A business operation and maintenance method and system based on cloud service architecture

Abstract

This invention discloses a business operation and maintenance method and system based on a cloud service architecture, relating to the field of cloud computing technology. It includes collecting and formatting various link traffic data for horizontal network performance monitoring and topology generation; collecting microservice performance indicator data for multi-dimensional anomaly detection and analysis; and performing automated repair through fault correlation analysis and location. The method described in this invention helps to quickly identify network bottlenecks and potential security issues, provides accurate basic data for horizontal network performance monitoring and topology generation, optimizes early warning and rapid location performance, effectively reduces the impact of system failures on business operations, realizes intelligent and automated fault handling, shortens fault handling time, reduces operation and maintenance workload, and improves system stability and reliability.

Description

Technical Field

[0001] This invention relates to the field of cloud computing technology, specifically to a business operation and maintenance method and system based on a cloud service architecture. Background Technology

[0002] With the rapid development of information technology, cloud computing, as an emerging computing model, has profoundly changed traditional IT infrastructure and service delivery methods. In the cloud computing environment, business operation and maintenance management has become a key link in ensuring service continuity and improving user experience. However, existing technologies still need to be improved in terms of data processing accuracy, monitoring comprehensiveness, and the degree of automation in fault handling. Enterprises have an increasing demand for business operation and maintenance based on cloud service architecture, and their requirements for operation and maintenance efficiency and quality are also constantly increasing. This has prompted related technologies to continuously develop in a more efficient and intelligent direction.

[0003] In recent years, related technologies have developed rapidly, and various cloud service architectures have sprung up like mushrooms after rain, providing resource management capabilities that offer elastic scaling and on-demand allocation. Based on this, business operation and maintenance methods have also been constantly evolving, from simple monitoring to today's intelligent and automated operation and maintenance, with increasingly rich technical means. However, existing technologies still have many shortcomings. In terms of link traffic data collection and monitoring, current technologies often lack effective data formatting processing, resulting in inaccurate monitoring results and difficulty in generating comprehensive network performance topology maps, thus affecting the accurate assessment of network status. For microservice performance monitoring, current technologies have failed to achieve multi-dimensional anomaly detection and analysis, failing to promptly identify potential performance bottlenecks, impacting service response speed and user experience. In terms of fault handling, current technologies lack effective fault correlation analysis, and the degree of automated repair is low, often requiring manual intervention. This not only increases operation and maintenance costs but also prolongs fault recovery time. Therefore, how to achieve accurate monitoring and topology generation of link traffic data, multi-dimensional monitoring and anomaly analysis of microservice performance, and rapid fault location and automated repair have become urgent problems to be solved in the current cloud service architecture business operation and maintenance field. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: existing cloud computing technologies suffer from incomplete link traffic monitoring, inaccurate microservice anomaly detection, and low efficiency in automated fault repair, as well as the problem of how to achieve comprehensive, efficient, and automated business operation and maintenance management.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a business operation and maintenance method based on a cloud service architecture, comprising collecting and formatting various types of link traffic data, performing horizontal network performance monitoring and topology generation; collecting microservice performance indicator data, performing multi-dimensional anomaly detection and analysis; and performing automated repair through fault correlation analysis and location.

[0007] As a preferred embodiment of the cloud service architecture-based business operation and maintenance method described in this invention, the following steps are included: collecting and formatting various types of link traffic data, including data encryption and compression transmission; achieving cross-platform compatibility of data collection through standardized interfaces supporting multiple protocols; and transmitting link traffic data from different container, virtual machine, and physical machine environments through a unified data format.

[0008] As a preferred embodiment of the cloud service architecture-based business operation and maintenance method described in this invention, the horizontal network performance monitoring and topology generation includes real-time monitoring of the horizontal network performance of key indicators such as bandwidth and latency in the business system by collecting network traffic data at the operating system kernel layer.

[0009] The collected link data is automatically mapped into service call path graphs and dependency graphs through the topology generation module, and provides a path location function for tracing each path to help identify and troubleshoot anomalies in the service call chain.

[0010] As a preferred embodiment of the cloud service architecture-based business operation and maintenance method described in this invention, the multi-dimensional anomaly detection and analysis includes continuous monitoring of performance fluctuations at each time point through time series analysis, automatic analysis of historical data of business performance, identification of potential system problems by recognizing performance change trends in different time periods, capturing abnormal trends in business operation and generating real-time alarms.

[0011] As a preferred embodiment of the cloud service architecture-based business operation and maintenance method described in this invention, the multi-dimensional anomaly detection and analysis further includes dynamically optimizing alarm thresholds and anomaly priority identification by learning from historical monitoring data, automatically adapting to changes in the system environment, and performing real-time optimization based on changes in business needs and system load.

[0012] As a preferred embodiment of the cloud service architecture-based business operation and maintenance method described in this invention, the fault correlation analysis and localization includes decomposing faults into physical layer, network layer and application layer through a hierarchical localization strategy, and constructing a knowledge graph to achieve cross-layer fault correlation analysis.

[0013] As a preferred embodiment of the cloud service architecture-based business operation and maintenance method described in this invention, the automated repair includes optimal resource scheduling after fault isolation, high-priority repair of critical node faults in a multi-node environment to reduce interference with business traffic, and coordination of the repair process using a real-time communication mechanism to dynamically adjust resource allocation.

[0014] Another objective of this invention is to provide a business operation and maintenance system based on a cloud service architecture, which can perform multi-dimensional anomaly detection and analysis by collecting performance index data of microservices, thus solving the problems of non-real-time and inaccurate anomaly detection in current cloud computing technologies.

[0015] As a preferred embodiment of the cloud service architecture-based business operation and maintenance system described in this invention, it includes a link monitoring and analysis module, a microservice monitoring module, and a fault location and repair module.

[0016] The link monitoring and analysis module is used to collect and format various link traffic data, perform horizontal network performance monitoring and topology generation; the microservice monitoring module is used to collect microservice performance indicator data, perform multi-dimensional anomaly detection and analysis; the fault location and repair module is used to perform fault correlation analysis and location, and perform automated repair through a distributed collaborative repair mechanism.

[0017] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement a business operation and maintenance method based on a cloud service architecture.

[0018] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a business operation and maintenance method based on a cloud service architecture.

[0019] The beneficial effects of this invention are as follows: The business operation and maintenance method based on cloud service architecture provided by this invention collects and formats various link traffic data, performs horizontal network performance monitoring and topology generation, effectively integrates link traffic data in different environments, achieves unified and standardized data processing, helps to quickly identify network bottlenecks and potential security issues, provides accurate basic data for horizontal network performance monitoring and topology generation, collects microservice performance index data, performs multi-dimensional anomaly detection and analysis, optimizes early warning and rapid location performance, can promptly detect abnormal fluctuations in service performance, provides in-depth analysis reports for the operation and maintenance team, effectively reduces the impact of system failures on business, achieves comprehensive monitoring of microservice operating status, and performs automated repair through fault correlation analysis and location, realizing intelligent and automated fault handling, shortening fault handling time and reducing operation and maintenance workload, and improving system stability and reliability. This invention achieves better results in terms of comprehensive data monitoring, real-time fault detection, and degree of operation and maintenance automation. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 The first embodiment of the present invention provides an overall flowchart of a business operation and maintenance method based on a cloud service architecture.

[0022] Figure 2 The third embodiment of the present invention provides an overall flowchart of a business operation and maintenance system based on a cloud service architecture. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1, referring to Figure 1 As an embodiment of the present invention, a business operation and maintenance method based on a cloud service architecture is provided, comprising:

[0025] S1: Collect and format various types of link traffic data to perform horizontal network performance monitoring and topology generation.

[0026] Furthermore, it collects and formats various types of link traffic data, including encrypting and compressing the data before transmission; it achieves cross-platform compatibility of data collection through standardized interfaces that support multiple protocols, and transmits link traffic data in different container, virtual machine, and physical machine environments through a unified data format.

[0027] It should be noted that horizontal network performance monitoring and topology generation includes real-time monitoring of key indicators of bandwidth and latency in the business system by collecting network traffic data at the operating system kernel layer.

[0028] The collected link data is automatically mapped into service call path graphs and dependency graphs through the topology generation module, and provides a path location function for tracing each path to help identify and troubleshoot anomalies in the service call chain.

[0029] It should also be noted that data encryption and compression are achieved using a JSON format data interface. JSON (JavaScript Object Notation Data Interface) is a specific data exchange format and interface used for data transmission and interaction between different computing systems. It is a lightweight data exchange format that is easy to read and write, and also easy for machines to parse and generate.

[0030] It should also be noted that collecting and formatting various types of link traffic data for horizontal network performance monitoring and topology generation effectively integrates link traffic data from different environments, achieving unified and standardized data processing. This helps to quickly identify network bottlenecks and potential security issues, providing accurate basic data for horizontal network performance monitoring and topology generation.

[0031] S2: Collect performance metrics data of microservices and perform multi-dimensional anomaly detection and analysis.

[0032] Furthermore, multi-dimensional anomaly detection and analysis includes continuous monitoring of performance fluctuations at various points in time through time series analysis, automatic analysis of historical data on business performance, identification of potential system problems by recognizing performance change trends in different time periods, capture of abnormal trends in business operations, and generation of real-time alarms.

[0033] It should be noted that multi-dimensional anomaly detection and analysis also includes dynamically optimizing alarm thresholds and anomaly priority identification by learning from historical monitoring data, automatically adapting to changes in the system environment, and performing real-time optimization based on changes in business needs and system load.

[0034] It should also be noted that eBPF technology collects network traffic data at the operating system kernel level, enabling real-time monitoring of key lateral network performance indicators such as bandwidth and latency in the business system; eBPF technology (Extended Berkeley Packet) eBPF (Extended Browsing Filter) is a high-performance kernel technology that allows predefined code to run within the Linux kernel without modifying the kernel source code or loading kernel modules. eBPF is used to efficiently collect network traffic data and microservice performance metrics. By running eBPF programs directly in kernel space, it reduces data copying between user space and kernel space. eBPF programs can filter and analyze network packets, system calls, and other kernel-level information in real time. eBPF enables fine-grained monitoring of network traffic and microservice performance data. eBPF programs can be loaded into the kernel to monitor and analyze data streams in real time, enabling multi-dimensional anomaly detection. eBPF maps store monitoring status and statistics, which can be used in subsequent anomaly detection algorithms to identify potential performance problems or security threats. In fault location and repair modules, eBPF provides dynamic tracing capabilities, monitoring and analyzing system calls, network events, etc., to help locate the root cause of faults. eBPF's collaborative mechanism can be combined with distributed repair systems to achieve automated repair processes. When an anomaly is detected, eBPF programs can trigger repair measures, reconfigure network settings, or restart the faulty service.

[0035] It should also be noted that by collecting performance metrics data of microservices and performing multi-dimensional anomaly detection and analysis, the early warning and rapid performance location have been optimized. This enables timely detection of abnormal fluctuations in service performance, provides in-depth analysis reports for the operations and maintenance team, effectively reduces the impact of system failures on business, and achieves comprehensive monitoring of the microservice operation status.

[0036] S3: Automated repair is performed through fault correlation analysis and location.

[0037] Furthermore, fault correlation analysis and localization includes decomposing faults into physical, network, and application layers through a hierarchical localization strategy, and constructing a knowledge graph to achieve cross-layer fault correlation analysis.

[0038] It should be noted that automated repair includes optimal resource scheduling after fault isolation, prioritizing the repair of critical node faults in a multi-node environment to reduce interference with business traffic, and using real-time communication mechanisms to coordinate the repair process and dynamically adjust resource allocation.

[0039] It should also be noted that fault correlation analysis and localization includes using relation mining algorithms to establish fault propagation chains, identify abnormal propagation paths, and locate the root cause of the fault based on fault frequency and abnormal correlation indicators. The specific steps are as follows: First, the system collects a large amount of log data, performance indicators, system events, and alarm information. This data covers all levels of the system, including hardware, network, operating system, middleware, and applications. Next, relation mining algorithms, such as association rule mining and graph theory algorithms, are used to process the collected data and establish a dependency model between system components. Based on the dependency model, the algorithm can infer the propagation path of the fault in the system. Through analysis of performance indicators and logs... Real-time analysis enables the algorithm to detect abnormal behaviors in the system, including excessively long response times, increased error rates, and abnormal resource utilization. Once an anomaly is detected, the algorithm traces the propagation path of the anomaly between system components. This involves dynamically updating and analyzing the path of the fault propagation chain to determine how the anomaly spreads from one component to another. The algorithm counts the frequency of failures in each component and analyzes the time-series characteristics of these failures to identify potential fault hotspots. By calculating correlation indicators between components, such as fault co-occurrence rate and impact range, the algorithm can assess the strength of the correlation between components. Combining fault frequency and correlation indicators, the algorithm can infer the most likely root cause of the fault.

[0040] It should also be noted that automated repair includes optimal resource scheduling after fault isolation through a distributed collaborative repair mechanism. The specific steps are as follows: Each node in the distributed system continuously monitors its own operating status, including key indicators such as CPU, memory, disk I / O, and network traffic. Nodes periodically report monitoring data to the central monitoring system or share it among nodes via a distributed protocol. The monitoring system analyzes the collected data and detects abnormal behavior using preset rules or machine learning models. Once an anomaly is detected, the system identifies potential fault points and marks them as fault events requiring further processing. The node that identifies the fault immediately sends a fault notification to other nodes in the system, ensuring that all relevant nodes can obtain fault information in a timely manner. Fault information is shared among nodes for collaborative fault handling. Based on the shared fault information, nodes use diagnostic algorithms... The system analyzes the causes of faults, identifying the root cause by analyzing the fault propagation path and scope of impact. Nodes reach a consensus through a negotiation mechanism, deciding on remedial measures. Based on the root cause and system status, specific remedial strategies are formulated, such as restarting services or switching to a backup node. The remedial strategies are broken down into executable tasks and assigned to the corresponding nodes. Nodes execute the assigned tasks while monitoring the system status to ensure smooth remediation. After the remedial tasks are completed, the system verifies the remediation effect to ensure the fault has been resolved. If the remediation is successful, the system status is updated, and normal operation is restored. If the remediation fails, the system re-enters the fault diagnosis phase. All data and operations during the fault handling process are recorded for subsequent analysis and auditing. The system adjusts monitoring parameters, optimizes diagnostic algorithms, and remedial strategies based on remediation experience to improve the efficiency of future fault handling.

[0041] It should also be noted that by performing fault correlation analysis and location, automated repair is achieved, realizing intelligent and automated fault handling, shortening fault handling time, reducing maintenance workload, and improving system stability and reliability.

[0042] Example 2 is an embodiment of the present invention, which provides a business operation and maintenance method based on a cloud service architecture. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0043] First, a distributed system consisting of six nodes is established, with each node representing a different service instance. Each node is configured to collect and format link traffic data. Network traffic data is collected at the operating system kernel level, and JSON-formatted data interfaces are used for data encryption and compression during transmission, ensuring the security and efficiency of data transmission between different computing systems. Next, cross-platform compatibility of data collection is achieved through standardized interfaces supporting multiple protocols, enabling unified transmission of link traffic data across different container, virtual machine, and physical machine environments. Based on data collection, horizontal network performance monitoring and topology generation are implemented. This involves real-time monitoring of key indicators such as bandwidth and latency in the business system, and the topology generation module automatically maps link data into service call path graphs and dependency graphs. Furthermore, eBPF technology is used to perform fine-grained monitoring of network traffic and microservice performance data at the kernel layer. Next, performance metrics data of microservices are collected, and multi-dimensional anomaly detection and analysis are achieved through time-series analysis and historical data learning. This step particularly focuses on identifying performance fluctuations and anomaly trends, as well as dynamically optimizing alarm thresholds and anomaly priorities. Finally, to test automated repair capabilities, various fault scenarios are simulated, and fault correlation analysis and localization strategies, combined with a distributed collaborative repair mechanism, are used to automatically detect, diagnose, and repair faults. This process focuses on observing the efficiency, accuracy, and speed of system recovery in fault handling. Experimental results demonstrate the innovation and practicality of this invention in distributed collaborative repair mechanisms, providing a new solution for the field of network monitoring and fault handling.

[0044] Example 3, referring to Figure 2 As an embodiment of the present invention, a business operation and maintenance system based on a cloud service architecture is provided, including a link monitoring and analysis module, a microservice monitoring module, and a fault location and repair module.

[0045] The link monitoring and analysis module is used to collect and format various link traffic data, perform horizontal network performance monitoring and topology generation; the microservice monitoring module is used to collect microservice performance indicator data, perform multi-dimensional anomaly detection and analysis; and the fault location and repair module is used to perform fault correlation analysis and location, and perform automated repair through a distributed collaborative repair mechanism.

[0046] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0047] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0048] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0049] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0050] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A service operation method of a cloud service framework, characterized by, The method comprises the following steps: Collecting and formatting various types of link traffic data for horizontal network performance monitoring and topology generation; Collecting performance indicator data of microservices for multi-dimensional anomaly detection and analysis; Automated repair through fault correlation analysis and positioning; The collecting and formatting of various types of link traffic data includes data encryption and compressed transmission; Cross-platform compatibility of data collection is achieved through standardized interfaces supported by multiple protocols, and link traffic data in different container, virtual machine and physical machine environments are transmitted through a unified data format; The horizontal network performance monitoring and topology generation includes real-time monitoring of horizontal network performance of key indicators such as bandwidth and delay in the business system by collecting network traffic data at the operating system kernel layer; The collected link data is automatically mapped into service call path graphs and dependency graphs by the topology generation module, and the path positioning function is provided for each trace to assist in identifying and troubleshooting anomalies in the service call chain; The multi-dimensional anomaly detection and analysis includes continuous monitoring of performance fluctuations at each time point, automatic analysis of historical business performance data, identification of potential system problems by identifying performance trends in different time periods, capture of abnormal trends in business operation and generation of real-time alarms; The multi-dimensional anomaly detection and analysis also includes dynamically optimizing alarm thresholds and anomaly priority identification through learning from historical monitoring data, automatically adapting to changes in the system environment, and real-time tuning according to changes in business requirements and system load; The fault correlation analysis and positioning includes decomposing faults into physical, network and application layers through hierarchical positioning strategies, and building a knowledge graph for cross-layer fault correlation analysis.

2. The business operation and maintenance method of the cloud service framework according to claim 1, characterized in that: The automated repair includes optimal scheduling of resources after fault isolation, high-priority repair of critical node faults in a multi-node environment to reduce interference with business traffic, and dynamic adjustment of resource allocation using real-time communication mechanisms to coordinate the repair process.

3. A system for service operation and maintenance method using the cloud service framework according to any one of claims 1-2, characterized in that: The method comprises a link monitoring and analysis module, a microservice monitoring module, and a fault positioning and repair module. The link monitoring and analysis module is used to collect and format various types of link traffic data for horizontal network performance monitoring and topology generation. The microservice monitoring module is used to collect performance indicator data of microservices for multi-dimensional anomaly detection and analysis. The fault positioning and repair module is used for fault correlation analysis and positioning, and automated repair through a distributed collaborative repair mechanism.

4. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the business operation and maintenance method of the cloud service architecture according to any one of claims 1 to 2.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the business operation and maintenance method of the cloud service architecture according to any one of claims 1 to 2.

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