A green computing power network management method, device, equipment, medium and product

Abstract

The present disclosure relates to a green computing power network management method, device, equipment, medium and product. The method comprises: after detecting a service request for a computing power network, analyzing the service request to obtain a service requirement, and determining a service requirement parameter based on the service requirement; determining a candidate computing power node set that meets a preset energy consumption requirement based on device parameters of each device in the computing power network; selecting a target computing power node that meets the service requirement parameter from the candidate computing power node set; determining a target computing power network routing allocation path based on the selected target computing power node; wherein the target computing power network routing allocation path is used to indicate a routing path between a source node and the target computing power node. The present disclosure can enable the computing power network to have diversified green node selection capabilities.

Description

Technical Field

[0001] This disclosure relates to the field of transmission and bearer networks, and in particular to a green computing power network management method, apparatus, equipment, medium, and product. Background Technology

[0002] A computing network is a new type of information infrastructure that allocates and flexibly schedules computing, storage, and network resources on demand among the cloud, network, and edge based on business needs. When new computing services are added or scheduled, the computing network's management and control system will allocate computing and network resources nearby based on business needs.

[0003] In existing technologies, the allocation of computing power nodes in a computing power network is achieved by assessing the availability of computing resources and the satisfaction of key network indicators. However, the allocation indicators used in this method are relatively singular, which leads to a decrease in the rationality of the allocation of computing power resources and computing network resources during the operation and maintenance of existing computing network services. Summary of the Invention

[0004] This disclosure provides a method, apparatus, equipment, medium, and product for managing green computing power networks.

[0005] According to a first aspect of this disclosure, a method for managing green computing power networks is provided, the method comprising:

[0006] After detecting a service request for the computing power network, the service request is parsed to obtain the service requirements, and the service requirements parameters are determined based on the service requirements.

[0007] Based on the device parameters of each device in the computing power network, a set of candidate computing power nodes that meet the preset energy consumption requirements is determined;

[0008] Select target computing power nodes that meet the business requirement parameters from the set of candidate computing power nodes;

[0009] The target computing network routing allocation path is determined based on the selected target computing power nodes; wherein, the target computing network routing allocation path is used to indicate the routing path between the source node and the target computing power node.

[0010] Furthermore, determining the business requirement parameters based on the business requirements includes:

[0011] Collect operational data and service data carried by each device in the computing network;

[0012] Based on the operational data and the business data, a routing fusion model for the computing power network is determined; wherein, the routing fusion model is used to indicate the routing path between the source node and each computing power device in the computing power network;

[0013] The business requirements are matched with the routing fusion model to obtain the business requirement parameters.

[0014] Further, the step of determining the set of candidate computing nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network includes:

[0015] The device parameters of each device in the computing power network are processed by a neural recurrent network to obtain a set of candidate computing power nodes that meet the preset energy consumption requirements.

[0016] Further, the step of selecting target computing power nodes that meet the business requirement parameters from the set of candidate computing power nodes includes:

[0017] The business requirement parameters are processed with the smallest granularity data identification to obtain the minimum requirement parameters that satisfy the business request;

[0018] From the set of candidate computing power nodes, candidate computing power nodes that meet the minimum requirement parameters are determined to obtain the target computing power node.

[0019] Further, determining the target computing network routing allocation path based on the selected target computing power nodes includes:

[0020] Based on the target computing power node and the source node, at least one candidate computing network routing allocation path is determined;

[0021] Based on the network performance indicators of each node in each of the candidate computing network routing allocation paths, a first optimal computing network routing allocation path is determined among the at least one candidate computing network routing allocation path.

[0022] Based on the network energy consumption index of each node in each of the first optimal computing network routing allocation paths, a second optimal computing network routing allocation path is determined in the first optimal computing network routing allocation path.

[0023] The target computing network routing allocation path is determined based on the second optimal computing network routing allocation path.

[0024] Further, determining the first optimal computing network routing allocation path among the at least one candidate computing network routing allocation path based on the network performance indicators of each node in each candidate computing network routing allocation path includes:

[0025] The first score of each candidate computing network routing path is determined based on the network performance index and first weight coefficient of each node in each candidate computing network routing path.

[0026] The first preset number of candidate computing network routing allocation paths with the highest first scores are determined as the first optimal computing network routing allocation path.

[0027] Further, determining the second optimal computing network routing allocation path based on the network energy consumption indicators of each node in each of the first optimal computing network routing allocation paths includes:

[0028] The second score of the first optimal computing network routing allocation path is determined based on the network energy consumption index and the second weight coefficient of each node in each first optimal computing network routing allocation path.

[0029] The first optimal computing network routing allocation path with the highest second score is determined as the second optimal computing network routing allocation path.

[0030] Further, determining the target computing network routing allocation path based on the second optimal computing network routing allocation path includes:

[0031] The second optimal computing network routing allocation path is verified using multiple performance verification metrics, and the performance verification results of the second optimal computing network routing allocation path are obtained.

[0032] If the performance verification result meets the preset index requirements, the target computing network routing allocation path is determined based on the second optimal computing network routing allocation path, and the service request is run on the target computing network routing allocation path in the computing power network.

[0033] If the verification result does not meet the preset index requirements, return to the step of determining the set of candidate computing nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network.

[0034] Furthermore, the method also includes:

[0035] If a second optimal computing network routing allocation path that meets the preset index requirements is not determined multiple times in a row, an alarm message is sent to the user that the computing power network cannot meet the service request.

[0036] According to a second aspect of this disclosure, a green computing power network management device is provided, the device comprising:

[0037] The parsing module is used to parse the service request after detecting a service request for the computing power network, obtain the service requirements, and determine the service requirement parameters based on the service requirements.

[0038] The computing module is used to determine a set of candidate computing nodes that meet preset energy consumption requirements based on the device parameters of each device in the computing power network.

[0039] The filtering module is used to filter target computing power nodes that meet the business requirement parameters from the set of candidate computing power nodes;

[0040] The determination module is used to determine the target computing network routing allocation path based on the selected target computing power nodes; wherein the target computing network routing allocation path is used to indicate the routing path between the source node and the target computing power node.

[0041] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0042] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the methods described above.

[0043] According to a fifth aspect of this disclosure, a computer program product is provided. The computer program product includes a computer program that, when executed by a processor, implements the methods described above in this disclosure.

[0044] This disclosure provides a green computing power network management method, apparatus, device, medium, and product. In this embodiment, firstly, after detecting a service request for the computing power network, the service request is parsed to obtain service requirements, and service requirement parameters are determined based on these requirements. Then, a set of candidate computing power nodes that meet preset energy consumption requirements is determined based on the device parameters of each device in the computing power network. Next, target computing power nodes that meet the service requirement parameters are selected from the candidate computing power node set. Finally, a target computing power network routing allocation path is determined based on the selected target computing power nodes. The target computing power network routing allocation path indicates the routing path between the source node and the target computing power node.

[0045] As described above, the technical solution of this disclosure determines a set of candidate computing nodes that meet preset energy consumption requirements based on the device parameters of each device in the computing power network. Then, it selects target computing nodes that meet business requirement parameters from the candidate computing node set. The target computing nodes selected in this way can meet the preset energy consumption requirements while meeting the business requirement parameters, giving the computing power network a diversified green node selection capability, thereby enabling the selection of more reasonable computing nodes. At the same time, the technical solution of this disclosure can determine the target computing network routing allocation path based on the selected target computing nodes. When the business request runs on the target computing network routing allocation path, it can not only ensure the network performance of the business request, but also reduce the network energy consumption. Attached Figure Description

[0046] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0047] Figure 1 A flowchart illustrating a green computing power network management method provided as an exemplary embodiment of this disclosure;

[0048] Figure 2 A schematic diagram of green computing power network management logic provided for an exemplary embodiment of this disclosure;

[0049] Figure 3 A schematic diagram of a routing fusion model provided for an exemplary embodiment of this disclosure;

[0050] Figure 4 A flowchart illustrating a green computing network management method provided as another exemplary embodiment of this disclosure;

[0051] Figure 5 A flowchart illustrating a green computing network management method provided as another exemplary embodiment of this disclosure;

[0052] Figure 6 A flowchart illustrating a green computing network management method provided as another exemplary embodiment of this disclosure;

[0053] Figure 7 A schematic diagram of green computing power network management logic provided for another exemplary embodiment of this disclosure;

[0054] Figure 8 A flowchart illustrating a green computing network management method provided as another exemplary embodiment of this disclosure;

[0055] Figure 9 A schematic block diagram of the functional modules of a green computing power network management device provided for an exemplary embodiment of the present disclosure;

[0056] Figure 10 A structural block diagram of an electronic device provided as an exemplary embodiment of this disclosure;

[0057] Figure 11 A structural block diagram of a computer system provided as an exemplary embodiment of this disclosure;

[0058] Figure 12 A structural block diagram of a computer program product provided for an exemplary embodiment of this disclosure. Detailed Implementation

[0059] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0060] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0061] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0062] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0063] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0064] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0065] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0066] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understood that the above notification and user authorization process is merely illustrative and does not constitute a limitation on the implementation of this disclosure; other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0067] In one embodiment, such as Figure 1 As shown, a green computing power network management method is provided, including the following steps:

[0068] Step 101: After detecting a service request for the computing power network, the service request is parsed to obtain the service requirements, and the service requirement parameters are determined based on the service requirements.

[0069] Here, the executing entity can be a computing power network management terminal. After detecting a service request for the computing power network, the computing power network management terminal can parse the service request to obtain the service requirements and determine the service requirement parameters based on the service requirements. These service requirements can include adding new computing power services and scheduling computing power services.

[0070] In one possible embodiment, such as Figure 2 As shown, Figure 2 An exemplary schematic diagram of green computing power network management logic is shown. After detecting a service request for the computing power network, the computing power network management terminal first parses the service request to determine the service requirements, which include adding computing power services and scheduling computing power services. Then, the computing power network management terminal determines the service requirement parameters based on the service requirements.

[0071] In one possible embodiment, determining business requirement parameters based on business needs includes the following steps:

[0072] Collect operational data from each device in the computing network, as well as the service data it carries;

[0073] Based on operational and business data, a routing fusion model for the computing power network is determined.

[0074] The business requirements are matched with the routing fusion model to obtain the business requirement parameters.

[0075] In this embodiment, after parsing the service request to obtain the service requirements, the computing network management terminal first collects the operating data and the service data carried by each device in the computing network. The devices in the computing network may include network devices, computing power devices, cooling devices, and environmental systems. Network devices are routers that carry and forward user requests; specifically, these routers include "access, aggregation, and core" layer routers. Computing power devices are computing servers that provide computing and training services to user needs within areas such as data centers and cloud resource pools. Cooling devices are devices that exchange heat or dissipate heat generated in server rooms and racks. Environmental systems primarily monitor and statistically analyze the power consumption and temperature of server rooms, racks, and equipment in real time. The computing network management terminal collects the operating data and the service data carried by the network devices, computing power devices, cooling devices, and environmental systems through direct protocol acquisition and interface synchronization. Specifically, the collected data includes device resource data, service information, computing power data, end-to-end path routing data, cooling parameters, power consumption data, ambient temperature data, transmission bandwidth, and performance data.

[0076] Then, the computing power network management terminal cleans and parses the collected operational and business data to determine the routing fusion model of the computing power network. This routing fusion model indicates the routing paths from source nodes to various computing devices within the computing power network. For example... Figure 3 As shown, Figure 3 An exemplary schematic diagram of the routing fusion model is shown. The routing path from the source node to the computing device in the computing power network is: source node - device 1 - device 2 - device 3 - device N - computing device. The source node is the network device, devices 1-N are the next-hop routes of the network devices, i.e., intermediate nodes, and the computing device is the destination node. The routing fusion model includes information fields such as province, city, region, data center / base station, rack, network / computing device name, device IP, device type, device / application port, IP prefix, power consumption, bandwidth / computing power occupied, local temperature, and network performance (bandwidth, latency, packet loss) for each device in the computing power network.

[0077] After that, as Figure 2 As shown, the computing power network management terminal matches business requirements with the routing fusion model and decomposes the business requirements to obtain the business requirement parameters under the routing fusion model. The business requirement parameters include: user information parameters, computing power requirement parameters, network requirement parameters, energy consumption threshold parameters, temperature range parameters, and computing power type parameters.

[0078] Specifically, for user information parameters, the computing power network management terminal determines the user-side access device based on the user information in the business requirements, thereby identifying the operator's edge-side router device in the routing fusion model and determining the source node in the routing fusion model. For computing power requirement parameters, the computing power network management terminal decomposes the data such as the quantity, specifications, and usage time period of computing power required to support business operation from the business requirements. For network requirement parameters, the computing power network management terminal decomposes the data supporting the stable operation of network-side services in the routing fusion model, such as bandwidth, latency, packet loss, number of packets, overhead, and utilization rate. For energy consumption threshold parameters, the computing power network management terminal defines the upper limit of energy consumption requirements for node devices such as computing power servers and routers. For temperature range parameters, the computing power network management terminal defines the local temperature range of the data center for node devices such as computing power servers and routers. For computing power type parameters, the computing power network management terminal identifies the type of computing power required by the business, including computing, storage, training, and multi-dimensional requirements such as sensitive / non-sensitive, large / medium / small scale, and fixed / flexible computing rules.

[0079] Step 102: Determine the set of candidate computing nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network.

[0080] Here, after determining the business requirement parameters based on business needs, the computing power network management terminal can determine a set of candidate computing power nodes that meet the preset energy consumption requirements based on the equipment parameters of each device in the computing power network. The equipment parameters of each device in the computing power network include: configuration, performance, operating indicators, power consumption, computing resource capacity, climate, rack local temperature, data center cooling capacity, available power capacity, green electricity coverage (the proportion of green energy used, such as photovoltaic power generation, hydropower, wind power, etc.), and computing power service type.

[0081] In one possible embodiment, determining a set of candidate computing nodes that meet preset energy consumption requirements based on the device parameters of each device in the computing power network includes the following steps:

[0082] By processing the device parameters of each device in the computing power network through a neural recurrent network, a set of candidate computing power nodes that meet the preset energy consumption requirements is obtained.

[0083] Specifically, after determining the business requirement parameters based on business needs, the computing power network management terminal processes the device parameters of each device in the computing power network through a neural recurrent network to obtain a set of candidate computing power nodes that meet the preset energy consumption requirements.

[0084] It should be noted that the neural recurrent network requires at least 6 months of device parameters as training data, and recent weekly data as target validation data for model verification. This ensures the robustness and accuracy of the model. When the validation results match the target validation data, the neural recurrent network can be released and used. When the validation results do not match the target validation data, the neural recurrent network parameters are adjusted and training continues. If the validation results do not match the target validation data more than a preset number of times, an abnormal validation result alarm is reported, and the neural recurrent network iteration is stopped and rolled back to the previous version.

[0085] Here, considering the frequency of network cutovers in important nodes such as data centers, aggregation rooms, and core buildings in the computing power network, the training time of the neural recurrent network can be flexibly set. For example, incremental data training can be performed on the neural recurrent network from 3 to 4 a.m. every day, while real-time iteration is carried out.

[0086] Step 103: Select target computing nodes that meet the business requirements parameters from the candidate computing node set.

[0087] After determining the set of candidate computing nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing network, the computing network management terminal can filter the target computing nodes that meet the business requirements parameters from the set of candidate computing nodes.

[0088] In one possible embodiment, selecting target computing power nodes that meet business requirement parameters from the candidate computing power node set includes the following steps:

[0089] The business requirement parameters are processed with the smallest granularity data identification to obtain the minimum requirement parameters that meet the business request;

[0090] From the set of candidate computing power nodes, identify the candidate computing power nodes that meet the minimum requirement parameters to obtain the target computing power node.

[0091] Specifically, such as Figure 2 As shown, after the computing power network management terminal determines the set of candidate computing power nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network, the computing power network management terminal first performs minimum granularity data identification processing on the business requirement parameters to obtain the minimum requirement parameters that meet the business request.

[0092] For example, when the network requirement parameters in the service requirement parameters are processed with minimum granularity data identification, the minimum bandwidth that meets the user's service activation requirements can be obtained; then, the computing power network management terminal determines the candidate computing power nodes that meet the minimum requirement parameters from the candidate computing power node set, and obtains the target computing power node.

[0093] Step 104: Determine the target computing network routing allocation path based on the selected target computing power nodes.

[0094] Here, as Figure 2 As shown, after the computing power network management terminal selects the target computing power nodes that meet the business requirements parameters from the candidate computing power node set, it can determine the target computing network routing allocation path based on the selected target computing power nodes. The target computing network routing allocation path indicates the routing path between the source node and the target computing power node.

[0095] In one possible embodiment, determining the target computing network routing allocation path based on the selected target computing power nodes includes the following steps:

[0096] Based on the target computing power node and the source node, determine at least one candidate computing network routing allocation path;

[0097] Based on the network performance indicators of each node in each candidate computing network routing allocation path, determine the first optimal computing network routing allocation path in at least one candidate computing network routing allocation path.

[0098] Based on the network energy consumption index of each node in each first optimal computing network routing allocation path, the second optimal computing network routing allocation path is determined in the first optimal computing network routing allocation path.

[0099] The target computing network routing path is determined based on the second optimal computing network routing allocation path.

[0100] Specifically, after the computing power network management terminal selects the target computing power node that meets the business requirement parameters from the candidate computing power node set, it first determines at least one candidate computing network routing allocation path based on the target computing power node and the source node. The source node is the uplink operator's core equipment (i.e., the router) that connects the user to the computing power network terminal, and the target computing power node is the computing power device. For example, the computing power network management terminal can determine candidate computing network routing allocation path A, candidate computing network routing allocation path B, and candidate computing network routing allocation path C based on the target computing power node and the source node.

[0101] Then, the computing network management terminal determines the first optimal computing network routing path from at least one candidate computing network routing path based on the network performance indicators of each node in each candidate computing network routing path. The network performance indicators of each node include node latency, node bandwidth, node utilization, node packet loss rate, node hop count, node overhead, and whether the node is a necessary node.

[0102] Continuing the previous example, the computing power network management terminal determines candidate computing network routing allocation paths A, B, and C based on the target computing power node and the source node. Candidate computing network routing allocation path A includes nodes 1, 2, and 3; candidate computing network routing allocation path B includes nodes 4, 5, and 6; and candidate computing network routing allocation path C includes nodes 7, 8, and 9. The computing power network management terminal determines the scores of candidate computing network routing allocation paths A, B, and C according to the network performance indicators of each node in candidate computing network routing allocation paths A, B, and C, and determines the two candidate computing network routing allocation paths with the highest scores as the first optimal computing network routing allocation paths.

[0103] Subsequently, the computing network management terminal determines the second optimal computing network routing path based on the network energy consumption indicators of each node in each first optimal computing network routing path. The network energy consumption indicators of each node include node energy consumption, node local temperature, node area cooling capacity, node power distribution capacity, etc.

[0104] Continuing the previous example, the computing network management terminal determines the first optimal computing network routing allocation path A and the first optimal computing network routing allocation path B. Based on the network energy consumption indicators of each node in the first optimal computing network routing allocation paths A and B, the computing network management terminal determines the scores of the first optimal computing network routing allocation paths A and B respectively, and determines the first optimal computing network routing allocation path with the highest score as the second optimal computing network routing allocation path. Finally, the computing network management terminal verifies the second optimal computing network routing allocation path from multiple performance verification indicators, obtains the performance verification results of the second optimal computing network routing allocation path, and determines the target computing network routing allocation path based on whether the verification results meet the preset energy consumption requirements.

[0105] As described above, the technical solution of this disclosure determines a set of candidate computing nodes that meet preset energy consumption requirements based on the device parameters of each device in the computing power network. Then, it selects target computing nodes that meet business requirement parameters from the candidate computing node set. The target computing nodes selected in this way can meet the preset energy consumption requirements while meeting the business requirement parameters, giving the computing power network a diversified green node selection capability, thereby enabling the selection of more reasonable computing nodes. At the same time, the technical solution of this disclosure can determine the target computing network routing allocation path based on the selected target computing nodes. When the business request runs on the target computing network routing allocation path, it can not only ensure the network performance of the business request, but also reduce the network energy consumption.

[0106] In one embodiment, such as Figure 4As shown, step 104, which determines the first optimal computing network routing allocation path among at least one candidate computing network routing allocation path based on the network performance indicators of each node in each candidate computing network routing allocation path, further includes the following steps:

[0107] Step 401: Determine the first score of the candidate computing network routing path based on the network performance indicators and first weight coefficient of each node in each candidate computing network routing allocation path.

[0108] Here, after determining at least one candidate computing network routing allocation path based on the target computing power node and the source node, the computing power network management terminal can determine the first score of the candidate computing network routing allocation path according to the network performance indicators and the first weight coefficient of each node in each candidate computing network routing allocation path.

[0109] In one possible embodiment, after the computing network management terminal determines at least one candidate computing network routing allocation path, it determines the first score of the candidate computing network routing allocation path based on the network performance indicators and a first weight coefficient of each node in each candidate computing network routing allocation path. For example, the candidate computing network routing allocation paths determined by the computing network management terminal are candidate computing network routing allocation paths A, B, and C. The first weight coefficient λ of candidate computing network routing allocation path A is 0.6. Candidate computing network routing allocation path A includes nodes 1, 2, and 3, whose network performance indicator scores are 30, 40, and 55 respectively. Therefore, the first score of candidate computing network routing allocation path A is (30+40+55)*0.6 = 75. The first scores of candidate computing network routing allocation paths B and C can be obtained using the same method, and will not be elaborated further here. It should be noted that the first weight coefficient λ reflects the weight of the network performance indicators in the candidate computing network routing allocation path and can be flexibly set according to requirements; no restrictions are imposed here.

[0110] Step 402: Determine the first preset number of candidate computing network routing allocation paths with the highest first scores as the first optimal computing network routing allocation paths.

[0111] Here, after the computing network management terminal determines the first score of the candidate computing network routing allocation path, it can determine the first preset number of candidate computing network routing allocation paths with the highest first score as the first optimal computing network routing allocation path.

[0112] In one possible embodiment, after determining the first score of the candidate network routing allocation paths, the computing power network management terminal determines the candidate network routing allocation paths with the highest first scores as the first optimal network routing allocation paths. For example, if the computing power network management terminal determines the first scores of candidate network routing allocation paths A, B, and C to be 75, 90, and 80 respectively, and the first preset number is 2, then the two candidate network routing allocation paths with the highest first scores, B and C, are determined as the first optimal network routing allocation paths. It should be noted that the first preset number can be flexibly set according to requirements and is not limited here.

[0113] In this embodiment, firstly, the computing network management terminal determines the first score of each candidate computing network routing path based on the network performance indicators and the first weight coefficient of each node in each candidate computing network routing path; then, the first preset number of candidate computing network routing paths with the highest first scores are determined as the first optimal computing network routing paths.

[0114] As described above, the computing network management terminal determines the first score of each candidate computing network routing allocation path based on network performance indicators and the first weight coefficient, and determines the first optimal computing network routing allocation path as the first preset number of candidate computing network routing allocation paths with the highest first scores. The determined first optimal computing network routing allocation path achieves optimal selection in terms of network performance, thereby improving the reliability of the computing network management method.

[0115] In one embodiment, such as Figure 5 As shown, step 104, which determines the second optimal computing network routing allocation path based on the network energy consumption indicators of each node in each first optimal computing network routing allocation path, also includes the following steps:

[0116] Step 501: Determine the second score of the first optimal computing network routing path based on the network energy consumption index and the second weight coefficient of each node in each first optimal computing network routing allocation path.

[0117] Here, the computing power network management terminal determines the first optimal computing network routing path in at least one candidate computing network routing path based on the network performance indicators of each node in each candidate computing network routing path. Then, it determines the second score of the first optimal computing network routing path based on the network energy consumption indicators and the second weight coefficient of each node in each first optimal computing network routing path.

[0118] In one possible embodiment, after the computing network management terminal determines the first optimal computing network routing allocation path, it determines the second score of the first optimal computing network routing allocation path based on the network energy consumption index and the second weight coefficient of each node in each first optimal computing network routing allocation path.

[0119] For example, the first optimal computing network routing allocation path determined by the computing network management terminal is the first optimal computing network routing allocation path A and B. The second weight coefficient β of the first optimal computing network routing allocation path A is 0.5. The first optimal computing network routing allocation path A includes node 1, node 2, and node 3. The network energy consumption index scores of these three nodes are 60, 70, and 70, respectively. Then, the second score of the first optimal computing network routing allocation path A is (60+70+70)*0.5=100. The second score of the first optimal computing network routing allocation path B can be obtained in the same way, which will not be elaborated here.

[0120] Step 502: Determine the first optimal computing network routing allocation path with the highest second score from the second preset number of paths as the second optimal computing network routing allocation path.

[0121] Here, after determining the second score of the first optimal computing network routing allocation path, the computing network management terminal can determine the first optimal computing network routing allocation path with the highest second score of the second preset number of paths as the second optimal computing network routing allocation path.

[0122] In one possible embodiment, after determining the second score of the first optimal computing network routing allocation path, the computing network management terminal determines the first optimal computing network routing allocation path with the highest second score among a second preset number of such paths as the second optimal computing network routing allocation path.

[0123] For example, if the computing network management terminal determines that the second scores of the first optimal computing network routing allocation paths A and B are 100 and 90 respectively, and the second preset quantity is 1, then the first optimal computing network routing allocation path A with the highest second score is determined as the second optimal computing network routing allocation path. It should be noted that the second preset quantity can be flexibly set according to needs and is not limited here.

[0124] In this embodiment, firstly, the computing network management terminal determines the second score of the first optimal computing network routing allocation path based on the network energy consumption index and the second weight coefficient of each node in each first optimal computing network routing allocation path; then, the computing network management terminal determines the first optimal computing network routing allocation path with the highest second score among the second preset number of such paths as the second optimal computing network routing allocation path.

[0125] As described above, the computing network management terminal determines the second score of the first optimal computing network routing allocation path based on the network energy consumption index and the second weight coefficient, and determines the first optimal computing network routing allocation path with the highest second score among the second preset number of such paths as the second optimal computing network routing allocation path. The determined second optimal computing network routing allocation path achieves optimal selection in terms of network performance and network energy consumption. The two optimal calculations further improve the reliability of the computing network management method.

[0126] In one embodiment, such as Figure 6 As shown, step 104, which determines the target computing network routing allocation path based on the second optimal computing network routing allocation path, also includes the following steps:

[0127] Step 601: Verify the second optimal computing network routing allocation path using multiple performance verification metrics and obtain the performance verification results of the second optimal computing network routing allocation path.

[0128] Here, after the computing power network management terminal determines the first optimal computing network routing allocation path with the highest second score of the second preset number of paths as the second optimal computing network routing allocation path, it can verify the second optimal computing network routing allocation path from multiple performance verification indicators and obtain the performance verification result of the second optimal computing network routing allocation path.

[0129] In one possible embodiment, such as Figure 7 As shown, Figure 7 An exemplary schematic diagram of another green computing power network management logic is shown. After the computing power network management terminal determines the second optimal computing network routing allocation path, it can call the digital twin computing model and input the second optimal computing network routing allocation path into the digital twin pre-verification model. The second optimal computing network routing allocation path is verified through digital twin pre-verification technology to obtain the performance verification results of the second optimal computing network routing allocation path. The performance verification indicators include computing power performance verification, business performance verification, and green energy saving verification.

[0130] Specifically, firstly, the digital twin computing model needs to be input before it can be invoked: service scale parameters such as computing power configuration requirements and business traffic size, occupancy data of computing and routing devices, network-side indicator thresholds (e.g., latency, bandwidth, packet loss, utilization, overhead), and green energy-saving indicator thresholds (e.g., energy consumption, local temperature) are input into the digital twin computing model. Then, based on the input parameter data, the digital twin computing model provides network simulation computing capabilities to verify the second optimal computing network routing allocation path. This verification specifically includes computing power performance verification, service performance verification, and green energy-saving verification. Among these, computing power performance verification mainly determines the connectivity of computing network service routing. The generalization of computing power network service routing is to determine whether it meets the basic requirements for data request and forwarding capabilities. Service performance verification mainly determines whether the service performance indicators are met, that is, whether the network performance meets the user's service needs. Green energy saving verification mainly determines whether the performance of the computing power server during operation meets the service needs, whether the energy consumption and temperature data of newly added services do not exceed the threshold, whether the energy consumption and temperature data after service scheduling do not exceed the pre-scheduling levels, and whether it does not affect the services in the original path after scheduling. Finally, the computing power network management terminal obtains the computing power performance verification results, service performance verification results, and green energy saving verification results of the second optimal computing network routing allocation path.

[0131] Step 602: If the performance verification results meet the preset index requirements, determine the target computing network routing allocation path based on the second optimal computing network routing allocation path, and run the service request on the target computing network routing allocation path in the computing power network.

[0132] Here, after obtaining the performance verification result of the second optimal computing network routing allocation path, if the performance verification result meets the preset indicator requirements, the computing network management terminal can determine the target computing network routing allocation path based on the second optimal computing network routing allocation path, and run the service request on the target computing network routing allocation path in the computing network.

[0133] In one possible embodiment, such as Figure 7 As shown, after obtaining the performance verification result of the second optimal computing network routing allocation path, the computing network management terminal judges the performance verification result. If the performance verification result meets the preset index requirements, the target computing network routing allocation path is determined based on the second optimal computing network routing allocation path.

[0134] For example, the computing power network management terminal determines the second optimal computing network routing allocation paths A and B. The verification results of the second optimal computing network routing allocation path A obtained from the digital twin pre-verification model are: computing power performance verification passed, service performance verification passed, and green energy saving verification failed. The verification results of the second optimal computing network routing allocation path B obtained from the digital twin pre-verification model are: computing power performance verification passed, service performance verification passed, and green energy saving verification passed. The preset indicator requirement is that the computing power performance verification result, service performance verification result, and green energy saving verification result all pass. Therefore, the second optimal computing network routing allocation path A does not meet the preset indicator requirement, while the second optimal computing network routing allocation path B meets the preset indicator requirement. The computing power network management terminal then determines the second optimal computing network routing allocation path B as the target computing network routing allocation path and runs the service request on the target computing network routing allocation path in the computing power network.

[0135] In one optional embodiment, during the execution of service requests on the target computing network routing path, the computing network management terminal performs overall monitoring of the computing network, i.e., detecting new service requests targeting the computing network. Specifically, the overall monitoring of the computing network by the computing network management terminal includes monitoring the operation of the underlying infrastructure and equipment, monitoring the operational quality of service requests on the target computing network routing path, and monitoring changes to the computing network. Monitoring indicators include network performance indicators, power consumption, energy saving indicators, and change requests. When a node on the target computing network routing path experiences network, energy consumption, or temperature deterioration, the computing network management terminal receives the corresponding alarm and switches all service requests for the corresponding node to a backup node or path to ensure stable operation of the service requests. When a new service is detected on the computing network, the target computing network routing path is calculated. The specific steps for calculating the target computing network routing path can be found in the above embodiment applied to the computing network management terminal, and will not be repeated here.

[0136] Step 603: If the verification result does not meet the preset index requirements, return to the step of determining the set of candidate computing nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network.

[0137] Here, after obtaining the performance verification results of the second optimal computing network routing allocation path, if the verification results do not meet the preset index requirements, the computing network management terminal can return to the step of determining the set of candidate computing nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing network.

[0138] In one possible embodiment, such as Figure 7As shown, after obtaining the performance verification result of the second optimal computing network routing allocation path, if the verification result does not meet the preset index requirements, the computing network management terminal marks the second optimal computing network routing allocation path as a blacklist and re-executes step 102, that is, re-determines the set of candidate computing nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing network. After determining the set of candidate computing nodes, firstly, the second optimal computing network routing allocation path is re-determined, and the re-determined second optimal computing network routing allocation path is input into the digital twin computing model. Then, the performance verification result of the second optimal computing network routing allocation path is obtained. Finally, the target computing network routing allocation path is determined based on the performance verification result. It should be noted that the specific steps for determining the target computing network routing allocation path based on the performance verification result can be found in the embodiments of steps 602 and 603, and will not be repeated here.

[0139] In this embodiment, firstly, the computing power network management terminal verifies the second optimal computing network routing allocation path from multiple performance verification indicators to obtain the performance verification result of the second optimal computing network routing allocation path; then, if the performance verification result meets the preset indicator requirements, the computing power network management terminal determines the target computing network routing allocation path based on the second optimal computing network routing allocation path and runs the service request on the target computing network routing allocation path in the computing power network; if the verification result does not meet the preset indicator requirements, the process returns to the step of determining the set of candidate computing power nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network.

[0140] As described above, the computing network management terminal obtains performance verification results by verifying the second optimal computing network routing allocation path. The target computing network routing allocation path determined based on the performance verification results meets preset indicator requirements, further improving the reliability of the computing network management method. Furthermore, if the performance verification fails, the computing network management terminal recalculates the candidate computing node set until a target computing network routing allocation path that meets the preset indicator requirements is determined based on the candidate computing node set, enhancing the flexibility of the computing network management method.

[0141] In one embodiment, such as Figure 8 As shown, a green computing power network management method is also provided, including the following steps:

[0142] Step 801: If a second optimal computing network route allocation path that meets the preset index requirements is not determined multiple times in a row, an alarm message is sent to the user indicating that the computing network cannot meet the service request.

[0143] In one possible embodiment, after returning to the step of determining the set of candidate computing nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing network, if the computing network management terminal fails to determine the second optimal computing network routing allocation path that meets the preset index requirements multiple times in a row, the computing network management terminal sends an alarm message to the user that the computing network cannot meet the service request.

[0144] For example, a parameter T is set, where T represents the number of times the computing network management terminal fails to determine the second optimal computing network routing allocation path that meets the preset index requirements. When T > 3, the computing network management terminal sends an alarm message to the user indicating that the computing network cannot meet the service request. It should be noted that the value of parameter T can be set according to the actual situation and is not restricted here.

[0145] In this embodiment, if the computing network management terminal fails to determine a second optimal computing network routing allocation path that meets the preset index requirements multiple times, it sends an alarm message to the user indicating that the computing network cannot meet the service request. While ensuring the rigor of the computing network management method, it notifies the user that the current computing network cannot meet the service request in an interactive manner, thereby improving the flexibility of the computing network management method.

[0146] As described above, this disclosed technical solution upgrades traditional computing networks to "green" computing networks through a novel computing network management method, endowing existing computing resources and network resources with green and energy-saving attributes. This disclosed technical solution can automatically allocate target computing nodes, thereby determining the target computing network routing path. Furthermore, the process of determining the target computing network routing path involves two calculations of definable weight coefficients, ensuring that the determined target computing network routing path achieves optimal results. This disclosed technical solution defines a novel green computing network service end-to-end routing fusion model, and defines all information fields in the model, which can be directly applied to the underlying infrastructure of the computing network. This disclosed technical solution can achieve daily operation monitoring of the computing network. When service additions, changes, or quality issues occur, it can automatically determine the target computing network routing path, and verify it based on digital twin pre-verification technology. Monitoring continues after the computing network data is updated, ensuring the reliability of service request operation.

[0147] By dividing each function into corresponding functional modules, this disclosure provides a green computing power network management device, which can be a server or a chip applied to a server. Figure 9 A schematic block diagram of the functional modules of a green computing power network management device provided for an exemplary embodiment of this disclosure. (See diagram below.) Figure 9 As shown, the green computing network management device includes:

[0148] The parsing module 901 is used to parse the service request after detecting a service request for the computing power network, obtain the service requirements, and determine the service requirement parameters based on the service requirements.

[0149] The computing module 902 is used to determine a set of candidate computing nodes that meet preset energy consumption requirements based on the device parameters of each device in the computing power network;

[0150] The filtering module 903 is used to filter target computing power nodes that meet the business requirement parameters from the set of candidate computing power nodes;

[0151] The determination module 904 is used to determine the target computing network routing allocation path based on the selected target computing power nodes; wherein the target computing network routing allocation path is used to indicate the routing path between the source node and the target computing power node.

[0152] In one embodiment, the parsing module 901 includes:

[0153] The data acquisition unit is used to collect the operating data of each device in the computing power network and the service data it carries;

[0154] The first determining unit is used to determine the routing fusion model of the computing power network based on the operating data and the business data; wherein the routing fusion model is used to indicate the routing path between the source node and each computing power device in the computing power network;

[0155] The second determining unit is used to match the business requirements with the routing fusion model to obtain the business requirement parameters.

[0156] In one embodiment, the arithmetic module 902 includes:

[0157] The third determining unit is used to process the device parameters of each device in the computing power network through a neural recurrent network to obtain a set of candidate computing power nodes that meet the preset energy consumption requirements.

[0158] In one embodiment, the filtering module 903 includes:

[0159] The minimum granularity data identification unit is used to perform minimum granularity data identification processing on the business requirement parameters to obtain the minimum requirement parameters that satisfy the business request.

[0160] The fourth determining unit is used to determine, from the set of candidate computing power nodes, a candidate computing power node that meets the minimum requirement parameters, and to obtain the target computing power node.

[0161] In one embodiment, determining module 904 includes:

[0162] The fifth determining unit is used to determine at least one candidate computing network routing allocation path based on the target computing power node and the source node;

[0163] The sixth determining unit is used to determine the first optimal computing network routing allocation path in the at least one candidate computing network routing allocation path based on the network performance indicators of each node in each candidate computing network routing allocation path.

[0164] The seventh determining unit is used to determine the second optimal computing network routing allocation path in the first optimal computing network routing allocation path based on the network energy consumption index of each node in each of the first optimal computing network routing allocation paths.

[0165] The eighth determining unit is used to determine the target computing network routing allocation path based on the second optimal computing network routing allocation path.

[0166] In one embodiment, determining module 904 includes:

[0167] The first scoring unit is used to determine the first score of the candidate computing network routing allocation path based on the network performance indicators and the first weight coefficient of each node in each candidate computing network routing allocation path.

[0168] The first optimal computing network routing allocation path determination unit is used to determine the first preset number of candidate computing network routing allocation paths with the highest first scores as the first optimal computing network routing allocation path.

[0169] In one embodiment, determining module 904 includes:

[0170] The second scoring unit is used to determine the second score of the first optimal computing network routing allocation path based on the network energy consumption index and the second weight coefficient of each node in each of the first optimal computing network routing allocation paths.

[0171] The second optimal computing network routing allocation path determination unit is used to determine the first optimal computing network routing allocation path with the highest second score from the second preset number of first optimal computing network routing allocation paths as the second optimal computing network routing allocation path.

[0172] In one embodiment, determining module 904 includes:

[0173] The verification unit is used to verify the second optimal computing network routing allocation path from multiple performance verification indicators and obtain the performance verification result of the second optimal computing network routing allocation path.

[0174] The running unit is configured to determine the target computing network routing allocation path based on the second optimal computing network routing allocation path, and run the service request on the target computing network routing allocation path in the computing power network, provided that the performance verification result meets the preset indicator requirements.

[0175] The return unit is used to return to the step of determining a set of candidate computing nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network if the verification result does not meet the preset index requirements.

[0176] In one embodiment, the apparatus further includes:

[0177] The alarm unit is used to send an alarm message to the user that the computing power network cannot meet the service request if a second optimal computing network routing allocation path that meets the preset index requirements is not determined multiple times in a row.

[0178] This disclosure also provides an electronic device, including: at least one processor; a memory for storing processor-executable instructions; wherein the at least one processor is configured to execute the instructions to implement the methods disclosed in this disclosure.

[0179] Figure 10 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of this disclosure. For example... Figure 10 As shown, the electronic device 1000 includes at least one processor 1001 and a memory 1002 coupled to the processor 1001. The processor 1001 can perform the corresponding steps in the methods disclosed in the embodiments of this disclosure.

[0180] The processor 1001 described above can also be called a central processing unit (CPU), which can be an integrated circuit chip with signal processing capabilities. Each step in the method disclosed in this embodiment can be implemented by the integrated logic circuitry in the processor 1001 or by software instructions. The processor 1001 can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this embodiment can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can be located in the memory 1002, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor 1001 reads information from the memory 1002 and, in conjunction with its hardware, completes the steps of the method described above.

[0181] Furthermore, various operations / processes according to this disclosure, implemented via software and / or firmware, can be transmitted from a storage medium or network to a computer system with a dedicated hardware architecture, such as... Figure 11 The computer system 1100 shown is equipped with the programs that constitute the software. When various programs are installed, the computer system is able to perform various functions, including functions such as those described above. Figure 11 A block diagram of a computer system provided for an exemplary embodiment of this disclosure.

[0182] Computer system 1100 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0183] like Figure 11As shown, the computer system 1100 includes a computing unit 1101, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 1102 or a computer program loaded from a storage unit 1108 into a random access memory (RAM) 1103. The RAM 1103 may also store various programs and data required for the operation of the computer system 1100. The computing unit 1101, ROM 1102, and RAM 1103 are interconnected via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0184] Multiple components in computer system 1100 are connected to I / O interface 1105, including: input unit 1106, output unit 1107, storage unit 1108, and communication unit 1109. Input unit 1106 can be any type of device capable of inputting information into computer system 1100. Input unit 1106 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 1107 can be any type of device capable of presenting information and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 1108 may include, but is not limited to, hard disks and optical disks. Communication unit 1109 allows computer system 1100 to exchange information / data with other devices via a network such as the Internet, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0185] The computing unit 1101 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1101 performs the various methods and processes described above. For example, in some embodiments, the methods disclosed in this disclosure can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1108. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1000 via ROM 1102 and / or communication unit 1109. In some embodiments, the computing unit 1101 can be configured to perform the methods disclosed in this disclosure by any other suitable means (e.g., by means of firmware).

[0186] This disclosure also provides a computer-readable storage medium, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is able to perform the methods disclosed in this disclosure.

[0187] The computer-readable storage medium in this disclosure can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The aforementioned computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specifically, the aforementioned computer-readable storage medium may include electrical connections based on one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0188] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0189] Figure 12 A computer program product 1200 is provided as an exemplary embodiment of the present disclosure. The computer program product 1200 includes a computer program 1201, wherein the computer program 1201, when executed by a processor, implements the methods disclosed in the embodiments of the present disclosure.

[0190] In embodiments of this disclosure, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or it can be connected to an external computer.

[0191] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0192] The modules, components, or units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the modules, components, or units do not necessarily constitute a limitation on the module, component, or unit itself.

[0193] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that can be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0194] The above description is merely an embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0195] While specific embodiments of this disclosure have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A green computing power network management method, characterized in that, include: After detecting a service request for the computing power network, the service request is parsed to obtain the service requirements, and the service requirements parameters are determined based on the service requirements. Based on the device parameters of each device in the computing power network, a set of candidate computing power nodes that meet the preset energy consumption requirements is determined; Select target computing power nodes that meet the business requirement parameters from the set of candidate computing power nodes; The target computing network routing allocation path is determined based on the selected target computing power nodes; wherein, the target computing network routing allocation path is used to indicate the routing path between the source node and the target computing power node; The step of determining the target computing network routing allocation path based on the selected target computing power nodes includes: Based on the target computing power node and the source node, at least one candidate computing network routing allocation path is determined; Based on the network performance indicators of each node in each of the candidate computing network routing allocation paths, a first optimal computing network routing allocation path is determined among the at least one candidate computing network routing allocation path. Based on the network energy consumption index of each node in each of the first optimal computing network routing allocation paths, a second optimal computing network routing allocation path is determined in the first optimal computing network routing allocation path. The target computing network routing allocation path is determined based on the second optimal computing network routing allocation path.

2. The method according to claim 1, characterized in that, The process of determining the business requirement parameters based on the business requirements includes: Collect operational data and service data carried by each device in the computing network; Based on the operational data and the business data, a routing fusion model for the computing power network is determined; wherein, the routing fusion model is used to indicate the routing path between the source node and each computing power device in the computing power network; The business requirements are matched with the routing fusion model to obtain the business requirement parameters.

3. The method according to claim 1, characterized in that, The process of determining the set of candidate computing nodes that meet preset energy consumption requirements based on the device parameters of each device in the computing power network includes: The device parameters of each device in the computing power network are processed by a neural recurrent network to obtain a set of candidate computing power nodes that meet the preset energy consumption requirements.

4. The method according to claim 1, characterized in that, The step of selecting target computing nodes that meet the business requirement parameters from the set of candidate computing nodes includes: The business requirement parameters are processed with the smallest granularity data identification to obtain the minimum requirement parameters that satisfy the business request; From the set of candidate computing power nodes, candidate computing power nodes that meet the minimum requirement parameters are determined to obtain the target computing power node.

5. The method according to claim 1, characterized in that, The step of determining the first optimal computing network routing allocation path from the at least one candidate computing network routing allocation path based on the network performance indicators of each node in each candidate computing network routing allocation path includes: The first score of each candidate computing network routing path is determined based on the network performance index and first weight coefficient of each node in each candidate computing network routing path. The first preset number of candidate computing network routing allocation paths with the highest first scores are determined as the first optimal computing network routing allocation path.

6. The method according to claim 5, characterized in that, The step of determining the second optimal computing network routing allocation path based on the network energy consumption indicators of each node in each of the first optimal computing network routing allocation paths includes: The second score of the first optimal computing network routing allocation path is determined based on the network energy consumption index and the second weight coefficient of each node in each first optimal computing network routing allocation path. The first optimal computing network routing allocation path with the highest second score is determined as the second optimal computing network routing allocation path.

7. The method according to claim 1, characterized in that, The step of determining the target computing network routing allocation path based on the second optimal computing network routing allocation path includes: The second optimal computing network routing allocation path is verified using multiple performance verification metrics, and the performance verification results of the second optimal computing network routing allocation path are obtained. If the performance verification result meets the preset index requirements, the target computing network routing allocation path is determined based on the second optimal computing network routing allocation path, and the service request is run on the target computing network routing allocation path in the computing power network. If the verification result does not meet the preset index requirements, return to the step of determining the set of candidate computing nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network.

8. The method according to claim 7, characterized in that, The method further includes: If a second optimal computing network routing allocation path that meets the preset index requirements is not determined multiple times in a row, an alarm message is sent to the user that the computing power network cannot meet the service request.

9. A green computing power network management device, characterized in that, include: The parsing module is used to parse the service request after detecting a service request for the computing power network, obtain the service requirements, and determine the service requirement parameters based on the service requirements. The computing module is used to determine a set of candidate computing nodes that meet preset energy consumption requirements based on the device parameters of each device in the computing power network. The filtering module is used to filter target computing power nodes that meet the business requirement parameters from the set of candidate computing power nodes; The determination module is used to determine the target computing network routing allocation path based on the selected target computing power nodes; wherein, the target computing network routing allocation path is used to indicate the routing path between the source node and the target computing power node; The determining module is further configured to: Based on the target computing power node and the source node, at least one candidate computing network routing allocation path is determined; Based on the network performance indicators of each node in each of the candidate computing network routing allocation paths, a first optimal computing network routing allocation path is determined among the at least one candidate computing network routing allocation path. Based on the network energy consumption index of each node in each of the first optimal computing network routing allocation paths, a second optimal computing network routing allocation path is determined in the first optimal computing network routing allocation path. The target computing network routing allocation path is determined based on the second optimal computing network routing allocation path.

10. An electronic device, characterized in that, include: At least one processor; Memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as described in any one of claims 1-8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-8.

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