DEST_PATH_IMAGE063
the method is characterized in that the number of iterations is reduced from e to 0 in a non-linear way, and in the early stage of the iteration, when the distance between the current optimal solution and the global optimal solution is far, the current optimal solution is reduced quickly and the value is small
Figure 975433DEST_PATH_IMAGE063
Is not beneficial to realizing the search of the coverage of the algorithm in the solution space, and in the later period of iteration,
Figure 269011DEST_PATH_IMAGE063
the value is reduced too slowly, so that the local development capability of the algorithm is limited, the searching capability of the algorithm is limited, and in order to solve the problem, a density decreasing factor is reconstructed, and the mathematical model of the density decreasing factor is as follows:
Figure 100002_DEST_PATH_IMAGE064
；
wherein,
Figure 100002_DEST_PATH_IMAGE065
indicating the start of an iteration
Figure 100002_DEST_PATH_IMAGE066
Value of,
Figure 100002_DEST_PATH_IMAGE067
indicating the end of an iteration
Figure 388277DEST_PATH_IMAGE063
The value, i.e. when t =0,
Figure 843529DEST_PATH_IMAGE065
=e，
Figure 743352DEST_PATH_IMAGE067
=0，
Figure 100002_DEST_PATH_IMAGE068
the smoothness of the control curve is verified when
Figure 100002_DEST_PATH_IMAGE069
If the value is not less than 1.05, the experimental result is optimal, and when the global search is carried out in the middle period before iteration,
Figure 230834DEST_PATH_IMAGE063
the value is large, the nonlinear degressive is slow, the algorithm continuously searches unknown areas, the exploration capability is strong, and in the later stage of iteration,
Figure 532502DEST_PATH_IMAGE063
the value is small, the nonlinear decreasing trend is gradually increased, the local searching performance of the algorithm is gradually enhanced, and accurate searching is performed around the optimal solution as far as possible so as to balance the global searching capacity and the local searching capacity of the algorithm;
if it is used
Figure 100002_DEST_PATH_IMAGE070
Entering a global search phase, collisions between objects, randomly selecting an object (mr) and using the updated t +1 iterationsReplacing the acceleration of the object:
Figure 100002_DEST_PATH_IMAGE071
；
wherein,
Figure 100002_DEST_PATH_IMAGE072
and
Figure 100002_DEST_PATH_IMAGE073
respectively the density, volume and acceleration of the object i,
Figure 100002_DEST_PATH_IMAGE074
and
Figure 100002_DEST_PATH_IMAGE075
is the acceleration, density and volume of a random object;
if it is used
Figure 100002_DEST_PATH_IMAGE076
Entering a local search stage, updating the acceleration of the object for t +1 times of iteration without collision between the objects:
Figure 100002_DEST_PATH_IMAGE077
；
wherein,
Figure 100002_DEST_PATH_IMAGE078
is the optimum acceleration of the object.
As a further description of the above technical solution:
in the clustering pre-processing, the acceleration is normalized to calculate the percent change:
Figure 100002_DEST_PATH_IMAGE079
；
where u and l are normalized ranges set to 0.9 and 0.1, respectively, determining the percentage of step size that each agent will change, if object i is far from the global optimum, the acceleration value will be high, which means the target will be in the global search, otherwise it will be in the local search phase, which explains how the search is shifted from the global phase to the local phase;
if it is used
Figure 784754DEST_PATH_IMAGE070
And in the global searching stage, calculating the position of the ith object in the t +1 th generation by using a formula:
Figure 100002_DEST_PATH_IMAGE080
；
wherein,
Figure 100002_DEST_PATH_IMAGE081
。
as a further description of the above technical solution:
in the cluster preprocessing, if
Figure 100002_DEST_PATH_IMAGE082
A local searching stage, updating the position thereof by using a formula;
Figure 100002_DEST_PATH_IMAGE083
；
wherein,
Figure 100002_DEST_PATH_IMAGE084
t is increased along with the number of iterations and is proportional to the transfer operator
Figure 100002_DEST_PATH_IMAGE085
Definition, T is at
Figure 100002_DEST_PATH_IMAGE086
The range increases with time, and is verified when
Figure 100002_DEST_PATH_IMAGE087
The experimental results are optimal, at a lower percentageThe larger difference between the optimal position and the current position than initially causes the step value of the random walk to be higher, but as the search progresses, the percentage is gradually increased to reduce the difference between the optimal position and the current position.
As a further description of the above technical solution:
in the clustering preprocessing, the moving direction F is changed by using a formula:
Figure 100002_DEST_PATH_IMAGE088
；
Figure 100002_DEST_PATH_IMAGE089
；
after verification, when
Figure 100002_DEST_PATH_IMAGE090
And (3) optimizing the experimental result, evaluating each object by using the objective function F, storing the optimal solution, and recording as the optimal solution
Figure 100002_DEST_PATH_IMAGE091
、
Figure 100002_DEST_PATH_IMAGE092
、
Figure DEST_PATH_IMAGE093
And
Figure DEST_PATH_IMAGE094
；
because the task scheduling is a discrete problem, the invention adopts natural number coding, the natural number coding can be changed into floating point numbers after being updated according to a position updating formula, the components of certain dimensionalities can exceed a specified value range, absolute values are sequentially taken from the floating point numbers, the absolute values are rounded downwards, and the remainder is taken, wherein the formula is as follows:
Figure DEST_PATH_IMAGE095
。
as a further description of the above technical solution:
in the clustering pretreatment, the iteration times are set, and the output is carried out when the maximum iteration times are reached
Figure 234190DEST_PATH_IMAGE091
Will be
Figure 495669DEST_PATH_IMAGE029
Decoding into a mapping distribution scheme of the virtual machine and the task, and then handing over to the cloud environment for processing.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the invention, the cloud computing task scheduling method has stronger local search capability, chaotic reverse learning is added in the early stage of the algorithm to improve the availability of an initial solution, the convergence speed is improved, and the nonlinear dynamic density decreasing factor is reconstructed, so that the algorithm can balance global search and local search better, and the final convergence result is better.
2. Aiming at the problems of very diversified video transcoding requirements and different difficulty of video task transcoding, the invention aims to improve the load balance of the system, performs clustering pretreatment before all video tasks are scheduled in the system, clusters different types of tasks based on different task characteristics, and then allocates each cluster of tasks to a virtual machine for resource processing, thereby improving the load balance degree of the system.
Drawings
FIG. 1 is a diagram of a step of cloud video transcoding task scheduling based on K-Means clustering and an improved AOA algorithm according to the present invention;
fig. 2 is a flow chart of implementation of cloud video transcoding task scheduling based on K-Means clustering and an improved AOA algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution: a cloud video transcoding task scheduling method based on K-Means clustering and an improved AOA algorithm comprises the following steps:
step S1: in cloud computing, a user submits transcoding requirements, a system acquires a video task data set and divides the video task data set into different GOPs through a video divider, the GOPs represent different video tasks, and the video task set is used
Figure 651844DEST_PATH_IMAGE001
For representing, gathering of virtual machines
Figure 714478DEST_PATH_IMAGE002
Is shown in which
Figure 323314DEST_PATH_IMAGE003
The tasks are represented as:
Figure 762385DEST_PATH_IMAGE004
，
Figure 914012DEST_PATH_IMAGE005
a number indicating the number of the task,
Figure 147547DEST_PATH_IMAGE006
which represents the length of the task or tasks,
Figure 509259DEST_PATH_IMAGE007
representing the amount of computation required for the task,
Figure 220863DEST_PATH_IMAGE008
the memory required for the task is represented,
Figure 351630DEST_PATH_IMAGE009
expressing the bandwidth required by the tasks, clustering the tasks by using a K-means clustering algorithm according to different task characteristics, and clustering the tasks with similar requirements in the same cluster;
step S2: the task scheduling strategy based on the improved Archimedes optimization algorithm is characterized in that a chaos mapping reverse learning strategy is added in the early stage of the algorithm, a nonlinear dynamic density decreasing factor is reconstructed, global search and local search capabilities of the algorithm are balanced, an optimal scheduling scheme is searched, and a video task set is distributed to virtual machine resources for transcoding scheduling processing.
Specifically, the clustering preprocessing on the video task in step S1 includes:
(1) Determining task samples to be clustered, and randomly selecting k tasks as initial clustering centers;
(2) Calculating the distance from each sample to a clustering center, and classifying the task samples to the closest cluster according to the distance;
sample to cluster centers
Figure 880700DEST_PATH_IMAGE010
The distance of (2) is calculated:
Figure 729707DEST_PATH_IMAGE011
；
determining from the nearest mean vector
Figure 979423DEST_PATH_IMAGE012
Cluster marking of (2):
Figure 230276DEST_PATH_IMAGE013
；
dividing tasks into corresponding clusters
Figure 805614DEST_PATH_IMAGE014
；
（3）Re-calculate the average for each cluster, resulting in a new cluster center:
Figure 876338DEST_PATH_IMAGE015
；
(4) Repeating the iteration of (2) and (3);
outputting a task clustering calculation result:
Figure 70690DEST_PATH_IMAGE016
and outputting the mutually different task sets among the k similar clusters.
Specifically, the step S2 of allocating the tasks and the virtual machine resources by using the improved archimedes optimization algorithm includes:
mapping distribution between the tasks in each cluster of the cluster preprocessing and the virtual machines is carried out, for example, if one cluster is taken as an example, n subtask sets are distributed to m virtual machines, the size of an object population is NP, the position of each object is represented by a vector X, and the position of the ith object can be coded as
Figure 176049DEST_PATH_IMAGE017
Wherein
Figure 922288DEST_PATH_IMAGE018
Each dimension component represents a virtual machine allocated to the task, for example, if the optimal solution is (1,5,4, …, m, …), it represents that virtual machine 1 receives task 1, virtual machine 5 receives task 2, and virtual machine 4 receives task 3;
two n x m matrices Time and S are defined as follows:
Figure 214729DEST_PATH_IMAGE019
；
Figure 337406DEST_PATH_IMAGE020
；
wherein
Figure 923371DEST_PATH_IMAGE021
Representing the time it takes for virtual machine j to process task i,
Figure 840511DEST_PATH_IMAGE022
indicating whether the task i is executed on the virtual machine, if
Figure 151407DEST_PATH_IMAGE022
A 1 indicates that task i is executed on virtual machine j, otherwise 0, and the execution time on virtual machine j is the sum of all the task times executed on it:
Figure 546616DEST_PATH_IMAGE023
。
specifically, in the clustering preprocessing, the time for all the virtual machines to complete all the tasks is set to be FT, where FT is the maximum value of the time for all the virtual machines to complete the tasks, and is:
Figure 360988DEST_PATH_IMAGE024
the energy consumption required by different virtual machines is also different, and the loss of the ith virtual machine in each unit operation is set as
Figure 589975DEST_PATH_IMAGE025
And the loss of all the virtual machines for completing all tasks is FC, then
Figure DEST_PATH_IMAGE096
The load balance can be represented by the ratio of the task running time and the starting time of the virtual machine, when the task is completed, the shorter the idle time of the virtual machine is, the better the load balance capability is, the FZ is set, and then
Figure DEST_PATH_IMAGE097
。
Specifically, in the clustering preprocessing, the fitness function is F,
Figure 388167DEST_PATH_IMAGE028
the smaller F is, the shorter the comprehensive time and cost for task completion is, the better the load balancing degree is, and the better the algorithm performance is;
each object position is represented by a vector X, and the ith object position can be encoded as
Figure 852646DEST_PATH_IMAGE029
Wherein
Figure 646159DEST_PATH_IMAGE030
At the time of initialization
Figure 905102DEST_PATH_IMAGE031
Is a random integer between 1 and m;
at initialization
Figure 925011DEST_PATH_IMAGE031
And then, adding Logical mapping reverse learning, wherein the formula is as follows:
Figure 927602DEST_PATH_IMAGE032
；
will be provided with
Figure 450987DEST_PATH_IMAGE031
Each dimension of (a) is mapped to a (0,1) interval, resulting in a new vector
Figure 552935DEST_PATH_IMAGE033
The value range of each dimension component in the vector A is (0,1), and then the vector A is used as an initial value to generate a new sequence
Figure 794560DEST_PATH_IMAGE034
，
Figure 335263DEST_PATH_IMAGE035
Is a chaotic parameter with the value range of
Figure 244313DEST_PATH_IMAGE036
，
Figure 471158DEST_PATH_IMAGE037
The closer to 4It is easy for the population to be evenly distributed, and finally, the population will be more evenly distributed
Figure 465658DEST_PATH_IMAGE034
Mapping back to (1,m), and then N of the above formulas
Figure 810052DEST_PATH_IMAGE038
Generating corresponding N reverse initial solutions
Figure 308030DEST_PATH_IMAGE039
；
Figure 79677DEST_PATH_IMAGE040
；
Figure 436840DEST_PATH_IMAGE041
And
Figure 584924DEST_PATH_IMAGE042
respectively, the upper and lower limits of the search space, where
Figure 202987DEST_PATH_IMAGE043
Is a number of 1, and the number of the main chain is 1,
Figure 145536DEST_PATH_IMAGE044
for m, N will be generated
Figure 849049DEST_PATH_IMAGE038
And N are
Figure 659879DEST_PATH_IMAGE039
Sorting function fitness values of the positions, and taking the first N as initialized populations
Figure 398028DEST_PATH_IMAGE045
；
Initializing volume (vol) and density (den) of the ith object:
Figure 245899DEST_PATH_IMAGE046
，
Figure 436709DEST_PATH_IMAGE047
wherein rand is a number [0,1]Randomly generated n-dimensional vectors;
acceleration of the initialization object (acc):
Figure 926596DEST_PATH_IMAGE048
，
Figure 394617DEST_PATH_IMAGE043
and
Figure 678968DEST_PATH_IMAGE044
respectively, the upper and lower limits of the search space, where
Figure 357074DEST_PATH_IMAGE043
The number of the carbon atoms is 1,
Figure 385073DEST_PATH_IMAGE044
is m, i.e.
Figure 832235DEST_PATH_IMAGE049
；
In this step, the variables are initialized and the variable with the best fitness value is stored, i.e.
Figure 287487DEST_PATH_IMAGE050
，
Figure 67269DEST_PATH_IMAGE051
，
Figure 898959DEST_PATH_IMAGE052
And
Figure 200627DEST_PATH_IMAGE053
；
density and volume update of ith object for t +1 th iteration:
Figure 92360DEST_PATH_IMAGE054
；
Figure 479479DEST_PATH_IMAGE055
；
wherein
Figure 724646DEST_PATH_IMAGE056
And
Figure 146400DEST_PATH_IMAGE057
is the best object-associated volume and density found so far, and rand is [0,1 ]]An n-dimensional random number in between.
Specifically, in the clustering preprocessing, collision occurs between objects, after a period of time, the objects try to reach an equilibrium state, and a transfer operator TF transfers search from global search to local search:
Figure 209034DEST_PATH_IMAGE058
；
the transition operator is gradually increased over time, from
Figure 817870DEST_PATH_IMAGE059
The number of the steps is increased to 1,
Figure 256942DEST_PATH_IMAGE060
and
Figure 657836DEST_PATH_IMAGE061
respectively representing the current iteration times and the maximum iteration times;
the density decrement factor also facilitates the search of the AOA from global to local, which decreases over time using the formula:
Figure 891371DEST_PATH_IMAGE062
；
the decreasing density factor of the standard AOA is key to coordinating global and local searches, as can be seen from the equation,
Figure 987503DEST_PATH_IMAGE063
the method is characterized in that the number of iterations is reduced from e to 0 in a non-linear way, and in the early stage of the iteration, when the distance between the current optimal solution and the global optimal solution is far, the current optimal solution is reduced quickly and the value is small
Figure 699107DEST_PATH_IMAGE063
Is not beneficial to realizing the search of the coverage of the algorithm in the solution space, and in the later period of iteration,
Figure 95454DEST_PATH_IMAGE063
the local development capability of the algorithm is limited due to too slow value reduction, the searching capability of the algorithm is limited, and in order to solve the problem, a density decreasing factor is reconstructed, and the mathematical model of the density decreasing factor is as follows:
Figure 375256DEST_PATH_IMAGE064
；
wherein,
Figure 958685DEST_PATH_IMAGE065
indicating the start of an iteration
Figure 473979DEST_PATH_IMAGE066
Value of,
Figure 724832DEST_PATH_IMAGE067
indicating the end of an iteration
Figure 300170DEST_PATH_IMAGE063
The value, i.e. when t =0,
Figure 370894DEST_PATH_IMAGE065
=e，
Figure 315979DEST_PATH_IMAGE067
=0，
Figure 421338DEST_PATH_IMAGE068
the smoothness degree of the control curve is verified,when in use
Figure 167577DEST_PATH_IMAGE069
If the value is not less than 1.05, the experimental result is optimal, and when the global search is carried out in the middle period before iteration,
Figure 460018DEST_PATH_IMAGE063
the value is large, the nonlinear degressive is slow, the algorithm continuously searches unknown areas, the exploration capability is strong, and in the later stage of iteration,
Figure 582695DEST_PATH_IMAGE063
the value is small, the nonlinear decreasing trend is gradually increased, the local searching performance of the algorithm is gradually enhanced, and accurate searching is performed around the optimal solution as far as possible so as to balance the global searching capacity and the local searching capacity of the algorithm;
if it is used
Figure 417927DEST_PATH_IMAGE070
Entering a global search stage, collision occurs between the objects, one object (mr) is randomly selected and the object acceleration is iterated for t +1 times by using the update:
Figure 335067DEST_PATH_IMAGE071
；
wherein,
Figure 645963DEST_PATH_IMAGE072
and
Figure 41172DEST_PATH_IMAGE073
respectively the density, volume and acceleration of the object i,
Figure 855545DEST_PATH_IMAGE074
and
Figure 333799DEST_PATH_IMAGE075
is the acceleration, density and volume of a random object;
if it is used
Figure 866412DEST_PATH_IMAGE076
Entering a local search stage, updating the acceleration of the object for t +1 times of iteration without collision between the objects:
Figure 799733DEST_PATH_IMAGE077
；
wherein,
Figure 734191DEST_PATH_IMAGE078
is the optimum acceleration of the object.
Specifically, in the clustering pre-processing, the acceleration is normalized to calculate the change percentage:
Figure 258713DEST_PATH_IMAGE079
；
where u and l are normalized ranges set to 0.9 and 0.1, respectively, determining the percentage of step size that each agent will change, if object i is far from the global optimum, the acceleration value will be high, which means the target will be in the global search, otherwise it will be in the local search phase, which explains how the search is shifted from the global phase to the local phase;
if it is not
Figure 888408DEST_PATH_IMAGE070
And in the global searching stage, calculating the position of the ith object in the t +1 th generation by using a formula:
Figure 891000DEST_PATH_IMAGE080
；
wherein,
Figure 679964DEST_PATH_IMAGE081
。
specifically, in the cluster preprocessing, if
Figure DEST_PATH_IMAGE098
A local searching stage, updating the position of the local searching stage by using a formula;
Figure 109808DEST_PATH_IMAGE083
；
wherein,
Figure 617013DEST_PATH_IMAGE084
t increases with the number of iterations, proportional to the transfer operator, using
Figure 49394DEST_PATH_IMAGE085
Definition, T is at
Figure 692865DEST_PATH_IMAGE086
The range increases with time, and is verified when
Figure 559189DEST_PATH_IMAGE087
The experimental result is optimal, starting with a lower percentage, with a larger difference between the optimal position and the current position such that the step value of the random walk will be higher, but the percentage is gradually increased as the search progresses to reduce the difference between the optimal position and the current position.
Specifically, in the clustering preprocessing, the moving direction F is changed by using a formula:
Figure 22532DEST_PATH_IMAGE088
；
Figure 632505DEST_PATH_IMAGE089
；
after verification, when
Figure 271428DEST_PATH_IMAGE090
And (3) optimizing the experimental result, evaluating each object by using the objective function F, storing the optimal solution, and recording as the optimal solution
Figure 43074DEST_PATH_IMAGE091
、
Figure 259292DEST_PATH_IMAGE092
、
Figure 407377DEST_PATH_IMAGE093
And
Figure 25440DEST_PATH_IMAGE094
；
because the task scheduling is a discrete problem, the invention adopts natural number coding, the natural number coding can be changed into floating point numbers after being updated according to a position updating formula, the components of certain dimensionalities can exceed a specified value range, absolute values are sequentially taken from the floating point numbers, the absolute values are rounded downwards, and the remainder is taken, wherein the formula is as follows:
Figure 92622DEST_PATH_IMAGE095
。
specifically, in the clustering preprocessing, the iteration times are set, and output is performed when the maximum iteration times are reached
Figure 530556DEST_PATH_IMAGE091
Will be
Figure 482332DEST_PATH_IMAGE029
Decoding into a mapping distribution scheme of the virtual machine and the task, and then handing over to the cloud environment for processing.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (10)

1. A cloud video transcoding task scheduling method based on K-Means clustering and an improved AOA algorithm is characterized by comprising the following steps:
step S1: in cloud computing, a user submits transcoding requirements, a system acquires a video task data set, and the video task data set is divided into different GOPs (group of pictures) through a video divider, and the GOPs are used as substitutesVideo tasks, video task sets, with different tables
Figure DEST_PATH_IMAGE001
For presentation, for collection of virtual machines
Figure DEST_PATH_IMAGE002
Is shown in which
Figure DEST_PATH_IMAGE003
The tasks are represented as:
Figure DEST_PATH_IMAGE004
，
Figure DEST_PATH_IMAGE005
a number indicating the number of the task,
Figure DEST_PATH_IMAGE006
which represents the length of the task or tasks,
Figure DEST_PATH_IMAGE007
representing the amount of computation required for the task,
Figure DEST_PATH_IMAGE008
the memory required by the task is represented,
Figure DEST_PATH_IMAGE009
expressing the bandwidth required by the tasks, clustering the tasks by using a K-means clustering algorithm according to different task characteristics, and clustering the tasks with similar requirements in the same cluster;
step S2: the task scheduling strategy based on the improved Archimedes optimization algorithm is characterized in that a chaos mapping reverse learning strategy is added in the early stage of the algorithm, a nonlinear dynamic density decreasing factor is reconstructed, global search and local search capabilities of the algorithm are balanced, an optimal scheduling scheme is searched, and a video task set is distributed to virtual machine resources for transcoding scheduling processing.
2. The cloud video transcoding task scheduling based on K-Means clustering and improved AOA algorithm as claimed in claim 1, wherein the clustering preprocessing of the video tasks in step S1 comprises:
(1) Determining task samples to be clustered, and randomly selecting k tasks as initial clustering centers;
(2) Calculating the distance from each sample to a clustering center, and classifying the task samples into the closest cluster according to the distance;
sample to cluster centers
Figure DEST_PATH_IMAGE010
The distance calculation of (2):
Figure DEST_PATH_IMAGE011
；
determining from the nearest mean vector
Figure DEST_PATH_IMAGE012
Cluster marking of (2):
Figure DEST_PATH_IMAGE013
；
dividing tasks into corresponding clusters
Figure DEST_PATH_IMAGE014
；
(3) The mean of each cluster is recalculated, resulting in a new cluster center:
Figure DEST_PATH_IMAGE015
；
(4) Repeating the iteration of (2) and (3);
outputting a task clustering calculation result:
Figure DEST_PATH_IMAGE016
outputting the mutually different task sets among the k similar clusters。
3. The cloud video transcoding task scheduling method based on K-Means clustering and improved AOA algorithm as claimed in claim 2, wherein the step S2 of allocating tasks and virtual machine resources by using an improved archimedes optimization algorithm comprises:
mapping distribution between the tasks in each cluster of the cluster preprocessing and the virtual machines is carried out, for example, if one cluster is taken as an example, n subtask sets are distributed to m virtual machines, the size of an object population is NP, the position of each object is represented by a vector X, and the position of the ith object can be coded as
Figure DEST_PATH_IMAGE017
Wherein
Figure DEST_PATH_IMAGE018
Each dimension component represents a virtual machine allocated to the task, for example, if the optimal solution is (1,5,4, …, m, …), it represents that virtual machine 1 receives task 1, virtual machine 5 receives task 2, and virtual machine 4 receives task 3;
two n x m matrices Time and S are defined as follows:
Figure DEST_PATH_IMAGE019
；
Figure DEST_PATH_IMAGE020
；
wherein
Figure DEST_PATH_IMAGE021
Representing the time it takes for virtual machine j to process task i,
Figure DEST_PATH_IMAGE022
indicating whether the task i is executed on the virtual machine, if so
Figure 384335DEST_PATH_IMAGE022
A 1 indicates that task i is executed on virtual machine j, otherwise 0, and the execution time on virtual machine j is the sum of all the task times executed on it:
Figure DEST_PATH_IMAGE023
。
4. the cloud video transcoding task scheduling method based on K-Means clustering and the improved AOA algorithm as claimed in claim 3, wherein in the clustering preprocessing, the time for all the virtual machines to complete all the tasks is set to be FT, and FT is the maximum value of the time for all the virtual machines to complete the tasks, and is as follows:
Figure DEST_PATH_IMAGE024
the energy consumption required by different virtual machines is also different, and the loss of the ith virtual machine in each unit operation is set as
Figure DEST_PATH_IMAGE025
And the loss of all the virtual machines for completing all tasks is FC, then
Figure DEST_PATH_IMAGE026
The load balance can be embodied by the ratio of the task running time and the boot time of the virtual machine, when the task is completed, the shorter the idle time of the virtual machine is, the better the load balance capability is, the FZ is set, and then
Figure DEST_PATH_IMAGE027
。
5. The cloud video transcoding task scheduling based on K-Means clustering and improved AOA algorithm as claimed in claim 4, wherein in the clustering pre-processing, the fitness function is F,
Figure DEST_PATH_IMAGE028
the smaller F, the more comprehensive the task is completedThe inter-cost is short, the load balancing degree is better, and the algorithm performance is better;
each object position is represented by a vector X, then the ith object position can be encoded as
Figure DEST_PATH_IMAGE029
Wherein
Figure DEST_PATH_IMAGE030
At the time of initialization
Figure DEST_PATH_IMAGE031
Is a random integer between 1 and m;
at initialization
Figure 661995DEST_PATH_IMAGE031
And then, adding Logical mapping reverse learning, wherein the formula is as follows:
Figure DEST_PATH_IMAGE032
；
will be provided with
Figure 972890DEST_PATH_IMAGE031
Each dimension of (a) is mapped to a (0,1) interval, resulting in a new vector
Figure DEST_PATH_IMAGE033
The value range of each dimension component in the vector A is (0,1), and then the vector A is used as an initial value to generate a new sequence
Figure DEST_PATH_IMAGE034
，
Figure DEST_PATH_IMAGE035
Is a chaotic parameter with the value range of
Figure DEST_PATH_IMAGE036
，
Figure 555050DEST_PATH_IMAGE035
The closer to 4, the more evenly the population is distributed and eventually will be
Figure 369422DEST_PATH_IMAGE034
Mapping back to (1,m), and then N of the above formulas
Figure 457464DEST_PATH_IMAGE034
Generating corresponding N reverse initial solutions
Figure DEST_PATH_IMAGE037
；
Figure DEST_PATH_IMAGE038
；
Figure DEST_PATH_IMAGE039
And
Figure DEST_PATH_IMAGE040
respectively, the upper and lower limits of the search space, where
Figure DEST_PATH_IMAGE041
Is a number of 1, and the number of the main chain is 1,
Figure DEST_PATH_IMAGE042
for m, N of
Figure 553858DEST_PATH_IMAGE034
And N are
Figure 18338DEST_PATH_IMAGE037
Sorting function fitness values of the positions, and taking the first N as initialized populations
Figure DEST_PATH_IMAGE043
；
Initializing volume (vol) and density (den) of the ith object:
Figure DEST_PATH_IMAGE044
，
Figure DEST_PATH_IMAGE045
wherein rand is a number [0,1]N-dimensional vectors randomly generated;
initializing the acceleration (acc) of the object:
Figure DEST_PATH_IMAGE046
，
Figure 890479DEST_PATH_IMAGE041
and
Figure 883843DEST_PATH_IMAGE042
respectively, the upper and lower limits of the search space, where
Figure 903751DEST_PATH_IMAGE041
The number of the carbon atoms is 1,
Figure 30976DEST_PATH_IMAGE042
is m, i.e.
Figure DEST_PATH_IMAGE047
；
In this step, the variables are initialized and the variable with the best fitness value is stored, i.e.
Figure DEST_PATH_IMAGE048
，
Figure DEST_PATH_IMAGE049
，
Figure DEST_PATH_IMAGE050
And
Figure DEST_PATH_IMAGE051
；
density and volume update of ith object for t +1 th iteration:
Figure DEST_PATH_IMAGE052
；
Figure DEST_PATH_IMAGE053
；
wherein
Figure DEST_PATH_IMAGE054
And
Figure DEST_PATH_IMAGE055
is the best object-associated volume and density found so far, and rand is [0,1 ]]An n-dimensional random number in between.
6. The task scheduling of cloud video transcoding based on K-Means clustering and improved AOA algorithm as claimed in claim 5, wherein in the clustering pre-processing, collision occurs between objects, after a period of time, the objects try to reach an equilibrium state, and the transfer operator TF transfers the search from global search to local search:
Figure DEST_PATH_IMAGE056
；
the transition operator is gradually increased over time, from
Figure DEST_PATH_IMAGE057
The number of the steps is increased to 1,
Figure DEST_PATH_IMAGE058
and
Figure DEST_PATH_IMAGE059
respectively representing the current iteration times and the maximum iteration times;
the density decrement factor also facilitates the search of the AOA from global to local, which decreases over time using the formula:
Figure DEST_PATH_IMAGE060
；
the decreasing density factor of the standard AOA is key to coordinating global and local searches, and as can be seen from the equation,
Figure DEST_PATH_IMAGE061
the method is characterized in that the number of iterations is reduced from e to 0 in a non-linear way, and in the earlier stage of iteration, when the distance between the current optimal solution and the global optimal solution is far, the reduction is fast and the value is small
Figure DEST_PATH_IMAGE062
Is not beneficial to realizing the search of the coverage of the algorithm in the solution space, and in the later period of iteration,
Figure 383722DEST_PATH_IMAGE061
the local development capability of the algorithm is limited due to too slow value reduction, the searching capability of the algorithm is limited, and in order to solve the problem, a density decreasing factor is reconstructed, and the mathematical model of the density decreasing factor is as follows:
Figure DEST_PATH_IMAGE063
；
wherein,
Figure DEST_PATH_IMAGE064
indicating the start of an iteration
Figure 344725DEST_PATH_IMAGE062
The value of the sum of the values,
Figure DEST_PATH_IMAGE065
indicating the end of an iteration
Figure 242143DEST_PATH_IMAGE061
The value, i.e. when t =0,
Figure 517266DEST_PATH_IMAGE064
=e，
Figure 426316DEST_PATH_IMAGE065
=0，
Figure DEST_PATH_IMAGE066
controlling the smoothness of the curve, and verifying when
Figure 168007DEST_PATH_IMAGE066
If the value is 1.05, the experimental result is optimal, and if the global search is performed in the early and middle stages of iteration,
Figure 162508DEST_PATH_IMAGE062
the value is larger, the nonlinear degressive is slower, the algorithm continuously searches unknown areas, has stronger exploration capacity, and in the later period of iteration,
Figure 241323DEST_PATH_IMAGE062
the value is small, the nonlinear decreasing trend is gradually increased, the local searching performance of the algorithm is gradually enhanced, and accurate searching is performed around the optimal solution as far as possible so as to balance the global searching capacity and the local searching capacity of the algorithm;
if it is not
Figure DEST_PATH_IMAGE067
Entering a global search stage, collision occurs between the objects, one object (mr) is randomly selected and the object acceleration is iterated for t +1 times by using the update:
Figure DEST_PATH_IMAGE068
；
wherein,
Figure DEST_PATH_IMAGE069
and
Figure DEST_PATH_IMAGE070
respectively the density, volume and acceleration of the object i,
Figure DEST_PATH_IMAGE071
and
Figure DEST_PATH_IMAGE072
is the acceleration, density and volume of a random object;
if it is not
Figure DEST_PATH_IMAGE073
Entering a local search stage, wherein no collision exists between objects, and updating the acceleration of the iterative object for t +1 times:
Figure DEST_PATH_IMAGE074
；
wherein,
Figure DEST_PATH_IMAGE075
is the optimum acceleration of the object.
7. The cloud video transcoding task scheduling based on K-Means clustering and improved AOA algorithm as claimed in claim 6, wherein in the clustering pre-processing, acceleration is normalized to calculate change percentage:
Figure DEST_PATH_IMAGE076
；
where u and l are normalized ranges set to 0.9 and 0.1, respectively, determining the percentage of step size that each agent will change, if object i is far from the global optimum, the acceleration value will be high, which means the target will be in the global search, otherwise it will be in the local search phase, which explains how the search is shifted from the global phase to the local phase;
if it is not
Figure 365399DEST_PATH_IMAGE067
And in the global searching stage, calculating the position of the ith object in the t +1 th generation by using a formula:
Figure DEST_PATH_IMAGE077
；
wherein,
Figure DEST_PATH_IMAGE078
。
8. the cloud video transcoding task scheduling based on K-Means clustering and improved AOA algorithm as claimed in claim 7, wherein in the clustering pre-processing, if yes, the cloud video transcoding task scheduling
Figure DEST_PATH_IMAGE079
A local searching stage, updating the position thereof by using a formula;
Figure DEST_PATH_IMAGE080
；
wherein,
Figure DEST_PATH_IMAGE081
t is increased along with the number of iterations and is proportional to the transfer operator
Figure DEST_PATH_IMAGE082
Definition, T is at
Figure DEST_PATH_IMAGE083
The range increases with time and, as verified,when in use
Figure DEST_PATH_IMAGE084
The experimental result is optimal, starting with a lower percentage, with a larger difference between the optimal position and the current position such that the step value of the random walk will be higher, but the percentage is gradually increased as the search progresses to reduce the difference between the optimal position and the current position.
9. The cloud video transcoding task scheduling method based on K-Means clustering and improved AOA algorithm as claimed in claim 8, wherein in the clustering pre-processing, the moving direction F is changed by using a formula:
Figure DEST_PATH_IMAGE085
；
Figure DEST_PATH_IMAGE086
；
after verification, when
Figure DEST_PATH_IMAGE087
And (3) optimizing the experimental result, evaluating each object by using the objective function F, storing the optimal solution, and recording as the optimal solution
Figure DEST_PATH_IMAGE088
、
Figure DEST_PATH_IMAGE089
、
Figure DEST_PATH_IMAGE090
And
Figure 950095DEST_PATH_IMAGE075
；
because the task scheduling is a discrete problem, the invention adopts natural number coding, the natural number coding can be changed into floating point numbers after being updated according to a position updating formula, the components of certain dimensionalities can exceed a specified value range, absolute values are sequentially taken from the floating point numbers, the absolute values are rounded downwards, and the remainder is taken, wherein the formula is as follows:
Figure DEST_PATH_IMAGE091
。
10. the cloud video transcoding task scheduling method based on K-Means clustering and improved AOA algorithm as claimed in claim 9, wherein in the clustering preprocessing, iteration times are set, and output is performed when the maximum iteration times are reached
Figure 166313DEST_PATH_IMAGE088
Will be
Figure DEST_PATH_IMAGE092
Decoding into a mapping distribution scheme of the virtual machine and the task, and then handing over to the cloud environment for processing.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117311993A (en) * 2023-11-28 2023-12-29 华东交通大学 Cloud computing load balancing method and system
CN118093208A (en) * 2024-04-26 2024-05-28 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) Deep learning task scheduling method for multi-user GPU cluster
CN118093208B (en) * 2024-04-26 2024-07-26 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) Deep learning task scheduling method for multi-user GPU cluster

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117311993A (en) * 2023-11-28 2023-12-29 华东交通大学 Cloud computing load balancing method and system
CN117311993B (en) * 2023-11-28 2024-03-08 华东交通大学 Cloud computing load balancing method and system
CN118093208A (en) * 2024-04-26 2024-05-28 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) Deep learning task scheduling method for multi-user GPU cluster
CN118093208B (en) * 2024-04-26 2024-07-26 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) Deep learning task scheduling method for multi-user GPU cluster

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