Resource scheduling method and device, equipment and storage medium

Abstract

The embodiment of the invention relates to the technical field of network communication, in particular to a resource scheduling method, device and equipment and a storage medium, and aims at quickly sensing the network environment of a metropolitan area network and scheduling network resources. The method comprises the steps that a controller receives cloud network resource data of all network nodes in a target area; the controller sends the analyzed cloud network resource data to a computing network scheduling platform; the computing network scheduling platform makes a decision on the cloud network resource data according to a pre-established expert database to generate a corresponding resource allocation strategy, and the expert database records equipment parameters of network equipment corresponding to each network node; the computing network scheduling platform sends the resource allocation strategy to the controller; the controller sends a configuration template corresponding to the resource allocation strategy to the network node; and the network node determines a corresponding network path according to the configuration template.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of network communication technology, and specifically, to a resource scheduling method, apparatus, device, and storage medium. Background Art

[0002] When metropolitan area network resources are insufficient or fail, network paths need to be switched. In existing technologies, network maintenance personnel generally query the cloud network resources in the target area, and then organize network engineers to discuss the optimal network path to orchestrate and schedule the network.

[0003] The existing technology requires manual arrangement of network paths and scheduling of network resources, which takes a long time and is inconvenient to operate. Summary of the Invention

[0004] The embodiments of the present application provide a resource scheduling method, apparatus, device and storage medium, which are intended to achieve rapid perception of the network environment of a metropolitan area network and schedule network resources.

[0005] A first aspect of an embodiment of the present application provides a resource scheduling method, the method comprising: The controller receives cloud network resource data of all network nodes in the target area; The controller sends the parsed cloud network resource data to the computing network scheduling platform; The computing network scheduling platform makes decisions on the cloud network resource data based on a pre-established expert database to generate a corresponding resource allocation strategy. The expert database records the device parameters of the network device corresponding to each of the network nodes. The computing network scheduling platform sends the resource allocation strategy to the controller; The controller sends the configuration template corresponding to the resource allocation strategy to the network node; The network node determines a corresponding network path according to the configuration template.

[0006] Optionally, before the controller receives the cloud network resource data of all network nodes in the target area, the method further includes: Collecting device parameters of all the network nodes in the target area; The equipment parameters are statistically sorted and the expert database is established.

[0007] Optionally, the method further includes: The network node feeds back response information to the controller; The controller sends the parsed response information to the computing network scheduling platform.

[0008] Optionally, the method further includes: When the network path is effective, the computing network scheduling platform determines all network nodes on the old network path; The network resources of all network nodes on the old network path are reclaimed.

[0009] Optionally, the computing network scheduling platform makes decisions on the cloud network resource data based on a pre-established expert database and generates corresponding resource allocation strategies, including: The computing network scheduling platform determines resource-deficient nodes and faulty nodes in the target area based on the cloud network resource data; According to the device information of each network node in the expert database, a corresponding data transmission path is determined for nodes in the target area except the faulty node, to obtain the resource allocation strategy.

[0010] Optionally, the method further includes: The computing network scheduling platform generates a corresponding network resource distribution topology map based on the cloud network resource data; Each node in the network resource distribution topology diagram is monitored in real time.

[0011] Optionally, the controller sending the configuration template corresponding to the resource allocation policy to the network node includes: The controller encapsulates the resource allocation policy to obtain the configuration template; The controller sends the configuration template to the network node according to the network address corresponding to each node.

[0012] A second aspect of an embodiment of the present application provides a resource scheduling device, the device comprising: The cloud network resource data receiving module is used for the controller to receive the cloud network resource data of all network nodes in the target area; A cloud network resource data sending module is used for the controller to send the parsed cloud network resource data to the computing network scheduling platform; A decision module, configured for the computing network scheduling platform to make decisions on the cloud network resource data based on a pre-established expert database and generate corresponding resource allocation strategies, wherein the expert database records the device parameters of the network device corresponding to each of the network nodes; A policy sending module, configured for the computing network scheduling platform to send the resource allocation policy to the controller; a configuration template sending module, configured for the controller to send the configuration template corresponding to the resource allocation policy to the network node; The network path determination module is used for the network node to determine the corresponding network path according to the configuration template.

[0013] Optionally, the device further comprises: A device parameter collection module, configured to collect device parameters of all the network nodes in the target area; The expert database establishment module is used to statistically organize the equipment parameters and establish the expert database.

[0014] Optionally, the device further comprises: A feedback response information module, configured for the network node to feed back response information to the controller; The response information forwarding module is used for the controller to send the parsed response information to the computing network scheduling platform.

[0015] Optionally, the device further comprises: An old node determination module, configured to, when the network path is effective, enable the computing network scheduling platform to determine all network nodes on the old network path; The resource recovery module is used to recover the network resources of all network nodes on the old network path.

[0016] Optionally, the decision module includes: A fault node determination submodule, configured for the computing network scheduling platform to determine resource-deficient nodes and faulty nodes in the target area based on the cloud network resource data; The resource allocation strategy determination submodule is used to determine the corresponding data transmission path for the nodes in the target area except the faulty node according to the device information of each network node in the expert database, and obtain the resource allocation strategy.

[0017] Optionally, the device further comprises: A resource distribution topology map generation module is used for the computing network scheduling platform to generate a corresponding network resource distribution topology map based on the cloud network resource data; The real-time monitoring module is used to perform real-time monitoring on each node in the network resource distribution topology diagram.

[0018] Optionally, the configuration template issuing module includes: A policy encapsulation submodule, configured for the controller to encapsulate the resource allocation policy to obtain the configuration template; The configuration template sending submodule is used for the controller to send the configuration template to the network node according to the network address corresponding to each node.

[0019] A third aspect of an embodiment of the present application provides a readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method described in the first aspect of the present application is implemented.

[0020] A fourth aspect of an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method described in the first aspect of the present application are implemented.

[0021] Using the resource scheduling method provided in the present application, the controller receives the cloud network resource data of all network nodes in the target area; the controller sends the parsed cloud network resource data to the computing network scheduling platform; the computing network scheduling platform makes decisions on the cloud network resource data based on a pre-established expert database and generates corresponding resource allocation strategies, and the expert database records the device parameters of the network devices corresponding to each of the network nodes; the computing network scheduling platform sends the resource allocation strategy to the controller; the controller sends the configuration template corresponding to the resource allocation strategy to the network node; the network node determines the corresponding network path based on the configuration template.

[0022] In this application, the cloud network resource data of the target area is received through the computing network scheduling platform, and decisions are made based on the cloud network resource data and a pre-established expert database to obtain the corresponding resource allocation strategy. The network resource allocation strategy is then sent to the corresponding network node through the controller, and the optimal network transmission path is found for each node, thereby realizing intelligent orchestration and scheduling of network resources and saving the time of network resource scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] Figure 1 This is a flow chart of a resource scheduling method proposed in one embodiment of the present application; Figure 2 This is a flowchart of intelligent scheduling of network resources in a metropolitan area network proposed in one embodiment of the present application; Figure 3 This is a schematic diagram of the resource analysis process proposed in one embodiment of the present application; Figure 4 This is a schematic diagram of the network resource intelligent scheduling process proposed in an embodiment of the present application Figure 5 is a schematic diagram of a resource scheduling device proposed in one embodiment of the present application; Figure 6 FIG. 1 is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] refer to Figure 1 , Figure 1 This is a flow chart of a resource scheduling method proposed in one embodiment of the present application. Figure 1 As shown, the specific steps include: S11: The controller receives cloud network resource data of all network nodes in the target area.

[0027] In this embodiment, the controller is the device that controls all network nodes in the target area. Network nodes are devices used for network data transmission in the target area, namely, new metropolitan area network (MAN) devices. Cloud-network resource data includes cloud and network resource data within network nodes. Through unified pooling, protocol innovations (such as SRv6), and an intelligent platform, cloud and network resource data from MAN devices enables agile service delivery, elastic resource expansion, and ultimate performance assurance. Its features include high-speed transmission, secure isolation, and intelligent operations and maintenance, making it the core infrastructure supporting 5G, cloud computing, and smart city development. Network resources include device port traffic, VLAN tag distribution, SRv6 / SDN path status, and QoS (Quality of Service) policy execution. Cloud resources include virtual machine / container load, storage IOPS, and edge computing node computing utilization.

[0028] Metropolitan area network (MAN) equipment has undergone a series of upgrades, with newer equipment now prevalent. Legacy MAN equipment employed a hierarchical tree / ring architecture, relying on high-end routers and core switches to carry multiple services (such as IP MAN and wireless bearer networks). Network functionality was tightly coupled to hardware, limiting scalability (capacity expansion required hardware replacement). Based on MPLS VPNs and traditional TCP / IP protocols, service isolation relied on complex configurations. Static routing policies limited traffic scheduling flexibility (e.g., fixed diversion of production / office traffic). High-end routers (such as the Cisco ASR series) and core switches were predominantly used, with limited single-machine processing capabilities. Transmission media relied on twisted pair / coaxial cables, resulting in low bandwidth limits (typically ≤10 Gbps). Manual configuration and monitoring were required, resulting in long recovery times (manual switching was required if a link was down). The lack of a centralized controller meant service provisioning took days.

[0029] New metropolitan area network equipment supports a flat architecture (such as the spine-leaf structure), modular horizontal expansion, and on-demand device node addition. It incorporates the concept of cloud-network convergence and decouples network functions from hardware through virtualization (NFV). It utilizes the IPv6+ / SRv6 protocols, supports network slicing and flexible service orchestration (such as on-demand bandwidth allocation for government and enterprise dedicated lines). It integrates EVPN (Ethernet VPN) technology to simplify multi-service bearer logic. It utilizes high-performance SR devices (such as the H3C CR16000) and intelligent routers (such as the RX8800) and supports 100G / 400G interfaces. It deploys an AD-WAN controller for intelligent traffic scheduling and automated operations and maintenance (such as real-time dynamic route adjustments). It also supports AI-powered predictive maintenance, using big data analytics to provide early warning of equipment failures.

[0030] In summary, legacy metropolitan area network (MAN) equipment is hardware-centric, with shortcomings such as rigid architecture and inefficient operations and maintenance. New MAN equipment leverages technologies such as IPv6+, cloud-network convergence, and intelligent control to achieve elastic expansion, business agility, and extreme performance, becoming key infrastructure in the 5G and cloud computing era.

[0031] In this embodiment, the cloud network resource data of all network nodes in the target area is received through the controller. The controller is connected to all network nodes in the target area. Each network node includes metropolitan area network devices, which will regularly report cloud and network resource data.

[0032] For example, the target area is a city, and the network nodes are metropolitan area network devices. Each metropolitan area network device regularly reports cloud and network resource data to the controller through messages.

[0033] S12: The controller sends the parsed cloud network resource data to the computing network scheduling platform.

[0034] In this embodiment, the computing network scheduling platform is used to calculate the optimal data transmission path of each network node based on the cloud network resource data and allocate corresponding network resources to each network node.

[0035] In this embodiment, after receiving the cloud network resource data, the controller parses the reported cloud network resource data and sends the parsed cloud network resource data to the computing network scheduling platform.

[0036] In this embodiment, when parsing cloud network resource data reported by metropolitan area network devices, invalid data caused by device false alarms (such as sudden traffic surges exceeding the physical port bandwidth limit) or network jitter can be eliminated, and traffic fluctuations can be smoothed using a time window sliding average algorithm. Data such as device port traffic, VLAN tag distribution, SRv6 / SDN path status, QoS (Quality of Service) policy execution, virtual machine / container load rate, storage IOPS, and edge computing node computing power utilization can be obtained from the cloud network resource data.

[0037] For example, the controller sends processed data to the computing network scheduling platform via Kafka (a distributed stream processing platform whose data transmission is designed for both efficiency and reliability). In Kafka's synchronous data transmission method, producers must wait for server confirmation (ACK) after sending a message, ensuring reliable data transmission. This method is suitable for high-reliability scenarios such as financial transactions. In asynchronous data transmission, producers use a callback mechanism to send messages in a non-blocking manner, improving transmission efficiency and making it suitable for high-throughput scenarios such as log collection. In batch transmission, multiple messages are merged into batches to reduce network overhead and optimize the balance between throughput and latency using the linger.ms parameter. The controller sends data to the computing network scheduling platform using the corresponding method in Kafka, ensuring stable data transmission.

[0038] S13: The computing network scheduling platform makes decisions on the cloud network resource data based on a pre-established expert database to generate a corresponding resource allocation strategy. The expert database records the device parameters of the network device corresponding to each of the network nodes.

[0039] In this embodiment, the expert database records the device parameters of each network node's corresponding network device. The expert database for metropolitan area network equipment features "technical verticalization, dynamic management, and scenario-specific expertise," covering areas such as network architecture design, device operation and maintenance, and security and compliance. Experts must possess both a deep understanding of protocols (such as MPLS TE and IPv6) and practical experience.

[0040] In this embodiment, after receiving the cloud network resource data, the computing network scheduling platform analyzes the cloud network resource data to determine the nodes with insufficient network resources or faulty nodes, and then allocates corresponding network resources to each network node in normal operation through the equipment parameters of each network node recorded in the expert database, and determines the optimal data transmission path. If a faulty node occurs, it is necessary to delete the faulty node on the original path, and then decide to allocate a new optimal path for the remaining normal nodes.

[0041] In this embodiment, the computing network scheduling platform makes decisions based on the cloud network resource data according to the pre-established expert database, and generates corresponding resource allocation strategies in the following specific steps: S13-1: The computing network scheduling platform determines resource-deficient nodes and faulty nodes in the target area based on the cloud network resource data.

[0042] In this embodiment, after receiving the cloud network resource data, the computing network scheduling platform determines the working status and load of the node based on the cloud network resource data of each node. When the bandwidth, traffic and other network resources of a node are normal, and each interface receives and sends data normally, the node is determined to be a normal node. When the bandwidth allocated to a node is low and the transmission speed is slow, it is regarded as a resource-deficient node. When a node does not report information or some port information is abnormal, the node is regarded as a faulty node.

[0043] For example, the computing network scheduling platform receives the cloud network resource data of each node. If the bandwidth of node A is low, node A will be regarded as a node with insufficient resources. If the data transmission between node B and other nodes is abnormal, the speed of receiving and sending data is too slow or stops sending data, node B will be regarded as a faulty node.

[0044] S13-2: Determine corresponding data transmission paths for nodes in the target area except the faulty node based on the device information of each network node in the expert database, and obtain the resource allocation strategy.

[0045] In this embodiment, after determining the resource-deficient nodes and faulty nodes, the faulty nodes are eliminated and the remaining nodes are analyzed. The Suanwang scheduling platform determines the amount of traffic that each node can carry, the data transmission speed of each node, the interface information of each node, etc. based on the equipment information of each network node in the expert database, and then determines the optimal data transmission path for the node through comprehensive analysis, and then formulates a corresponding resource allocation strategy.

[0046] For example, the computing network scheduling platform needs to formulate a strategy for data transmission between node A and node B. There are nodes C and D between node A and node B. According to the data in the expert database, it is known that the supported load of node C is greater than that of node D, and according to the reported cloud network resource data, it is known that the load flow at node D is greater than that of node C. At this time, the transmission path from A to B is selected as A to C to B.

[0047] S14: The computing network scheduling platform sends the resource allocation strategy to the controller.

[0048] In this embodiment, after determining the optimal path for data transmission of each node, the computing network scheduling platform generates a configuration arrangement of the network resource allocation strategy and sends the resource allocation strategy to the controller.

[0049] S15: The controller sends the configuration template corresponding to the resource allocation strategy to the network node.

[0050] In this embodiment, the configuration template corresponding to the resource allocation policy is a configuration template obtained by encapsulating the resource allocation policy through a preset template.

[0051] In this embodiment, after receiving the network resource allocation policy sent by the computing network scheduling platform, the controller encapsulates the resource allocation policy into a template, and sends the encapsulated policy to the network device in the network node.

[0052] For example, the controller encapsulates resource allocation policies using NETCONF configuration templates (a standardized XML document designed based on the NETCONF protocol for automated configuration and management of network devices. Its core function is to encapsulate configuration operations and parameters through a predefined XML structure, and implement efficient and secure device management and control in combination with the protocol layering mechanism). After encapsulation, the template is sent to the controller.

[0053] In this embodiment, the controller sends the configuration template corresponding to the resource allocation policy to the network node, including: S15-1: The controller encapsulates the resource allocation policy to obtain the configuration template.

[0054] In this embodiment, after receiving the resource allocation policy, the controller encapsulates the resource allocation policy using a preset template, thereby obtaining a configuration template of the resource allocation policy.

[0055] S15-2: The controller sends the configuration template to the network node according to the network address corresponding to each node.

[0056] In this embodiment, the controller stores the network address corresponding to each network node, and sends the configuration template to the corresponding network address, so that each network node can receive the corresponding configuration template.

[0057] S16: The network node determines a corresponding network path according to the configuration template.

[0058] In this embodiment, after receiving the configuration template, the network node loads the configuration template so that the policy in the configuration template takes effect in the network device, thereby determining the corresponding network path.

[0059] For example, the resource allocation strategy in the configuration template is that node A is connected to node C through node B. After receiving the corresponding configuration template, node B loads the template, determines the corresponding interface connection with node A and the corresponding interface connection with node C, and forms a corresponding network path.

[0060] In this embodiment, the controller receives the cloud network resource data of each network node in the target area, and makes decisions through the computing network scheduling platform to select the optimal path and achieve reasonable network resource allocation, which is conducive to the stable development of various services under the new metropolitan area network.

[0061] In another embodiment of the present application, before the controller receives the cloud network resource data of all network nodes in the target area, the method further includes: S21: Collect device parameters of all the network nodes in the target area.

[0062] In this embodiment, it is first necessary to establish an expert database. When establishing the expert database, the device parameters of all network nodes in the target area are first collected.

[0063] S22: Statistically organize the equipment parameters and establish the expert database.

[0064] In this embodiment, after receiving the device parameters of all network nodes in the target area, the device parameters are statistically sorted and then a corresponding expert database is established.

[0065] In this embodiment, by pre-establishing an expert database, the device parameters of all network nodes in the target area are mastered, which is beneficial for the network scheduling platform to make decisions and reasonably allocate resources to each device.

[0066] In this embodiment, the expert database can also integrate the experience of network architecture design experts to quantitatively model the computing power of nodes within the metropolitan area network (such as GPU clusters and edge servers), defining metrics such as computing capacity and load ratio. Historical optimization cases in the expert database are used to refine metropolitan area network path allocation rules (such as forcing high-priority services to connect directly to core nodes) and form a policy template library. Security expert recommendations (such as prioritizing quantum encryption transmission nodes) are integrated to ensure that path allocation meets security and compliance requirements. Based on user service types (such as 4K video and industrial control), the service classification model in the expert database is invoked to decompose computing power requirements (floating-point computing power and latency sensitivity) and network QoS requirements. Based on traffic scheduling models (such as node load prediction models) in the expert database, path status is dynamically monitored and path switching is triggered (such as enabling backup links when load exceeds 80%). In abnormal scenarios (such as fiber breaks), expert fault handling plans are invoked, enabling fast rerouting via BGP. The prediction accuracy of the scheduling algorithm is continuously optimized using metropolitan area network operation and maintenance data in the expert database (such as historical path performance logs). A reinforcement learning model designed by AI experts is introduced to enable self-evolution of computing network resource allocation strategies.

[0067] In another embodiment of the present application, the method further includes: S31: The network node feeds back response information to the controller.

[0068] In this embodiment, the response information is information sent by the network node to the controller, and is used to return the application status of the path.

[0069] In this embodiment, after the network node obtains the configuration template and applies the network path on the template, the network device on the network node returns corresponding response information to the controller.

[0070] S32: The controller sends the parsed response information to the computing network scheduling platform.

[0071] In this embodiment, after receiving the response information sent by the network node, the controller parses the response information to determine whether the network path is successfully applied, and then sends the application result obtained by parsing to the computing network scheduling platform.

[0072] For example, the response information includes successful response information and failed response information. The successful response information indicates that the network node successfully loaded the template configuration and successfully applied the new path. The failed response information indicates that the network node did not successfully load the template configuration and failed to apply the new path.

[0073] In this embodiment, after the network node receives the configuration template and loads it for operation, it feeds back response information to the controller and the computing network scheduling platform, so that the controller and the computing network scheduling platform can timely grasp the application status of the new path, which is conducive to better allocation of network resources.

[0074] In another embodiment of the present application, the method further includes: S41: When the network path is effective, the computing network scheduling platform determines all network nodes on the old network path.

[0075] In this embodiment, when the network path is effective, the computing network scheduling platform determines all network nodes on the old network path based on the cloud network resource data. These network nodes are not on the new network path, so theoretically there is no need to allocate bandwidth, traffic and other network resources.

[0076] S42: Reclaim the network resources of all network nodes on the old network path.

[0077] In this embodiment, after determining the network nodes on the old network path, the computing network scheduling platform reclaims the network resources allocated to the network nodes on the old network path.

[0078] In this embodiment, whether to reclaim network resources is determined according to actual situations in actual applications. In some cases, such as when certain nodes are required to serve as standby nodes, the network resources of the network nodes may not be reclaimed.

[0079] In this embodiment, when the new path takes effect, the network resources on the old path are promptly recovered, which is beneficial to saving network resources and reasonably arranging network resources.

[0080] For example, a metropolitan area network device A, device B, and device C report cloud and network resource data to the controller. The controller processes the data reported by the three devices, removes redundant data, and extracts valid information, including the traffic load, bandwidth, delay, service quality policy, etc. of each device, and sends this information to the computing network scheduling platform. After receiving the information of the three metropolitan area network devices, the computing network scheduling platform identifies the resource-deficient nodes and faulty nodes in the three metropolitan area network devices based on the received data, and sends a call request to the expert database to call the device information of the three devices, including the device model, device port information, the maximum load supported by the device, etc. Parameters are then used to make decisions, determining the optimal path. For example, if the original path is ABC and analysis reveals that node B is faulty, node B needs to be removed and the optimal path for AC needs to be found. A decision is made to determine that node D between A and C is operating normally and is not heavily loaded. After comprehensive consideration, the new path is determined to be ADC. This policy is then issued to the controller, which encapsulates it and sends the corresponding configuration template to devices A, D, and C. The devices read and load the corresponding template, then load the configuration into their own devices. Devices A and C close the ports connected to device B and connect to the port of device D, forming a new path. After forming the new path, devices A, D, and C report to the controller that the template response was successful, that the path has been loaded, and that the new path is being used for data transmission. The controller then processes this information and transmits it to the computing network scheduling platform. Upon receiving the successful path application message, the computing network scheduling platform begins resource scheduling and reclaims the network resources allocated to device B through the controller.

[0081] In another embodiment of the present application, the method further includes: S51: The computing network scheduling platform generates a corresponding network resource distribution topology map based on the cloud network resource data.

[0082] In this embodiment, the network resource distribution topology diagram includes the relative position of each network node and the connection relationship between each network node.

[0083] In this embodiment, the computing network scheduling platform determines the location of each network node and the remaining network nodes connected to the network node based on the cloud network resource data, and generates a corresponding network resource distribution topology map.

[0084] S52: Perform real-time monitoring on each node in the network resource distribution topology diagram.

[0085] In this embodiment, after generating the network resource distribution topology map, the computing network scheduling platform monitors each node in the network resource distribution topology map in real time through the controller, periodically receives the cloud network resource data sent by each node, and determines the operating status of the equipment on the node based on the cloud network resource data.

[0086] In this embodiment, the operation status of the network nodes in the target area is monitored in real time by generating a network resource distribution topology map, thereby ensuring real-time perception of the network status of the target area.

[0087] In another embodiment of the present application, reference Figure 2 , Figure 2 This is a flowchart of intelligent scheduling of network resources in a metropolitan area network proposed in an embodiment of the present application. Figure 2 As shown, first, an expert database is established. Each network node then reports cloud and network resource data to the controller. The controller reports cloud and network resource data to the computing network scheduling platform through Kafka. The computing network scheduling platform analyzes the nodes with insufficient cloud and network resources and the causes of node failures. The expert database is then used to make decisions, allocating the optimal network path for the network nodes in the target area. The allocation policy configuration is then orchestrated and sent to the controller. The controller then encapsulates the NETCONF configuration template and sends the configuration template to the new city equipment (new metropolitan area network equipment) to open up new network paths and finally reclaim resources on the old paths.

[0088] In another embodiment of the present application, reference Figure 3 , Figure 3 This is a schematic diagram of the resource analysis process proposed in one embodiment of the present application. Figure 3 As shown, the computing network scheduling platform first establishes an expert database, and then after the cloud and network resource data are generated in the new city equipment and cloud resource pool, the cloud and network resource data are reported to the controller. The controller parses the cloud and network resource data and sends it to the computing network scheduling platform. After receiving the cloud and network resource data, the computing network scheduling platform forms a cloud and network resource distribution topology map.

[0089] In another embodiment of the present application, reference Figure 4 , Figure 4 This is a schematic diagram of the network resource intelligent scheduling process proposed in an embodiment of the present application. Figure 4 As shown, the computing network scheduling platform monitors and analyzes insufficient cloud network resources or node failures, decides the optimal selection and allocation policy configuration through the expert database, and then intelligently orchestrates the allocation policy configuration and sends the policy to the controller. The controller encapsulates the policy and then sends the policy configuration template to the network node (new city device / cloud resource pool) to make the network resource scheduling effective. The device side then returns the success / failure response configuration template to the controller. The controller parses the success / failure response configuration template and returns the success / failure response result to the computing network scheduling platform.

[0090] In another embodiment of the present application, after the controller recovers the resources on the old path, it can restart the faulty node to troubleshoot the fault. If the faulty node resumes normal operation, the node is monitored to be back to normal. When the node re-reports data, the node is re-planned to the network path based on the cloud and network resource data of the node to ensure that the network node is not idle and the device resources are not wasted. If the fault cannot be eliminated after the restart, the device ID, location and other information of the device are sent to the management interface to notify the maintenance personnel to repair or retract the device. After the device is repaired, the device can automatically report the information. At the same time, the staff also clicks on the fault repair completion information on the management interface. At this time, after receiving the information, the network scheduling platform will re-add the device to the path planning consideration. It can also use the device as a backup device. When a fault is detected in an adjacent device, the path will be switched to the device, thereby ensuring the stable operation of the entire metropolitan area network.

[0091] In the above embodiments of the present application, by establishing an expert database, the computing network scheduling platform schedules network resources for the target area based on the cloud network resource data, analyzes the shortage of network resources or node failures in real time, realizes the optimal network resource selection and allocation, intelligently orchestrates network resource policy configuration, and automatically triggers service scheduling, thereby realizing flexible scheduling of new metropolitan area network business traffic and flexible switching of network paths, ensuring the provision of stable network services to users.

[0092] Based on the same inventive concept, an embodiment of the present application provides a resource scheduling device. Figure 5 , Figure 5 FIG is a schematic diagram of a resource scheduling device 500 proposed in an embodiment of the present application. Figure 5 As shown, the device includes: The cloud network resource data receiving module 501 is used for the controller to receive the cloud network resource data of all network nodes in the target area; The cloud network resource data sending module 502 is used for the controller to send the parsed cloud network resource data to the computing network scheduling platform; A decision module 503 is configured for the computing network scheduling platform to make decisions on the cloud network resource data based on a pre-established expert database to generate a corresponding resource allocation strategy. The expert database records the device parameters of the network device corresponding to each of the network nodes. A policy issuing module 504 is configured for the computing network scheduling platform to send the resource allocation policy to the controller; A configuration template sending module 505 is configured for the controller to send the configuration template corresponding to the resource allocation policy to the network node; The network path determination module 506 is configured for the network node to determine a corresponding network path according to the configuration template.

[0093] Optionally, the device further comprises: A device parameter collection module, configured to collect device parameters of all the network nodes in the target area; The expert database establishment module is used to statistically organize the equipment parameters and establish the expert database.

[0094] Optionally, the device further comprises: A feedback response information module, configured for the network node to feed back response information to the controller; The response information forwarding module is used for the controller to send the parsed response information to the computing network scheduling platform.

[0095] Optionally, the device further comprises: An old node determination module, configured to, when the network path is effective, enable the computing network scheduling platform to determine all network nodes on the old network path; The resource recovery module is used to recover the network resources of all network nodes on the old network path.

[0096] Optionally, the decision module includes: A fault node determination submodule, configured for the computing network scheduling platform to determine resource-deficient nodes and faulty nodes in the target area based on the cloud network resource data; The resource allocation strategy determination submodule is used to determine the corresponding data transmission path for the nodes in the target area except the faulty node according to the device information of each network node in the expert database, and obtain the resource allocation strategy.

[0097] Optionally, the device further comprises: A resource distribution topology map generation module is used for the computing network scheduling platform to generate a corresponding network resource distribution topology map based on the cloud network resource data; The real-time monitoring module is used to perform real-time monitoring on each node in the network resource distribution topology diagram.

[0098] Optionally, the configuration template issuing module includes: A policy encapsulation submodule, configured for the controller to encapsulate the resource allocation policy to obtain the configuration template; The configuration template sending submodule is used for the controller to send the configuration template to the network node according to the network address corresponding to each node.

[0099] Based on the same inventive concept, another embodiment of the present application provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the resource scheduling method as described in any of the above embodiments of the present application.

[0100] Based on the same inventive concept, another embodiment of the present application provides an electronic device, Figure 5 This is a schematic diagram of an electronic device 500 proposed in one embodiment of the present application, including a memory 502, a processor 501 and a computer program stored in the memory and executable on the processor, wherein the processor implements the resource scheduling method described in any of the above embodiments of the present application when executed.

[0101] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0102] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0103] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, devices, or computer program products. Therefore, the embodiments of the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0107] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0108] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0109] The above is a detailed introduction to the resource scheduling method, device, equipment and storage medium provided by this application. Specific examples are used in this article to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method of this application and its core ideas. At the same time, for general technical personnel in this field, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on this application.

Claims

1. A resource scheduling method, characterized in that: The method comprises: The controller receives cloud network resource data of all network nodes in the target area; The controller sends the parsed cloud network resource data to the computing network scheduling platform; The computing network scheduling platform makes decisions on the cloud network resource data based on a pre-established expert database to generate a corresponding resource allocation strategy. The expert database records the device parameters of the network device corresponding to each of the network nodes. The computing network scheduling platform sends the resource allocation strategy to the controller; The controller sends the configuration template corresponding to the resource allocation strategy to the network node; The network node determines a corresponding network path according to the configuration template.

2. The resource scheduling method according to claim 1, characterized in that: Before the controller receives the cloud network resource data of all network nodes in the target area, the method further includes: Collecting device parameters of all the network nodes in the target area; The equipment parameters are statistically sorted and the expert database is established.

3. The resource scheduling method according to claim 1, characterized in that: The method further comprises: The network node feeds back response information to the controller; The controller sends the parsed response information to the computing network scheduling platform.

4. The resource scheduling method according to claim 3, characterized in that: The method further comprises: When the network path is effective, the computing network scheduling platform determines all network nodes on the old network path; The network resources of all network nodes on the old network path are reclaimed.

5. The resource scheduling method according to claim 1, characterized in that: The computing network scheduling platform makes decisions based on the cloud network resource data based on a pre-established expert database and generates corresponding resource allocation strategies, including: The computing network scheduling platform determines resource-deficient nodes and faulty nodes in the target area based on the cloud network resource data; According to the device information of each network node in the expert database, a corresponding data transmission path is determined for nodes in the target area except the faulty node, to obtain the resource allocation strategy.

6. The resource scheduling method according to claim 5, characterized in that: The method further comprises: The computing network scheduling platform generates a corresponding network resource distribution topology map based on the cloud network resource data; Each node in the network resource distribution topology diagram is monitored in real time.

7. The resource scheduling method according to claim 1, characterized in that: The controller sends the configuration template corresponding to the resource allocation strategy to the network node, including: The controller encapsulates the resource allocation policy to obtain the configuration template; The controller sends the configuration template to the network node according to the network address corresponding to each node.

8. A resource scheduling device, characterized in that: The device comprises: The cloud network resource data receiving module is used for the controller to receive the cloud network resource data of all network nodes in the target area; A cloud network resource data sending module is used for the controller to send the parsed cloud network resource data to the computing network scheduling platform; A decision module, configured for the computing network scheduling platform to make decisions on the cloud network resource data based on a pre-established expert database and generate corresponding resource allocation strategies, wherein the expert database records the device parameters of the network device corresponding to each of the network nodes; A policy sending module, configured for the computing network scheduling platform to send the resource allocation policy to the controller; a configuration template sending module, configured for the controller to send the configuration template corresponding to the resource allocation policy to the network node; The network path determination module is used for the network node to determine the corresponding network path according to the configuration template.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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