Calculation method and device for assisting networking of intelligent calculation center

Abstract

The invention provides a computing method and device for assisting networking of an intelligent computing center. The method provided by the invention comprises the steps of determining an input parameter based on a networking structure type of an intelligent calculation center; determining the number of network structure layers corresponding to the networking structure type based on the relationship between the input parameters; determining networking parameter dimensions according to the networking structure type, and calculating a networking parameter value corresponding to each networking parameter dimension based on the number of layers of the network structure and the input parameters; wherein layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type are determined according to the number of layers of the network structure, the layer dimension parameters are configuration parameters of each layer in the networking structure, and the layer connection dimension parameters are connection configuration parameters among the layers in the networking structure. According to the computing method and device for assisting networking of the intelligent computing center, the automatic networking computing function is achieved, the method and device are used for assisting networking of the intelligent computing center, and universality is high.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a computing method and device for assisting the networking of intelligent computing centers. Background Art

[0002] As the development of artificial intelligence is in full swing, the demand for computing power is growing at an exponential rate. The demand for the construction of intelligent computing centers is very strong, and the scale of construction is getting larger and larger. This has resulted in the establishment of an intelligent computing center requiring a large number of servers and matching network equipment. When building an intelligent computing center, it is necessary to provide customers with customized networking solutions. The number of hardware devices required for networking, including servers, switches, optical modules, optical fibers or optical cables, etc., must be listed in detail in the solution. Then using manual calculations is time-consuming and labor-intensive, and due to the different understandings and abilities of different program writers, errors are very likely to occur, which will have a very adverse impact on the reporting or bidding process to customers. Therefore, there is an urgent need for a software to realize the function of networking computing.

[0003] Currently, no publicly released network computing software has been found in the market. Therefore, there is an urgent need for a method to realize the automatic network computing function to assist the intelligent computing center in networking, and it is highly versatile. Summary of the invention

[0004] In view of this, the present application provides a computing method and device for assisting the networking of an intelligent computing center, which is used to realize automatic networking computing functions to assist the networking of an intelligent computing center and has strong versatility.

[0005] Specifically, the present application is implemented through the following technical solutions: A first aspect of the present application provides a computing method for assisting intelligent computing center networking, the method comprising: Determine input parameters based on the networking structure type of the intelligent computing center, wherein different networking structure types have different dimensions of the input parameters; Based on the relationship between the input parameters, determining the number of network structure layers corresponding to the networking structure type; Determine the networking parameter dimensions according to the networking structure type, and calculate the networking parameter values ​​corresponding to each of the networking parameter dimensions based on the number of network structure layers and the input parameters; Among them, the layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type are determined according to the number of network structure layers, the layer dimension parameters are the configuration parameters of each layer in the networking structure, and the layer connection dimension parameters are the connection configuration parameters between the layers in the networking structure.

[0006] A second aspect of the present application provides a computing device for assisting intelligent computing center networking, the device comprising: a determination module and a computing module; wherein: The determination module is used to determine the input parameters based on the networking structure type of the intelligent computing center, wherein the dimensions of the input parameters are different for different networking structure types; The determination module is further configured to determine the number of network structure layers corresponding to the networking structure type based on the relationship between the input parameters; The calculation module is used to determine the networking parameter dimension according to the networking structure type, and calculate the networking parameter value corresponding to each networking parameter dimension based on the number of network structure layers and the input parameters; Among them, the layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type are determined according to the number of network structure layers, the layer dimension parameters are the configuration parameters of each layer in the networking structure, and the layer connection dimension parameters are the connection configuration parameters between the layers in the networking structure.

[0007] The computing method and device for assisting the networking of the intelligent computing center provided in the present application, on the one hand, determine the input parameters based on the networking structure type of the intelligent computing center. Only a few parameters necessary for networking need to be input to generate the number of hardware devices required for networking and realize networking. The input parameters all contain simple and easy-to-understand instructions, and there is no need to have an in-depth understanding of the architecture of the intelligent computing center network, which lowers the threshold for users. On the other hand, by digitizing the hardware equipment required for networking, the differences between different product models from different manufacturers are ignored, and the versatility is strong and easier to be accepted by users. On the third hand, machines are used to replace previous manual calculations and verifications, and there is no need for tedious and error-prone paper and pen calculations and verifications. When faced with the requirements of more projects, it saves time and effort, is less prone to errors, improves work efficiency, and reduces the probability of manual errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flowchart of the first embodiment of the computing method for assisting intelligent computing center networking provided by the present application; Figure 2 A structural diagram of Example 1 of a computing device for assisting intelligent computing center networking provided in this application. DETAILED DESCRIPTION

[0009] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0010] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0011] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0012] Specific embodiments are given below to introduce the technical solution of the present application in detail.

[0013] Figure 1 This is a flowchart of the first embodiment of the calculation method for assisting the intelligent computing center network provided by this application. Figure 1 , the method provided in this embodiment may include: S101. Determine input parameters based on the networking structure type of the intelligent computing center, wherein the dimensions of the input parameters are different for different networking structure types.

[0014] Specifically, the networking structure type of the intelligent computing center refers to the network architecture and layout method used to connect and manage computing resources, storage devices, network devices and other related components in the intelligent computing center. Common networking structure types include centralized structure, distributed structure, hybrid structure, multi-layer structure, leaf-spine structure, hyper-converged structure and edge computing structure.

[0015] Furthermore, different network structure types correspond to different dimensions of input parameters. Common input parameters include at least the number of servers, the number of network cards per server, the number of ports per network card, the number of switch ports, the network convergence ratio, and the type of connection medium between devices.

[0016] In specific implementation, the network structure type based on the intelligent computing center is used to determine the input parameters, including: (1) Based on the networking structure type of the intelligent computing center, determine the structural parameters of the corresponding structure, and determine the dimensions of the input parameters according to the structural parameters and the configuration attributes of the intelligent computing center.

[0017] Specifically, structural parameters refer to key technical indicators and configuration parameters related to the networking structure type. Structural parameters correspond to the networking structure type. When the networking structure type is a multi-layer structure (including core layer, aggregation layer and access layer), the corresponding structural parameters include core layer bandwidth, aggregation layer bandwidth, access layer bandwidth, redundancy and delay. When the networking structure type is a leaf-spine structure, the corresponding structural parameters include the number of leaf-layer switch ports, the number of spine-layer switch ports, topology and delay.

[0018] Furthermore, the configuration attributes of the intelligent computing center involve various technical and architectural parameters, which directly affect the computing power, network performance, storage capacity, reliability and scalability of the intelligent computing center. The configuration attributes of the intelligent computing center generally include resource configuration attributes, network configuration attributes, storage configuration attributes, virtualization and containerization attributes, security configuration attributes, management and monitoring attributes, scalability and flexibility attributes, energy efficiency attributes, and reliability and high availability attributes.

[0019] In specific implementation, based on the networking structure type of the intelligent computing center, a demand analysis is performed to determine the structural parameters of the intelligent computing center under the networking structure type. Further, based on the determined structural parameters, the configuration attributes of the intelligent computing center are refined. Based on the refined configuration attributes, the specific input parameters and their dimensions are determined.

[0020] (2) Based on the networking requirements of the intelligent computing center, enter the input parameters for each dimension.

[0021] Specifically, the networking requirements of the intelligent computing center include computing requirements, network requirements, storage requirements, virtualization requirements, security requirements, management requirements, scalability requirements, energy efficiency requirements, and reliability requirements.

[0022] In the specific implementation, based on a specific requirement in the networking requirements of the intelligent computing center, input the input parameters of the dimension corresponding to the requirement. Based on the same method, input the input parameters of each dimension.

[0023] For example, in one embodiment, the input parameters of each dimension include: number of servers 96, number of network cards of a single server 4, number of ports of a single network card 1, number of switch ports 32, networking convergence ratio 1:1, 1 switch port connected to 1 optical fiber connected to 1 network card (connection type between server and switch), 1 switch port connected to 1 optical fiber connected to 1 network card (connection type between switches).

[0024] Optionally, after inputting the input parameters of each dimension, the method further includes: determining the value range of the input parameters of each dimension based on the performance requirements of the intelligent computing center and the networking structure type; traversing the input parameters of each dimension in turn to check whether the input parameters of each dimension satisfy the value range; when the input parameters of any dimension do not satisfy the value range, redetermining the input parameters until the input parameters of each dimension satisfy the value range.

[0025] Specifically, based on the networking structure type of the intelligent computing center, determine the input parameters of each dimension. Furthermore, by considering the performance requirements of the intelligent computing center, limit the input parameters of the corresponding dimensions (for example, in the above example, the value range of the number of ports of a single network card is 1-2), and determine the value range of the input parameters of each dimension. By traversing the input parameters of each dimension, check whether its value meets the value range, and adjust the input parameters whose values ​​do not meet the value range, until the values ​​of the input parameters of each dimension meet the value range.

[0026] S102: Determine the number of network structure layers corresponding to the networking structure type based on the relationship between the input parameters.

[0027] Specifically, in combination with the above description, the input parameters at least include the number of servers, the number of network cards of a single server, the number of ports of a single network card, the number of switch ports, and the networking convergence ratio.

[0028] Furthermore, the number of network structure layers corresponds to the type of network structure. For example, in combination with the above description, when the network structure type is a multi-layer structure (including a core layer, a convergence layer, and an access layer), the corresponding number of network structure layers is three. When the network structure type is a leaf-spine structure (including a leaf layer and a spine layer), the corresponding number of network structure layers is two.

[0029] In a specific implementation, determining the number of network structure layers corresponding to the networking structure type based on the relationship between the input parameters includes: (1) Calculating the number of leaf-layer switches based on the input parameters; wherein the input parameters include at least the number of servers, the number of network cards of a single server, the number of ports of a single network card, the number of switch ports, and a network convergence ratio.

[0030] Specifically, leaf-layer switches are key components that connect computing nodes and spine-layer switches. The number of leaf-layer switches affects the throughput, latency, and reliability of the network.

[0031] In a specific implementation, the calculating the number of leaf-layer switches based on the input parameters includes: (i) Determine the total number of ports based on the number of servers, the number of network cards of a single server, and the number of ports of a single network card.

[0032] Specifically, the total number of ports refers to the sum of the number of ports of all devices on the server. The total number of ports is equal to the product of the number of servers, the number of network cards on a single server, and the number of ports on a single network card.

[0033] In specific implementation, the total number of ports can be determined according to the following formula: Total number of ports = number of servers × number of network cards per server × number of ports per network card (ii) Determining the number of uplink ports of the leaf switch based on the number of switch ports and the networking convergence ratio.

[0034] Specifically, the number of uplink ports of a leaf switch refers to the number of ports that each leaf switch uses to connect to upper-layer network devices (usually spine switches or core switches).

[0035] In specific implementation, the number of uplink ports of the leaf switch can be determined according to the following formula: The number of uplink ports of the leaf switch = the number of switch ports / (network convergence ratio + 1) (iii) Determining the number of downlink ports of the leaf switch based on the number of uplink ports of the leaf switch and the number of switch ports.

[0036] Specifically, the number of downstream ports of a leaf switch refers to the number of ports of each leaf switch connected to a computing node or other terminal device.

[0037] In specific implementation, the number of downstream ports of the leaf switch can be determined according to the following formula: The number of downstream ports of the leaf switch = the number of switch ports - the number of upstream ports of the leaf switch (iiii) Determining the number of leaf switches based on the total number of ports and the number of downstream ports of the leaf switches.

[0038] In specific implementation, the number of leaf-layer switches can be determined according to the following formula: Number of leaf switches = total number of ports / number of downstream ports of leaf switches (2) Based on the relationship between the number of leaf-layer switches and the number of switch ports, determine the number of network structure layers corresponding to the networking structure type.

[0039] Specifically, the relationship between the number of leaf-layer switches and the number of switch ports determines the number of network structure layers.

[0040] In a specific implementation, the number of network structure layers corresponding to the networking structure type is determined based on the size relationship between the number of leaf-layer switches and the number of switch ports, including: when the number of leaf-layer switches is not greater than the number of switch ports, the number of network structure layers is determined to be two layers; when the number of leaf-layer switches is greater than the number of switch ports, the number of network structure layers is determined to be three layers.

[0041] Specifically, when the number of leaf switches exceeds the port limit of each switch (i.e., the number of switch ports), it is usually necessary to design the network structure into three layers (for example, adding a convergence layer to connect multiple leaf switches) to effectively manage and connect all devices. By adding a convergence layer, it can help manage a large number of leaf switches, so that each leaf switch does not have to be directly connected to the core layer, but is connected through the convergence layer. This design helps to improve the flexibility and scalability of the network while reducing the burden on the core layer switches.

[0042] The method provided in this embodiment provides a scientific method for determining the number of network structure layers: first, the number of leaf-layer switches is determined according to the relevant parameters in the input parameters and the calculation formula of the number of leaf-layer switches, and then the number of network structure layers is determined by comparing the calculated number of leaf-layer switches with the number of switch ports in the input parameters. After determining the number of network structure layers corresponding to the networking structure type, various networking parameters and their values ​​can be calculated based on the determined number of network structure layers and the input parameters, that is, the number of hardware devices required for networking can be determined, thereby realizing networking.

[0043] S103. Determine the networking parameter dimension according to the networking structure type, and calculate the networking parameter values ​​corresponding to each of the networking parameter dimensions based on the number of network structure layers and the input parameters; wherein, the layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type are determined according to the number of network structure layers, the layer dimension parameters are the configuration parameters of each layer in the networking structure, and the layer connection dimension parameters are the connection configuration parameters between the layers in the networking structure.

[0044] Specifically, the networking parameter dimension is related to the number of network structure layers and input parameters, and the networking parameter dimension includes layer dimension parameters and layer connection dimension parameters. The layer dimension parameters are configuration parameters of each layer in the networking structure, and the layer dimension parameters at least include the number of switches and the number of ports. The layer connection dimension parameters are connection configuration parameters between layers in the networking structure, and the layer connection dimension parameters at least include the number of optical modules and the number of optical fibers and optical cables.

[0045] In a specific implementation, determining the layer dimension parameter and the layer connection dimension parameter corresponding to the networking structure type according to the number of network structure layers includes: (1) Based on the number of network structure layers, determine the function of each network layer in the network structure corresponding to the number of network structure layers.

[0046] Specifically, the functions of each layer of the network in different network structures are different. For example, in combination with the above description, when the network structure has two layers (including the core layer and the leaf layer), the function of the core layer is responsible for network aggregation and routing, connecting to the upper network or edge network, and processing the aggregation and distribution of traffic within the data center. The function of the leaf layer is to connect with computing nodes and other terminal devices, provide high-density port connections to support a large number of computing nodes, carry most of the data traffic within the data center, and provide uplink connections to the core layer. For another example, when the network structure has three layers (including the core layer, the aggregation layer, and the leaf layer), the function of the core layer is responsible for cross-network routing and aggregation, processing traffic from the aggregation layer and other core devices, and performing security and performance optimization. The function of the aggregation layer is to connect multiple leaf layer switches, aggregate the energy of the leaf layer switches to the core layer, provide redundant paths and load balancing, and connect and distribute data. The function of the leaf layer is to connect with computing nodes and other terminal devices, provide a large number of downlink ports to support connections to all computing nodes and other devices, and process communications between devices within the data center.

[0047] In specific implementation, based on the number of network structure layers, the network structure corresponding to the number of network structure layers is determined. Functional analysis is performed on each layer of the determined network structure to determine the function of each layer of the network.

[0048] (2) Based on the function of each layer of the network in the network structure, determine the layer dimension parameters and layer connection dimension parameters corresponding to each layer of the network.

[0049] Specifically, the layer dimension parameters and layer connection dimension parameters corresponding to each layer of the network are different. For example, combined with the above description, when the network structure has two layers (including the core layer and the leaf layer), the layer dimension parameters corresponding to the core layer may include the number of ports, the number of switches, the size of the routing table, the processing capacity, etc., and the layer connection dimension parameters corresponding to the core layer may include the number of optical modules, the number of optical fibers, the number of optical cables, bandwidth, delay, and redundancy. The layer dimension parameters corresponding to the leaf layer may include the number of ports, the number of switches, the port rate, the port utilization, etc., and the layer connection dimension parameters corresponding to the leaf layer may include the number of optical modules, the number of optical fibers, the number of optical cables, bandwidth, delay, and redundancy.

[0050] In specific implementation, based on the functions of each network layer in the determined network structure, the layer dimension parameters and layer connection dimension parameters corresponding to each network layer are determined.

[0051] (3) Combining the layer dimension parameters and layer connection dimension parameters corresponding to each layer of the network to obtain the layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type.

[0052] In specific implementation, the layer dimension parameters and layer connection dimension parameters corresponding to each layer of the network are combined and added, and the combined layer dimension parameters and layer connection dimension parameters are determined as the layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type.

[0053] For example, in combination with the above example, when the number of network structure layers is two (including the core layer and the leaf layer), in this step, the layer dimension parameters corresponding to the core layer and the layer dimension parameters corresponding to the leaf layer are combined and added as the layer dimension parameters corresponding to the networking structure type. The layer connection dimension parameters corresponding to the core layer and the layer connection dimension parameters corresponding to the leaf layer are combined and added as the layer connection dimension parameters corresponding to the networking structure type.

[0054] As a preferred embodiment, the calculating the networking parameter values ​​corresponding to each networking parameter dimension based on the number of network structure layers and the input parameters includes: (1) Based on the number of layers of the network structure, determine the layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type.

[0055] Specifically, the specific implementation process and implementation principle of this step can be referred to the description in the above embodiment, which will not be repeated here.

[0056] (2) Determine the layer dimension parameters based on the number of servers, the number of network cards of a single server, the number of ports of a single network card, the number of switch ports, and the networking convergence ratio in the input parameters.

[0057] Specifically, the layer dimension parameters correspond to the network structure type. In this embodiment, the network structure type is described as a leaf-spine structure. Combined with the above description, the layer dimension parameters at least include the number of switches and the number of ports. The number of switches includes the number of leaf-layer switches and the number of spine-layer switches. The number of ports includes the number of uplink ports of leaf-layer switches, the number of downlink ports of leaf-layer switches, the number of redundant ports of leaf-layer switches, and the number of redundant ports of spine-layer switches.

[0058] Method for determining the number of leaf-layer switches: Number of leaf switches = total number of ports / number of downstream ports of leaf switches Total number of ports = number of servers × number of network cards per server × number of ports per network card How to determine the number of spine switches: Number of spine switches = Number of leaf switches × Number of uplink ports on leaf switches / Number of switch ports Method for determining the number of uplink ports of a leaf switch: The number of uplink ports of the leaf switch = the number of switch ports / (network convergence ratio + 1) Method for determining the number of downstream ports of a leaf switch: The number of downstream ports of the leaf switch = the number of switch ports - the number of upstream ports of the leaf switch Method for determining the number of redundant ports of leaf switches: The number of redundant ports of a leaf switch = the number of leaf switches × (the number of switch ports - (the number of uplink ports of a leaf switch + the number of downlink ports of a leaf switch)) How to determine the number of redundant ports of the spine switch: The number of redundant ports of the spine switch = the number of spine switches × the number of switch ports - the number of leaf switches × the number of uplink ports of the leaf switch (3) Determine the layer connection dimension parameters based on the number of servers, the number of network cards of a single server, the number of ports of a single network card, and the type of connection medium between devices in the input parameters.

[0059] Specifically, the layer connection dimension parameter corresponds to the network structure type. In this embodiment, the network structure type is described as a leaf-spine structure. The layer connection dimension parameter is determined based on the number of servers, the number of network cards of a single server, the number of ports of a single network card, and the type of connection medium between devices in the input parameters, including: (1) Determine a first coefficient based on the type of connection medium between devices in the input parameter.

[0060] Specifically, the connection medium type between devices refers to the physical transmission medium used to connect different devices (such as servers and switches) in the intelligent computing center. Common connection media types include optical fiber, copper cable, hybrid optical fiber and copper cable connection, and wireless connection.

[0061] It should be noted that different connection medium types correspond to different first coefficients, and the first coefficient represents how many ports each optical module corresponds to under the connection medium type. For example, when the connection medium type is optical fiber, the first coefficient is 1.

[0062] (2) Determine the total number of ports based on the number of servers, the number of network cards on a single server, and the number of ports on a single network card in the input parameters.

[0063] Specifically, in combination with the above description, the total number of ports is equal to the product of the number of servers, the number of network cards of a single server, and the number of ports of a single network card.

[0064] In specific implementation, based on the input server number, the number of network cards of a single server, and the number of ports of a single network card, the total number of ports can be obtained by multiplying the three.

[0065] (3) Determine the number of optical modules and the number of optical fibers and optical cables based on the first coefficient and the total number of ports.

[0066] Specifically, the number of optical modules and the number of optical fibers and optical cables is equal to the product of the first coefficient and the total number of ports.

[0067] In specific implementation, the calculated total number of ports is multiplied by the determined first coefficient to obtain the number of optical modules and the number of optical fibers and optical cables, that is, the layer connection dimension parameter.

[0068] The method provided in this embodiment provides a scientific method for determining the dimensions and values ​​of networking parameters: when determining the dimensions of networking parameters, first determine the functions of each layer of the network in the network structure corresponding to the number of layers of the network structure based on the determined number of layers of the network structure, determine the layer dimension parameters and layer connection dimension parameters corresponding to each layer of the network based on the functions of each layer of the network, and then determine the dimensions of the networking parameters corresponding to the networking structure type. When determining the values ​​of each networking parameter, determine the layer dimension parameters based on the relevant parameters in the input parameters and the calculation formula of the layer dimension parameters, determine the layer connection dimension parameters based on the relevant parameters in the input parameters and the calculation formula of the layer connection dimension parameters, and then determine the values ​​of each networking parameter. After determining the dimensions and values ​​of the networking parameters, the number of hardware devices required for networking is determined, and networking can be conveniently implemented in the future.

[0069] The calculation method for assisting the networking of the intelligent computing center provided in this embodiment, on the one hand, determines the input parameters based on the networking structure type of the intelligent computing center. Only a few parameters necessary for networking need to be input to generate the number of hardware devices required for networking and realize networking. The input parameters all contain simple and easy-to-understand instructions, and there is no need to have an in-depth understanding of the architecture of the intelligent computing center network, which lowers the threshold for users. On the other hand, by digitizing the hardware equipment required for networking, the differences between different product models from different manufacturers are ignored, and the versatility is strong, which is easier to be accepted by users. On the third hand, machines are used to replace previous manual calculations and verifications, and there is no need for tedious and error-prone paper and pen calculations and verifications. When faced with the requirements of more projects, it saves time and effort, is less prone to errors, improves work efficiency, and reduces the probability of manual errors.

[0070] Corresponding to the aforementioned embodiment of a computing method for assisting the networking of intelligent computing centers, the present application also provides an embodiment of a computing device for assisting the networking of intelligent computing centers.

[0071] Figure 2 This is a schematic diagram of the structure of the computing device embodiment 1 of the auxiliary intelligent computing center network provided by this application. Figure 2 , the device provided in this embodiment includes: a determination module 210 and a calculation module 220; wherein, The determination module 210 is used to determine the input parameters based on the networking structure type of the intelligent computing center, wherein the dimensions of the input parameters are different for different networking structure types; The determination module 210 is further configured to determine the number of network structure layers corresponding to the networking structure type based on the relationship between the input parameters; The calculation module 220 is used to determine the networking parameter dimension according to the networking structure type, and calculate the networking parameter value corresponding to each networking parameter dimension based on the number of network structure layers and the input parameters; Among them, the layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type are determined according to the number of network structure layers, the layer dimension parameters are the configuration parameters of each layer in the networking structure, and the layer connection dimension parameters are the connection configuration parameters between the layers in the networking structure.

[0072] The computing device for assisting the networking of the intelligent computing center provided in this embodiment, on the one hand, determines the input parameters based on the networking structure type of the intelligent computing center. Only a few parameters necessary for networking need to be input to generate the number of hardware devices required for networking and realize networking. The input parameters all contain simple and easy-to-understand instructions, and there is no need to have an in-depth understanding of the architecture of the intelligent computing center network, which lowers the threshold for users. On the other hand, by digitizing the hardware devices required for networking, the differences between different product models from different manufacturers are ignored, and the versatility is strong, which is easier to be accepted by users. On the third hand, machines are used to replace previous manual calculations and verifications, and there is no need for tedious and error-prone paper and pen calculations and verifications. When faced with the requirements of more projects, it saves time and effort, is less prone to errors, improves work efficiency, and reduces the probability of manual errors.

[0073] The device of this embodiment can be used to perform Figure 1 The steps, specific implementation principles and implementation processes of the method embodiment shown are similar and will not be repeated here.

[0074] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0075] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0076] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A computing method for assisting intelligent computing center networking, characterized in that: The method comprises: Determine input parameters based on the networking structure type of the intelligent computing center, wherein different networking structure types have different dimensions of the input parameters; Based on the relationship between the input parameters, determining the number of network structure layers corresponding to the networking structure type; Determine the networking parameter dimensions according to the networking structure type, and calculate the networking parameter values ​​corresponding to each of the networking parameter dimensions based on the number of network structure layers and the input parameters; Among them, the layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type are determined according to the number of network structure layers, the layer dimension parameters are the configuration parameters of each layer in the networking structure, and the layer connection dimension parameters are the connection configuration parameters between the layers in the networking structure.

2. The method according to claim 1, characterized in that The input parameters are determined based on the networking structure type of the intelligent computing center, including: Based on the networking structure type of the intelligent computing center, determine the structural parameters of the corresponding structure, and determine the dimensions of the input parameters according to the structural parameters and the configuration attributes of the intelligent computing center; Based on the networking requirements of the intelligent computing center, enter the input parameters for each dimension.

3. The method according to claim 2, characterized in that After inputting the input parameters of each dimension, the method further includes: Determine the value range of the input parameter of each dimension based on the performance requirements of the intelligent computing center and the type of the networking structure; Traverse the input parameters of each dimension in turn, and check whether the input parameters of each dimension meet the value range; When the input parameters of any dimension do not satisfy the value range, the input parameters are re-determined until the input parameters of each dimension satisfy the value range.

4. The method according to claim 1, characterized in that: The determining, based on the relationship between the input parameters, the number of network structure layers corresponding to the networking structure type includes: Based on the input parameters, the number of leaf-layer switches is calculated; wherein the input parameters include at least the number of servers, the number of network cards of a single server, the number of ports of a single network card, the number of switch ports, and the network convergence ratio; Based on the relationship between the number of leaf-layer switches and the number of switch ports, the number of network structure layers corresponding to the networking structure type is determined.

5. The method according to claim 4, characterized in that The calculating the number of leaf-layer switches based on the input parameters includes: Determine the total number of ports based on the number of servers, the number of network cards of a single server, and the number of ports of a single network card; Determining the number of uplink ports of the leaf layer switch based on the number of switch ports and the networking convergence ratio; Determine the number of downlink ports of the leaf switch based on the number of uplink ports of the leaf switch and the number of switch ports; The number of leaf switches is determined based on the total number of ports and the number of downstream ports of the leaf switches.

6. The method according to claim 4, characterized in that The determining the number of network structure layers corresponding to the networking structure type based on the relationship between the number of leaf layer switches and the number of switch ports includes: When the number of leaf-layer switches is not greater than the number of switch ports, determining that the number of layers of the network structure is two; When the number of leaf-layer switches is greater than the number of switch ports, the number of layers of the network structure is determined to be three.

7. The method according to claim 1, characterized in that The layer dimension parameters include at least the number of switches and the number of ports, and the layer connection dimension parameters include at least the number of optical modules, optical fibers, and optical cables; the layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type are determined according to the number of network structure layers, including: Based on the number of network structure layers, determining the function of each network layer in the network structure corresponding to the number of network structure layers; Based on the function of each layer of the network in the network structure, determine the layer dimension parameters and layer connection dimension parameters corresponding to each layer of the network; The layer dimension parameters and layer connection dimension parameters corresponding to each layer of the network are combined to obtain the layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type.

8. The method according to claim 1, characterized in that The calculating, based on the number of network structure layers and the input parameters, the networking parameter values ​​corresponding to the networking parameter dimensions includes: Based on the number of layers of the network structure, determine the layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type; Determine the layer dimension parameter based on the number of servers, the number of network cards of a single server, the number of ports of a single network card, the number of switch ports, and the networking convergence ratio in the input parameters; The layer connection dimension parameters are determined based on the number of servers, the number of network cards of a single server, the number of ports of a single network card, and the type of connection medium between devices in the input parameters.

9. The method according to claim 8, characterized in that Determining the layer connection dimension parameters based on the number of servers, the number of network cards of a single server, the number of ports of a single network card, and the type of connection medium between devices in the input parameters includes: Determine a first coefficient based on the type of connection medium between devices in the input parameter; Determine the total number of ports based on the number of servers, the number of network cards of a single server, and the number of ports of a single network card in the input parameters; Based on the first coefficient and the total number of ports, the number of optical modules and the number of optical fibers and optical cables are determined.

10. A computing device for assisting intelligent computing center networking, characterized in that: The device comprises: a determination module and a calculation module; wherein, The determination module is used to determine the input parameters based on the networking structure type of the intelligent computing center, wherein the dimensions of the input parameters are different for different networking structure types; The determination module is further configured to determine the number of network structure layers corresponding to the networking structure type based on the relationship between the input parameters; The calculation module is used to determine the networking parameter dimension according to the networking structure type, and calculate the networking parameter value corresponding to each networking parameter dimension based on the number of network structure layers and the input parameters; Among them, the layer dimension parameters and layer connection dimension parameters corresponding to the networking structure type are determined according to the number of network structure layers, the layer dimension parameters are the configuration parameters of each layer in the networking structure, and the layer connection dimension parameters are the connection configuration parameters between the layers in the networking structure.

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