CN115640128A - Cloud resource scheduling method in hybrid cloud mode - Google Patents
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Abstract
The invention relates to the technical field of cloud resources, and discloses a cloud resource scheduling method in a hybrid cloud mode, which comprises the following steps: the method comprises the steps that firstly, a cloud resource platform is built, a local area network is set, server nodes connected with the local area network are connected, data in a server are transmitted through the server nodes, the transmitted data are stored in the cloud resource platform, meanwhile, the cloud resource platform is provided with a plurality of storage units, and the transmitted data are stored in a distributed mode according to a time sequence; and step two, storage analysis, namely analyzing the storage units in the cloud resource platform through the inner monitoring unit, and dividing the storage units through the storage unit analysis. According to the cloud resource scheduling method in the hybrid cloud mode, the storage unit is analyzed according to the method and the device, the storage unit is analyzed according to the real-time state of the storage unit, the accuracy of resource scheduling is improved, and therefore path errors and cost increase of resource scheduling caused by resource scheduling are effectively prevented.
Description
Technical Field
The invention relates to the technical field of cloud resources, in particular to a cloud resource scheduling method in a hybrid cloud mode.
Background
Cloud computing is a novel service mode, a large amount of computing resources stored on a data center cluster are uniformly managed in a resource pool mode and are provided for users to use as required, and under the environment of cloud computing, the users can conveniently use resources such as computing and storage without spending a large amount of manpower and financial resources to purchase and maintain data storage equipment for data acquisition, and can obtain stronger processing capacity, storage space and better professional services; at present, cloud computing is widely applied to various industries, the IT operation and maintenance level of enterprises is improved, and the development of various industries is invisibly promoted.
In the prior art, the accuracy of resource scheduling cannot be determined in the process of resource scheduling by a cloud resource platform, the path of resource scheduling cannot be ensured to be accurate, and the cost of resource scheduling cannot be controlled, so that the invention provides a cloud resource scheduling method in a hybrid cloud mode.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a cloud resource scheduling method in a hybrid cloud mode, which solves the problems in the background technology.
(II) technical scheme
In order to achieve the above purpose, the invention provides the following technical scheme: a cloud resource scheduling method in a hybrid cloud mode comprises the following specific steps: the method comprises the steps that firstly, a cloud resource platform is built, a local area network is set, server nodes connected with the local area network are connected, data in a server are transmitted through the server nodes, the transmitted data are stored in the cloud resource platform, meanwhile, the cloud resource platform is provided with a plurality of storage units, and the transmitted data are stored in a distributed mode according to a time sequence; step two, storage analysis, namely analyzing the storage units in the cloud resource platform through an internal monitoring unit, and dividing the storage units through storage unit analysis; step three, task analysis, namely, the cloud resource platform receives a task instruction sent by any server node in real time, collects the real-time task instruction received by the cloud resource platform, analyzes the collected real-time task instruction, and divides the real-time task instruction according to the analysis result of the real-time task instruction; reasonably matching the analyzed storage unit with the real-time task instruction, accurately scheduling data stored in the storage unit, and matching the data scheduling with the real-time task instruction; analyzing the operation of the cloud resource platform, acquiring error frequency of server data reception and error frequency of data transmission in the cloud resource platform, and comparing the error frequency of server data reception and the error frequency of data transmission in the cloud resource platform with a data reception error frequency threshold and a data transmission error frequency threshold respectively: if any numerical value of the error frequency of server data receiving and the error frequency of data transmission in the cloud resource platform is greater than the corresponding threshold value, marking the corresponding server as a problem server, and sending the serial number of the problem server to a mobile phone terminal of a manager; and if the error frequency of server data receiving and the error frequency of data transmission in the cloud resource platform are both smaller than the corresponding threshold values, marking the corresponding server as a normal server.
Preferably, the internal monitoring platform in the second step is used for analyzing the storage unit, and analyzing the storage unit according to the real-time state of the storage unit, wherein the analysis process specifically comprises the following steps: step S1: collecting each storage unit in the cloud resource platform, and marking the storage units as i, wherein i is a natural number greater than 1; step S2: acquiring the remaining data storage capacity, the ratio of the remaining data storage capacity to the total amount and the longest data storage duration in each storage unit, and respectively marking the remaining data storage capacity, the ratio of the remaining data storage capacity to the total amount and the longest data storage duration in each storage unit as SCi, BZi and ZCi; obtaining an analysis coefficient Xi of a storage unit through an analysis formula; and step S3: the analysis coefficient Xi of the storage unit is compared with an analysis coefficient threshold.
Preferably, the comparison process of the analysis coefficients of the storage unit is as follows: if the analysis coefficient Xi of the storage unit is larger than or equal to the analysis coefficient threshold value, marking the corresponding storage unit as a task receiving unit, and sending the task receiving unit to the cloud resource platform; if the analysis coefficient Xi of the storage unit is smaller than the analysis coefficient threshold value, the corresponding storage unit is marked as a task rejection unit, and the task rejection unit is sent to the cloud resource platform.
Preferably, in the third step, the task instructions are analyzed to acquire the processing coefficients of the task instructions, and the task analysis steps are as follows: step SS1: monitoring a cloud resource platform in real time, acquiring a task instruction received in real time in the cloud resource platform, and marking the task instruction received in real time as o, wherein o is a natural number greater than 1; the server receives the task instructions in real time and sends the task instructions to the server; step SS2: acquiring the processing required time corresponding to the task instruction received in real time and the interval time between the task instruction sending time and the current time, and respectively marking the processing required time corresponding to the task instruction received in real time and the interval time between the task instruction sending time and the current time as XQo and JGo; acquiring a processing coefficient Zo of a real-time task instruction through a processing formula; step SS3: and sequencing the processing coefficients Zo of the real-time task instructions according to the numerical value from large to small, and sending the real-time task instructions to the cloud resource platform according to the sequence.
Preferably, the resource scheduling in step four includes the following steps: the method comprises the steps of collecting task receiving units in a cloud resource platform, marking a server corresponding to a real-time task instruction as a task server, analyzing the task server and the task receiving units, matching the task server and the task receiving units corresponding to required data, transmitting the data of the task receiving units with the consistent required data and the task server through nodes, and scheduling the data in the task receiving units into the task server.
(III) advantageous effects
Compared with the prior art, the invention provides a cloud resource scheduling method in a hybrid cloud mode, which has the following beneficial effects:
according to the cloud resource scheduling method in the hybrid cloud mode, the storage unit is analyzed according to the real-time state of the storage unit, so that the accuracy of resource scheduling is improved, and the path error and the cost increase of the resource scheduling caused by the resource scheduling are effectively prevented; according to the method, the task instructions are analyzed and the processing coefficients of all the task instructions are acquired, so that the division is performed according to the processing coefficients of the task instructions, the processing efficiency of the task instructions is improved, and meanwhile, data scheduling in a cloud resource platform is performed according to the task instructions; according to the cloud resource scheduling method and device, data of all the servers are collected, the data are stored in a distributed mode, analysis is carried out according to the task instructions sent by the servers, the servers corresponding to the task instructions are judged, data transmission is carried out on the corresponding servers, and therefore the efficiency and the accuracy of cloud resource scheduling are improved.
Drawings
Fig. 1 is a flowchart of a cloud resource scheduling method in a hybrid cloud mode according to 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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: the cloud resource scheduling method specifically comprises the following steps: the method comprises the steps that firstly, a cloud resource platform is built, a local area network is set, server nodes connected with the local area network are connected, data in a server are transmitted through the server nodes, the transmitted data are stored in the cloud resource platform, meanwhile, the cloud resource platform is provided with a plurality of storage units, and the transmitted data are stored in a distributed mode according to the time sequence; analyzing storage, namely analyzing a storage unit in the cloud resource platform through an internal supervision unit, and dividing the storage unit through storage unit analysis; step three, task analysis, namely, the cloud resource platform receives a task instruction sent by any server node in real time, collects the real-time task instruction received by the cloud resource platform, analyzes the collected real-time task instruction, and divides the real-time task instruction according to the analysis result of the real-time task instruction; reasonably matching the analyzed storage unit with the real-time task instruction, and accurately scheduling the data stored in the storage unit, wherein the data scheduling is matched with the real-time task instruction; analyzing the operation of the cloud resource platform, acquiring the error frequency of server data reception and the error frequency of data transmission in the cloud resource platform, and comparing the error frequency of server data reception and the error frequency of data transmission in the cloud resource platform with a data reception error frequency threshold value and a data transmission error frequency threshold value respectively: if any value of the error frequency of server data receiving and the error frequency of data transmission in the cloud resource platform is greater than the corresponding threshold value, marking the corresponding server as a problem server, and sending the number of the problem server to a mobile phone terminal of a manager; if the error frequency of data receiving of the server in the cloud resource platform and the error frequency of data transmission are both smaller than the corresponding threshold values, the corresponding server is marked as a normal server, the internal supervision platform in the step two is used for analyzing the storage unit, the storage unit is analyzed according to the real-time state of the storage unit, and the analysis process is specifically as follows: step S1: collecting each storage unit in the cloud resource platform, and marking the storage unit as i, wherein i is a natural number greater than 1; step S2: acquiring the residual data storage capacity, the ratio of the residual data storage capacity to the total amount and the longest data storage time length in each storage unit, and respectively marking the residual data storage capacity, the ratio of the residual data storage capacity to the total amount and the longest data storage time length in each storage unit as SCi, BZi and ZCi; acquiring an analysis coefficient Xi of a storage unit through an analysis formula; and step S3: comparing the analysis coefficient Xi of the storage unit with an analysis coefficient threshold value, wherein the comparison process of the analysis coefficient of the storage unit is as follows: if the analysis coefficient Xi of the storage unit is larger than or equal to the analysis coefficient threshold, marking the corresponding storage unit as a task receiving unit, and sending the task receiving unit to the cloud resource platform; if the analysis coefficient Xi of the storage unit is smaller than the analysis coefficient threshold, marking the corresponding storage unit as a task rejection unit, and sending the task rejection unit to the cloud resource platform, wherein the task instructions are analyzed in the third step to acquire the processing coefficient of each task instruction, and the task analysis step specifically comprises the following steps: step SS1: monitoring a cloud resource platform in real time, acquiring a task instruction received in real time in the cloud resource platform, and marking the task instruction received in real time as o, wherein o is a natural number greater than 1; the server receives the task instructions in real time and sends the task instructions to the server; step SS2: acquiring the processing required time corresponding to the task instruction received in real time and the interval time between the task instruction sending time and the current time, and respectively marking the processing required time corresponding to the task instruction received in real time and the interval time between the task instruction sending time and the current time as XQo and JGo; acquiring a processing coefficient Zo of a real-time task instruction through a processing formula; and step SS3: sequencing the processing coefficients Zo of the real-time task instructions according to the numerical value from large to small, and sending the real-time task instructions to the cloud resource platform according to the sequence, wherein the resource scheduling step in the fourth step is as follows: the method comprises the steps of collecting task receiving units in a cloud resource platform, marking a server corresponding to a real-time task instruction as a task server, analyzing the task server and the task receiving units, matching the task server and the task receiving units corresponding to required data, transmitting the data of the task receiving units with the consistent required data and the task server through nodes, and scheduling the data in the task receiving units into the task server.
Working; when the cloud resource platform works, the cloud resource platform is built, a local area network is set, server nodes connected with the local area network are connected, data in the server are transmitted through the server nodes, the transmitted data are stored in the cloud resource platform, meanwhile, the cloud resource platform is provided with a plurality of storage units, and the transmitted data are stored in a distributed mode according to a time sequence; analyzing the storage units in the cloud resource platform through an internal supervision unit, and dividing the storage units through storage unit analysis; the method comprises the steps of task analysis, wherein a cloud resource platform receives a task instruction sent by any server node in real time, collects the real-time task instruction received by the cloud resource platform, analyzes the collected real-time task instruction, and divides the real-time task instruction according to the analysis result of the real-time task instruction; reasonably matching the analyzed storage unit with the real-time task instruction, accurately scheduling data stored in the storage unit, and matching the data scheduling with the real-time task instruction; the operation of the cloud resource platform is analyzed, the error frequency of data receiving and the error frequency of data transmission of the server in the cloud resource platform are collected, and the error frequency of data receiving and the error frequency of data transmission of the server in the cloud resource platform are respectively compared with a data receiving error frequency threshold and a data transmission error frequency threshold.
In summary, according to the cloud resource scheduling method in the hybrid cloud mode, the storage unit is analyzed according to the method and the device, and the storage unit is analyzed according to the real-time state of the storage unit, so that the accuracy of resource scheduling is improved, and the resource scheduling is effectively prevented from generating path errors and causing the cost of the resource scheduling to be increased; according to the method, the task instructions are analyzed and the processing coefficients of all the task instructions are acquired, so that the task instructions are divided according to the processing coefficients of the task instructions, the processing efficiency of the task instructions is improved, and meanwhile, data scheduling in a cloud resource platform is performed according to the task instructions; according to the cloud resource scheduling method and device, data of all the servers are collected, the data are stored in a distributed mode, analysis is carried out according to the task instructions sent by the servers, the servers corresponding to the task instructions are judged, data transmission is carried out on the corresponding servers, and therefore the efficiency and the accuracy of cloud resource scheduling are improved.
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. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. A cloud resource scheduling method in a hybrid cloud mode is characterized in that: the cloud resource scheduling method specifically comprises the following steps: the method comprises the steps that firstly, a cloud resource platform is built, a local area network is set, server nodes connected with the local area network are connected, data in a server are transmitted through the server nodes, the transmitted data are stored in the cloud resource platform, meanwhile, the cloud resource platform is provided with a plurality of storage units, and the transmitted data are stored in a distributed mode according to the time sequence; analyzing storage, namely analyzing a storage unit in the cloud resource platform through an internal supervision unit, and dividing the storage unit through storage unit analysis; step three, task analysis, namely, the cloud resource platform receives a task instruction sent by any server node in real time, collects the real-time task instruction received by the cloud resource platform, analyzes the collected real-time task instruction and divides the real-time task instruction according to the analysis result of the real-time task instruction; reasonably matching the analyzed storage unit with the real-time task instruction, accurately scheduling data stored in the storage unit, and matching the data scheduling with the real-time task instruction; analyzing the operation of the cloud resource platform, acquiring error frequency of server data reception and error frequency of data transmission in the cloud resource platform, and comparing the error frequency of server data reception and the error frequency of data transmission in the cloud resource platform with a data reception error frequency threshold and a data transmission error frequency threshold respectively: if any numerical value of the error frequency of server data receiving and the error frequency of data transmission in the cloud resource platform is greater than the corresponding threshold value, marking the corresponding server as a problem server, and sending the serial number of the problem server to a mobile phone terminal of a manager; and if the error frequency of server data receiving and the error frequency of data transmission in the cloud resource platform are both smaller than the corresponding threshold values, marking the corresponding server as a normal server.
2. The method according to claim 1, wherein the method comprises: the inner supervision platform in the step two is used for analyzing the storage unit and analyzing the storage unit according to the real-time state of the storage unit, and the analysis process is as follows: step S1: collecting each storage unit in the cloud resource platform, and marking the storage unit as i, wherein i is a natural number greater than 1; step S2: acquiring the residual data storage capacity, the ratio of the residual data storage capacity to the total amount and the longest data storage time length in each storage unit, and respectively marking the residual data storage capacity, the ratio of the residual data storage capacity to the total amount and the longest data storage time length in each storage unit as SCi, BZi and ZCi; obtaining an analysis coefficient Xi of a storage unit through an analysis formula; and step S3: the analysis coefficient Xi of the storage unit is compared with an analysis coefficient threshold.
3. The method according to claim 1, wherein the method comprises: the comparison process of the analysis coefficients of the storage unit is as follows: if the analysis coefficient Xi of the storage unit is larger than or equal to the analysis coefficient threshold, marking the corresponding storage unit as a task receiving unit, and sending the task receiving unit to the cloud resource platform; if the analysis coefficient Xi of the storage unit is smaller than the analysis coefficient threshold value, the corresponding storage unit is marked as a task rejection unit, and the task rejection unit is sent to the cloud resource platform.
4. The method according to claim 1, wherein the method comprises: in the third step, the task instructions are analyzed to acquire the processing coefficients of the task instructions, and the task analysis steps are as follows: step SS1: monitoring a cloud resource platform in real time, acquiring a task instruction received in real time in the cloud resource platform, and marking the task instruction received in real time as o, wherein o is a natural number greater than 1; the server receives the task instructions in real time and sends the task instructions to the server; step SS2: acquiring the processing required duration corresponding to the task instruction and the interval duration between the task instruction sending time and the current time which are received in real time, and respectively marking the processing required duration corresponding to the task instruction and the interval duration between the task instruction sending time and the current time as XQo and JGo; acquiring a processing coefficient Zo of a real-time task instruction through a processing formula; step SS3: and sequencing the processing coefficients Zo of the real-time task instructions according to the numerical value from large to small, and sending the real-time task instructions to the cloud resource platform according to the sequence.
5. The method according to claim 1, wherein the method comprises: the resource scheduling in step four comprises the following steps: the method comprises the steps of collecting task receiving units in a cloud resource platform, marking a server corresponding to a real-time task instruction as a task server, analyzing the task server and the task receiving units, matching the task server and the task receiving units corresponding to required data, transmitting the data of the task receiving units with the consistent required data and the task server through nodes, and scheduling the data in the task receiving units into the task server.
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Application publication date: 20230124 |