CN115988075A - Cloud data migration method and device based on artificial fish swarm algorithm - Google Patents
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Abstract
The invention discloses a cloud data migration method and device based on an artificial fish swarm algorithm, and relates to the field of emerging technologies. The cloud data migration method and device based on the artificial fish swarm algorithm comprise the following application steps: s1, acquiring resource utilization conditions and bandwidth utilization conditions of each rear-end server in unit time; s2, receiving a migration request sent by a web service; the method can obviously improve the load condition of the back-end server of the cloud migration cluster, is beneficial to improving user experience, and can be used as the fitness function of the artificial fish school algorithm process by presetting the target function capable of describing the resource utilization condition and the load degree in the cloud migration environment in advance, the individuals with large fitness value can attract the individuals with small fitness value to move towards the target function, meanwhile, the convergence of the optimal solution is realized in an iteration mode, and when the iteration times reach the maximum, the artificial fish school algorithm obtains the global optimal solution, namely the final solution.
Description
Technical Field
The invention relates to the emerging technical field, in particular to a cloud data migration method and device based on an artificial fish swarm algorithm.
Background
With the continuous development of the current internet technology, the scale of internet resources is continuously and rapidly increased, and the rapid development of cloud computing and big data technology is promoted. Cloud computing is an emerging computing mode, and a large number of infrastructure resources are gathered by utilizing a mature virtualization technology, so that the data center resources can provide services to the outside as required. Based on the rapid development of cloud computing, a plurality of cloud manufacturers including the ari cloud, the Tencent cloud, the Huacheng cloud, the space wing cloud and the like are started at home at present, and a large amount of user data are stored by the cloud service providers. With the continuous increase of various cloud manufacturers, more choices are provided for users, if a user needs to replace a cloud manufacturer and wants to continue to use previous data in the using process, a cloud migration service needs to be used, a distributed architecture is often adopted for cloud migration, and a plurality of servers are arranged at the back end to wait for executing a migration task.
In general, after receiving a migration request from a user, a foreground allocates migration tasks according to the following two strategies, one of which is to sequentially allocate the tasks to different physical servers by using a RoundRobin algorithm, thereby realizing load balancing; the other method is to adopt the minimum connection number as the basis of task scheduling, although the two methods are easy to implement, the load condition of the server is identified only by polling or depending on the connection number, which is not comprehensive enough, and the actual conditions of all nodes cannot be directly reflected, so that the problem that the migration task is reasonably distributed and the cloud migration server is fully and efficiently utilized is a problem which needs to be considered, and therefore, a cloud data migration method and device based on an artificial fish swarm algorithm are provided to solve the existing problems.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a cloud data migration method and device based on an artificial fish swarm algorithm, which solve the problems that the load condition of a cloud migration cluster rear-end server can be obviously improved, the user experience is improved, an objective function capable of describing the resource utilization condition and the load degree in a cloud migration environment is preset in advance to serve as a fitness function in the artificial fish swarm algorithm process, an individual with a large fitness value can attract an individual with a small fitness value to move towards the individual, the convergence of an optimal solution is realized in an iteration mode, and when the iteration times reach the maximum, the artificial fish swarm algorithm obtains a global optimal solution, namely a final solution.
In order to realize the purpose, the invention is realized by the following technical scheme: a cloud data migration method based on an artificial fish school algorithm comprises the following application steps:
s1, acquiring resource utilization conditions and bandwidth utilization conditions of each rear-end server in unit time;
s2, receiving a migration request sent by a web service;
and S3, transmitting the acquired information to an artificial fish school calculation module to generate a corresponding migration strategy.
Further, the resource utilization condition and the bandwidth utilization condition in the S1 are obtained through a monitor;
and each back-end server is regarded as Agent and is responsible for local data acquisition.
Further, the Agent obtains the CPU utilization rate, the memory occupancy rate, the network bandwidth utilization rate and the load condition information through a Linux command.
Further, the monitor is responsible for data collection and forwarding information to the artificial fish school calculation module.
Further, the artificial fish school calculating module in S3 includes:
s31, modeling of a data set: acquiring performance index data of each server in unit time, and uniformly representing the resource use condition on a single server by using DV (distance vector), wherein the artificial fish school is equivalent to a cloud migration cluster, and each artificial fish in the artificial fish school is regarded as an independent back-end server;
s32, clustering behavior of the artificial fishes: the current self-center position of each artificial fish is an initial position, the adjacent artificial fish is expressed into a set, and whether the artificial fish moves to the center of the set is judged according to the number of the companions in the current visual field and the current fitness value;
whether the number of the peers in the current visual field is less than 0, if so, clustering fails, otherwise, the central position of the set is calculated, whether the fitness value of the central position of the set is greater than that of the initial position is judged, if so, the artificial fish moves to the central position,
wherein the current position of the artificial fish i is X i With itself as the center, the artificial fish nearby can be expressed as
The function value of response is recorded as Y j Visual is the Visual field range of the artificial fish;
s33, rear-end collision behavior of the artificial fish: x j The number of other individuals in the surrounding visual field is denoted as n f Judgment of Y j /n f >δY i If the situation is true, if the foraging behavior is not carried out, the rear-end collision is not successful,
where δ is the crowding factor; delta Y i Indicating the degree of congestion;
s34, foraging behavior of the artificial fish: the current position where the artificial fish i exists is X i Then move within the field of view to select a next position X to move to j If X is j Food concentration at the location higher than X i And if no more optimal state is found after repeated iteration times, the random movement is carried out.
Further, the initial population X = { X ] of the artificial fish population in S31 1 ,X 2 ,…,X m In which X is i The position of the ith artificial fish is shown, and i belongs to any number from 1 to m;
the fitness value of the artificial fish i is Y i Generally, the fitness value Y is described by an objective function i The more reasonable the objective function is established, the larger the fitness value will be, expressed as: y is i ＝f(X i ),f(X i ) Is defined as f (X) i )＝DV i ；DV i To represent resource usage on a single server i.
Further, whether the number of the peers in the current visual field is smaller than 0 or not is judged, clustering fails if the number of the peers in the current visual field is smaller than 0, otherwise, the center position of the set is calculated, whether the fitness value of the center position of the set is larger than that of the initial position or not is judged, and if yes, the artificial fish moves to the center position.
Further, the performance collection procedure is a CS architecture.
Further, it is characterized byThe formula of the moving step length is X j ＝X i +Visual*Rand()，
Wherein Visual × Rang () is a random function.
A cloud data migration device based on an artificial fish swarm algorithm comprises:
the acquisition module is used for acquiring the resource utilization condition and the bandwidth utilization condition of each back-end server in unit time;
the request module is used for receiving a migration request sent by a web service;
and the artificial fish swarm calculation module is used for receiving and processing the information and generating a corresponding migration strategy.
The invention has the following beneficial effects:
(1) According to the cloud data migration method and device based on the artificial fish swarm algorithm, the cloud migration process and the artificial fish swarm algorithm are combined, the rear-end server of the cloud migration cluster is simulated into the artificial fish, and the server with the best current performance is obtained through cyclic search of the cluster behavior, the tail-end collision activity and the foraging behavior, so that migration tasks can be distributed to the server, and the strategy is obviously superior to that of a conventional polling and minimum connection distribution method.
(2) According to the cloud data migration method and device based on the artificial fish swarm algorithm, the performance statistical information is reported to the monitoring module in real time through the back-end server, and powerful basis is provided for efficient data migration.
(3) The cloud data migration method and device based on the artificial fish swarm algorithm can fully utilize the performance of each back-end server of the cloud migration cluster. Actually performing operation, triggering 10000 migration tasks, wherein the data volume migrated by each task is different, and after the calculation of an artificial fish school module of a cloud migration system, distributing the tasks to each server according to a polling strategy at first because all servers do not work;
due to the fact that the performance consumption of the servers is different due to the difference of the data volume of each migration task, after all the servers work, the subsequent migration tasks are gradually distributed to the servers with smaller data migration volume, namely the servers with smaller resource consumption, and the migration tasks can be reasonably and effectively distributed after the performance of all the servers of the cloud migration cluster is calculated through the artificial fish swarm algorithm.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
FIG. 1 is a diagram of a task dynamic allocation architecture of a cloud migration system according to the present invention;
fig. 2 is a schematic diagram of the operation mode of the performance acquisition system of the present invention.
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.
In the description of the present invention, it is to be understood that the terms "opening," "upper," "lower," "thickness," "top," "middle," "length," "inner," "peripheral," and the like are used in an orientation or positional relationship merely to facilitate description of the invention and to simplify the description, and are not intended to indicate or imply that the referenced components or elements must be in a particular orientation, constructed and operative in a particular orientation, and are not to be construed as limiting the invention.
Referring to fig. 1-2, an embodiment of the present invention provides a technical solution: a cloud data migration method based on an artificial fish school algorithm comprises the following application steps:
s1, acquiring resource utilization conditions and bandwidth utilization conditions of each rear-end server in unit time;
s2, receiving a migration request sent by a web service;
and S3, transmitting the acquired information to an artificial fish school calculation module to generate a corresponding migration strategy.
Specifically, the resource utilization and bandwidth utilization in S1 are obtained by a monitor, the performance collection process is a CS architecture, the working mode adopted by the entire performance collection system is an active mode, an SNMP protocol is used, the backend servers periodically and actively report various performance data to the monitor, and the monitor module stores the latest performance data of each backend server;
and each back-end server is regarded as Agent and is responsible for local data acquisition.
Specifically, the Agent obtains information such as CPU utilization rate, memory occupancy rate, network bandwidth utilization rate and load condition through a Linux command.
Specifically, the monitor is responsible for data collection and forwarding information to the artificial fish school calculation module.
Specifically, the artificial fish school calculation module in S3 includes:
s31, modeling of a data set: acquiring performance index data of each server in unit time, uniformly expressing the resource use condition on a single server by using DV (distance vector), wherein the artificial fish swarm is equivalent to a cloud migration cluster, each artificial fish in the artificial fish swarm is regarded as an independent back-end server,
the initial population X = { X) of the artificial fish population in S31 1 ,X 2 ,…,X m In which X is i Indicating the position of the ith artificial fish, wherein i belongs to any one number from 1 to m;
the adaptability value of the artificial fish i is Y i Generally, the fitness value Y is described by an objective function i The more reasonable the objective function is established, the larger the fitness value will be, expressed as: y is i ＝f(X i ),f(X i ) Is defined as f (X) i )＝DV i ；
S32, clustering behavior of the artificial fishes: the current self central position of each artificial fish is an initial position, the adjacent artificial fish is represented as a set, whether the number of the companions in the current visual field is less than 0 is judged, clustering fails if the number of the companions is less than 0, otherwise, the central position of the set is calculated, whether the central position fitness value of the set is greater than the fitness value of the initial position is judged, and if the central position fitness value of the set is greater than the fitness value of the initial position, the artificial fish moves to the central position;
whether the number of the peers in the current visual field is less than 0, if so, clustering fails, otherwise, the central position of the set is calculated, whether the fitness value of the central position of the set is greater than that of the initial position is judged, if so, the artificial fish moves to the central position,
wherein the current position of the artificial fish i is X i Centered on itself, the artificial fish nearby can be represented as a set S i ＝{X i ||X j -X i | is less than or equal to Visual }, wherein X is less than or equal to Visual }, and j the fitness function value is recorded as Y for the position to be moved to next step j Visual is the Visual field range of the artificial fish;
s33, rear-end collision behavior of the artificial fish: x j The number of other individuals in the surrounding visual field is denoted as n f Judgment of Y j /n f >δY i If the situation is true, if the foraging behavior is not carried out, the rear-end collision is not successful,
where δ is the crowding factor; delta Y i Indicating the degree of congestion;
s34, foraging behavior of the artificial fish: the current position where the artificial fish i exists is X i Then move within the field of view, select a next position X to move to j If X is j Food concentration at the location higher than X i Then, the step length is moved to the direction according to the formula of the step length X j ＝X i If the optimal state is not found after repeated iteration times, random movement is carried out, wherein X j For the position to be moved next, X i Visual + Rang () is a random function for the current position.
A cloud data migration device based on an artificial fish swarm algorithm comprises:
the acquisition module is used for acquiring the resource utilization condition and the bandwidth utilization condition of each back-end server in unit time;
the request module is used for receiving a migration request sent by a web service;
and the artificial fish swarm calculation module is used for receiving and processing the information and generating a corresponding migration strategy.
When the method is used, when the optimal target server in the cloud migration cluster corresponding to each task to be migrated is searched, firstly, m artificial fish swarm populations are generated, the position of each artificial fish swarm needs to be initialized randomly, under the general condition, a calculated objective function value is used as the fitness of each artificial fish, and f (X) in the f (X) is used i )＝DV i As a formula for calculating the objective function value, the larger the value is, the more satisfied the performance is, and the more satisfied the migration requirement is;
the artificial fish tries to move to a central node with a higher self-adaptability value in a certain range, then the food concentration values between the artificial fishes are compared nearby, the artificial fish with a large value is more attractive, and the artificial fish with a small value can be attracted to move to the artificial fish;
with the progress of the iterative process, the artificial fish with lower fitness in the population is continuously closer to the artificial fish with higher fitness than the artificial fish, and finally the artificial fish is gathered near the position with the highest fitness value, so that the position of the artificial fish with the highest fitness is considered to be optimal in performance, and the optimal target server for processing the cloud migration task is obtained.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or intervening elements may also be present; when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present, and further, as used herein, connected may include wirelessly connected; the term "and/or" is used to include any and all combinations of one or more of the associated listed items.
Any process or method descriptions in flow charts or otherwise described herein may be understood as: represents modules, segments or portions of code which include one or more executable instructions for implementing specific logical functions or steps of a process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
It will be understood by those skilled in the art that all or part of the steps of the method for implementing the above embodiments may be implemented by a program instructing associated hardware, and the program may be stored in a computer readable storage medium, and when executed, the program includes the steps of (method steps), the storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (10)
1. A cloud data migration method based on an artificial fish school algorithm is characterized by comprising the following steps: the method comprises the following application steps:
s1, acquiring resource utilization conditions and bandwidth utilization conditions of each rear-end server in unit time;
s2, receiving a migration request sent by a web service;
and S3, transmitting the acquired information to an artificial fish school calculation module to generate a corresponding migration strategy.
2. The artificial fish swarm algorithm-based cloud data migration method according to claim 1, wherein the cloud data migration method comprises the following steps: the resource utilization condition and the bandwidth utilization condition in the S1 are obtained through a monitor;
and each back-end server is regarded as Agent and is responsible for local data acquisition.
3. The artificial fish swarm algorithm-based cloud data migration method according to claim 2, wherein the cloud data migration method comprises the following steps: the Agent obtains the CPU utilization rate, the memory occupancy rate, the network bandwidth utilization rate and the load condition information through a Linux command.
4. The artificial fish school algorithm-based cloud data migration method according to claim 3, wherein the cloud data migration method comprises the following steps: the monitor is responsible for data collection and information forwarding to the artificial fish school calculation module.
5. The artificial fish school algorithm-based cloud data migration method according to claim 1, wherein the method comprises the following steps: the artificial fish school calculating module in the S3 comprises:
s31, modeling of a data set: acquiring performance index data of each server in unit time, and uniformly representing the resource use condition on a single server by using DV (distance vector), wherein the artificial fish school is equivalent to a cloud migration cluster, and each artificial fish in the artificial fish school is regarded as an independent back-end server;
s32, clustering behavior of the artificial fishes: the current self-center position of each artificial fish is an initial position, the adjacent artificial fish is expressed into a set, and whether the artificial fish moves to the center of the set is judged according to the number of the companions in the current visual field and the current fitness value;
whether the number of the peers in the current visual field is less than 0, if so, clustering fails, otherwise, the central position of the set is calculated, whether the fitness value of the central position of the set is greater than that of the initial position is judged, if so, the artificial fish moves to the central position,
wherein the current position of the artificial fish i is X i With itself as the center, the artificial fish nearby is shown as a set S i ＝{X i ||X j -X i | ≦ Visual }, wherein X j Move to the next stepThe value of fitness function of the moved position is recorded as Y j Visual is the Visual field range of the artificial fish;
s33, rear-end collision behavior of the artificial fish: x j The number of other individuals in the surrounding visual field is denoted as n f Judgment of Y j /n f >δY i If the result is true, if the foraging behavior is not carried out, the rear-end collision is unsuccessful,
wherein δ is a crowding factor; delta Y i Indicating the degree of congestion;
s34, foraging behavior of the artificial fish: the current position where the artificial fish i exists is X i Then move within the field of view to select a next position X to move to j If X is j The food concentration of the location is higher than X i And if no better state is found after repeated iterations, the mobile terminal moves randomly.
6. The artificial fish swarm algorithm-based cloud data migration method according to claim 5, wherein the cloud data migration method comprises the following steps: the initial population X = { X) of the artificial fish population in S31 1 ,X 2 ,…,X m In which X is i Indicating the position of the ith artificial fish, wherein i belongs to any one number from 1 to m;
the fitness value of the artificial fish i is Y i Generally, the fitness value Y is described by an objective function i The more reasonably the objective function is established, the greater the fitness value will be, which is formulated as: y is i ＝f(X i ),f(X i ) Is defined as f (X) i )＝DV i ；DV i To represent resource usage on a single server i.
7. The artificial fish swarm algorithm-based cloud data migration method according to claim 6, wherein the cloud data migration method comprises the following steps: and S32, judging whether the number of the peers in the current visual field is less than 0, if so, failing to gather, otherwise, calculating the central position of the set, judging whether the fitness value of the central position of the set is greater than that of the initial position, and if so, moving the artificial fish to the central position.
8. The artificial fish swarm algorithm-based cloud data migration method according to claim 2, wherein the cloud data migration method comprises the following steps: the performance collection procedure is a CS architecture.
9. The artificial fish swarm algorithm-based cloud data migration method according to claim 5, wherein the cloud data migration method comprises the following steps: the moving step formula is X j ＝X i +Visual*Rand()，
Where Rang () is a random function.
10. The utility model provides a cloud data migration device based on artificial fish school algorithm which characterized in that includes:
the acquisition module is used for acquiring the resource utilization condition and the bandwidth utilization condition of each back-end server in unit time;
the request module is used for receiving a migration request sent by a web service;
and the artificial fish swarm calculation module is used for receiving and processing the information and generating a corresponding migration strategy.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117376403A (en) * | 2023-10-08 | 2024-01-09 | 无锡安鑫卓越智能科技有限公司 | Cloud data migration method and system |
| CN117376403B (en) * | 2023-10-08 | 2024-05-14 | 上海知享家信息技术服务有限公司 | Cloud data migration method and system |
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