Processing method and device of computing resources, electronic equipment and storage medium

Abstract

The invention provides a computing resource processing method and device, electronic equipment and a storage medium, and relates to the technical field of computers. Computing nodes are controlled to initiate registration to a management service so as to be included in a monitoring network, resource use indexes of the computing nodes are periodically collected and reported to the management service, and the online state of each computing node is monitored in real time through a distributed heartbeat mechanism. And the resource state panoramic view of the computing cluster is generated and updated based on the node information in the registration request and the reported resource use index, so that the problem that a dynamic distributed monitoring network is not established in the prior art can be solved; the problems of incomplete monitoring coverage and delayed state perception due to the fact that the computing nodes are difficult to be effectively monitored, the resource index acquisition is discontinuous, the node online state monitoring is lagged and cluster-level all-dimensional resource state presentation is lacked in the prior art are solved.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a method and apparatus for processing computing resources, electronic equipment, and storage medium. Background Technology

[0002] Computing resource management is a core supporting technology for insurance business systems and is widely used in the operation and maintenance of service clusters in multi-tenant environments. With the accelerating digital transformation of the insurance industry, the scale of computing clusters is growing exponentially, and traditional resource monitoring systems are gradually becoming insufficient to meet the current demands for dynamic and refined resource management. In related technical fields, existing solutions mostly construct a basic resource management framework through the coordinated operation of centralized monitoring, passive data collection, and threshold alarms. Specifically, this system covers the entire process from node status collection to resource allocation decisions, including key steps such as IP identifier registration, periodic indicator collection, and threshold determination. In terms of architectural design, the centralized architecture relies on a single management node to aggregate various types of data; passive monitoring relies on a periodic polling mechanism to obtain resource status information; and the threshold alarm function triggers corresponding response actions based on pre-set indicator thresholds.

[0003] However, existing methods for handling computing resources have significant shortcomings. Most of these solutions directly employ static IP configuration and single-point data collection, failing to establish a dynamically updated distributed monitoring network. This design flaw can lead to systemic problems such as incomplete node coverage and delayed status awareness. Specifically, in traditional solutions, manual intervention is required to configure cluster nodes when expansion is needed. This not only introduces node registration latency but also risks monitoring blind spots. Threshold-based alarm mechanisms can only judge single-dimensional indicators, lacking the ability to correlate and analyze multi-dimensional resource indicators such as CPU, memory, disk, and network, making it difficult to comprehensively reflect the actual operating status of resources. Furthermore, passive monitoring sampling intervals are typically greater than 30 seconds, failing to capture instantaneous resource fluctuations in a timely manner. With manual management, the complexity of operations and maintenance increases non-linearly when the number of nodes in the cluster exceeds 50, further increasing management difficulty. These limitations directly result in computing resource utilization consistently falling below 60%, increasing the risk of service interruption by more than three times compared to the ideal state. This is especially pronounced during peak insurance business periods, where resource allocation lag becomes even more significant, becoming a key bottleneck restricting the stable and efficient operation of the insurance business. Summary of the Invention

[0004] This disclosure provides a method and apparatus for processing computing resources, an electronic device, and a storage medium. Its main objective is to at least partially solve one of the technical problems in the related art.

[0005] According to a first aspect of this disclosure, a method for processing computing resources is provided, comprising: The control compute node initiates a registration request to the management service to register the compute node in the monitoring network; The resource usage metrics of the computing nodes are collected periodically, and the collection results are reported to the management service. The online status of each computing node is monitored in real time through a distributed heartbeat mechanism; Based on the node information contained in the registration request and the reported resource usage indicators, a panoramic view of the computing cluster's resource status is generated and updated.

[0006] Optionally, the control computing node initiates a registration request to the management service, including: Obtain the network address and resource configuration metadata of the computing node; The network address is used as a unique identifier to send registration information to the management service.

[0007] Optionally, the resource usage metrics include at least one of processor utilization, memory utilization, storage utilization, and network connection count.

[0008] Optionally, the real-time monitoring of the online status of each computing node through a distributed heartbeat mechanism includes: Control each computing node to send a heartbeat signal to the management service at a preset cycle; The management service determines whether a heartbeat signal is received within a set time to judge whether each computing node is online and updates the status information of each computing node.

[0009] Optionally, generating and updating the panoramic view of the resource status of the computing cluster includes: Based on the resource usage metrics and node status reported by each computing node, a view reflecting the overall resource distribution and usage of the cluster is dynamically constructed.

[0010] Optional, also includes: When the resource usage metric of at least one computing node is detected to exceed a preset threshold, an analysis report is generated that includes the node identifier, current load data, and resource adjustment suggestions.

[0011] According to a second aspect of this disclosure, a computing resource processing apparatus is provided, comprising: The registration unit is used to control the computing node to initiate a registration request to the management service in order to register the computing node in the monitoring network; The data collection unit is used to periodically collect the resource usage indicators of the computing nodes and report the collection results to the management service. The monitoring unit is used to monitor the online status of each computing node in real time through a distributed heartbeat mechanism; The reporting unit is used to generate and update a panoramic view of the resource status of the computing cluster based on the node information contained in the registration request and the reported resource usage indicators.

[0012] Optionally, the registration unit is also used for: Obtain the network address and resource configuration metadata of the computing node; The network address is used as a unique identifier to send registration information to the management service.

[0013] Optionally, the resource usage metrics include at least one of processor utilization, memory utilization, storage utilization, and network connection count.

[0014] Optionally, the monitoring unit is also used for: Control each computing node to send a heartbeat signal to the management service at a preset cycle; The management service determines whether a heartbeat signal is received within a set time to judge whether each computing node is online and updates the status information of each computing node.

[0015] Optionally, the reporting unit is also used for: Based on the resource usage metrics and node status reported by each computing node, a view reflecting the overall resource distribution and usage of the cluster is dynamically constructed.

[0016] Optional, also includes: The generation unit is used to generate an analysis report containing node identifiers, current load data, and resource adjustment suggestions when it detects that the resource usage index of at least one computing node exceeds a preset threshold.

[0017] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0018] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0019] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0020] The computing resource processing method, apparatus, electronic device, and storage medium disclosed herein control computing nodes to register with a management service to include them in a monitoring network, periodically collect resource usage indicators of the computing nodes and report them to the management service, monitor the online status of each computing node in real time through a distributed heartbeat mechanism, and generate and update a panoramic view of the computing cluster's resource status based on the node information in the registration request and the reported resource usage indicators. Therefore, it can solve the problems in the prior art where the lack of a dynamic distributed monitoring network leads to difficulties in effectively including computing nodes in monitoring, discontinuous collection of resource indicators, lagging monitoring of node online status, and a lack of comprehensive cluster-level resource status presentation, resulting in incomplete monitoring coverage and delayed status perception. It achieves the technical effect of effectively including computing cluster nodes in monitoring, continuously grasping resource usage indicators, and real-time monitoring of node online status, forming a panoramic view of cluster resource status, and providing comprehensive and real-time status data support for subsequent resource management.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0022] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A flowchart illustrating a method for processing computing resources provided in an embodiment of this disclosure; Figure 2 A schematic diagram of the structure of a computing resource processing device provided in an embodiment of this disclosure; Figure 3 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0023] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0024] The following description, with reference to the accompanying drawings, outlines a method, apparatus, electronic device, and storage medium for processing computing resources according to embodiments of the present disclosure.

[0025] Figure 1This is a flowchart illustrating a method for processing computing resources provided in an embodiment of the present disclosure.

[0026] like Figure 1 As shown, the method includes the following steps: Step 101: Control the computing node to initiate a registration request to the management service to register the computing node in the monitoring network.

[0027] In the embodiments of this disclosure, to address the problems of manual configuration and potential delays or omissions in the inclusion of computing nodes into the monitoring network in existing technologies, this technical solution controls the computing node to actively initiate a registration request to the management service. This formally connects the computing node to the monitoring network, establishes an association between the computing node and the management service, and enables the management service to identify and perform subsequent monitoring and management of the computing node. The registration request can carry identification information to distinguish the computing node, supporting the management service's unique identification and recording of the computing node, ensuring that each computing node is accurately included in the monitoring network. As one implementation method, the computing node can automatically obtain its own IP address, hostname, and other identification information and basic resource information upon startup. Based on this information, it prepares registration data and initiates a registration request to the management service. The management service checks whether the IP address is already registered to perform corresponding operations—if the IP is not registered, a new node record is created; if the IP is already registered, the node status is updated, ultimately completing the registration of the computing node in the monitoring network.

[0028] This technology avoids the inefficiency and error-proneness of manual configuration of computing node registration in existing technologies, enabling automatic and efficient integration of computing nodes into the monitoring network, reducing registration latency and monitoring blind spots, and providing a foundation for subsequent resource monitoring and status management of computing nodes, effectively improving the management efficiency and scalability of the monitoring network for computing nodes.

[0029] Step 102: Periodically collect the resource usage indicators of the computing nodes and report the collection results to the management service.

[0030] In the embodiments of this disclosure, to address the problems of insufficient real-time performance and incomplete indicator coverage in the prior art for collecting computing node resource status, which makes it difficult to accurately grasp the node's operating status, this technical solution controls computing nodes to collect their own resource usage indicators according to a preset period and reports the collected resource usage status data to a management service. This allows the management service to continuously obtain the resource consumption of each computing node, laying a data foundation for subsequently building a computing cluster resource status view. The resource usage indicators can cover various key parameters reflecting the operating status of computing nodes to comprehensively characterize the node's resource load. As one implementation method, computing nodes can collect their own multi-dimensional resource usage indicators, such as CPU utilization, memory usage, disk space utilization, and network connection count, according to a set period (e.g., every 5 seconds), and report the collection results to the management service in real time. The management service then stores and performs basic analysis processing on the reported data to ensure the continuity and availability of resource status data.

[0031] This technology can solve the problems of excessively long sampling intervals and single indicators in existing passive monitoring, and realize the periodic and comprehensive collection and reporting of the resource usage status of computing nodes. This enables management services to grasp the resource load of each node in real time and accurately, providing continuous and complete data support for subsequent cluster resource management decisions, effectively improving the timeliness and accuracy of resource status perception, and avoiding management decision deviations caused by missing or delayed resource information.

[0032] Step 103: Monitor the online status of each computing node in real time through a distributed heartbeat mechanism.

[0033] In the embodiments of this disclosure, to address the problems of lack of real-time monitoring of the online status of computing nodes and the ease with which offline nodes may go undetected in the prior art, this technical solution constructs a distributed heartbeat mechanism to achieve real-time monitoring of the online status of each computing node. This ensures that the management service can dynamically grasp the connectivity and operating status of each computing node, avoiding blind spots in node status perception caused by single-point monitoring failures. The distributed heartbeat mechanism operates by having computing nodes actively send status signals, which the management service receives and verifies accordingly, ensuring the real-time performance and reliability of the monitoring process. Furthermore, this mechanism does not rely on a single monitoring node and can adapt to the status monitoring needs of cluster expansion. As one implementation method, each computing node sends heartbeat packets to the management service according to a preset period. The management service continuously receives and records the heartbeat signals from each node. If no heartbeat packet is received from a computing node within a set time, it is preliminarily determined that the node may be offline or experiencing an anomaly, thereby achieving real-time tracking of the node's online status.

[0034] This technology can solve the problems of slow fault response and lagging node status perception in existing technologies. By using a distributed heartbeat mechanism to achieve real-time monitoring of the online status of computing nodes, it can promptly detect offline or abnormal node situations, avoid monitoring blind spots and fault escalation caused by unclear node status, and provide timely status information for subsequent node fault investigation and service recovery. This effectively improves the real-time performance and reliability of computing cluster operation status monitoring and ensures the stability of the overall cluster operation and maintenance.

[0035] Step 104: Based on the node information contained in the registration request and the reported resource usage indicators, generate and update a panoramic view of the computing cluster's resource status.

[0036] In the embodiments of this disclosure, to address the problem of fragmented and unsystematic resource status information in existing computing clusters, which makes it difficult for operations and maintenance personnel to fully grasp the distribution and load of cluster resources, this technical solution integrates node information contained in computing node registration requests with resource usage indicators periodically reported by each computing node to construct and dynamically update a panoramic view of the computing cluster's resource status. This allows the management end to systematically obtain the identity identifiers and corresponding resource operating status of all computing nodes within the cluster, achieving a holistic perception of cluster resources. The panoramic view of resource status can aggregate basic node identification data and dynamic resource data in real time, ensuring that the view information remains synchronized with the actual operating status of the cluster, meeting the needs for overall monitoring of cluster resources. As one implementation method, the node information in the registration request may include unique identifiers such as the computing node's IP address and hostname. The reported resource usage indicators may cover CPU utilization, available memory, disk space utilization, and network connection count. Based on this information, the management service continuously integrates and updates the panoramic view of resource status, clearly presenting the online status of each node, resource consumption trends, and overall cluster resource occupancy, providing a comprehensive reference for cluster resources for operations and maintenance.

[0037] This technology can solve the problems of fragmented cluster resource status information and insufficient overall perception in existing technologies, and realize a panoramic and real-time presentation of computing cluster resource status. This allows operation and maintenance personnel to quickly and comprehensively grasp the overall resource distribution and load of the cluster, providing sufficient status support for subsequent resource allocation, scaling up and down and other decisions. It effectively improves the precision and efficiency of cluster resource management and avoids management deviations caused by incomplete understanding of resource status.

[0038] The computing resource processing method disclosed herein controls computing nodes to register with a management service to be included in the monitoring network, periodically collects resource usage indicators of the computing nodes and reports them to the management service, monitors the online status of each computing node in real time through a distributed heartbeat mechanism, and generates and updates a panoramic view of the computing cluster's resource status based on the node information in the registration request and the reported resource usage indicators. Therefore, it can solve the problems in the prior art caused by the lack of a dynamic distributed monitoring network, which makes it difficult to effectively include computing nodes in the monitoring, the collection of resource indicators is discontinuous, the monitoring of node online status is lagging, and there is a lack of comprehensive cluster-level resource status presentation, resulting in incomplete monitoring coverage and delayed status perception. It achieves the technical effect of effectively including computing cluster nodes in the monitoring, continuously grasping resource usage indicators, and real-time monitoring of node online status, forming a panoramic view of cluster resource status, and providing comprehensive and real-time status data support for subsequent resource management.

[0039] Within the technical framework disclosed in step 101, the control computing node initiates a registration request to the management service, including but not limited to: obtaining the network address and resource configuration metadata of the computing node; and sending registration information to the management service using the network address as a unique identifier.

[0040] Specifically, within the technical framework of step 101, during the registration request process, the computing node first actively acquires its network address and resource configuration metadata through its built-in system information collection module. The network address is specifically the computing node's IP address, which is directly read by calling the operating system's network configuration query interface (such as the `ifconfig` command in Linux or the `ipconfig` command in Windows). The resource configuration metadata covers basic hardware configuration information such as the computing node's CPU model and number of cores, total memory capacity, total disk storage space, and network adapter model, obtained by accessing the system hardware information database or calling the hardware detection interface. After completing the information collection, the computing node embeds the aforementioned network address (IP address) as a unique identifier field into the registration information data packet, supplementing the data packet with the remaining resource configuration metadata as auxiliary information fields. Then, it sends the registration information data packet to the management service's preset registration port through a preset network communication protocol (such as a custom communication protocol based on TCP or the standard HTTP protocol). Upon receiving the registration information, the management service first extracts the IP address field from the data packet and compares it with the stored list of node IPs to determine whether the computing node has completed registration, providing a basis for the subsequent registration process.

[0041] Using IP address as the unique identifier for registration can effectively avoid duplicate registration or identifier confusion of computing nodes in the monitoring network, ensuring the uniqueness of node identification. Furthermore, collecting and reporting resource configuration metadata in advance allows the management service to grasp the basic hardware capabilities of the node during the node registration phase, eliminating the need for subsequent additional resource configuration query requests, reducing the number of network interactions, and providing initial reference for subsequent resource scheduling and load allocation, thereby improving the efficiency and practicality of the registration process.

[0042] Within the technical framework disclosed in the foregoing embodiments, the resource usage indicators include at least one of processor utilization, memory utilization, storage utilization, and network connection count.

[0043] Specifically, processor utilization, memory utilization, storage utilization, and network connection count can be collected in the following ways: For processor utilization, the ` / proc / stat` file is read periodically (e.g., every 5 seconds), and the ratio of the sum of CPU user-mode and system-mode time occupied per unit time (e.g., the last 5 seconds) to the total time is calculated to obtain the actual utilization. For memory utilization, the ` / proc / meminfo` file is used to extract MemTotal (total memory), MemFree (free memory), Buffers (cache), and Cached (page cache) data, and the actual utilization is calculated using the formula "(MemTotal - MemFree - Buffers - Cached) / MemTotal". For storage utilization, the `statfs` system function is called to obtain the total number of bytes and the number of available bytes for a specified storage partition (e.g., the ` / data` partition), and the ratio between the two is calculated. For network connection count, `ss -t state` is executed. The ESTABLISHED command counts the number of connection entries in the returned results to obtain the current number of active network connections, while filtering out connections corresponding to the local loopback address (127.0.0.1) to exclude invalid data.

[0044] By collecting data based on system kernel files and standard system functions, the authenticity and real-time nature of various resource usage metrics are ensured. Dedicated calculation logic is designed for different metrics (such as excluding cached data for memory usage), enabling the metrics to accurately reflect the actual resource load status of computing nodes and providing more reliable data support for subsequent analysis and decision-making in management services.

[0045] Within the technical framework disclosed in step 103, the real-time monitoring of the online status of each computing node through the distributed heartbeat mechanism includes, but is not limited to: controlling each computing node to send a heartbeat signal to the management service at a preset period; and determining whether the heartbeat signal is received within a set time based on the management service, so as to determine whether each computing node is online and update the status information of each computing node.

[0046] Specifically, under the technical framework of step 103, the process of monitoring the online status of each computing node in real time through a distributed heartbeat mechanism is as follows: First, a fixed heartbeat sending period (e.g., once every 3 seconds) is preset for each computing node. This period can be pre-configured on the management server and synchronized to each computing node according to the scale of the computing cluster and network stability. Each computing node sends a heartbeat signal to the management service through a preset network communication link (reusing the same TCP connection as the registration request or establishing an independent lightweight UDP connection) according to this period. The heartbeat signal needs to carry the unique node ID (or the corresponding network address) generated by the computing node during registration in step 101 and the basic running status identifier of the current node (such as simplified status information such as "normal" or "resource shortage"). The management server maintains an independent heartbeat receiving timer for each registered compute node, setting a heartbeat timeout period (e.g., twice the heartbeat cycle, i.e., 6 seconds). Upon receiving a heartbeat signal from a compute node, the management server immediately updates the timestamp of the corresponding heartbeat receiving timer and marks the node status as "online." If the heartbeat receiving timer for a compute node fails to update within the set timeout period (i.e., no heartbeat signal is received), the management server determines that the node is offline or at risk of an anomaly, updates its status to "offline / abnormal," and synchronizes the updated node status information to the system database to ensure that subsequent resource monitoring and scheduling processes can obtain the latest node status data.

[0047] By combining the design of sending heartbeat signals at fixed intervals with timeout judgments, offline or abnormal conditions of computing nodes can be quickly detected, avoiding the problem of delayed status perception in traditional passive monitoring. The heartbeat signal carries a unique node ID, which ensures that the management service can accurately locate abnormal nodes, while the status information is synchronized to the database, providing an accurate basis for subsequent fault diagnosis and resource allocation, effectively reducing monitoring blind spots and improving the reliability of computing cluster status monitoring.

[0048] Under the technical solution framework disclosed in step 104, the generation and updating of the panoramic view of the resource status of the computing cluster includes, but is not limited to: dynamically constructing a view reflecting the overall resource distribution and usage of the cluster based on the resource usage indicators and node status reported by each computing node.

[0049] Specifically, within the technical framework of step 104, the process of generating and updating a panoramic view of the computing cluster's resource status is as follows: The management service first receives and stores the resource usage metrics (including CPU utilization, memory utilization, disk space utilization, and network connection count) periodically reported by each computing node, along with the node status (online / offline) confirmed through a distributed heartbeat mechanism. Then, the management service initiates a data integration module, binding each computing node's IP address (unique identifier) ​​to its corresponding real-time resource usage metrics and current online status according to the association logic of "node identifier - resource data - node status," forming a single-node resource status dataset. Next, based on all single-node resource status datasets, the management service further performs cluster-level aggregate calculations by resource type, such as calculating the total cluster CPU utilization (the weighted average of the CPU utilization of each online node) and the total cluster memory usage. The system calculates the total memory usage of each online node, the total disk utilization of the cluster (the average disk utilization of each online node), and the total number of network connections in the cluster (the sum of network connections of each online node). It also calculates the total number of nodes in the cluster and the number of online nodes. Finally, the management service integrates the single-node resource status dataset with the cluster-level aggregated data into structured view data, constructing a panoramic view of the resource status. This view includes detailed resource usage information for each node (e.g., the node with IP 192.168.1.101 has a CPU utilization of 65%, a memory utilization of 70%, and is online), as well as statistics on the overall resource distribution and usage of the cluster (e.g., 28 / 30 nodes are online, average CPU utilization is 62%, average memory utilization is 68%). Whenever a new resource usage metric is reported or a node status changes, the management service immediately repeats the above data integration and view construction process to achieve dynamic updates to the panoramic view.

[0050] By associating node identifiers, resource data, and node status, the system presents both the details of individual node resources and the overall cluster status, avoiding fragmented resource information and allowing operations personnel to simultaneously grasp both local and overall resource status. The view updates in real time with data reporting, ensuring that the cluster resource status obtained by operations personnel is synchronized with the actual operating situation, solving the problem of information lag in traditional static views. The integrated presentation of multi-dimensional resource data also provides comprehensive and intuitive data support for subsequent resource allocation and scaling decisions, improving the efficiency and accuracy of cluster resource management.

[0051] Within the framework of the technical solutions disclosed in the foregoing embodiments, this disclosure further includes: when the resource usage index of at least one computing node is detected to exceed a preset threshold, generating an analysis report containing node identifier, current load data, and resource adjustment suggestions.

[0052] Specifically, within the framework of the above technical solution, when it is necessary to detect that the resource usage indicators of computing nodes exceed preset thresholds and generate an analysis report, the specific process is as follows: First, the management server pre-configures corresponding preset thresholds for each resource usage indicator (processor utilization, memory utilization, storage utilization, and network connection count). For example, the processor utilization threshold is set to 80%, the memory utilization threshold to 85%, the storage utilization threshold to 90%, and the network connection count threshold to 1000 (the thresholds can be adjusted based on historical operating data and maintenance experience of the insurance business cluster). After receiving the resource usage indicators periodically reported by each computing node, the management service compares each indicator of each node with the corresponding preset threshold in real time. If it finds that at least one indicator of any node exceeds the preset threshold (such as a node's CPU utilization reaching 88%), the analysis report generation process is immediately triggered. At this point, the management service first extracts the unique identifier of the node (i.e., the network address registered in step 101, such as IP address 192.168.2.105), then collects the specific values ​​of the node's current over-threshold indicators (88% CPU utilization) and other related resource indicators in real time (such as memory utilization of 72%, storage utilization of 65%, and network connections of 850), forming a current load data set. Subsequently, the management service combines its cluster operation and maintenance experience in insurance business scenarios (such as the node carrying auto insurance claims data processing tasks, where high CPU usage may affect claims processing timeliness) to generate resource adjustment suggestions (such as temporarily migrating some non-core claims tasks on the node to low-load nodes, or starting a backup computing node to share the load). Finally, the management service integrates the node identifier, current load data (including over-threshold indicators and other related indicators), and resource adjustment suggestions into a structured analysis report (in JSON or tabular format), and synchronizes it to the system database storage, while also pushing it to the management interface of the operation and maintenance personnel to ensure that the operation and maintenance personnel can view it in real time.

[0053] By setting precise resource thresholds and comparing them in real time, overloaded computing nodes can be quickly located, avoiding service lag caused by resource overload. The unique node identifiers and detailed load data included in the analysis report can help operations and maintenance personnel directly pinpoint the problem nodes and understand the load details. Meanwhile, resource adjustment suggestions combined with business scenarios provide clear guidance for operations and maintenance operations, effectively shortening the response time from problem discovery to problem resolution and ensuring the stable operation of insurance businesses (such as auto insurance claims and policy entry).

[0054] It should be noted that the embodiments of this disclosure may include multiple steps. For ease of description, these steps are numbered, but these numbers are not a limitation on the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of this disclosure do not limit this.

[0055] Corresponding to the above-described method for processing computing resources, this disclosure also proposes a computing resource processing apparatus. Since the apparatus embodiments of this disclosure correspond to the method embodiments described above, details not disclosed in the apparatus embodiments can be referred to the method embodiments described above, and will not be repeated here.

[0056] Figure 2 This is a schematic diagram of the structure of a computing resource processing device provided in an embodiment of the present disclosure, as shown below. Figure 2 As shown, it includes: Registration unit 21 is used to control the computing node to initiate a registration request to the management service in order to register the computing node in the monitoring network; The data collection unit 22 is used to periodically collect the resource usage indicators of the computing nodes and report the collection results to the management service. Monitoring unit 23 is used to monitor the online status of each computing node in real time through a distributed heartbeat mechanism; The reporting unit 24 is used to generate and update a panoramic view of the resource status of the computing cluster based on the node information contained in the registration request and the reported resource usage indicators.

[0057] The computing resource processing device provided in this disclosure controls computing nodes to register with a management service to be included in the monitoring network, periodically collects resource usage indicators of the computing nodes and reports them to the management service, monitors the online status of each computing node in real time through a distributed heartbeat mechanism, and generates and updates a panoramic view of the resource status of the computing cluster based on the node information in the registration request and the reported resource usage indicators. Therefore, it can solve the problems in the prior art that are difficult to effectively include computing nodes in the monitoring, discontinuous collection of resource indicators, lagging monitoring of node online status and lack of comprehensive cluster-level resource status presentation due to the lack of a dynamic distributed monitoring network, resulting in incomplete monitoring coverage and delayed status perception. It achieves the technical effect of effectively including computing cluster nodes in the monitoring, continuously grasping resource usage indicators and real-time monitoring of node online status, forming a panoramic view of cluster resource status, and providing comprehensive and real-time status data support for subsequent resource management.

[0058] Furthermore, in one possible implementation of this embodiment, the registration unit 21 is also used for: Obtain the network address and resource configuration metadata of the computing node; The network address is used as a unique identifier to send registration information to the management service.

[0059] Furthermore, in one possible implementation of this embodiment, the resource usage metrics include at least one of processor utilization, memory utilization, storage utilization, and network connection count.

[0060] Furthermore, in one possible implementation of this embodiment, the monitoring unit 23 is also used for: Control each computing node to send a heartbeat signal to the management service at a preset cycle; The management service determines whether a heartbeat signal is received within a set time to judge whether each computing node is online and updates the status information of each computing node.

[0061] Furthermore, in one possible implementation of this embodiment, the reporting unit 24 is also used for: Based on the resource usage metrics and node status reported by each computing node, a view reflecting the overall resource distribution and usage of the cluster is dynamically constructed.

[0062] Furthermore, in one possible implementation of this embodiment, such as Figure 2 As shown, it also includes: The generation unit 25 is used to generate an analysis report containing node identifier, current load data and resource adjustment suggestions when the resource usage index of at least one computing node is detected to exceed a preset threshold.

[0063] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and the principle is the same, so it is not limited in this embodiment.

[0064] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0065] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 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.

[0066] like Figure 3As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 302 or a computer program loaded from storage unit 308 into RAM (Random Access Memory) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O (Input / Output) interface 305 is also connected to the bus 304.

[0067] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0068] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as methods for processing computing resources. For example, in some embodiments, methods for processing computing resources may be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, computing unit 301 may be configured to perform the aforementioned computing resource processing method by any other suitable means (e.g., by means of firmware).

[0069] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0070] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0071] In the context of this disclosure, a machine-readable medium 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. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0072] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0073] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0074] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0075] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0076] The various numerical designations such as "first," "second," etc., used in this disclosure are merely for ease of description and are not intended to limit the scope of the embodiments of this disclosure, nor do they indicate a sequential order.

[0077] At least one of the features described in this disclosure can also be described as one or more, and multiple features can be two, three, four or more, and this disclosure does not impose any limitations. In the embodiments of this disclosure, for a technical feature, the technical features in that technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no sequential order or size order among the technical features described by "first", "second", "third", "A", "B", "C" and "D".

[0078] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0079] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for processing computing resources, characterized in that, include: The control compute node initiates a registration request to the management service to register the compute node in the monitoring network; The resource usage metrics of the computing nodes are collected periodically, and the collection results are reported to the management service. The online status of each computing node is monitored in real time through a distributed heartbeat mechanism; Based on the node information contained in the registration request and the reported resource usage indicators, a panoramic view of the computing cluster's resource status is generated and updated.

2. The method according to claim 1, characterized in that, The control computing node initiates a registration request to the management service, including: Obtain the network address and resource configuration metadata of the computing node; The network address is used as a unique identifier to send registration information to the management service.

3. The method according to claim 1, characterized in that, The resource usage metrics include at least one of processor utilization, memory utilization, storage utilization, and network connection count.

4. The method according to claim 1, characterized in that, The method of real-time monitoring of the online status of each computing node through a distributed heartbeat mechanism includes: Control each computing node to send a heartbeat signal to the management service at a preset cycle; The management service determines whether a heartbeat signal is received within a set time to judge whether each computing node is online and updates the status information of each computing node.

5. The method according to claim 1, characterized in that, The generation and updating of the panoramic view of the resource status of the computing cluster includes: Based on the resource usage metrics and node status reported by each computing node, a view reflecting the overall resource distribution and usage of the cluster is dynamically constructed.

6. The method according to claim 1, characterized in that, Also includes: When the resource usage metric of at least one computing node is detected to exceed a preset threshold, an analysis report is generated that includes the node identifier, current load data, and resource adjustment suggestions.

7. A computing resource processing apparatus, characterized in that, include: The registration unit is used to control the computing nodes to initiate registration requests to the management service in order to include the computing nodes in the unified monitoring network; The data collection unit is used to periodically collect the resource usage indicators of the computing nodes and report the collection results to the management service. The monitoring unit is used to monitor the online status of each computing node in real time through a distributed heartbeat mechanism; The reporting unit is used to generate and update a panoramic view of the resource status of the computing cluster based on the node information contained in the registration request and the reported resource usage indicators.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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