Service operation and maintenance method and system based on cloud service architecture

Abstract

The invention discloses a service operation and maintenance method and system based on a cloud service framework, and relates to the technical field of cloud computing, and the method comprises the steps: collecting and formatting various link flow data, and carrying out the transverse network performance monitoring and topology generation; acquiring performance index data of the micro-service, and performing multi-dimensional anomaly detection and analysis; and through fault correlation analysis and positioning, automatic repair is carried out. The method provided by the invention is helpful for quickly identifying network bottlenecks and potential safety problems, provides accurate basic data for transverse network performance monitoring and topology generation, optimizes early warning and quick positioning performance, effectively reduces the influence of system faults on services, realizes intelligentization and automation of fault processing, and improves the working efficiency. The fault processing time is shortened, the operation and maintenance workload is reduced, and the stability and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing technology, and in particular to a business operation and maintenance method and system based on a cloud service architecture. Background Art

[0002] With the rapid development of information technology, cloud computing, as an emerging computing model, has profoundly changed the traditional IT infrastructure and service delivery methods. In the cloud computing environment, business operation and maintenance management has become a key link to ensure service continuity and improve user experience. However, the existing technology still needs to be improved in terms of data processing accuracy, comprehensiveness of monitoring, and the degree of automation of 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 improving, which has prompted related technologies to continue to develop in a more efficient and intelligent direction.

[0003] In recent years, relevant technologies have made great progress, and various cloud service architectures have sprung up, providing elastic scaling and on-demand resource management capabilities. On this basis, business operation and maintenance methods are also constantly evolving, from the initial simple monitoring to today's intelligent and automated operation and maintenance, and technical means are becoming increasingly rich. However, the existing related technologies still have many shortcomings. In terms of link traffic data collection and monitoring, the existing technologies often lack effective data formatting processing, resulting in inaccurate monitoring results and difficulty in generating a comprehensive network performance topology map, which affects the accurate assessment of the network status. For microservice performance monitoring, the existing technologies fail to achieve multi-dimensional anomaly detection and analysis, and cannot timely discover potential performance bottlenecks, affecting the response speed of the service and user experience. In terms of fault handling, the existing technologies lack effective fault correlation analysis, and the degree of automated repair is not high, often requiring manual intervention, which not only increases the operation and maintenance costs, but also prolongs the 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 issues 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 the present invention is: the existing cloud computing technology has the problems of incomplete link traffic monitoring, inaccurate microservice anomaly detection, low efficiency of automatic fault repair, and how to achieve comprehensive, efficient and automated business operation and maintenance management.

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

[0007] As a preferred solution of the business operation and maintenance method based on cloud service architecture described in the present invention, the collection and formatting of various link traffic data includes data encryption and compression transmission; cross-platform compatibility of data collection is achieved through a standardized interface supported by multiple protocols, and link traffic data in different containers, virtual machines and physical machine environments are transmitted through a unified data format.

[0008] As a preferred solution of the business operation and maintenance method based on cloud service architecture described in the present invention, the horizontal network performance monitoring and topology generation include collecting network traffic data at the operating system kernel layer to perform real-time monitoring of the horizontal network performance of key indicators such as bandwidth and delay in the business system.

[0009] The collected data of various links are automatically mapped into service call path diagrams and dependency diagrams through the topology generation module, and a path positioning function is provided for tracking each path to assist in identifying and troubleshooting anomalies in the service call chain.

[0010] As a preferred solution of the business operation and maintenance method based on cloud service architecture described in the present 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 identifying performance change trends in different time periods, capturing abnormal trends in business operations and generating real-time alarms.

[0011] As a preferred solution of the business operation and maintenance method based on cloud service architecture described in the present invention, the multi-dimensional anomaly detection and analysis also includes dynamically optimizing alarm thresholds and anomaly priority identification through learning of historical monitoring data, automatically adapting to system environmental changes, and performing real-time tuning according to changes in business needs and system load.

[0012] As a preferred solution of the business operation and maintenance method based on cloud service architecture described in the present invention, the fault correlation analysis and positioning includes decomposing the fault into physical layer, network layer and application layer through a layered positioning strategy, and constructing a knowledge graph to realize cross-level fault correlation analysis.

[0013] As a preferred solution of the business operation and maintenance method based on cloud service architecture described in the present invention, the automated repair includes optimal scheduling of resources after fault isolation, repairing key node failures with high priority in a multi-node environment, reducing interference with business traffic, and using real-time communication mechanisms to coordinate the repair process and dynamically adjust resource allocation.

[0014] Another object of the present invention is to provide a business operation and maintenance system based on a cloud service architecture, which can collect performance indicator data of microservices to perform multi-dimensional anomaly detection and analysis, thereby solving the problem that current cloud computing technology has non-real-time and inaccurate anomaly detection.

[0015] As a preferred solution of the business operation and maintenance system based on the cloud service architecture described in the present 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 types of link traffic data, perform horizontal network performance monitoring and topology generation; the microservice monitoring module is used to collect performance indicator data of microservices, 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, wherein the memory stores a computer program, and the processor executes the computer program to implement a business operation and maintenance method based on a cloud service architecture.

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

[0019] Beneficial effects of the present invention: The business operation and maintenance method based on cloud service architecture provided by the present invention collects and formats various link traffic data, performs horizontal network performance monitoring and topology generation, effectively integrates link traffic data in different environments, realizes 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 performance indicator data of microservices, performs multi-dimensional anomaly detection and analysis, optimizes early warning and rapid positioning performance, can timely discover abnormal fluctuations in service performance, and provide in-depth analysis reports for the operation and maintenance team, effectively reduces the impact of system failures on the business, and realizes comprehensive monitoring of the operating status of microservices. Through fault correlation analysis and positioning, automatic repair is performed, and intelligent and automated fault handling is realized, which shortens the fault handling time and reduces the operation and maintenance workload, and improves the stability and reliability of the system. The present invention achieves better results in terms of comprehensiveness of data monitoring, real-time fault detection, and degree of automation of operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0021] Figure 1 An overall flow chart of a business operation and maintenance method based on a cloud service architecture provided for the first embodiment of the present invention.

[0022] Figure 2 An overall flow chart of a business operation and maintenance system based on a cloud service architecture provided for the third embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

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

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

[0026] Furthermore, the collection and formatting of various types of link traffic data include data encryption and compression 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.

[0027] It should be noted that horizontal network performance monitoring and topology generation include real-time monitoring of 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.

[0028] The collected data of various links are automatically mapped into service call path diagrams and dependency diagrams through the topology generation module, and a path positioning function is provided for tracking each path to assist in identifying and troubleshooting anomalies in the service call chain.

[0029] It should also be noted that the JSON format data interface is used for data encryption and compression transmission. JSON format data interface (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 is also easy for machines to parse and generate.

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

[0031] S2: Collect performance indicator 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 identifying performance change trends in different time periods, capturing abnormal trends in business operations and generating real-time alerts.

[0033] It should be noted that 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 system environmental changes, and performing real-time tuning based on changes in business needs and system load.

[0034] It should also be noted that the eBPF technology collects network traffic data at the operating system kernel layer, and performs real-time monitoring of the horizontal network performance of key indicators such as bandwidth and latency in the business system; eBPF technology (Extended Berkeley Packet eBPF is a high-performance kernel technology that allows predefined code to be run in the Linux kernel without changing the kernel source code or loading kernel modules. eBPF technology is used to efficiently collect link traffic data and performance indicator data of microservices. By running eBPF programs directly in kernel space, the copying of data between user space and kernel space is reduced. eBPF programs can filter and analyze network packets, system calls and other kernel-level information in real time. With eBPF technology, fine-grained monitoring of network traffic and microservice performance data can be achieved. eBPF programs can be loaded into the kernel to monitor and analyze data streams in real time, thereby performing multi-dimensional anomaly detection. eBPF maps are used to store monitoring status and statistical data, which can be used in subsequent anomaly detection algorithms to identify potential performance problems or security threats. In the fault location and repair module, eBPF technology provides dynamic tracking capabilities, which can monitor and analyze system calls, network events, etc., to help locate the root cause of the fault. The collaborative mechanism of eBPF can be combined with a distributed repair system to realize an automated repair process. When an anomaly is detected, the eBPF program can trigger repair measures, reconfigure network settings, or restart the faulty service.

[0035] It should also be noted that collecting performance indicator data of microservices and conducting multi-dimensional anomaly detection and analysis optimizes early warning and rapid positioning performance, can promptly detect abnormal fluctuations in service performance, and provide in-depth analysis reports for the operation and maintenance team, effectively reducing the impact of system failures on the business and achieving comprehensive monitoring of the operating status of microservices.

[0036] S3: Perform automated repairs through fault correlation analysis and location.

[0037] Furthermore, fault correlation analysis and location include decomposing the fault into the physical layer, network layer and application layer through a layered location strategy, and constructing a knowledge graph to achieve cross-level fault correlation analysis.

[0038] It should be noted that automated repair includes optimal scheduling of resources after fault isolation, repairing key node failures with high priority in a multi-node environment, reducing 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 location includes the use of relationship mining algorithms to establish a fault propagation chain, identify abnormal transmission 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 will collect a large amount of log data, performance indicators, system events, and alarm information. These data cover all levels of the system, including hardware, network, operating system, middleware, and application programs. Next, the collected data is processed using relationship mining algorithms, such as association rule mining and graph theory algorithms, to 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. By analyzing performance indicators and logs, the system can identify the root cause of the fault. Real-time analysis: the algorithm can detect abnormal behaviors in the system, including long response times, increased error rates, and abnormal resource utilization. Once an anomaly is detected, the algorithm will track the transmission path of the anomaly between system components, which involves dynamic updates and path analysis of the fault propagation chain to determine how the anomaly propagates from one component to another. The algorithm will count the frequency of failures in each component and analyze the time series characteristics of these failures to identify possible failure hotspots. By calculating the correlation indicators between components, such as the co-occurrence rate of faults and the scope of impact, the algorithm can evaluate the strength of the correlation between components. Combining the 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. The node regularly reports monitoring data to the central monitoring system or shares it between nodes through a distributed protocol; the monitoring system analyzes the collected data and detects abnormal behavior through preset rules or machine learning models; once an anomaly is detected, the system will identify potential failure points and mark them as failure events that require further processing; the node that identifies the failure immediately sends a fault notification to other nodes in the system to ensure that all relevant nodes can obtain fault information in a timely manner, and the fault information is shared between nodes to collaboratively handle the fault; the node uses the diagnostic algorithm based on the shared fault information The method analyzes the cause of the failure, determines the root cause of the failure by analyzing the fault propagation path and the scope of impact; reaches a consensus between nodes through a negotiation mechanism and decides on the repair measures to be taken. According to the root cause of the failure and the system status, a specific repair strategy is formulated, such as restarting the service and switching to the backup node; the repair strategy is decomposed into executable tasks and assigned to the corresponding nodes; the nodes perform the assigned tasks and monitor the system status at the same time to ensure the smooth progress of the repair operation; after the repair task is completed, the system verifies the repair effect to ensure that the fault has been resolved; if the repair is successful, the system status is updated and normal operation is restored; if the repair fails, the fault diagnosis stage is re-entered; all data and operations in the fault handling process are recorded for subsequent analysis and auditing; the system adjusts monitoring parameters, optimizes diagnostic algorithms and repair strategies based on repair experience to improve the efficiency of future fault handling.

[0041] It should also be noted that through fault correlation analysis and location, automated repair is carried out, which makes fault handling intelligent and automated, shortens fault handling time and reduces operation and maintenance workload, and improves 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, each node represents a different service instance, and each node is configured to collect and format link traffic data. Network traffic data is collected at the operating system kernel layer, and data encryption and compression transmission are performed using the JSON format data interface to ensure the security and efficiency of data transmission between different computing systems. Next, through a standardized interface supported by multiple protocols, cross-platform compatibility of data collection is achieved, so that link traffic data in different containers, virtual machines, and physical machine environments can be transmitted uniformly; based on data collection, horizontal network performance monitoring and topology generation are implemented, which involves real-time monitoring of key indicators such as bandwidth and latency in the business system, and automatically mapping link data into service call path graphs and dependency graphs through the topology generation module. In addition, eBPF technology is used to perform fine-grained monitoring of network traffic and microservice performance data at the kernel layer. Next, performance indicator data of microservices is collected, and multi-dimensional anomaly detection and analysis are achieved through time series analysis and historical data learning. In this step, special attention is paid to the identification of performance fluctuations and abnormal trends, as well as the dynamic optimization of alarm thresholds and abnormal priorities. Finally, in order to test the automated repair capability, multiple fault scenarios are simulated, and fault correlation analysis and location strategies are used in combination with a distributed collaborative repair mechanism to automatically detect, diagnose and repair faults. In this process, the efficiency, accuracy of fault handling and the speed of system recovery are observed. The experimental results show that the innovation and practicality of the present invention in the distributed collaborative repair mechanism provide new solutions for the field of network monitoring and fault handling.

[0044] Example 3, reference Figure 2 , is an embodiment of the present invention, and provides a business operation and maintenance system based on a cloud service architecture, 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, conduct horizontal network performance monitoring and topology generation; the microservice monitoring module is used to collect performance indicator data of microservices, conduct multi-dimensional anomaly detection and analysis; the fault location and repair module is used to perform fault correlation analysis and location, and perform automated repairs through a distributed collaborative repair mechanism.

[0046] If the function is implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

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

[0048] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0049] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in 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 rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A business operation and maintenance method based on a cloud service architecture, characterized in that: include: Collect and format various link traffic data to conduct horizontal network performance monitoring and topology generation; Collect performance indicator data of microservices and conduct multi-dimensional anomaly detection and analysis; Automatic repair is performed through fault correlation analysis and location.

2. The business operation and maintenance method based on the cloud service framework according to claim 1, characterized in that: The collecting and formatting of various link traffic data includes data encryption and compression transmission; Cross-platform compatibility of data collection is achieved through standardized interfaces supported by multiple protocols, and link flow data in different container, virtual machine and physical machine environments are transmitted through a unified data format.

3. The business operation and maintenance method based on the cloud service framework according to claim 2, characterized in that: 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 data of various links are automatically mapped into service call path diagrams and dependency diagrams through the topology generation module, and a path positioning function is provided for tracking each path to assist in identifying and troubleshooting anomalies in the service call chain.

4. The business operation and maintenance method based on the cloud service framework according to claim 3, characterized in that: 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 identifying performance change trends in different time periods, capturing abnormal trends in business operations and generating real-time alarms.

5. The business operation and maintenance method based on the cloud service framework according to claim 4, characterized in that: 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 system environmental changes, and performing real-time tuning according to changes in business needs and system load.

6. The business operation and maintenance method based on the cloud service framework according to claim 5, characterized in that: The fault correlation analysis and location includes decomposing the fault into the physical layer, network layer and application layer through a layered location strategy, and constructing a knowledge graph to realize cross-layer fault correlation analysis.

7. The business operation and maintenance method based on the cloud service framework according to claim 6, characterized in that: The automated repair includes optimal resource scheduling after fault isolation, repairing critical node failures with high priority in a multi-node environment, reducing interference with business traffic, and utilizing real-time communication mechanisms to coordinate the repair process and dynamically adjust resource allocation.

8. A system using the business operation and maintenance method based on the cloud service architecture as claimed in any one of claims 1 to 7, characterized in that: Includes link monitoring and analysis module, microservice monitoring module, and fault location and repair module; 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 performance indicator data of microservices and perform multi-dimensional anomaly detection and analysis; The fault location and repair module is used to perform fault correlation analysis and location, and perform automatic repair through a distributed collaborative repair mechanism.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the business operation and maintenance method based on the cloud service architecture described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the business operation and maintenance method based on the cloud service architecture described in any one of claims 1 to 7 are implemented.

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