CN111597023B - Cluster intelligent scheduling method and device based on learning state - Google Patents
Cluster intelligent scheduling method and device based on learning state Download PDFInfo
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Abstract
The invention provides a cluster intelligent scheduling method and device based on a learning state. Comprising the following steps: receiving a student cluster starting instruction, searching a student cluster schedule, and when the student cluster schedule is not searched, newly building the student cluster schedule to be added; the method comprises the steps of obtaining lesson information of current lessons, establishing a learning state weight algorithm, calculating the lesson information according to the learning state weight algorithm, obtaining a weight value of each lesson student, associating the weight value with the corresponding lesson student, inserting the weight value into a student cluster schedule to be added, and obtaining a student cluster schedule to be used; according to the method, the virtual machine is reasonably distributed according to the learning weight value, so that the learning efficiency of students is improved, the uncertainty of cluster scheduling is solved, and the cluster starting speed is also improved.
Description
Technical Field
The invention relates to the technical field of computer software, in particular to a cluster intelligent scheduling method and device based on a learning state.
Background
With the rapid development of big data and artificial intelligence, more and more industries begin to use big data and add elements of artificial intelligence. In the field of education, as the demands of countries for talents of higher education are gradually increased and demands for development of time generation are increased, more and more courses of big data and related professions are presented in each higher education institution. However, due to the complexity of the big data courses and the special requirements for the experimental environment, the traditional experimental environment cannot meet the current teaching requirements, and the normal teaching activities such as teaching, experiments and the like need to be completed by means of the experimental platform, so that various big data platforms for higher teaching are developed, and the big data platforms are also commonly displayed in the line of sight of the masses as the bamboo shoots in the spring after raining.
The existing big data related platform performs related work through the cluster, in the working process, the cluster usually needs to virtualize a plurality of virtual machines at the same time so as to meet the experimental environment, but the use of a large number of virtual machines can have higher requirements on the cluster performance, but the cluster performance of the existing big data related platform can not meet the requirements, so that the cluster operation speed is slow, and for students, how long the waiting time for experiment can influence the attention and the enthusiasm of the students, and the learning efficiency of the students is reduced.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
In view of the above, the invention provides a cluster intelligent scheduling method and device based on a learning state, which aims to solve the technical problem that the prior art cannot realize intelligent scheduling of clusters according to the learning state.
The technical scheme of the invention is realized as follows:
in one aspect, the invention provides a learning state-based cluster intelligent scheduling method, which comprises the following steps:
s1, receiving a student cluster starting instruction, searching a student cluster schedule, and when the student cluster schedule is not searched, newly building a student cluster schedule to be added;
s2, acquiring the lesson information of the current lesson students, establishing a learning state weight algorithm, calculating the lesson information according to the learning state weight algorithm, acquiring a weight value of each lesson student, associating the weight value with the corresponding lesson student, inserting the weight value into a student cluster schedule to be added, and acquiring a student cluster schedule to be used;
s3, creating a student cluster according to the student cluster schedule to be used, and distributing virtual machines.
On the basis of the above technical solution, preferably, in step S1, a student cluster starting instruction is received, a student cluster schedule is searched, when the student cluster schedule is not searched, a new student cluster schedule to be added is built, and further, the method further comprises the steps of receiving the student cluster starting instruction, searching the student cluster schedule, and when the student cluster schedule is searched, sequentially starting the student clusters according to the student cluster schedule; when the student cluster schedule is not found, a new student cluster schedule to be added is built.
On the basis of the above technical solution, preferably, when the student cluster schedule is found, the student clusters are started sequentially according to the student cluster schedule, and further comprising the steps of, when the student cluster schedule is found, obtaining login information of a current lesson student, wherein the login information comprises: and searching the login information of the current lesson student in the local record, sequentially starting the student clusters according to the student cluster schedule if the login information of the current lesson student exists in the local record, and creating a to-be-added student cluster schedule if the login information of the current lesson student does not exist in the local record.
On the basis of the above technical solution, preferably, in step S2, the current lesson information of the lesson student is obtained, and the method further includes the following steps, where the lesson information includes: basic information of students and corresponding learning state information, wherein the basic information comprises: student class information, ID information, and MAC address information of a corresponding client machine; the learning state information includes: pre-lesson data, post-lesson job completion data, attendance data, and classroom interaction data.
On the basis of the above technical solution, preferably, in step S2, a learning state weight algorithm is established, the lesson information is calculated according to the learning state weight algorithm, a weight value of each lesson student is obtained, the weight value is associated with the corresponding lesson student and is inserted into a student cluster schedule to be added, a student cluster schedule to be used is obtained, and further the method further comprises the steps of establishing a learning state weight algorithm, calculating the lesson information according to the learning state weight algorithm, obtaining a weight value of each lesson student, associating the weight value with basic information of the corresponding lesson student, establishing a corresponding relation, sorting the weight values of each lesson student according to the value size in order from high to low, sorting the weight value with higher value is higher, obtaining a sorted weight value order, and inserting the corresponding lesson student into the added student cluster schedule according to the weight value order.
On the basis of the above technical solution, preferably, the learning state weight algorithm is:
wherein w is the total weight value, n is the number of categories of statistical parameters, s i (i ε {1,2,., n }) is a score for a single category, t i Weight value for a single statistical category, and
on the basis of the above technical solution, preferably, in step S3, after creating a student cluster according to the student cluster schedule to be used and allocating a virtual machine, the method further includes the steps of extracting student data in the student cluster schedule to be used, storing the extracted data in a local record, and clearing all information in the student cluster schedule to be used after receiving an instruction to stop student cluster.
Still further preferably, the learning state-based cluster intelligent scheduling apparatus includes:
the new building module is used for receiving the student cluster starting instruction, searching the student cluster scheduling table, and when the student cluster scheduling table is not searched, building a new student cluster scheduling table to be added;
the calculation module is used for acquiring the lesson information of the current lesson students, establishing a learning state weight algorithm, calculating the lesson information according to the learning state weight algorithm, acquiring a weight value of each lesson student, correlating the weight value with the corresponding lesson student, inserting the weight value into a student cluster schedule to be added, and acquiring the student cluster schedule to be used;
and the cluster creation module is used for creating a student cluster according to the student cluster schedule to be used and distributing virtual machines.
In a second aspect, the learning state-based cluster intelligent scheduling method further includes a device, where the device includes: the system comprises a memory, a processor and a learning state based cluster intelligent scheduling method program stored on the memory and executable on the processor, wherein the learning state based cluster intelligent scheduling method program is configured to implement the steps of the learning state based cluster intelligent scheduling method as described above.
In a third aspect, the intelligent cluster scheduling method based on a learning state further includes a medium, where the medium is a computer medium, and the computer medium stores an intelligent cluster scheduling method program based on a learning state, where the intelligent cluster scheduling method program based on a learning state implements the steps of the intelligent cluster scheduling method based on a learning state as described above when being executed by a processor.
Compared with the prior art, the intelligent cluster scheduling method based on the learning state has the following beneficial effects:
(1) By introducing a weight value algorithm, the scheduling of the student clusters is realized through the weight value algorithm, the uncertainty of cluster scheduling can be solved, and the cluster starting speed can be improved.
(2) Through the learning state information of the students and the cluster scheduling hooks, fairness of cluster scheduling is improved, the students with good learning attitudes can obtain longer virtual machine operation time, overlong waiting time of the students is avoided, meanwhile, enthusiasm of the students is improved, and the students can complete teaching activities better.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart of a first embodiment of a learning state-based intelligent scheduling method for clusters according to the present invention;
fig. 3 is a schematic diagram of functional modules of a first embodiment of the learning state-based cluster intelligent scheduling method.
Detailed Description
The following description of the embodiments of the present invention will clearly and fully describe the technical aspects of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the apparatus, and in actual practice the apparatus may include more or less components than those illustrated, or certain components may be combined, or different arrangements of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a cluster intelligent scheduling method program based on a learning state may be included in a memory 1005 as one medium.
In the device shown in fig. 1, the network interface 1004 is mainly used for establishing a communication connection between the device and a server storing all data required in the cluster intelligent scheduling method system based on the learning state; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the cluster intelligent scheduling method equipment based on the learning state can be arranged in the cluster intelligent scheduling method equipment based on the learning state, and the cluster intelligent scheduling method equipment based on the learning state calls the cluster intelligent scheduling method program based on the learning state stored in the memory 1005 through the processor 1001 and executes the cluster intelligent scheduling method based on the learning state provided by the implementation of the invention.
Referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of a cluster intelligent scheduling method based on learning status according to the present invention.
In this embodiment, the intelligent cluster scheduling method based on the learning state includes the following steps:
s10: and receiving a student cluster starting instruction, searching a student cluster schedule, and when the student cluster schedule is not searched, newly building the student cluster schedule to be added.
It should be appreciated that a cluster is a computer system that performs computing work in a highly tight, coordinated manner by a set of loosely-integrated computer software and/or hardware connections. In a sense, they can be regarded as a computer. Individual computers in a clustered system are often referred to as nodes, typically connected by a local area network, but there are other possible ways of connection. In this embodiment, clusters typically use Kubernets virtualization technology. Because of the specificity of the big data environment, in order to meet the teaching activities, a virtual environment needs to be created in a virtual machine, and the more advanced Docker container technology cannot meet the requirement and can only select the Kubernets virtualization technology. The cluster schedule is a call form which is generated according to the current existing cluster and can call the virtual machine generated by the cluster, and the student cluster schedule is a call form which is generated according to the learning state of the student and can call the virtual machine when the student is performing a network course and needs to use the virtual machine to perform course activities, so that the enthusiasm and concentration of the student for learning can be increased.
It should be understood that, before receiving the instruction for starting the student cluster, the system searches whether a student cluster schedule exists, and if the student cluster schedule can be found, the system determines the identity of the student currently in class, specifically, obtains login information of the student currently in class, where the login information includes: the ID information and the MAC address information of the corresponding client machine, and the login information of the current lesson student is searched in the local record, if the login information can be found, the judgment is passed, and the system sequentially starts the student clusters according to the student cluster schedule; if not, the judgment is failed, and the system builds a student cluster schedule to be added.
It should be understood that if the system does not find the student cluster schedule, the system will create a student cluster schedule to be added, and supplement the student cluster schedule to be added according to the current information of the students on class.
S20: the method comprises the steps of obtaining lesson information of current lessons, establishing a learning state weight algorithm, calculating the lesson information according to the learning state weight algorithm, obtaining a weight value of each lesson student, associating the weight value with the corresponding lesson student, inserting the weight value into a student cluster schedule to be added, and obtaining the student cluster schedule to be used.
It should be appreciated that to supplement the student cluster schedule to be added, the system will obtain lesson information for the current lesson student, including: basic information of students and corresponding learning state information, wherein the basic information comprises: student class information, ID information, and MAC address information of a corresponding client machine; the learning state information includes: pre-lesson data, post-lesson work completion data, attendance data and classroom interaction data, wherein the data are used for archiving and storing student identity information so as to enable direct verification when the cluster is started next time, and reasonably distributing the cluster according to learning state information.
It should be understood that after that, the learning weight value of the students needs to be calculated, a learning state weight algorithm is established, the lesson information is calculated according to the learning state weight algorithm, the weight value of each lesson student is obtained, the weight value is associated with the basic information of the corresponding lesson student, a corresponding relation is established, the weight value of each lesson student is ordered according to the order from high to low of the numerical value, the higher the numerical value is, the weight value order after the ordering is obtained, the corresponding lesson students are inserted into the student cluster adding schedule according to the weight value order, and the students with the same weight are inserted according to the student number order, so that the learning state of one student can be more directly judged, the clusters are reasonably scheduled, and the cluster scheduling speed is improved.
It should be appreciated that the learning state weight algorithm is:
wherein w is the total weight value, n is the number of categories of statistical parameters, s i (i ε {1,2,., n }) is a score for a single category, t i Weight value for a single statistical category, and
it should be appreciated that assuming the number of categories of statistical parameters is n, the score for a single category is s i (i e {1,2,., n }) then the weight of the single statistical category
Then there is a certain student's current total weight +.>
After the current total weight values of all students are calculated, the current total weight values can be sequentially inserted into the weight sequence table according to the respective weight values.
Taking 4 common statistical parameters of pre-lesson study, post-lesson operation completion, attendance checking and classroom interaction as examples, the current value of n is 4.
Assuming that the teacher has placed x total before the current course t Pre-learning task before secondary lesson, y t Post-class operation, z t Record of secondary attendance, p t And (5) interaction in class.
Taking student a as an example, he completes x a Secondary pre-study, submit y a After class operation, check in z a Secondary attendance checking, take part in p a And (5) interaction in class. Score of pre-lesson pre-study for student a
Score ∈of post-class operation>
Sign-in score
Classroom interaction score->
After the statistical scores of the categories are obtained, the weights of the categories can be obtained respectively, and the weights are provided with
Then the total weight w of student a a ＝t 1 +t 2 +t 3 +t 4 Wherein w is a ∈[0,1]。
S30: and creating a student cluster according to the student cluster schedule to be used, and distributing virtual machines.
It should be understood that when the teacher stops lecturing, i.e. the system receives the instruction of stopping the student cluster, the system will extract the student data in the student cluster schedule to be used, and store the extracted student data in the local record for recording, so as to perform identity verification and call later, and then clear all the information in the student cluster schedule to be used.
It should be understood that, in this embodiment, a cluster intelligent scheduling flowchart based on learning states specifically includes the following operations:
first, the system sequentially starts the student clusters according to the cluster schedule.
After a student logs in a platform, the platform records information such as the ID of the student and the MAC address of the current client machine, the student can apply for a virtual machine to a server by using the two types of information as a unique certificate of an application cluster through a client, the server returns to the client after preparing a required cluster environment, and the client enters the cluster environment through a return address.
Before the system does not generate the student cluster schedule, the system can randomly start clusters corresponding to different clients. After the system generates the student cluster schedule, the system starts the cluster mode from the original random sequence start to the sequence start according to the cluster schedule, in the cluster schedule sequence table, the cluster with high priority is arranged at the front position, and as a result, the record with the front position is started preferentially.
When a teacher chooses to play a class, the system starts to calculate the weight information of the students currently in class according to the cluster schedule. The method comprises the steps of acquiring basic information of all students in a current class, associating the basic information with various information of the learning state of the students, calculating the weight value of each student according to a learning state weight algorithm, then inserting the weight values of the students into a cluster schedule according to the high-to-low sequence of the weight values of the students, and inserting the students with the same weight according to the student number sequence. After the teaching activities are completed, the teacher chooses to go to lessons, and the information in the cluster scheduling table is cleared.
It should be noted that, when all students log on the platform for the first time, as all the student weight values are 0, the order in the cluster schedule is arranged according to the student number size from low to high.
After each student weight value is obtained, the student weight values are sequentially inserted into the cluster scheduling table from high to low according to the total weight, so that the generation of the cluster scheduling table is completed, in such a way, the fairness of the operation of the students is improved, the students with good learning attitudes can obtain longer operation time, the increasing enthusiasm of the students to learn is also increased, in order to obtain the longer operation time, the students need to actively complete all teaching activities of teacher arrangement on time, meanwhile, the uncertainty of cluster scheduling is solved due to the addition of a learning state weight algorithm, and the starting speed of the clusters can be improved.
It should be noted that the foregoing is merely illustrative, and does not limit the technical solutions of the present application in any way.
As described above, it is easy to find that, in this embodiment, by receiving a student cluster start instruction, a student cluster schedule is searched, and when the student cluster schedule is not searched, a new student cluster schedule to be added is built; the method comprises the steps of obtaining lesson information of current lessons, establishing a learning state weight algorithm, calculating the lesson information according to the learning state weight algorithm, obtaining a weight value of each lesson student, associating the weight value with the corresponding lesson student, inserting the weight value into a student cluster schedule to be added, and obtaining a student cluster schedule to be used; according to the student cluster scheduling table to be used, a student cluster is created, and virtual machines are allocated, and the embodiment calculates the learning weight value of a lesson student by using a weight value algorithm, so that the virtual machines are reasonably allocated according to the learning weight value, the learning efficiency of the student is improved, the uncertainty of cluster scheduling is solved, and the cluster starting speed is also improved.
In addition, the embodiment of the invention also provides a cluster intelligent scheduling device based on the learning state. As shown in fig. 3, the learning state-based cluster intelligent scheduling apparatus includes: a new module 10, a calculation module 20, and a cluster creation module 30.
The new module 10 is configured to receive a student cluster start instruction, search a student cluster schedule, and when the student cluster schedule is not searched, newly build a student cluster schedule to be added;
the computing module 20 is configured to obtain lesson information of a current lesson student, establish a learning state weight algorithm, calculate the lesson information according to the learning state weight algorithm, obtain a weight value of each lesson student, associate the weight value with a corresponding lesson student, and insert the weight value into a student cluster schedule to be added to obtain a student cluster schedule to be used;
the cluster creation module 30 is configured to create a student cluster according to the student cluster schedule to be used, and allocate a virtual machine.
In addition, it should be noted that the above embodiment of the apparatus is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select some or all modules according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
In addition, technical details which are not described in detail in the present embodiment may refer to the learning state-based intelligent scheduling method provided in any embodiment of the present invention, and are not described herein.
In addition, the embodiment of the invention also provides a medium, which is a computer medium, wherein the computer medium is stored with a cluster intelligent scheduling method program based on a learning state, and the cluster intelligent scheduling method program based on the learning state realizes the following operations when being executed by a processor:
s1, receiving a student cluster starting instruction, searching a student cluster schedule, and when the student cluster schedule is not searched, newly building a student cluster schedule to be added;
s2, acquiring the lesson information of the current lesson students, establishing a learning state weight algorithm, calculating the lesson information according to the learning state weight algorithm, acquiring a weight value of each lesson student, associating the weight value with the corresponding lesson student, inserting the weight value into a student cluster schedule to be added, and acquiring a student cluster schedule to be used;
s3, creating a student cluster according to the student cluster schedule to be used, and distributing virtual machines.
Further, the cluster intelligent scheduling method program based on the learning state also realizes the following operations when being executed by the processor:
receiving a student cluster starting instruction, searching a student cluster schedule, and sequentially starting the student clusters according to the student cluster schedule when the student cluster schedule is searched; when the student cluster schedule is not found, a new student cluster schedule to be added is built.
Further, the cluster intelligent scheduling method program based on the learning state also realizes the following operations when being executed by the processor:
when the student cluster scheduling table is found, login information of the students on the current lesson is obtained, wherein the login information comprises: and searching the login information of the current lesson student in the local record, sequentially starting the student clusters according to the student cluster schedule if the login information of the current lesson student exists in the local record, and creating a to-be-added student cluster schedule if the login information of the current lesson student does not exist in the local record.
Further, the cluster intelligent scheduling method program based on the learning state also realizes the following operations when being executed by the processor:
the lesson information includes: basic information of students and corresponding learning state information, wherein the basic information comprises: student class information, ID information, and MAC address information of a corresponding client machine; the learning state information includes: pre-lesson data, post-lesson job completion data, attendance data, and classroom interaction data.
Further, the cluster intelligent scheduling method program based on the learning state also realizes the following operations when being executed by the processor:
establishing a learning state weight algorithm, calculating the lesson information according to the learning state weight algorithm, acquiring a weight value of each lesson student, associating the weight value with basic information of the corresponding lesson student, establishing a corresponding relation, sequencing the weight value of each lesson student according to the value from high to low, acquiring a weight value sequence with higher value, and inserting the corresponding lesson student into an added student cluster schedule according to the weight value sequence.
Further, the cluster intelligent scheduling method program based on the learning state also realizes the following operations when being executed by the processor:
the learning state weight algorithm is as follows:
wherein w is the total weight value, n is the number of categories of statistical parameters, s i (i ε {1,2,., n }) is a score for a single category, t i Weight value for a single statistical category, and
further, the cluster intelligent scheduling method program based on the learning state also realizes the following operations when being executed by the processor:
and after receiving the instruction for stopping the student cluster, extracting the student data in the student cluster schedule to be used, storing the student data in the local record, and clearing all information in the student cluster schedule to be used.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (9)
1. A cluster intelligent scheduling method based on learning state is characterized in that: comprises the following steps of;
s1, receiving a student cluster starting instruction, searching a student cluster schedule, and when the student cluster schedule is not searched, newly building a student cluster schedule to be added;
s2, acquiring the lesson information of the current lesson students, establishing a learning state weight algorithm, calculating the lesson information according to the learning state weight algorithm, acquiring a weight value of each lesson student, associating the weight value with the corresponding lesson student, inserting the weight value into a student cluster schedule to be added, and acquiring a student cluster schedule to be used; the lesson information includes: basic information of students and corresponding learning state information, wherein the basic information comprises: student class information, ID information, and MAC address information of a corresponding client machine; the learning state information includes: pre-lesson data, post-lesson job completion data, attendance data and classroom interaction data;
s3, creating a student cluster according to the student cluster schedule to be used, and distributing virtual machines.
2. The learning state-based cluster intelligent scheduling method as claimed in claim 1, wherein: in step S1, receiving a student cluster starting instruction, searching a student cluster schedule, and when the student cluster schedule is not searched, newly building a student cluster schedule to be added; when the student cluster schedule is not found, a new student cluster schedule to be added is built.
3. The learning state-based cluster intelligent scheduling method as claimed in claim 2, wherein: when the student cluster schedule is found, sequentially starting the student clusters according to the student cluster schedule, and further comprising the following steps of obtaining login information of the students on the current lesson when the student cluster schedule is found, wherein the login information comprises: and searching the login information of the current lesson student in the local record, sequentially starting the student clusters according to the student cluster schedule if the login information of the current lesson student exists in the local record, and creating a to-be-added student cluster schedule if the login information of the current lesson student does not exist in the local record.
4. The learning state-based cluster intelligent scheduling method as claimed in claim 1, wherein: in step S2, a learning state weight algorithm is established, the lesson information is calculated according to the learning state weight algorithm, the weight value of each lesson student is obtained, the weight value is associated with the corresponding lesson student and is inserted into a student cluster scheduling table to be added, the student cluster scheduling table to be used is obtained, the method further comprises the steps of establishing the learning state weight algorithm, calculating the lesson information according to the learning state weight algorithm, obtaining the weight value of each lesson student, associating the weight value with the basic information of the corresponding lesson student, establishing a corresponding relation, ranking the weight value of each lesson student according to the value from high to low, ranking the weight value with higher value is higher, the ranked weight value sequence is obtained, and the corresponding lesson students are inserted into the student cluster scheduling table to be added according to the weight value sequence.
5. The intelligent scheduling method for clusters based on learning states as set forth in claim 4, wherein: the learning state weighting algorithm comprises the following steps:
wherein w is the total weight value, n is the number of categories of statistical parameters, s i (i ε {1,2,., n }) is a score for a single category, t i Weight value for a single statistical category, and
6. the intelligent scheduling method for clusters based on learning states as set forth in claim 4, wherein: in step S3, after creating a student cluster according to the student cluster schedule to be used and allocating a virtual machine, the method further includes the steps of extracting student data in the student cluster schedule to be used, storing the extracted student data in a local record, and clearing all information in the student cluster schedule to be used after receiving a command for stopping the student cluster.
7. The utility model provides a cluster intelligent scheduling device based on learning state which characterized in that, cluster intelligent scheduling device based on learning state includes:
the new building module is used for receiving the student cluster starting instruction, searching the student cluster scheduling table, and when the student cluster scheduling table is not searched, building a new student cluster scheduling table to be added;
the calculation module is used for acquiring the lesson information of the current lesson students, establishing a learning state weight algorithm, calculating the lesson information according to the learning state weight algorithm, acquiring a weight value of each lesson student, correlating the weight value with the corresponding lesson student, inserting the weight value into a student cluster schedule to be added, and acquiring the student cluster schedule to be used; the lesson information includes: basic information of students and corresponding learning state information, wherein the basic information comprises: student class information, ID information, and MAC address information of a corresponding client machine; the learning state information includes: pre-lesson data, post-lesson job completion data, attendance data and classroom interaction data;
and the cluster creation module is used for creating a student cluster according to the student cluster schedule to be used and distributing virtual machines.
8. An apparatus, the apparatus comprising: a memory, a processor and a learning state based cluster intelligent scheduling method program stored on the memory and executable on the processor, the learning state based cluster intelligent scheduling method program configured to implement the steps of the learning state based cluster intelligent scheduling method of any one of claims 1 to 6.
9. A medium, characterized in that the medium is a computer medium, on which a learning state based cluster intelligent scheduling method program is stored, which, when being executed by a processor, implements the steps of the learning state based cluster intelligent scheduling method according to any one of claims 1 to 6.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105022670A (en) * | 2015-07-17 | 2015-11-04 | 中国海洋大学 | Heterogeneous distributed task processing system and processing method in cloud computing platform |
| CN107370783A (en) * | 2016-05-13 | 2017-11-21 | 北京金山云网络技术有限公司 | A kind of dispatching method and device of cloud computing cluster resource |
| CN108519911A (en) * | 2018-03-23 | 2018-09-11 | 上饶市中科院云计算中心大数据研究院 | The dispatching method and device of resource in a kind of cluster management system based on container |
| CN109032801A (en) * | 2018-07-26 | 2018-12-18 | 郑州云海信息技术有限公司 | A kind of request scheduling method, system and electronic equipment and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105022670A (en) * | 2015-07-17 | 2015-11-04 | 中国海洋大学 | Heterogeneous distributed task processing system and processing method in cloud computing platform |
| CN107370783A (en) * | 2016-05-13 | 2017-11-21 | 北京金山云网络技术有限公司 | A kind of dispatching method and device of cloud computing cluster resource |
| CN108519911A (en) * | 2018-03-23 | 2018-09-11 | 上饶市中科院云计算中心大数据研究院 | The dispatching method and device of resource in a kind of cluster management system based on container |
| CN109032801A (en) * | 2018-07-26 | 2018-12-18 | 郑州云海信息技术有限公司 | A kind of request scheduling method, system and electronic equipment and storage medium |
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