CN112565320B - Load balancing method and device - Google Patents
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- CN112565320B CN112565320B CN201910914101.2A CN201910914101A CN112565320B CN 112565320 B CN112565320 B CN 112565320B CN 201910914101 A CN201910914101 A CN 201910914101A CN 112565320 B CN112565320 B CN 112565320B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/4557—Distribution of virtual machine instances; Migration and load balancing
Abstract
The embodiment of the invention provides a load balancing method and a device, wherein the method comprises the following steps: the method comprises the steps of obtaining at least one task to be analyzed sent by terminal equipment, distributing the task to be analyzed to a first virtual machine according to task type identifications included by each task to be analyzed, adding the task to be analyzed to task queues corresponding to the task type identifications through the first virtual machine, determining a special data analysis task and/or a basic data analysis task from the task queues corresponding to the task type identifications through the first virtual machine, obtaining basic data results corresponding to the distributed special data analysis task from a second virtual machine, obtaining the special data analysis task distributed by the first virtual machine through a third virtual machine, and processing the special data analysis task according to the basic data results, so that load balance under the non-independent networking network environment is achieved.
Description
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to a load balancing method and apparatus.
Background
Fifth Generation mobile communication technology (5G) business has been started, and network optimization projects are also on schedule and face a data analysis job that is more extensive than the 4th Generation mobile communication technology (4G) network optimization data size. In addition, 4G and 5G coexist in a network environment of Non-independent Networking (NSA), and therefore, it is necessary to optimize a 4G network together with 5G network optimization.
The load balancing algorithm in the current network optimization scheme adopts a polling method, which specifically comprises the following steps: requests from the terminal equipment are allocated to the servers in turn, for example a total of N servers, a first request being allocated to server 1 and a second request being allocated to server 2 until the nth request is allocated to server N, and the cycle is then restarted.
However, the current load balancing algorithm does not consider the situation of coexistence of 4G and 5G in the NSA network environment, and therefore providing a load balancing method capable of being used in the NSA network environment is urgently needed.
Disclosure of Invention
The embodiment of the invention provides a load balancing method and device, aiming at solving the problem that the coexistence of 4G and 5G under the NSA network environment is not considered in the load balancing algorithm in the prior art.
The embodiment of the invention provides a load balancing method, which is applied to a server of a cloud platform and comprises the following steps:
the method comprises the steps of obtaining at least one task to be analyzed sent by terminal equipment, wherein each task to be analyzed comprises a task type identifier, and the task type identifier comprises a 4G task type or a 5G task type;
distributing the tasks to be analyzed to a first virtual machine according to task type identifications included by each task to be analyzed, and adding the tasks to be analyzed to task queues corresponding to the task type identifications through the first virtual machine;
determining a dedicated data analysis task and/or a basic data analysis task from a task queue corresponding to the task type identifier through the first virtual machine;
acquiring a basic data result corresponding to the distributed special data analysis task from a second virtual machine, acquiring the special data analysis task distributed by the first virtual machine through a third virtual machine, and processing the special data analysis task according to the basic data result;
the first virtual machine, the second virtual machine, and the third virtual machine are respectively virtual machines corresponding to the task type identifier.
The embodiment of the present invention further provides a load balancing apparatus, which is arranged in a server of a cloud platform, and includes:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring at least one task to be analyzed sent by terminal equipment, each task to be analyzed comprises a task type identifier, and the task type identifier comprises a 4G task type or a 5G task type;
the first distribution module is used for distributing the tasks to be analyzed to a first virtual machine according to task type identifications included by each task to be analyzed and adding the tasks to be analyzed to a task queue corresponding to the task type identifications through the first virtual machine;
the second allocation module is used for determining a dedicated data analysis task and/or a basic data analysis task from a task queue corresponding to the task type identifier through the first virtual machine;
the processing module is used for acquiring a basic data result corresponding to the distributed special data analysis task from a second virtual machine, acquiring the special data analysis task distributed by the first virtual machine through a third virtual machine, and processing the special data analysis task according to the basic data result;
the first virtual machine, the second virtual machine, and the third virtual machine are respectively virtual machines corresponding to the task type identifier.
In the embodiment of the invention, at least one task to be analyzed sent by a terminal device is obtained, the task to be analyzed is allocated to a first virtual machine according to a task type identifier included by each task to be analyzed, the task to be analyzed is added to a task queue corresponding to the task type identifier through the first virtual machine, a special data analysis task and/or a basic data analysis task are/is determined from the task queue corresponding to the task type identifier through the first virtual machine, the special data analysis task allocated by the first virtual machine is obtained through a third virtual machine, a basic data result corresponding to the allocated special data analysis task is obtained from the second virtual machine, and the special data analysis task is processed according to the basic data result, so that the task to be analyzed is allocated to the first virtual machine corresponding to the task type identifier of the task to be analyzed by a server to be processed under an NSA network environment, therefore, load balancing is realized, and physical resources of the server are better utilized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a flowchart illustrating steps of a load balancing method according to an embodiment of the present invention;
fig. 2A is a flowchart illustrating steps of another load balancing method according to an embodiment of the present invention;
fig. 2B is a block diagram of a structure of each virtual machine created in a server of a cloud platform according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a load balancing apparatus according to an embodiment 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 some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a load balancing method according to an embodiment of the present invention, where the load balancing method is applied to a server of a cloud platform, and includes the following steps:
The tasks to be analyzed comprise a weak coverage analysis task, an interference analysis task, a Key Performance Indicator (KPI) and a Key Performance Indicator (Key Performance Indicator) index analysis task waiting analysis task. The task type identifier may be a fourth Generation mobile communication technology (4G) task type or a fifth Generation mobile communication technology (5G) task type. If the task type identifier of one task to be analyzed is the 4G task type, the task to be analyzed is the 4G task to be analyzed; and if the task type identifier of the task to be analyzed is the 5G task type, the task to be analyzed is the 5G task to be analyzed.
The tasks to be analyzed are, for example, an interference analysis task, a weak coverage analysis task, a KPI analysis task, and the like, where the interference analysis task is, for example, intersystem interference or intersystem interference, and the intersystem interference is usually pilot frequency interference, and there are no perfect radio transmitters and receivers in the world. Scientific theory has shown that ideal filters are not realizable, i.e., the signal cannot be strictly bound within a specified operating frequency. Therefore, the transmitter transmits a part of the power to other frequencies while transmitting on the designated channel, and the receiver receives the power on other frequencies while receiving on the designated channel, thereby generating intersystem interference. An intra-system interference system is usually co-channel interference, for example, in a Time Division Long Term Evolution (TD-LTE) system, although different users in the same cell cannot use the same frequency resource, adjacent cells may use the same frequency resource. Interference will occur between devices using the same frequency resources in the same system, also referred to as intra-system interference. The weak coverage analysis task for example analyses which cells are weak coverage cells. The KPI analysis task is, for example, the success rate of switching between stations, the power of switching between stations, the call drop rate, etc.
102, distributing the tasks to be analyzed to a first virtual machine according to the task type identification included by each task to be analyzed, and adding the tasks to be analyzed to a task queue corresponding to the task type identification through the first virtual machine.
And the first virtual machine is a virtual machine corresponding to the task type identifier. And allocating the task to be analyzed to the first virtual machine, and adding the task to be analyzed to a task queue corresponding to the task type identifier through the first virtual machine, namely, the task to be analyzed, the task type identifier, the first virtual machine corresponding to the task type identifier and the task queue corresponding to the task type identifier have a corresponding relation. For example, referring to table 1 below, table 1 shows a corresponding relationship between a task to be analyzed, a task type identifier, a first virtual machine corresponding to the task type identifier, and a task column corresponding to the task type identifier. If the task to be analyzed comprises a task 1 to be analyzed, a task 2 to be analyzed, a task 3 to be analyzed and a task 4 to be analyzed, the type identifier of the task 1 to be analyzed is a 4G task type, the type identifier of the task 2 to be analyzed is a 5G task type, the type identifier of the task 3 to be analyzed is a 4G task type, the type identifier of the task 4 to be analyzed is a 5G task type, the first virtual machine corresponding to the 4G task type is a first 4G virtual machine, the first virtual machine corresponding to the 5G task type is a first 5G virtual machine, the task queue corresponding to the 4G task type is a 4G task queue, and the task queue corresponding to the 5G task type is a 5G task queue, adding the task 1 to be analyzed and the task 3 to be analyzed into the 4G task queue, and adding the task 2 to be analyzed and the task 4 to be analyzed into the 4G task queue. The details are shown in table 1 below:
TABLE 1
And 103, determining a special data analysis task and/or a basic data analysis task from a task queue corresponding to the task type identification through the first virtual machine.
It should be noted that the second virtual machine is a virtual machine corresponding to the task type identifier. For example, the second virtual machine corresponding to the 4G task type is a second 4G virtual machine, and the second virtual machine corresponding to the 5G task type is a second 5G virtual machine. If the special data analysis task and the basic data analysis task are in the 4G task queue, determining the special data analysis task as the 4G special data analysis task and determining the basic data analysis task as the 4G basic data analysis task; and if the special data analysis task and the basic data analysis task are in the 5G task queue, determining the special data analysis task as the 5G special data analysis task and determining the basic data analysis task as the 5G basic data analysis task. For example, the first 4G virtual machine determines a 4G-specific data analysis task and a 4G-basic data analysis task from the 4G task queue, and allocates the 4G-basic data analysis task to the second 4G virtual machine, and the first 4G virtual machine may allocate the 4G-specific data analysis task to a third 4G virtual machine, so that in step 104, the third virtual machine (for example, the third 4G virtual machine) may obtain the 4G-specific data analysis task allocated by the first 4G virtual machine; the first 5G virtual machine determines a 5G-specific data analysis task and a 5G-basic data analysis task from the 5G task queue, and allocates the 5G-basic data analysis task to the second 5G virtual machine, and the first 5G virtual machine may allocate the 5G-specific data analysis task to the third 5G virtual machine, so that in step 104, the third virtual machine (for example, the third 5G virtual machine) may acquire the 5G-specific data analysis task allocated by the first 5G virtual machine.
For example, as shown in table 2 below, if the task to be analyzed 1 is a 4G basic data analysis task, the task to be analyzed 3 is a 4G dedicated data analysis task, the task to be analyzed 2 is a 5G basic data analysis task, and the task to be analyzed 4 is a 5G dedicated data analysis task. Distributing the task 1 to be analyzed to a second 4G virtual machine, distributing the task 3 to be analyzed to a third 4G virtual machine, wherein the third 4G virtual machine can acquire the task 3 to be analyzed; and distributing the task 2 to be analyzed to a second 5G virtual machine, distributing the task 4 to be analyzed to a third 4G virtual machine, wherein the third 5G virtual machine can acquire the task 4 to be analyzed.
TABLE 2
For example, after the task 1 to be analyzed is allocated to the second 4G virtual machine, the second 4G virtual machine may determine whether the second 4G virtual machine stores the 4G basic data result corresponding to the task 1 to be analyzed, and if the 4G basic data result corresponding to the task 1 to be analyzed is stored, the task 1 to be analyzed is not processed; and if the 4G basic data result corresponding to the task 1 to be analyzed is not stored, processing the task 1 to be analyzed to obtain the 4G basic data result corresponding to the task 1 to be analyzed.
It should be noted that the basic data analysis task is a task of analyzing basic data, such as a cell identifier, a base station identifier, a cell center frequency point, and a cell physical identifier, included in a cell information table. For example, the task of analyzing the data in the neighbor cell information table, the data in the neighbor cell information table includes a neighbor cell network type (the neighbor cell network type includes at least one of 3G, 4G, and 5G), a base station identifier, a neighbor base station identifier, and an X2 interface link indication. The dedicated data analysis task is, for example, an interference analysis task, and the interference analysis task is, for example, to analyze whether a cell is subjected to continuous high interference, blocking interference, spurious interference, second harmonic interference, and the like in the co-channel user system.
And 104, acquiring a basic data result corresponding to the distributed special data analysis task from the second virtual machine, acquiring the special data analysis task distributed by the first virtual machine through the third virtual machine, and processing the special data analysis task according to the basic data result.
The third virtual machine corresponding to the 4G task type is a third 4G virtual machine, and the third virtual machine corresponding to the 5G task type is a third 5G virtual machine. If the 4G task queue includes the task 1 to be analyzed and the task 3 to be analyzed, and the task 1 to be analyzed is a 4G basic data analysis task, the second 4G virtual machine may transmit a 4G basic data result corresponding to the task 1 to be analyzed to a third 4G virtual machine, and the third virtual machine (for example, the third 4G virtual machine) may obtain a 4G basic data result stored by the second 4G virtual machine and corresponding to the task 1 to be analyzed, where the basic data result corresponding to the task 1 to be analyzed is a 4G basic data result. If the 5G task queue comprises the task 2 to be analyzed and the task 4 to be analyzed, and the task 2 to be analyzed is a 5G basic data analysis task, transmitting a 5G basic data result corresponding to the task 2 to be analyzed to a third 5G virtual machine through a second 5G virtual machine, and enabling the third virtual machine (for example, the third 5G virtual machine) to obtain a 5G basic data result corresponding to the task 2 to be analyzed, which is stored by the second 5G virtual machine, wherein the basic data result corresponding to the task 2 to be analyzed is a 5G basic data result.
It should be noted that, as the task to be analyzed may be allocated to the first virtual machine according to the task type identifier included in the task to be analyzed, for example, referring to table 1 and table 2 above, according to the 4G task type included in the task to be analyzed, task 1 to be analyzed and task 3 to be analyzed are allocated to the first 4G virtual machine, and according to the 5G task type included in the task to be analyzed, task 2 to be analyzed and task 4 to be analyzed are allocated to the first 5G virtual machine. The first 4G virtual machine distinguishes that the task 1 to be analyzed is a basic data analysis task, the task 1 to be analyzed is delivered to the second 4G virtual machine for processing, the first 4G virtual machine distinguishes that the task 3 to be analyzed is a special data analysis task, the task 3 to be analyzed is delivered to the third 4G virtual machine for processing, the third 4G virtual machine obtains a basic data result corresponding to the task 3 to be analyzed from the second 4G virtual machine, and the task 3 to be analyzed is processed according to the basic data result corresponding to the task 3 to be analyzed. Similarly, the first 5G virtual machine distinguishes that the task 4 to be analyzed is a dedicated data analysis task, hands the task 4 to be analyzed to a third 5G virtual machine for processing, and the third 5G virtual machine obtains a basic data result corresponding to the task 4 to be analyzed from the second 5G virtual machine and processes the task 4 to be analyzed according to the basic data result corresponding to the task 4 to be analyzed. The tasks to be analyzed of the 4G task type are processed through the virtual machines corresponding to the 4G task type, the tasks to be analyzed of the 5G task type are processed through the virtual machines corresponding to the 5G task type, and therefore load balance between the tasks to be analyzed of the 4G task type and the tasks to be analyzed of the 5G task type is achieved under the NSA network environment with the 4G task and the 5G task coexisting, and therefore server resources of the cloud platform can be reasonably utilized.
Referring to fig. 2A and fig. 2B, fig. 2A is a flowchart illustrating steps of another load balancing method according to an embodiment of the present invention, and fig. 2B is a block diagram illustrating structures of virtual machines created in a server of a cloud platform according to an embodiment of the present invention. The load balancing method comprises the following steps:
The sum of the sizes of the hard disk spaces of the Web virtual machine, the result summarizing virtual machine, the first 4G virtual machine, the second 4G virtual machine, each third 4G virtual machine, the first 5G virtual machine, the second 5G virtual machine and each third 5G virtual machine is less than or equal to the size of the hard disk space in the physical resources of the cloud platform; a represents the number of cores of the central processing unit CPU of the third 4G virtual machine, b represents the number of the third 4G virtual machines, c represents the number of cores of the CPU of the third 5G virtual machine, d represents the number of the third 5G virtual machines, e represents the number of cores of the CPU in the physical resources of the cloud platform, m represents the number of cores of the CPU of the Web virtual machine, n represents the number of cores of the CPU of the result summary virtual machine, j represents the memory size of the third 4G virtual machine, k represents the memory size of the third 5G virtual machine, h represents the memory size in the physical resources of the cloud platform, p represents the memory size of the CPU of the Web virtual machine, and q represents the memory size of the result summary virtual machine.
The number of created virtual machines and the configuration information of the virtual machines may refer to table 3 below, where table 3 shows the physical resources of the cloud platform, the number of cores of the CPU configured for each virtual machine, the memory size, and the hard disk space size. The physical resources of the cloud platform in table 3 include, but are not limited to, the total kernel number, the memory size, and the hard disk space size of the cloud platform.
TABLE 3
Taking physical resources of the cloud platform (the physical resources include the number of cores in the physical resources of the cloud platform, the size of a memory in the physical resources of the cloud platform, and the size of a hard disk space in the physical resources of the cloud platform) as an example, where the number of cores in the physical resources of the cloud platform is 12, the size of a memory in the physical resources of the cloud platform is 64G, and the size of a hard disk space in the physical resources of the cloud platform is 10240G. When each virtual machine in the above table 3 is created, the conditions that a × b + c × d ≧ e-m-n, j × b + k × d ≧ h-p-q, the sum of the sizes of the respective hard disk spaces of the Web virtual machine, the result summarizing virtual machine, the first 4G virtual machine, the second 4G virtual machine, each third 4G virtual machine, the first 5G virtual machine, the second 5G virtual machine, and each third 5G virtual machine is less than or equal to the size of the hard disk space in the physical resource of the cloud platform need to be satisfied.
The Web virtual machine can add the received tasks to be analyzed into a queue, the queue adopts a first-in first-out strategy, the Web virtual machine can distinguish the task types of the tasks to be analyzed through the task type identification of each task to be analyzed, namely, distinguish which tasks to be analyzed are 4G tasks to be analyzed and which tasks to be analyzed are 5G tasks to be analyzed, distribute the distinguished 4G tasks to be analyzed to a first 4G virtual machine, and distribute the distinguished 5G tasks to be analyzed to the first 5G virtual machine. If the task type identifier included in the target task to be analyzed in each task to be analyzed is distinguished by the Web virtual machine as the 4G task type, step 203 is executed, and if the task type identifier included in the target task to be analyzed in each task to be analyzed is distinguished by the Web virtual machine as the 5G task type, step 204 is executed. For example, as shown in table 1 above, if the Web virtual machine obtained by the Web virtual machine includes a task to be analyzed 1, a task to be analyzed 2, a task to be analyzed 3, and a task to be analyzed 4, when the task to be analyzed 1 is the target task to be analyzed, because the task type of the task to be analyzed 1 is identified as the 4G task type, the task to be analyzed 1 is allocated to the first 4G virtual machine, similarly, the task to be analyzed 3 is allocated to the first 4G virtual machine, and the task to be analyzed 2 and the task to be analyzed 4 are allocated to the first 5G virtual machine.
It should be noted that, the 4G tasks to be analyzed in the 4G task queue adopt a first-in first-out strategy. For example, when the task 1 to be analyzed is used as a target task to be analyzed, it is first distinguished that the task 1 to be analyzed is a 4G task, a task queue corresponding to a task type identifier included in the task 1 to be analyzed is a 4G task queue, the task 1 to be analyzed is first added to the 4G task queue, then the task 3 to be analyzed is added to the 4G task queue, then the task 1 to be analyzed is first output from the 4G task queue, and then the task 3 to be analyzed is first output from the 4G task queue. Step 205 is performed after step 203 is performed.
And 204, determining that the first virtual machine is a first 5G virtual machine, distributing the target task to be analyzed to the first 5G virtual machine, and adding the target task to be analyzed to a 5G task queue corresponding to a task type identifier included in the target task to be analyzed through the first 5G virtual machine.
It should be noted that, the 5G tasks to be analyzed in the 5G task queue adopt a first-in first-out strategy. For example, when the task 2 to be analyzed is used as a target task to be analyzed, the task 2 to be analyzed is firstly distinguished to be a 4G task to be analyzed, a task queue corresponding to a task type identifier included in the task 2 to be analyzed is a 5G task queue, the task 2 to be analyzed is firstly added into the 5G task queue, then the task 4 to be analyzed is added into the 5G task queue, then the task 2 to be analyzed is firstly output from the 5G task queue, and then the task 4 to be analyzed is firstly output from the 5G task queue. Step 206 is performed after step 204 is performed.
And step 205, determining the 4G special data analysis task and/or the 4G basic data analysis task from the 4G task queue through the first 4G virtual machine.
Under the condition that the first 4G virtual machine determines the 4G basic data analysis task, the 4G basic data analysis task is distributed to a second 4G virtual machine; and under the condition that the first 4G virtual machine determines the 4G special data analysis task, distributing the 4G special data analysis task to a third 4G virtual machine.
In this step, the dedicated data analysis task in step 103 is a 4G dedicated data analysis task, the basic data analysis task is a 4G basic data analysis task, and the second virtual machine is a second 4G virtual machine.
Step 207 is performed after step 205 is performed.
And step 206, determining the 5G special data analysis task and/or the 5G basic data analysis task from the 5G task queue through the first 5G virtual machine.
And when the first 5G virtual machine determines the 5G basic data analysis task, the 5G basic data analysis task is allocated to the second 5G virtual machine, and when the first 5G virtual machine determines the 5G special data analysis task, the 5G special data analysis task is allocated to the third 5G virtual machine.
In this step, the dedicated data analysis task in step 103 is a 5G dedicated data analysis task, the basic data analysis task is a 5G basic data analysis task, and the second virtual machine is a second 5G virtual machine.
Step 210 is performed after step 206 is performed.
And step 207, judging whether to store the 4G basic data result corresponding to the distributed 4G basic data analysis task through the second 4G virtual machine.
If the first 4G virtual machine allocates the 4G basic data analysis task to the second 4G virtual machine when the 4G basic data analysis task is determined in step 206, it is determined whether to store the 4G basic data result corresponding to the 4G basic data analysis task allocated to the first 4G virtual machine, if the 4G basic data result corresponding to the allocated 4G basic data analysis task is not stored, step 208 is performed, and if the 4G basic data result corresponding to the allocated 4G basic data analysis task is stored, the allocated 4G basic data analysis task is not analyzed by the second 4G virtual machine.
And 208, analyzing the 4G basic data analysis task through the second 4G virtual machine to form a 4G basic data result, and storing the 4G basic data result. Step 208 is executed, and then step 209 is executed.
It should be noted that, when a plurality of third 4G virtual machines exist, state information of each third 4G virtual machine corresponding to the 4G task type may be obtained from the first 4G virtual machine, where the state information of the third 4G virtual machines includes an idle state or a busy state; and selecting the third 4G virtual machine with the state information in the idle state from each third 4G virtual machine according to the state information of each third 4G virtual machine. It should be noted that, after the first 4G virtual machine distinguishes that the task to be analyzed is the 4G dedicated data analysis task, the distinguished 4G dedicated data analysis task may be allocated to a third 4G virtual machine whose state information is an idle state, and then the first 4G virtual machine may release the occupied physical resources. When the first 4G virtual machine is required to execute the task, the physical resources can be allocated to the first 5G virtual machine again.
It should be noted that, the distinguished 4G dedicated data analysis task is allocated to the third 4G virtual machine whose state information is in an idle state, and because the current load of the third 4G virtual machine in the idle state is low, the physical resource allocated to the third 4G virtual machine with a low current load can be better utilized to process the 4G dedicated data analysis task.
And step 210, judging whether to store a 5G basic data result corresponding to the 5G basic data analysis task through the second 5G virtual machine.
If the 5G basic data result corresponding to the 5G basic data analysis task is not stored, go to step 211; if the 5G base data result corresponding to the 5G base data analysis task is stored, step 212 is performed.
And step 211, analyzing the 5G basic data analysis task through the second 5G virtual machine to form a 5G basic data result, and storing the 5G basic data result. Step 212 is performed after step 211 is performed.
And step 212, acquiring the 5G private data analysis task allocated by the first 5G virtual machine through the third 5G virtual machine with the idle state information, and acquiring the stored 5G basic data result corresponding to the allocated 5G basic data analysis task from the second 5G virtual machine, so as to process the allocated 5G private data analysis task according to the 5G basic data result corresponding to the allocated 5G basic data analysis task.
It should be noted that when a plurality of third 5G virtual machines exist, state information of each third 5G virtual machine corresponding to the 5G task type may be obtained through the first 5G virtual machine, where the state information of the third 5G virtual machine includes an idle state or a busy state; and selecting the third 5G virtual machines with the state information in the idle state from each third 5G virtual machine according to the state information of each third 5G virtual machine. It should be noted that, after the first 5G virtual machine distinguishes that the task to be analyzed is the 5G dedicated data analysis task, the distinguished 5G dedicated data analysis task may be allocated to a third 5G virtual machine whose state information is an idle state, and then the first 5G virtual machine may release the occupied physical resources. When the first 5G virtual machine is required to execute the task, the physical resources can be allocated to the first 5G virtual machine again.
It should be noted that, the distinguished 5G dedicated data analysis task is allocated to the third 5G virtual machine whose state information is in an idle state, and since the current load of the third 5G virtual machine in the idle state is low, the physical resource allocated to the third 5G virtual machine with a low current load can be better utilized to process the 5G dedicated data analysis task.
And step 213, sending the 4G private data result processed by the allocated 4G private data analysis task to the result summarizing virtual machine through the third 4G virtual machine.
Step 213 executes step 215 after execution.
It should be noted that, if the interference analysis task in the 4G dedicated data analysis task is processed, the interference analysis task, for example, whether an analysis cell has a task interfered by continuous high interference, blocking interference, stray interference, second harmonic interference, etc. in the co-frequency user system, if a certain cell is subjected to continuous high interference in the co-frequency user system, the 4G processing result corresponding to the continuous high interference analysis task in the co-frequency user system is yes, and if the cell is not subjected to continuous high interference in the co-frequency user system, the 4G dedicated data result corresponding to the continuous high interference analysis task in the co-frequency user system is no. Similarly, if a cell has blocking interference, the result of the 4G dedicated data corresponding to the blocking interference analysis task is yes, and if the cell does not have blocking interference, the result of the 4G processing corresponding to the blocking interference analysis task is no, and the 4G processing results of other interference analysis tasks can be analogized in turn, which is not described herein again.
And step 214, sending the 5G special data result processed by the allocated 5G special data analysis task to the result summarizing virtual machine through the third 5G virtual machine.
Step 215 is performed after step 214 is performed. The 5G private data result is similar to the 4G private data result in step 213, except that the private data result in step 213 is a 4G private data result.
It should be noted that the priority of the first 4G virtual machine, the first 5G virtual machine, the second 4G virtual machine, and the second 5G virtual machine is lower than the priority of the Web virtual machine and the result summarizing virtual machine, and after the first 4G virtual machine, the first 5G virtual machine, the second 4G virtual machine, and the second 5G virtual machine finish executing the task, the physical resources occupied by the first 4G virtual machine, the first 5G virtual machine, the second 4G virtual machine, and the second 5G virtual machine may be released, and when the first 4G virtual machine, the first 5G virtual machine, the second 4G virtual machine, and the second 5G virtual machine need to execute the task again, the physical resources are allocated to the respective virtual machines again.
It should be noted that the target result includes a 4G-specific data result corresponding to the 4G-specific data analysis task and/or a 5G-specific data result corresponding to the 5G-specific data analysis task. The basic data result corresponding to the basic data analysis task does not need to be sent to the terminal equipment, the result summarizing virtual machine can summarize the 4G special data result and/or the 5G special data result to obtain a target result, the target result is sent to the terminal equipment, and the terminal equipment can display the target result, so that a user can obtain the target result, and network optimization can be carried out according to the target result.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a load balancing apparatus according to an embodiment of the present invention. The load balancing device may be disposed in a server of a cloud platform, and the load balancing device 300 includes the following modules:
an obtaining module 310, configured to obtain at least one to-be-analyzed task sent by a terminal device, where each to-be-analyzed task includes a task type identifier, and the task type identifier includes a 4G task type or a 5G task type;
the first allocation module 320 is configured to allocate the tasks to be analyzed to the first virtual machine according to the task type identifier included in each task to be analyzed, and add the tasks to be analyzed to the task queue corresponding to the task type identifier through the first virtual machine;
a second allocating module 330, configured to determine, by the first virtual machine, a dedicated data analysis task and/or a basic data analysis task from a task queue corresponding to the task type identifier;
a processing module 340, configured to obtain a basic data result corresponding to the allocated dedicated data analysis task from the second virtual machine, obtain the dedicated data analysis task allocated by the first virtual machine through a third virtual machine, and process the dedicated data analysis task according to the basic data result;
the first virtual machine, the second virtual machine and the third virtual machine are respectively virtual machines corresponding to the task type identification.
Optionally, the first allocating module 320 is specifically configured to determine that the first virtual machine is a first 4G virtual machine if a task type identifier included in a target task to be analyzed in each task to be analyzed is a 4G task type, allocate the target task to be analyzed to the first 4G virtual machine, add the target task to be analyzed to a 4G task queue corresponding to the task type identifier included in the target task to be analyzed through the first 4G virtual machine, and set a task queue corresponding to the task type identifier as the 4G task queue; alternatively, the first and second electrodes may be,
if the task type identifier included in the target task to be analyzed in each task to be analyzed is a 5G task type, determining that the first virtual machine is a first 5G virtual machine, distributing the target task to be analyzed to the first 5G virtual machine, adding the target task to be analyzed to a 5G task queue corresponding to the task type identifier included in the target task to be analyzed through the first 5G virtual machine, and setting the task queue corresponding to the task type identifier as the 5G task queue.
Optionally, the second allocating module 330 is specifically configured to determine, by the first 4G virtual machine, a 4G dedicated data analysis task and/or a 4G basic data analysis task from the 4G task queue, and allocate the 4G basic data analysis task to a second 4G virtual machine, where the dedicated data analysis task is the 4G dedicated data analysis task, the basic data analysis task is the 4G basic data analysis task, and the second virtual machine is a second 4G virtual machine; and/or the presence of a gas in the gas,
determining a 5G private data analysis task and/or a 5G basic data analysis task from the 5G task queue through the first 5G virtual machine, and distributing the 5G basic data analysis task to a second 5G virtual machine, wherein the private data analysis task is the 5G private data analysis task, the basic data analysis task is the 5G basic data analysis task, and the second virtual machine is the second 5G virtual machine.
Optionally, the load balancing apparatus may further include:
a first selection module, configured to obtain, by the first 4G virtual machine, state information of each third 4G virtual machine corresponding to the 4G task type, where the state information of the third 4G virtual machine includes an idle state or a busy state; according to the state information of each third 4G virtual machine, selecting the third 4G virtual machine with the state information in an idle state from each third 4G virtual machine, and taking the third 4G virtual machine with the state information in the idle state as the third virtual machine; and/or the presence of a gas in the gas,
a second selection module, configured to obtain, by the first 5G virtual machine, state information of each third 5G virtual machine corresponding to the 5G task type, where the state information of the third 5G virtual machine includes an idle state or a busy state; and selecting a third 5G virtual machine with idle state information from each third 5G virtual machine according to the state information of each third 5G virtual machine, and taking the third 5G virtual machine with idle state information as the third virtual machine.
Optionally, the load balancing apparatus may further include:
a first determining module, configured to determine, if the 4G basic data analysis task is allocated to a second 4G virtual machine, whether to store a 4G basic data result corresponding to the allocated 4G basic data analysis task by the second 4G virtual machine; if the 4G basic data result corresponding to the 4G basic data analysis task is not stored, analyzing the 4G basic data analysis task through the second 4G virtual machine to form the 4G basic data result, and storing the 4G basic data result; alternatively, the first and second electrodes may be,
a second determining module, configured to determine, if the 5G basic data analysis task is allocated to a second 5G virtual machine, whether to store a 5G basic data result corresponding to the allocated 5G basic data analysis task by using the second 5G virtual machine; if the 5G basic data result corresponding to the 5G basic data analysis task is not stored, analyzing the 5G basic data analysis task through the second 5G virtual machine to form the 5G basic data result, and storing the 5G basic data result.
Optionally, the processing module 340 is specifically configured to obtain, by using the state information, the 4G dedicated data analysis task allocated by the first 4G virtual machine for a third 4G virtual machine in an idle state, and obtain, from the second 4G virtual machine, a stored 4G basic data result corresponding to the allocated 4G dedicated data analysis task, so as to process the allocated 4G dedicated data analysis task according to the 4G basic data result corresponding to the allocated 4G dedicated data analysis task; and/or the presence of a gas in the gas,
and acquiring, by a third 5G virtual machine whose state information is an idle state, the 5G-dedicated data analysis task allocated by the first 5G virtual machine, and acquiring, from the second 5G virtual machine, a stored 5G-base data result corresponding to the allocated 5G-base data analysis task, so as to process the allocated 5G-dedicated data analysis task according to the 5G-base data result corresponding to the allocated 5G-base data analysis task.
Optionally, the load balancing apparatus may further include:
a sending module, configured to send, by the third 4G virtual machine, a 4G private data result obtained by processing the allocated 4G private data analysis task to a result summarizing virtual machine, and/or send, by the third 5G virtual machine, a 5G private data result obtained by processing the allocated 5G private data analysis task to the result summarizing virtual machine;
a sending module, configured to send the 4G processing result to a result summarizing virtual machine through the third 4G virtual machine, and/or send the 5G processing result to the result summarizing virtual machine through the third 5G virtual machine;
the sending module is further configured to summarize the 4G private data result and/or the 5G private data result by the result summarization virtual machine to determine a target result, and send the target result to a terminal device, so that the terminal device displays the target result.
Optionally, the first obtaining module 310 is specifically configured to obtain, by using a Web virtual machine, the at least one task to be analyzed that is sent by the terminal device;
the load balancing apparatus may further include:
a creating module, configured to create the Web virtual machine, the result summarizing virtual machine, the first 4G virtual machine, the second 4G virtual machine, each third 4G virtual machine, the first 5G virtual machine, the second 5G virtual machine, and each third 5G virtual machine according to physical resources of the cloud platform;
the sum of the sizes of the hard disk spaces of the Web virtual machine, the result summarizing virtual machine, the first 4G virtual machine, the second 4G virtual machine, each third 4G virtual machine, the first 5G virtual machine, the second 5G virtual machine and each third 5G virtual machine is less than or equal to the size of the hard disk space in the physical resources of the cloud platform; wherein a denotes the number of cores of a Central Processing Unit (CPU) of the third 4G virtual machine, b denotes the number of the third 4G virtual machines, c denotes the number of cores of a CPU of the third 5G virtual machine, d denotes the number of the third 5G virtual machines, e denotes the number of cores of a CPU in physical resources of the cloud platform, and m denotes the number of cores of a Central Processing Unit (CPU) of the Web virtual machine, the n represents the number of cores of the CPU of the result summarizing virtual machine, the j represents the memory size of the third 4G virtual machine, the k represents the memory size of the third 5G virtual machine, the h represents the memory size in the physical resources of the cloud platform, the p represents the memory size of the CPU of the Web virtual machine, and the q represents the memory size of the result summarizing virtual machine.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be 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 terminal 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 terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The load balancing method and apparatus provided by the present invention are introduced in detail, and a specific example is applied in the text to explain the principle and the implementation of the present invention, and the description of the above embodiment is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (16)
1. A load balancing method is characterized in that a server applied to a cloud platform comprises the following steps:
the method comprises the steps of obtaining at least one task to be analyzed sent by terminal equipment, wherein each task to be analyzed comprises a task type identifier, and the task type identifier comprises a 4G task type or a 5G task type;
distributing the tasks to be analyzed to a first virtual machine according to task type identifications included by each task to be analyzed, and adding the tasks to be analyzed to task queues corresponding to the task type identifications through the first virtual machine;
determining a dedicated data analysis task and/or a basic data analysis task from a task queue corresponding to the task type identifier through the first virtual machine;
acquiring a basic data result corresponding to the distributed special data analysis task from a second virtual machine, acquiring the special data analysis task distributed by the first virtual machine through a third virtual machine, and processing the special data analysis task according to the basic data result;
the first virtual machine, the second virtual machine, and the third virtual machine are respectively virtual machines corresponding to the task type identifier.
2. The method according to claim 1, wherein the allocating the task to be analyzed to a first virtual machine according to a task type identifier included in each task to be analyzed, and adding the task to be analyzed to a task queue corresponding to the task type identifier through the first virtual machine comprises:
if the task type identifier included in the target task to be analyzed in each task to be analyzed is a 4G task type, determining that the first virtual machine is a first 4G virtual machine, distributing the target task to be analyzed to the first 4G virtual machine, adding the target task to be analyzed to a 4G task queue corresponding to the task type identifier included in the target task to be analyzed through the first 4G virtual machine, and setting the task queue corresponding to the task type identifier as the 4G task queue; alternatively, the first and second electrodes may be,
if the task type identifier included in the target task to be analyzed in each task to be analyzed is a 5G task type, determining that the first virtual machine is a first 5G virtual machine, distributing the target task to be analyzed to the first 5G virtual machine, adding the target task to be analyzed to a 5G task queue corresponding to the task type identifier included in the target task to be analyzed through the first 5G virtual machine, and setting the task queue corresponding to the task type identifier as the 5G task queue.
3. The method according to claim 2, wherein the determining, by the first virtual machine, a dedicated data analysis task and/or a basic data analysis task from a task queue corresponding to the task type identifier and allocating the basic data analysis task to a second virtual machine comprises:
determining, by the first 4G virtual machine, a 4G private data analysis task and/or a 4G basic data analysis task from the 4G task queue, and allocating the 4G basic data analysis task to a second 4G virtual machine, where the private data analysis task is the 4G private data analysis task, the basic data analysis task is the 4G basic data analysis task, and the second virtual machine is the second 4G virtual machine; and/or the presence of a gas in the gas,
determining a 5G private data analysis task and/or a 5G basic data analysis task from the 5G task queue through the first 5G virtual machine, and distributing the 5G basic data analysis task to a second 5G virtual machine, wherein the private data analysis task is the 5G private data analysis task, the basic data analysis task is the 5G basic data analysis task, and the second virtual machine is the second 5G virtual machine.
4. The method of claim 3, wherein prior to said obtaining base data results corresponding to the assigned private data analysis task from the second virtual machine and obtaining the private data analysis task assigned by the first virtual machine via a third virtual machine, further comprising:
acquiring state information of each third 4G virtual machine corresponding to the 4G task type through the first 4G virtual machine, wherein the state information of the third 4G virtual machine comprises an idle state or a busy state; according to the state information of each third 4G virtual machine, selecting the third 4G virtual machine with the state information in an idle state from each third 4G virtual machine, and taking the third 4G virtual machine with the state information in the idle state as the third virtual machine; and/or the presence of a gas in the gas,
acquiring state information of each third 5G virtual machine corresponding to the 5G task type through the first 5G virtual machine, wherein the state information of the third 5G virtual machine comprises an idle state or a busy state; and selecting a third 5G virtual machine with idle state information from each third 5G virtual machine according to the state information of each third 5G virtual machine, and taking the third 5G virtual machine with idle state information as the third virtual machine.
5. The method of claim 4, further comprising, after said assigning the underlying data analysis task to a second virtual machine:
if the 4G basic data analysis task is allocated to a second 4G virtual machine, judging whether to store a 4G basic data result corresponding to the allocated 4G basic data analysis task through the second 4G virtual machine; if the 4G basic data result corresponding to the 4G basic data analysis task is not stored, analyzing the 4G basic data analysis task through the second 4G virtual machine to form the 4G basic data result, and storing the 4G basic data result; alternatively, the first and second electrodes may be,
if the 5G basic data analysis task is allocated to a second 5G virtual machine, judging whether to store a 5G basic data result corresponding to the allocated 5G basic data analysis task through the second 5G virtual machine; if the 5G basic data result corresponding to the 5G basic data analysis task is not stored, analyzing the 5G basic data analysis task through the second 5G virtual machine to form the 5G basic data result, and storing the 5G basic data result.
6. The method according to claim 5, wherein the obtaining, from the second virtual machine, a base data result corresponding to the allocated private data analysis task and obtaining, by a third virtual machine, the private data analysis task allocated by the first virtual machine, and processing the private data analysis task according to the base data result comprises:
acquiring, by a third 4G virtual machine whose state information is an idle state, the 4G dedicated data analysis task allocated by the first 4G virtual machine, and acquiring, from the second 4G virtual machine, a stored 4G base data result corresponding to the allocated 4G dedicated data analysis task, to process the allocated 4G dedicated data analysis task according to the 4G base data result corresponding to the allocated 4G dedicated data analysis task; and/or the presence of a gas in the gas,
and acquiring, by a third 5G virtual machine whose state information is an idle state, the 5G-dedicated data analysis task allocated by the first 5G virtual machine, and acquiring, from the second 5G virtual machine, a stored 5G-base data result corresponding to the allocated 5G-base data analysis task, so as to process the allocated 5G-dedicated data analysis task according to the 5G-base data result corresponding to the allocated 5G-base data analysis task.
7. The method of claim 5 or 6, further comprising, after said processing said dedicated data analysis task according to said underlying data results:
sending, by the third 4G virtual machine, the 4G private data result obtained by processing the allocated 4G private data analysis task to a result summarization virtual machine, and/or sending, by the third 5G virtual machine, the 5G private data result obtained by processing the allocated 5G private data analysis task to the result summarization virtual machine;
and summarizing the 4G special data result and/or the 5G special data result through the result summarizing virtual machine to determine a target result, and sending the target result to terminal equipment so that the terminal equipment can display the target result.
8. The method according to claim 7, wherein the obtaining of the at least one task to be analyzed sent by the terminal device comprises:
acquiring the at least one task to be analyzed sent by the terminal equipment through a Web virtual machine;
before the acquiring of at least one task to be analyzed sent by the terminal device, the method further includes:
according to physical resources of the cloud platform, creating the Web virtual machine, the result summarizing virtual machine, the first 4G virtual machine, the second 4G virtual machine, each third 4G virtual machine, the first 5G virtual machine, the second 5G virtual machine and each third 5G virtual machine;
the sum of the sizes of the hard disk spaces of the Web virtual machine, the result summarizing virtual machine, the first 4G virtual machine, the second 4G virtual machine, each third 4G virtual machine, the first 5G virtual machine, the second 5G virtual machine and each third 5G virtual machine is less than or equal to the size of the hard disk space in the physical resources of the cloud platform; the a represents the number of cores of the central processing unit CPU of the third 4G virtual machine, the b represents the number of the third 4G virtual machines, the c represents the number of cores of the CPU of the third 5G virtual machine, the d represents the number of the third 5G virtual machine, the e represents the number of cores of the CPU in the physical resources of the cloud platform, the m represents the number of cores of the CPU of the Web virtual machine, the n represents the number of cores of the CPU of the result summary virtual machine, the j represents the memory size of the third 4G virtual machine, the k represents the memory size of the third 5G virtual machine, the h represents the memory size in the physical resources of the cloud platform, the p represents the memory size of the CPU of the Web virtual machine, and the q represents the memory size of the result summary virtual machine.
9. The utility model provides a load balancing device which characterized in that sets up in the server of cloud platform, includes:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring at least one task to be analyzed sent by terminal equipment, each task to be analyzed comprises a task type identifier, and the task type identifier comprises a 4G task type or a 5G task type;
the first distribution module is used for distributing the tasks to be analyzed to a first virtual machine according to task type identifications included by each task to be analyzed and adding the tasks to be analyzed to a task queue corresponding to the task type identifications through the first virtual machine;
the second allocation module is used for determining a dedicated data analysis task and/or a basic data analysis task from a task queue corresponding to the task type identifier through the first virtual machine;
the processing module is used for acquiring a basic data result corresponding to the distributed special data analysis task from a second virtual machine, acquiring the special data analysis task distributed by the first virtual machine through a third virtual machine, and processing the special data analysis task according to the basic data result;
the first virtual machine, the second virtual machine, and the third virtual machine are respectively virtual machines corresponding to the task type identifier.
10. The apparatus according to claim 9, wherein the first allocation module is specifically configured to, if a task type identifier included in a target task to be analyzed in each task to be analyzed is a 4G task type, determine that the first virtual machine is a first 4G virtual machine, allocate the target task to be analyzed to the first 4G virtual machine, add the target task to be analyzed to a 4G task queue corresponding to the task type identifier included in the target task to be analyzed by using the first 4G virtual machine, and list a task queue corresponding to the task type identifier as the 4G task queue; alternatively, the first and second electrodes may be,
if the task type identifier included in the target task to be analyzed in each task to be analyzed is a 5G task type, determining that the first virtual machine is a first 5G virtual machine, distributing the target task to be analyzed to the first 5G virtual machine, adding the target task to be analyzed to a 5G task queue corresponding to the task type identifier included in the target task to be analyzed through the first 5G virtual machine, and setting the task queue corresponding to the task type identifier as the 5G task queue.
11. The apparatus according to claim 10, wherein the second allocating module is specifically configured to determine, by the first 4G virtual machine, a 4G-specific data analysis task and/or a 4G-based data analysis task from the 4G task queue, and allocate the 4G-based data analysis task to a second 4G virtual machine, where the 4G-specific data analysis task is the 4G-specific data analysis task, the 4G-based data analysis task is the 4G-based data analysis task, and the second virtual machine is a second 4G virtual machine; and/or the presence of a gas in the gas,
determining a 5G private data analysis task and/or a 5G basic data analysis task from the 5G task queue through the first 5G virtual machine, and distributing the 5G basic data analysis task to a second 5G virtual machine, wherein the private data analysis task is the 5G private data analysis task, the basic data analysis task is the 5G basic data analysis task, and the second virtual machine is the second 5G virtual machine.
12. The apparatus of claim 11, further comprising:
a first selection module, configured to acquire, by using the first 4G virtual machine, state information of each third 4G virtual machine corresponding to the 4G task type, where the state information of the third 4G virtual machine includes an idle state or a busy state; according to the state information of each third 4G virtual machine, selecting the third 4G virtual machine with the state information in an idle state from each third 4G virtual machine, and taking the third 4G virtual machine with the state information in the idle state as the third virtual machine; and/or the presence of a gas in the gas,
a second selection module, configured to obtain, by the first 5G virtual machine, state information of each third 5G virtual machine corresponding to the 5G task type, where the state information of the third 5G virtual machine includes an idle state or a busy state; and selecting a third 5G virtual machine with idle state information from each third 5G virtual machine according to the state information of each third 5G virtual machine, and taking the third 5G virtual machine with idle state information as the third virtual machine.
13. The apparatus of claim 12, further comprising:
a first determining module, configured to determine, if the 4G basic data analysis task is allocated to a second 4G virtual machine, whether to store a 4G basic data result corresponding to the allocated 4G basic data analysis task by the second 4G virtual machine; if the 4G basic data result corresponding to the 4G basic data analysis task is not stored, analyzing the 4G basic data analysis task through the second 4G virtual machine to form the 4G basic data result, and storing the 4G basic data result; alternatively, the first and second electrodes may be,
a second determining module, configured to determine, if the 5G basic data analysis task is allocated to a second 5G virtual machine, whether to store a 5G basic data result corresponding to the allocated 5G basic data analysis task by using the second 5G virtual machine; if the 5G basic data result corresponding to the 5G basic data analysis task is not stored, analyzing the 5G basic data analysis task through the second 5G virtual machine to form the 5G basic data result, and storing the 5G basic data result.
14. The apparatus according to claim 13, wherein the processing module is specifically configured to obtain, by the third 4G virtual machine whose state information is an idle state, the 4G-specific data analysis task allocated by the first 4G virtual machine, and obtain, from the second 4G virtual machine, the stored 4G base data result corresponding to the allocated 4G-specific data analysis task, so as to process the allocated 4G-specific data analysis task according to the 4G base data result corresponding to the allocated 4G-specific data analysis task; and/or the presence of a gas in the gas,
and acquiring, by a third 5G virtual machine whose state information is an idle state, the 5G-dedicated data analysis task allocated by the first 5G virtual machine, and acquiring, from the second 5G virtual machine, a stored 5G-base data result corresponding to the allocated 5G-base data analysis task, so as to process the allocated 5G-dedicated data analysis task according to the 5G-base data result corresponding to the allocated 5G-base data analysis task.
15. The apparatus of claim 13 or 14, further comprising:
a sending module, configured to send, by the third 4G virtual machine, a 4G private data result obtained by processing the allocated 4G private data analysis task to a result summarizing virtual machine, and/or send, by the third 5G virtual machine, a 5G private data result obtained by processing the allocated 5G private data analysis task to the result summarizing virtual machine;
the sending module is further configured to summarize the 4G private data result and/or the 5G private data result by the result summarization virtual machine to determine a target result, and send the target result to a terminal device, so that the terminal device displays the target result.
16. The apparatus according to claim 15, wherein the obtaining module is specifically configured to obtain, by using a Web virtual machine, the at least one task to be analyzed that is sent by the terminal device;
the device further comprises:
a creating module, configured to create the Web virtual machine, the result summarizing virtual machine, the first 4G virtual machine, the second 4G virtual machine, each third 4G virtual machine, the first 5G virtual machine, the second 5G virtual machine, and each third 5G virtual machine according to physical resources of the cloud platform;
the sum of the sizes of the hard disk spaces of the Web virtual machine, the result summarizing virtual machine, the first 4G virtual machine, the second 4G virtual machine, each third 4G virtual machine, the first 5G virtual machine, the second 5G virtual machine and each third 5G virtual machine is less than or equal to the size of the hard disk space in the physical resources of the cloud platform; the a represents the number of cores of the central processing unit CPU of the third 4G virtual machine, the b represents the number of the third 4G virtual machines, the c represents the number of cores of the CPU of the third 5G virtual machine, the d represents the number of the third 5G virtual machine, the e represents the number of cores of the CPU in the physical resources of the cloud platform, the m represents the number of cores of the CPU of the Web virtual machine, the n represents the number of cores of the CPU of the result summary virtual machine, the j represents the memory size of the third 4G virtual machine, the k represents the memory size of the third 5G virtual machine, the h represents the memory size in the physical resources of the cloud platform, the p represents the memory size of the CPU of the Web virtual machine, and the q represents the memory size of the result summary virtual machine.
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Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103634330A (en) * | 2012-08-20 | 2014-03-12 | 曙光信息产业(北京)有限公司 | Automatic resource distribution method in cloud calculation environment |
| US9888055B2 (en) * | 2013-03-15 | 2018-02-06 | Profitbricks Gmbh | Firewall for a virtual network and related techniques |
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| CN105897600A (en) * | 2016-06-15 | 2016-08-24 | 北京小米移动软件有限公司 | Method and device for controlling router load balance |
| CN106775948B (en) * | 2016-12-23 | 2020-09-04 | 安徽师范大学 | Cloud task scheduling method and device based on priority |
| CN109144666A (en) * | 2018-07-30 | 2019-01-04 | 上海思询信息科技有限公司 | A kind of method for processing resource and system across cloud platform |
| CN110247807A (en) * | 2019-06-26 | 2019-09-17 | 苏州浪潮智能科技有限公司 | A kind of port management method and device |
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