Intelligent cloud network scheduling method, storage medium, equipment and computer program product

Abstract

The invention discloses an intelligent cloud network scheduling method, a storage medium, equipment and a computer program product, and the method comprises the steps: searching network nodes of user groups of the same type and indexes of the same region in a cross-domain network according to log information, storing the network nodes in a node list, and marking the states of the network nodes; exploring the network nodes in the node list by adopting a path exploration algorithm to form a plurality of links, and finding out an optimal link; the optimal link is preferentially used for data forwarding, and for calculation tasks in the data, the calculation tasks are distributed to intercommunication network nodes for calculation. Calculation tasks are calculated while being transmitted, so that network node blockage caused by centralized calculation tasks is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of network intelligent terminal scheduling, and specifically, relates to an intelligent cloud network scheduling method, storage medium, device and computer program product. Background Art

[0002] With the rapid development of the information age, intelligent cloud networks have become increasingly complex, and as a control component in the cloud network, as the central brain of the entire cloud network scheduling, it will become crucial. At the same time, through network control, idle resources in the network can be intelligently discovered and utilized to improve network resource utilization.

[0003] In order to improve the utilization of network resources by using intelligent network scheduling in complex networks, multi-path exploration is used to find and evaluate multiple possible network data transmission nodes and find the optimal process. It is necessary to explore the problem of resource waste that each branch must traverse. At the same time, during the data transmission process, the data with computing tasks arrives at the destination node for centralized computing, which will cause the node to be overstressed and the network to be blocked. Summary of the invention

[0004] In response to the problems existing in the prior art, the present invention provides an intelligent cloud network scheduling method, storage medium, device and computer program product, which can calculate computing tasks as they are transmitted and avoid network node congestion due to the concentration of computing tasks.

[0005] To achieve the above technical objectives, the present invention adopts the following technical solution: an intelligent cloud network scheduling method, specifically comprising the following steps:

[0006] Step S1: Find network nodes with the same type of user group and the same regional indicators in the cross-domain network based on log information, store them in a node list, and mark the status of the network nodes;

[0007] Step S2: Use a path exploration algorithm to explore the network nodes in the node list to form several links and find an optimal link;

[0008] Step S3: Prioritize using the optimal link for data forwarding, and for computing tasks in the data, distribute the computing tasks to interconnected network nodes for computing.

[0009] Further, a ping command is executed for the network nodes in the node list to confirm connectivity to the destination network node. If connected, the network node status is marked as available; otherwise, the network node status is marked as unavailable.

[0010] Furthermore, in step S2, the network nodes in the node list are explored using a path exploration algorithm to form a number of links. The specific process is as follows:

[0011] i. Determine the starting point and destination network node of cloud network scheduling;

[0012] ii. Searching for a network node that is interoperable with the starting point of the cloud network scheduling from the network nodes in the available state in the node list;

[0013] iii. The searched network node continues to search for network nodes that are interoperable with it;

[0014] iv. Repeat step iii until intercommunication with the destination network node is achieved and several links are obtained.

[0015] Furthermore, in the process of exploration using the path exploration algorithm, the number of interconnections between each network node and its neighboring nodes is recorded, and the total number of interconnections between all network nodes and their neighboring nodes in each link is counted, and the link with the largest total number is taken as the optimal link.

[0016] Furthermore, the specific process of step S3 allocating computing tasks to the intercommunication network nodes for calculation is: determining the idle computing power on the intercommunication network nodes, dividing the computing tasks into sub-computing tasks according to the idle computing power, allocating the sub-computing tasks to the corresponding intercommunication network nodes for calculation, and transmitting the calculation results from the intercommunication network nodes to the destination network nodes.

[0017] Furthermore, the calculation process of the idle computing power on the interconnected network nodes is:

[0018]

[0019] Among them, C br is the idle computing power, n is the number of logic operation chips, i is the index of n, f(a i ) is the mapping function of logical operation, α i is the mapping ratio coefficient of the i-th logic operation chip, q1 (TOPS) is the redundant computing power of the logic operation; m is the number of parallel computing chips, j is the index of m, f(b j ) is the mapping function for parallel computing, β j is the mapping ratio coefficient of the jth parallel computing chip, q2 (FLOPS) is the redundant computing power of parallel computing; p is the number of neural network acceleration chips, k is the index of p, and f(c k ) is the mapping function of neural network acceleration, γ k is the mapping ratio coefficient of the kth neural network acceleration chip, and q3 (FLOPS) is the redundant computing power of neural network acceleration.

[0020] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the intelligent cloud network scheduling method.

[0021] Furthermore, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the intelligent cloud network scheduling method is implemented.

[0022] Furthermore, the present invention also provides a computer program product, including a computer program, which implements the intelligent cloud network scheduling method when executed by a processor.

[0023] Compared with the prior art, the present invention has the following beneficial effects: the intelligent cloud network scheduling method of the present invention can optimize the path and speed of data transmission by finding available network nodes and building links based on them, which helps to reduce network delays, improve bandwidth utilization and reduce transmission errors, and uses the link with the largest number of interconnected nodes as the optimal link, so that computing tasks are allocated to interconnected network nodes for calculation, thereby improving computing efficiency and alleviating the pressure caused by centralized node computing when the computing tasks reach the destination node, avoiding network node downtime and improving user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of the intelligent cloud network scheduling method of the present invention;

[0025] Figure 2 This is a schematic diagram of path exploration performed by the path exploration algorithm in the present invention. DETAILED DESCRIPTION

[0026] The technical solution of the present invention is further explained below in conjunction with the accompanying drawings.

[0027] like Figure 1 Flow chart of the intelligent cloud network scheduling method of the present invention, which specifically includes the following steps:

[0028] Step S1: In the cross-domain network, find the network nodes with the same type of user group and the same regional indicators according to the log information, store them in the node list, and mark the status of the network nodes. Specifically, execute the ping command for the network nodes in the node list to confirm the connectivity to the destination network node. If connected, mark the network node status as available, otherwise, mark the network node status as unavailable. This avoids traversing each branch of multi-path exploration, and only traverses the nodes with available status, which reduces resource waste and reduces algorithm complexity.

[0029] Step S2, the network nodes in the node list are explored using a path exploration algorithm to form several links and find an optimal link; when the optimal path is selected to transmit data with computing tasks, since the en route nodes have the most neighbor interconnected nodes, a certain part of the computing tasks can be undertaken for the en route nodes, which reduces the computing pressure of the en route nodes and also reduces the pressure of centralized computing of the destination nodes.

[0030] In the present invention, the network nodes in the node list are explored using a path exploration algorithm to form a number of links. The specific process is as follows:

[0031] i. Determine the starting point and destination network node of cloud network scheduling;

[0032] ii. Searching for a network node that is interoperable with the starting point of the cloud network scheduling from the network nodes in the available state in the node list;

[0033] iii. the network node to be found continues to search for network nodes that are interoperable with it;

[0034] iv. Repeat step iii until intercommunication with the destination network node is achieved and several links are obtained.

[0035] In the process of exploration using the path exploration algorithm, the number of interconnections between each network node and its neighbor nodes is recorded, and the total number of interconnections between all network nodes and neighbor nodes in each link is counted, and the link with the largest total number is taken as the optimal link. In order to maximize resource utilization, the interconnected nodes in the optimal link need to undertake part of the data calculation tasks. The more interconnections between the neighboring nodes of the path node, the greater the possibility of sharing the data calculation tasks.

[0036] Step S3: Prioritize the use of the optimal link for data forwarding. For the computing tasks in the data, transfer the computing tasks to the interconnected network nodes for computing. The specific process is: determine the idle computing power on the interconnected network nodes, divide the computing tasks into sub-computing tasks according to the idle computing power, assign the sub-computing tasks to the corresponding interconnected network nodes for computing, and transfer the computing results from the interconnected network nodes to the destination network nodes. Sharing data computing tasks by neighboring nodes can directly reduce the computing pressure of the path nodes and indirectly reduce the pressure of centralized computing of the destination nodes.

[0037] The calculation process of the idle computing power on the interconnected network nodes is as follows:

[0038]

[0039] Among them, C br is the idle computing power, n is the number of logic operation chips, i is the index of n, f(a i ) is the mapping function of logical operation, α iis the mapping ratio coefficient of the i-th logic operation chip, q1 (TOPS) is the redundant computing power of the logic operation; m is the number of parallel computing chips, j is the index of m, f(b j ) is the mapping function for parallel computing, β j is the mapping ratio coefficient of the jth parallel computing chip, q2 (FLOPS) is the redundant computing power of parallel computing; p is the number of neural network acceleration chips, k is the index of p, and f(c k ) is the mapping function of neural network acceleration, γ k is the mapping ratio coefficient of the kth neural network acceleration chip, and q3 (FLOPS) is the redundant computing power of neural network acceleration.

[0040] In a technical solution of the present invention, a computer-readable storage medium is also provided, storing a computer program, wherein the computer program enables a computer to execute the intelligent cloud network scheduling method.

[0041] In a technical solution of the present invention, an electronic device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the intelligent cloud network scheduling method is implemented.

[0042] In a technical solution of the present invention, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the intelligent cloud network scheduling method is implemented.

[0043] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0044] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0045] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. An intelligent cloud network scheduling method, characterized in that: The specific steps include: Step S1: Find network nodes with the same type of user group and the same regional indicators in the cross-domain network based on log information, store them in a node list, and mark the status of the network nodes; Step S2: Use a path exploration algorithm to explore the network nodes in the node list to form several links and find an optimal link; Step S3: Prioritize using the optimal link for data forwarding, and for computing tasks in the data, distribute the computing tasks to interconnected network nodes for computing.

2. The intelligent cloud network scheduling method according to claim 1, characterized in that: Execute the ping command for the network nodes in the node list to confirm the connectivity to the destination network node. If connected, mark the network node status as available; otherwise, mark the network node status as unavailable.

3. The intelligent cloud network scheduling method according to claim 2, characterized in that: In step S2, the network nodes in the node list are explored using a path exploration algorithm to form a number of links. The specific process is as follows: i. Determine the starting point and destination network node of cloud network scheduling; ii. Searching for a network node that is interoperable with the starting point of the cloud network scheduling from the network nodes in the available state in the node list; iii. The searched network node continues to search for network nodes that are interoperable with it; iv. Repeat step iii until intercommunication with the destination network node is achieved and several links are obtained.

4. The intelligent cloud network scheduling method according to claim 3, characterized in that: In the process of exploration using the path exploration algorithm, the number of interconnections between each network node and its neighboring nodes is recorded, and the total number of interconnections between all network nodes and their neighboring nodes in each link is counted, and the link with the largest total number is taken as the optimal link.

5. The intelligent cloud network scheduling method according to claim 4, characterized in that: The specific process of step S3 allocating computing tasks to the intercommunication network nodes for calculation is: determining the idle computing power on the intercommunication network nodes, dividing the computing tasks into sub-computing tasks according to the idle computing power, allocating the sub-computing tasks to the corresponding intercommunication network nodes for calculation, and transmitting the calculation results from the intercommunication network nodes to the destination network nodes.

6. The intelligent cloud network scheduling method according to claim 5, characterized in that: The calculation process of the idle computing power on the interconnected network nodes is as follows: Among them, C br is the idle computing power, n is the number of logic operation chips, i is the index of n, f(a i ) is the mapping function of logical operation, α i is the mapping ratio coefficient of the i-th logic operation chip, q1 (TOPS) is the redundant computing power of the logic operation; m is the number of parallel computing chips, j is the index of m, f(b j ) is the mapping function for parallel computing, β j is the mapping ratio coefficient of the jth parallel computing chip, q2 (FLOPS) is the redundant computing power of parallel computing; p is the number of neural network acceleration chips, k is the index of p, and f(c k ) is the mapping function of neural network acceleration, γ k is the mapping ratio coefficient of the kth neural network acceleration chip, and q3 (FLOPS) is the redundant computing power of neural network acceleration.

7. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables the computer to execute the intelligent cloud network scheduling method as described in any one of claims 1-6.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the intelligent cloud network scheduling method as described in any one of claims 1 to 6 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the intelligent cloud network scheduling method described in any one of claims 1-6 is implemented.

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