Operation and maintenance management method and system of big data center, medium and equipment

Abstract

The invention provides an operation and maintenance management method and system of a big data center, a medium and equipment, and relates to the technical field of data management. According to the technical scheme provided by the invention, different monitoring indexes and monitoring modes can be adopted for the special equipment in the big data center, the special equipment carries out monitoring through a multi-level cache mechanism and under each level, and the multi-level cache architecture based on the flow level can monitor the data flow according to the importance and resource requirements of the data flow. The data are accurately guided to the most suitable storage and processing environment, so that the data processing burden can be reduced, the monitoring precision under the flow peak condition is improved, and the operation and maintenance management effect is improved.

Description

Technical Field

[0001] The present application relates to the field of data management technology, and specifically to an operation and maintenance management method, system, medium and equipment for a big data center. Background Art

[0002] With the rapid development of information technology, big data centers have become key infrastructure for storing and processing massive amounts of information. These centers need to process huge amounts of data and must ensure that they operate efficiently and safely. Therefore, it is particularly important to effectively operate and maintain big data centers to ensure the stability and reliability of the system.

[0003] In related technologies, data flow monitoring is usually used to achieve operation and maintenance management. This method helps identify potential problems by monitoring data flow in real time. However, in actual applications, large data centers often have large traffic peaks, which makes the monitoring system unable to accurately capture anomalies, resulting in inaccurate monitoring and affecting the effectiveness of operation and maintenance management. Summary of the invention

[0004] The present application provides an operation and maintenance management method, system, medium and equipment for a big data center, which can achieve accurate monitoring of the big data center and improve the operation and maintenance management effect.

[0005] In a first aspect, the present application provides an operation and maintenance management method for a big data center, the method comprising: Obtaining a first operation data stream of a proprietary device and a second operation data stream of a common device in a big data center; According to the traffic level of the first running data flow, the first running data flow is allocated to a cache level corresponding to the traffic level; Using the first monitoring indicator of the proprietary device to monitor the data flow at each cache level, to obtain a first monitoring result corresponding to the proprietary device; Using the second monitoring indicator of the common device to monitor the second operation data flow, and obtaining a second monitoring result corresponding to the common device; An operation and maintenance management result of the big data center is generated according to the first monitoring result and the second monitoring result.

[0006] By adopting the above technical solutions, different monitoring indicators and monitoring methods are adopted for the proprietary devices in the big data center. The proprietary devices are monitored separately at each level through a multi-level caching mechanism. The multi-level caching architecture based on the traffic level can accurately guide the data flow to the most suitable storage and processing environment according to its importance and resource requirements, thereby reducing the data processing burden, improving the monitoring accuracy under traffic peak conditions, and thus improving the operation and maintenance management effect.

[0007] Optionally, before obtaining the first operation data stream of the proprietary device in the big data center and the second operation data stream of the common device, the method further includes: Obtain the hardware configuration information and task configuration information of each device in the big data center; The device whose hardware configuration information meets the standard hardware configuration information and whose task configuration information is in the task running state is determined as the proprietary device.

[0008] By adopting the above technical solution, the hardware configuration information and task configuration information of each device in the big data center are obtained to achieve accurate classification of the devices. Specifically, by comparing the hardware configuration information with the standard hardware configuration and combining the running status of the task configuration information, the proprietary devices can be accurately identified. Through this accurate identification and classification, the system can better respond to different computing needs and improve the operation and maintenance management effect of the big data center.

[0009] Optionally, allocating the first operating data flow to a cache level corresponding to the traffic level according to the traffic level of the first operating data flow includes: Acquire the historical operation data flow of the proprietary device, determine the flow level corresponding to the proprietary device, and the flow level corresponds to multiple flow intervals; Determine the traffic level of the first operation data flow according to the traffic interval in which the real-time traffic of the proprietary device is located; Allocate the first running data flow to a cache level corresponding to the traffic level.

[0010] By adopting the above technical solution, it is possible to accurately classify data flows and assign them to corresponding cache levels. This not only optimizes the use of cache resources, but also effectively improves the efficiency of data processing and the response speed of the system. In the case of traffic peaks, this solution can significantly improve monitoring accuracy, ensure priority processing of key data flows, and ultimately improve the overall operation and maintenance management of the big data center.

[0011] Optionally, the acquiring the historical operation data flow of the proprietary device and determining the flow level corresponding to the proprietary device includes: Obtaining the historical operation data flow of the proprietary device, and determining the average flow value and flow change curve corresponding to the historical operation data flow; A flow level corresponding to the proprietary device is defined according to the average flow value and the flow change curve.

[0012] By adopting the above technical solutions, we can achieve fine classification and management of traffic, so that the equipment can get the most appropriate resource allocation under different traffic conditions. This not only improves the operating efficiency and stability of the system, but also enhances the adaptability to traffic fluctuations, effectively optimizing the operation and maintenance management effect of the big data center.

[0013] Optionally, the flow level includes the number of flow levels and a flow interval, and the flow level corresponding to the proprietary device is defined according to the average flow value and the flow change curve, including: The number of flow levels of the proprietary device is determined according to the average flow value, and the flow interval is determined according to the flow change curve.

[0014] By adopting the above technical solutions and this detailed traffic division, the system can more effectively allocate resources and manage traffic, ensuring optimal performance under different load conditions. This not only improves the efficiency of data flow processing, but also enhances the flexibility and stability of the system, thereby significantly optimizing the overall operation and maintenance management of the big data center.

[0015] Optionally, after the second monitoring indicator of the common device is used to monitor the second operation data flow and a second monitoring result corresponding to the common device is obtained, the method further includes: Obtaining a first processing duration corresponding to the first monitoring result and a second processing duration corresponding to the second monitoring result; Determining whether the first processing duration is the same as the second processing duration; If the first processing time is different from the second processing time, the traffic level is adjusted according to a difference between the first processing time and the second processing time.

[0016] By adopting the above technical solution, the second monitoring result corresponding to the ordinary device is obtained. Then, the system obtains the first processing time corresponding to the first monitoring result and the second processing time corresponding to the second monitoring result, and determines whether the two are the same. If the processing time is different, the system adjusts the traffic level according to their difference. This process ensures the balance of processing time, thereby optimizing resource allocation and improving the operating efficiency and response speed of the system. By dynamically adjusting the traffic level, the solution enhances the adaptability of the system under different load conditions, making the operation and maintenance management of the big data center more accurate and efficient.

[0017] Optionally, after generating the operation and maintenance management result of the big data center according to the first monitoring result and the second monitoring result, the method further includes: The operation and maintenance management results of the big data center are output visually.

[0018] By adopting the above technical solutions, complex monitoring data and analysis results are presented in an intuitive form, which is easy to understand and analyze quickly. The intuitive graphical interface enables it to more quickly identify potential problems and optimization space, improving the efficiency and accuracy of operation and maintenance management.

[0019] In a second aspect, the present application provides an operation and maintenance management system for a big data center, the system comprising: A data acquisition module, used to acquire a first operation data stream of a proprietary device and a second operation data stream of a common device in a big data center; A level allocation module, configured to allocate the first operation data flow to a cache level corresponding to the traffic level according to the traffic level of the first operation data flow; A first monitoring module, configured to monitor the data flow at each cache level using a first monitoring indicator of the proprietary device, and obtain a first monitoring result corresponding to the proprietary device; A second monitoring module, configured to monitor the second operation data flow using a second monitoring indicator of the common device to obtain a second monitoring result corresponding to the common device; A result output module is used to generate an operation and maintenance management result of the big data center according to the first monitoring result and the second monitoring result.

[0020] In a third aspect, the present application provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing any one of the above methods.

[0021] In a fourth aspect, the present application provides an electronic device, comprising a processor, a memory and a transceiver, wherein the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any one of the methods described above.

[0022] In summary, the beneficial effects brought about by the technical solution of this application include: By adopting the above technical solutions, different monitoring indicators and monitoring methods are adopted for the proprietary devices in the big data center. The proprietary devices are monitored separately at each level through a multi-level caching mechanism. The multi-level caching architecture based on the traffic level can accurately guide the data flow to the most suitable storage and processing environment according to its importance and resource requirements, thereby reducing the data processing burden, improving the monitoring accuracy under traffic peak conditions, and thus improving the operation and maintenance management effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of an operation and maintenance management method of a big data center according to an embodiment of the present application; Figure 2 This is a schematic diagram of the structure of an operation and maintenance management system of a big data center according to an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a specific way.

[0027] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0028] See also Figure 1 , which is a flow chart of a method for operation and maintenance management of a big data center provided in an embodiment of the present application. The method can be implemented by a computer program, can be implemented by a single-chip microcomputer, or can be run on an operation and maintenance management system of a big data center based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application. The specific steps of the method for operation and maintenance management of a big data center are described in detail below.

[0029] S101: Acquire a first operation data stream of a proprietary device and a second operation data stream of a common device in a big data center.

[0030] Among them, a big data center refers to a large-scale, high-performance data processing facility for storing, managing and analyzing massive amounts of data. In the embodiments of the present application, it can be understood as a complex system environment that integrates advanced computing, storage and network equipment, capable of processing PB-level or even EB-level structured and unstructured data. The big data center is mainly used for large-scale data storage, high-performance computing, intelligent data management, providing cloud services, supporting decision analysis, ensuring high availability and promoting innovation. It can safely and efficiently store massive amounts of data, perform complex data analysis, artificial intelligence training, scientific simulation and other computing-intensive tasks, intelligently classify, index, backup and restore data, and provide flexible computing and storage services for various applications and users. In the context of this application, the big data center particularly emphasizes its complex operating environment and high-performance requirements, requiring advanced operation and maintenance management methods to optimize resource allocation, improve operating efficiency, and ensure the stability and performance of the overall system.

[0031] Among them, proprietary devices refer to high-performance computing devices with specific hardware configurations, performance characteristics, and uses in large data center environments. Such devices usually have highly customized hardware architectures and are optimized for specific types of data processing and computing tasks. Proprietary devices are used to perform key performance-intensive tasks within large data centers, such as complex data analysis, artificial intelligence model training, scientific computing simulations, etc. They are able to handle requirements such as large-scale parallel computing, high-speed data transmission and storage, and have higher processing power and efficiency than ordinary devices. The operating characteristics of these devices often include high throughput, low latency, and powerful computing power, which can meet the strict requirements of real-time, scalability, and high performance in big data processing. In the operation and management of big data centers, performance monitoring, resource allocation, and optimized use of proprietary devices are crucial to the efficiency and reliability of the entire system.

[0032] Among them, the first operation data stream refers to the data stream generated and processed by the proprietary device during operation in the large data center environment. In the embodiments of the present application, it can be understood as a real-time data stream generated by the proprietary device when performing a specific task. These data streams reflect the operating status, performance indicators and processing capabilities of the device. The first operation data stream usually has a higher priority and complexity. It contains key parameters of the device operation, such as CPU usage, memory usage, data throughput, task execution efficiency and other information. These data streams are used to monitor and analyze the operation of proprietary devices in real time, helping system administrators and automation management systems to make timely decisions and adjustments.

[0033] Among them, the second operation data stream of ordinary equipment refers to the data stream generated and processed by ordinary equipment during operation in a large data center environment. In the embodiment of the present application, it can be understood as a real-time data stream generated by ordinary equipment other than proprietary equipment. These data streams reflect the operating status, performance indicators and processing capabilities of ordinary equipment, but usually have lower priority and complexity. The second operation data stream contains various parameters of the operation of ordinary equipment, such as CPU usage, memory usage, network traffic, storage usage and other information. These data streams are used to monitor and analyze the operation of ordinary equipment.

[0034] In this embodiment, in the operation and maintenance management process of the big data center, obtaining the first operation data stream of the proprietary device and the second operation data stream of the common device is a key step to achieve efficient and intelligent management. The implementation of this step is to fully grasp the operating status of all devices in the data center, laying the foundation for subsequent resource optimization and performance improvement. In the specific implementation, the system first connects to each device of the big data center through a preset data acquisition interface. For proprietary devices, the system will focus on collecting its high-priority first operation data stream, which usually includes key performance indicators such as CPU usage, memory usage, data throughput, and task execution efficiency. At the same time, the system will also collect the second operation data stream of ordinary devices, including but not limited to conventional operating parameters such as CPU usage, memory usage, network traffic, and storage usage. The data collection process uses real-time monitoring technology to ensure that the information obtained can reflect the current status of the device in a timely manner. In order to process a large amount of real-time data, the system will adopt a distributed data processing framework to improve the efficiency and scalability of data processing. In addition, in order to ensure the integrity and accuracy of the data, the system will use data verification and cleaning algorithms to remove outliers and redundant information. The operation data stream obtained in this way can fully reflect the operating status of the big data center and provide a reliable data basis for subsequent analysis and decision-making.

[0035] Based on the above embodiment, as an optional implementation, the confirmation of the proprietary device may be implemented by using steps S201-S202.

[0036] S201: Obtain hardware configuration information and task configuration information of each device in the big data center.

[0037] Among them, hardware configuration information refers to a detailed set of technical parameters that describe the hardware composition and performance characteristics of the computing device. In the embodiments of the present application, it can be understood as the specific hardware specifications and performance indicators of various devices in the big data center, especially proprietary devices, including but not limited to processor type and frequency, memory capacity and type, storage device specifications, network interface bandwidth, GPU or dedicated accelerator model and quantity and other key hardware parameters. This information is used to accurately describe and identify the computing power, storage capacity and network performance of each device in the big data center, and provide a basic basis for device classification, task allocation and resource management.

[0038] Among them, task configuration information refers to a detailed description of the characteristics, requirements and execution parameters of various computing tasks performed in a large data center environment. In the embodiment of the present application, it can be understood as a series of configuration parameters and requirements for specific computing tasks or workloads, including but not limited to task type (such as data analysis, machine learning, scientific computing, etc.), required computing resources (such as the number of CPU cores, memory size, GPU requirements, etc.), expected execution time, data input and output requirements, priority levels, dependencies, parallelism requirements and other key information.

[0039] In the efficient operation and maintenance management of the big data center, in order to fully understand the hardware characteristics and current load of all devices in the data center. In specific implementation, the system first connects to each device in the big data center through a preset management interface or agent. For hardware configuration information, the system automatically collects key parameters such as the processor type and frequency, memory capacity and type, storage device specifications, network interface bandwidth, GPU or dedicated accelerator model and quantity of each device. This information can usually be obtained through the API or hardware management tools provided by the operating system. At the same time, the system also collects task configuration information, including the type of task currently running, the required computing resources, the expected execution time, data input and output requirements, priority level, etc. This information can be obtained through the interface of the task scheduling system or log analysis. In order to ensure the accuracy and real-time nature of the information, the system will regularly update this configuration information and use a version control mechanism to track changes. During the data collection process, the system will use distributed data processing technology to efficiently process information from a large number of devices. In addition, in order to ensure the integrity and consistency of the data, the system will use a data verification algorithm to detect and correct possible errors or outliers. The hardware configuration information and task configuration information obtained in this way can fully reflect the resource status and workload characteristics of the big data center.

[0040] S202: Determine a device whose hardware configuration information meets standard hardware configuration information and whose task configuration information is in a task running state as a proprietary device.

[0041] Among them, standard hardware configuration information refers to a benchmark set of hardware specifications and performance indicators that proprietary equipment in a large data center should have. In the embodiment of the present application, it can be understood as a set of predefined hardware parameter thresholds or specification ranges for identifying and classifying high-performance dedicated equipment in a data center. These standard configuration information generally include, but are not limited to, the minimum requirements for the number of processor cores and the main frequency, the benchmark values ​​of memory capacity and bandwidth, the standards for storage device type and capacity, the lower limit of network interface bandwidth, the performance indicators of dedicated accelerators (such as GPUs or FPGAs), and other key hardware features. Standard hardware configuration information is used to establish an objective benchmark to distinguish between proprietary equipment and ordinary equipment, ensuring that proprietary equipment has the ability to perform high-performance computing tasks.

[0042] In specific implementation, the system will first read the pre-defined standard hardware configuration information, which will serve as the basis for determining whether the device is a proprietary device. Subsequently, the system will traverse the hardware configuration information of all devices in the big data center and compare the actual configuration of each device with the standard configuration. During the comparison process, the system will check whether key hardware parameters such as processor performance, memory capacity, storage specifications, network bandwidth, and dedicated accelerators meet or exceed the standard requirements. At the same time, the system will also check the task configuration information of each device to determine whether it is in the task running state. Only devices that meet both the hardware configuration standards and are executing tasks will be identified as proprietary devices. This dual judgment mechanism ensures that the identified proprietary devices not only have high-performance hardware, but are also effectively performing computing tasks, avoiding idle resources. In order to achieve the automation and real-time nature of this process, the system will use efficient data processing algorithms to quickly complete the comparison and analysis of a large amount of device information. In addition, the system will also use a dynamic update mechanism to regularly refresh device status information to adapt to changes in device configuration or dynamic changes in task status.

[0043] S102: According to the traffic level of the first running data flow, the first running data flow is allocated to a cache level corresponding to the traffic level.

[0044] Among them, traffic level refers to a metric for classifying and evaluating the scale and importance of network data traffic in a large data center. In the embodiment of the present application, it can be understood as a hierarchical classification of network traffic based on factors such as data transmission volume, processing priority, and business importance. This classification usually includes multiple levels, such as low, medium, high, and critical levels, and each level corresponds to different data processing requirements and resource allocation strategies. It can help the system allocate more network bandwidth and computing resources for high-level traffic, ensure the smooth operation of key businesses, and provide differentiated service quality assurance for data streams of different importance. By distinguishing traffic levels, the system can more reasonably distribute network loads between multiple devices or nodes to avoid overloads at certain key nodes. By identifying and prioritizing high-level traffic, the system can improve overall data processing efficiency and response speed.

[0045] Among them, the cache level refers to the multi-level data storage and processing architecture implemented within the large data center, which is designed to optimize data access speed and system performance. In the embodiment of the present application, it can be understood as a hierarchical data cache structure, which usually includes multiple levels, such as cache, memory cache, local disk cache, and distributed storage cache. These levels are organized into a hierarchical structure according to the frequency, importance, and processing requirements of data access. The cache level is used to achieve fast access and efficient processing of data while balancing system performance and storage costs. In practical applications, high-level caches (such as cache and memory cache) are usually used to store frequently accessed hot data or key business data, providing the fastest access speed; while low-level caches (such as local disk cache and distributed storage cache) are used to store data with low access frequency but still requiring fast response. Through this hierarchical structure, the system can store the data in the most appropriate cache level according to its characteristics and access mode, thereby improving data processing efficiency as a whole. In this application, the cache level is closely integrated with the traffic level, and data with high-level traffic is usually stored in the high-level cache first to ensure fast access and processing.

[0046] In this embodiment, the system first analyzes the received first running data flow to determine the traffic level to which it belongs. This process involves evaluating multiple characteristics of the data flow, including factors such as data volume, processing priority, and business importance. Once the traffic level is determined, the system will allocate the data flow to the corresponding cache level according to the preset mapping relationship. For example, high-level traffic may be allocated to the cache or memory cache layer, while lower-level traffic may be allocated to the local disk cache or distributed storage cache layer. This allocation mechanism is dynamic, and the system will continuously monitor the characteristics and access patterns of the data flow and adjust its position in the cache level when necessary. In order to achieve efficient execution of this process, the system adopts an intelligent scheduling algorithm that can complete the analysis and allocation decision of the data flow in a very short time. At the same time, the system also implements an adaptive cache management strategy, which can dynamically adjust the allocation strategy according to the load conditions of each cache level to avoid overload at a certain level.

[0047] By distributing different levels of data flow to multiple cache levels, the system achieves balanced load distribution and reduces the pressure on a single storage layer. Again, this layered processing method significantly improves the processing capacity of the data center under traffic peak conditions and maintains high monitoring accuracy even under high load. Based on the above embodiment, as an optional implementation, step S102 also includes S301-S303.

[0048] S301: Acquire the historical operation data flow of the proprietary device, determine the traffic level corresponding to the proprietary device, and the traffic level corresponds to multiple traffic intervals.

[0049] Among them, the historical operation data stream refers to the data stream record of the past period of time that has been processed or stored in the big data center. In the embodiment of the present application, it can be understood as a time series set of various operation data collected and saved by the system during the previous operation, including but not limited to network traffic data, equipment performance indicators, task execution records, resource utilization and other information. These historical data are usually stored in a structured or semi-structured form, containing key information such as timestamps, data sources, data types, and data values.

[0050] Among them, the traffic interval refers to a range definition method for quantitatively classifying the data transmission volume in the network traffic management of a large data center. In the embodiment of the present application, it can be understood as a specific numerical range set for different traffic levels based on the historical operation data flow and the current system state. These ranges are usually expressed in data transmission rate (such as the number of bits or bytes per second) or the amount of data per unit time. For example, there may be low traffic intervals (such as 0-100Mbps), medium traffic intervals (such as 100-500Mbps), high traffic intervals (such as 500Mbps-1Gbps), etc. The division of traffic intervals is not statically fixed, but can be dynamically adjusted according to the overall load of the system and business needs. By comparing the real-time monitored data flow with the preset traffic interval, the system can quickly determine the traffic level to which the current data flow belongs.

[0051] In specific implementation, the system will first extract the historical operation data flow of the proprietary device over a period of time (such as the last 30 days) from the data storage. These data contain multi-dimensional information such as the device's network traffic, processing task type, resource utilization, etc. The system will conduct in-depth analysis of these historical data, including statistical distribution characteristics, time series trends, periodic patterns, etc., to fully understand the operating characteristics of the device. Based on these analysis results, the system will determine an appropriate traffic level for the proprietary device. This traffic level is not a single value, but corresponds to multiple traffic intervals.

[0052] The process of determining traffic levels and corresponding traffic intervals is dynamic and adaptive. The system continuously monitors the real-time operation of proprietary devices and compares it with historical data. If a device's traffic pattern is found to have changed significantly, the system automatically adjusts its traffic level and corresponding traffic interval. This dynamic adjustment mechanism ensures the accuracy and timeliness of traffic classification, allowing the system to quickly adapt to changes in device load.

[0053] Based on the above embodiment, as an optional implementation, step S301 also includes S3011-S3012.

[0054] S3011: Obtain the historical operation data flow of the proprietary device, and determine the average flow value and flow change curve corresponding to the historical operation data flow.

[0055] In specific implementation, the system first extracts the historical operation data flow of the proprietary device for a certain period of time in the past from the data storage. These data usually contain multi-dimensional information such as the device's network traffic, data transmission rate, processing task type, etc., and each record is accompanied by an accurate timestamp. The system preprocesses these raw data, including data cleaning, outlier detection and processing, to ensure the accuracy of subsequent analysis. After the data preprocessing is completed, the system calculates the average flow value of the historical operation data flow. This average value is not just a simple arithmetic average, but a weighted average that takes into account the time weight to more accurately reflect the typical flow level of the device. At the same time, the system also generates a flow change curve, which shows the change trend of the device flow over time. The average flow value provides a benchmark for the overall flow level of the device, which helps to quickly determine the load status of the device. The flow change curve reveals the dynamic characteristics of the device flow, including peaks, valleys, periodic fluctuations, etc. S3012: Define the flow level corresponding to the proprietary equipment according to the average flow value and the flow change curve.

[0056] In specific implementation, the system will first comprehensively consider the characteristics of the average flow value and the flow change curve. The average flow value provides a benchmark for the overall flow level of the device, while the flow change curve reflects the dynamic change characteristics of the flow, including peak values, fluctuation ranges, and periodic patterns. The system will use a multi-dimensional evaluation method to define the flow level. First, based on the average flow value, the system will preliminarily classify the device into a basic flow level. Then, the system will analyze the characteristics of the flow change curve, such as fluctuation amplitude, peak frequency, and duration, and adjust the preliminary classification level. For example, if a device has a medium average flow value, but its flow change curve shows frequent high peaks and long duration, the system may upgrade its flow level to a higher level. This comprehensive evaluation method not only takes into account the general flow conditions of the device, but also fully considers its dynamic characteristics, thereby providing a more accurate and predictive flow level definition.

[0057] Based on the above embodiment, as an optional implementation, the flow level includes the number of flow levels and the flow interval. The number of flow levels of the proprietary device is determined according to the average flow value, and the flow interval is determined according to the flow change curve.

[0058] The number of traffic levels refers to the total number of different levels or layers set for classifying and managing data flows in the network traffic management of large data centers. In the embodiments of the present application, it can be understood as the number of discrete traffic classification levels set by the system for proprietary devices based on historical operating data flows and current system status. This number is usually an integer, for example, 3 levels (low, medium, high), 5 levels (very low, low, medium, high, very high), or more levels may be set, depending on the complexity of the system and management requirements.

[0059] The number of traffic levels is set to achieve more sophisticated and flexible traffic management strategies. By discretizing continuous traffic data into a limited number of levels, the system can perform resource allocation and priority management more efficiently.

[0060] S302: Determine the traffic level of the first running data flow according to the traffic interval of the real-time traffic of the proprietary device.

[0061] In specific implementation, the system first monitors the flow data of proprietary devices in real time. These real-time data are continuously collected by the monitoring module on the device and transmitted to the central management system at a high frequency. The system compares these real-time flow data with pre-defined flow intervals. These flow intervals are determined based on the previously analyzed historical operation data flow, average flow value and flow change curve, which represent different levels of flow levels. When the system detects the first operation data flow, it immediately calculates its current flow value and determines which pre-defined flow interval this flow value falls into. According to the flow interval, the system then assigns the corresponding flow level to this first operation data flow. For example, if the current flow falls in a higher flow interval, the system may assign it to a high priority flow level; conversely, if the flow is low, it may be assigned to a lower flow level. Because the judgment is based on real-time data, the system can respond quickly to sudden high flow situations or unexpected low flow periods, thereby maintaining the stability of overall performance.

[0062] S303: Allocate the first running data flow to a cache level corresponding to the traffic level.

[0063] In this embodiment, in order to accurately guide the data flow to the most suitable storage and processing environment according to its importance and resource requirements, the overall system performance is optimized. In specific implementation, the system will first select the corresponding cache level according to the previously determined traffic level of the first running data flow. These cache levels are usually pre-designed multi-level storage structures, each of which has its specific performance characteristics and resource configuration. The system will guide the first running data flow to the selected cache level in real time through a special data routing module. This process involves operations such as marking of data packets, updating of routing tables, and reservation of cache resources. At the same time, the system will record this allocation operation, including information such as data flow identification, allocation time, target cache level, etc., for subsequent monitoring and optimization. This cache level allocation method based on traffic level significantly improves the efficiency of data processing. High-priority data flows are allocated to high-performance cache layers, which can obtain faster read and write speeds and lower latency, thereby speeding up the processing speed of key services. The implementation of this step of allocating the first running data flow to the cache level corresponding to the traffic level has greatly improved the data management capabilities and processing efficiency of the big data center. By combining traffic classification with a multi-level cache architecture, the system achieves smarter and more flexible resource scheduling and performance optimization, effectively improving overall operation and maintenance management efficiency and service quality.

[0064] S103: Use the first monitoring indicator of the proprietary device to monitor the data flow at each cache level to obtain a first monitoring result corresponding to the proprietary device.

[0065] The first monitoring indicator refers to a key quantitative parameter used to evaluate and monitor whether the first operating data stream is in an abnormal state in the operation and maintenance management of a large data center. In the embodiment of the present application, it can be understood as a set of operation and maintenance indicators specifically set for the operating status and performance of the first operating data stream. These indicators may include but are not limited to data flow volatility, processing delay, error rate, resource occupancy rate, etc. The first monitoring indicator is mainly used for operation and maintenance purposes, and its core task is to ensure the stability and reliability of the first operating data stream and to detect potential problems or anomalies in a timely manner. By monitoring these indicators in real time, the system can quickly identify whether the first operating data stream deviates from the normal operating parameter range.

[0066] Among them, the first monitoring result corresponding to the proprietary device refers to the specific data and evaluation results obtained by monitoring and analyzing the first monitoring index during the operation of the specific proprietary device. In the embodiment of the present application, it can be understood that the system is based on the preset first monitoring index, and the quantitative result obtained after real-time monitoring and evaluation of the performance and state of the proprietary device processing the first operation data flow. These results may include but are not limited to specific numerical values ​​or state descriptions such as real-time traffic data, processing delay time, resource utilization, error rate, etc. of the device. The first monitoring result corresponding to the proprietary device provides an intuitive reflection of the current operating state of the proprietary device, so that the operation and maintenance personnel can quickly determine whether the device is in normal working condition. By comparing these results with the pre-set normal operating parameter range, the system can promptly detect potential anomalies or performance problems.

[0067] The first monitoring result is also used to evaluate the effectiveness of the current cache level allocation strategy. By comparing the performance of the dedicated devices in different cache levels, it can be determined whether the current allocation strategy is reasonable and whether it needs to be adjusted to improve the overall system efficiency.

[0068] In this embodiment, the system first deploys monitoring modules for the proprietary devices in each cache level, and these modules are responsible for collecting data related to the first monitoring indicator in real time. The first monitoring indicator may include key performance parameters such as data flow, processing delay, resource utilization, error rate, etc. The monitoring module continuously collects real-time data of these indicators through embedded sensors or software probes. The collected raw data is then transmitted to the central data processing unit, where preliminary cleaning, standardization and aggregation are performed. The processed data is used to calculate the specific values ​​of each first monitoring indicator, and these calculation results constitute the first monitoring results corresponding to the proprietary device. The system will compare these results with the pre-set normal operating parameter range to determine whether the current operating state is within an acceptable range. At the same time, the system will also analyze the time series changes of these results to identify potential trends or abnormal patterns.

[0069] Specifically, by simultaneously monitoring the proprietary devices at each cache level, the system can obtain real-time performance data at different levels, including key indicators such as data flow, processing delay, and resource utilization. This parallel processing method ensures that the monitoring process itself will not become a bottleneck of the system, but can be carried out simultaneously with the data processing tasks without causing significant performance impact on the main business process.

[0070] S104: Use the second monitoring indicator of the common device to monitor the second operation data flow, and obtain a second monitoring result corresponding to the common device.

[0071] Among them, the second monitoring index of common equipment refers to a series of key parameters for evaluating and quantifying the performance and operating status of non-proprietary equipment (i.e., common equipment) in the big data center. In the embodiment of the present application, it can be understood as a set of quantitative indicators specifically used to monitor and evaluate the efficiency, stability and resource utilization of common equipment in processing the second operating data stream. These indicators may include but are not limited to processing speed, response time, throughput, error rate, CPU usage, memory usage, disk I / O speed, etc.

[0072] Among them, the second monitoring result refers to the specific data and evaluation results obtained by real-time monitoring and analysis of the application of the second monitoring indicator to the common device. In the embodiment of the present application, it can be understood that the system is based on the preset second monitoring indicator, and the quantitative data and state description obtained after the performance and state of the common device processing the second operation data stream are continuously monitored and evaluated. These results may include but are not limited to specific numerical values ​​or status reports such as the real-time processing speed, response time, throughput, error rate, resource utilization rate of the device. By comparing these results with the pre-defined normal operating parameter range, the system can promptly detect potential anomalies or performance problems. For example, if the monitoring results show that the CPU usage of a certain common device continues to be abnormally high or the response time suddenly increases, it may indicate that the device is about to face a performance bottleneck or failure risk.

[0073] In specific implementation, the system will first deploy monitoring modules for each common device, which are responsible for collecting data related to the second monitoring indicators in real time. The second monitoring indicators usually include key performance parameters such as processing speed, response time, throughput, error rate, CPU usage, memory usage, etc. The monitoring module continuously collects real-time data of these indicators through embedded sensors or software probes. The collected raw data is then transmitted to the central data processing unit, where preliminary cleaning, standardization and aggregation are performed. The processed data is used to calculate the specific values ​​of each second monitoring indicator, and these calculation results constitute the second monitoring results corresponding to the common equipment. The system will compare these results with the pre-set normal operating parameter range to determine whether the current operating status is within an acceptable range. At the same time, the system will also analyze the time series changes of these results to identify potential trends or abnormal patterns.

[0074] In an optional implementation, step S104 further includes S401 - S403 .

[0075] S401: Obtain a first processing duration corresponding to a first monitoring result and a second processing duration corresponding to a second monitoring result.

[0076] The first processing duration refers to the time period required for the proprietary device to process the first operation data stream. In the embodiment of the present application, it can be understood as the time taken from the proprietary device starting to receive the first operation data stream to completing the entire processing process of the data stream. This time period includes the total time required for a series of operations such as data reception, analysis, processing, calculation, and storage.

[0077] The second processing duration refers to the time period required for the common device to process the second operation data stream. In the embodiment of the present application, it can be understood as the time taken from the common device starting to receive the second operation data stream to completing the entire processing process of the data stream. This time period covers the total time taken for a series of operations such as data reception, analysis, processing, calculation, and storage.

[0078] In specific implementation, the system first extracts the time information of the proprietary device processing the first operation data stream from the first monitoring result, which includes the total time consumed in data reception, analysis, processing, calculation and storage. At the same time, the corresponding time information of the ordinary device processing the second operation data stream is extracted from the second monitoring result. These two sets of time data constitute the first processing duration and the second processing duration respectively.

[0079] The process of obtaining these processing times usually involves multiple technical links. The system sets timestamps at the inlet and outlet of the data flow, and obtains the preliminary processing time by calculating the difference between the two timestamps. However, considering the possible interruptions, waiting or parallel processing during the data processing process, the system will also combine other relevant indicators, such as CPU usage time, I / O waiting time, etc., to perform more detailed time calculations and adjustments to ensure that the obtained processing time can accurately reflect the actual data processing efficiency.

[0080] S402: Determine whether the first processing duration is the same as the second processing duration.

[0081] In specific implementation, the system will first obtain the first processing time and the second processing time calculated in the previous step, and then accurately compare the two time values. The system uses a special comparison algorithm to determine whether the two time values ​​are equal. Considering the possible small errors in floating-point calculations, a very small tolerance value is usually set, such as 1 microsecond. If the difference between the two processing times is less than this tolerance value, the system will consider them to be the same in a practical sense.

[0082] S403: If the first processing time is different from the second processing time, adjusting the traffic level according to the difference between the first processing time and the second processing time.

[0083] In the operation and management of a big data center, when it is found that the first processing time is longer than the second processing time, it means that the proprietary device is slow in processing the first running data stream. In this case, further refining the hierarchy by adjusting the flow level to increase the number of parallel processing and thus balance the processing time is a key optimization step. This process aims to fully utilize the processing power of the proprietary device, improve the overall system efficiency, and ensure the balance of data processing.

[0084] In specific implementation, the system will first accurately calculate the difference between the first processing time and the second processing time. This difference directly reflects the processing efficiency disadvantage of proprietary equipment compared to ordinary equipment. Based on this difference, the system will start the detailed adjustment process of the traffic level.

[0085] S105: Generate operation and maintenance management results of the big data center according to the first monitoring result and the second monitoring result.

[0086] During specific implementation, the system will first collect and organize the first monitoring results (performance data of proprietary devices) and the second monitoring results (performance data of ordinary devices). These data usually include key indicators such as processing time, resource utilization, throughput, error rate, etc. Then, the system will use a series of data analysis techniques, such as statistical analysis, trend analysis, correlation analysis, etc., to deeply process and interpret these data. For the analysis of proprietary devices, it is necessary to make full use of the fine-grained data provided by its multi-level cache mechanism. The system will analyze the performance indicators of each cache level (such as L1, L2, and L3 cache) separately, including cache hit rate, access latency, etc. This hierarchical analysis can provide more accurate performance insights, especially during traffic peaks.

[0087] Based on these analysis results, the system will generate a comprehensive operation and maintenance management report. This report usually includes overall system performance evaluation, performance comparison between devices, resource utilization analysis, abnormality and risk identification, optimization suggestions, and future trend forecasts. When generating the report, the system will pay special attention to the performance differences between proprietary devices and ordinary devices, as well as the performance of each cache layer of proprietary devices, so as to achieve more refined and intelligent operation and maintenance management.

[0088] Based on the above embodiment, as an optional implementation method, the operation and maintenance management results of the big data center are output in a visualized manner.

[0089] During the specific implementation, the operation and maintenance management results generated based on the first monitoring results and the second monitoring results are converted into various charts, dashboards and interactive interfaces. First, the system will select appropriate visualization forms according to different types of data and analysis results. For example, for time series data, such as the changing trend of processing time, a line chart or an area chart can be used; for resource utilization, a dashboard or a heat map can be used; for performance comparison between different devices, a bar chart or a radar chart can be used. During the implementation process, the system will adopt responsive design principles to ensure that the visualization results can be well displayed on different devices and screen sizes. At the same time, interactive functions will be introduced to allow users to drill, filter and customize views to meet the needs of people at different levels. For example, senior managers may pay more attention to overall performance indicators and trends, while technicians may need to view more detailed device-level data.

[0090] The following is an embodiment of the system of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the system of the present application, please refer to the embodiment of the method of the application.

[0091] See also Figure 2 , which shows a schematic diagram of the structure of an operation and maintenance management system of a big data center provided by an exemplary embodiment of the present application. The system can be implemented as all or part of the system through software, hardware, or a combination of both. The operation and maintenance management system of the big data center includes: A data acquisition module, used to acquire a first operation data stream of a proprietary device and a second operation data stream of a common device in a big data center; A level allocation module, used for allocating the first running data flow to a cache level corresponding to the traffic level according to the traffic level of the first running data flow; A first monitoring module, configured to monitor data flows at each cache level using a first monitoring indicator of a proprietary device, and obtain a first monitoring result corresponding to the proprietary device; A second monitoring module, used to monitor the second operation data flow using a second monitoring indicator of the common device to obtain a second monitoring result corresponding to the common device; The result output module is used to generate the operation and maintenance management results of the big data center according to the first monitoring result and the second monitoring result.

[0092] Based on the above embodiment, as an optional embodiment, the data acquisition module is also used to obtain the hardware configuration information and task configuration information of each device in the big data center; the device whose hardware configuration information meets the standard hardware configuration information and whose task configuration information is in the task running state is determined as a proprietary device.

[0093] On the basis of the above embodiments, as an optional embodiment, the hierarchical allocation module is also used to obtain the historical operation data flow of the proprietary device, determine the traffic level corresponding to the proprietary device, and the traffic level corresponds to multiple traffic intervals; determine the traffic level of the first operation data flow according to the traffic interval where the real-time traffic of the proprietary device is located; and allocate the first operation data flow to the cache level corresponding to the traffic level.

[0094] Based on the above embodiment, as an optional embodiment, the hierarchical allocation module is also used to obtain the historical operation data flow of the proprietary device, determine the average flow value and flow change curve corresponding to the historical operation data flow; based on the average flow value and the flow change curve, define the flow level corresponding to the proprietary device.

[0095] Based on the above embodiment, as an optional embodiment, the hierarchical allocation module is further used to determine the number of flow levels of the dedicated equipment according to the average flow value, and determine the flow interval according to the flow change curve.

[0096] Based on the above embodiment, as an optional embodiment, the second monitoring module is also used to obtain a first processing time corresponding to the first monitoring result, and a second processing time corresponding to the second monitoring result; determine whether the first processing time is the same as the second processing time; if the first processing time is different from the second processing time, adjust the traffic level according to the difference between the first processing time and the second processing time.

[0097] Based on the above embodiment, as an optional embodiment, the result output module is also used to visualize the operation and maintenance management results of the big data center.

[0098] The present application also provides a computer storage medium that can store multiple instructions, which are suitable for being loaded and executed by a processor as described above. Figure 1 The operation and maintenance management method of the big data center in the embodiment shown in the figure can be found in the specific execution process. Figure 1 The specific description of the illustrated embodiment will not be repeated here.

[0099] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0100] The communication bus 302 is used to realize the connection and communication between these components.

[0101] The user interface 303 may include a standard wired interface or a wireless interface.

[0102] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0103] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0104] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of an operation and maintenance management method of a big data center.

[0105] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call an application program stored in the memory 305 for an operation and maintenance management method of a big data center. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0106] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more methods in the above embodiments.

[0107] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0108] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0112] If the integrated unit 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 memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0113] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure.

Claims

1. A method for operation and maintenance management of a big data center, characterized in that: The method comprises: Obtaining a first operation data stream of a proprietary device and a second operation data stream of a common device in a big data center; According to the traffic level of the first running data flow, the first running data flow is allocated to a cache level corresponding to the traffic level; Using the first monitoring indicator of the proprietary device to monitor the data flow at each cache level, to obtain a first monitoring result corresponding to the proprietary device; Using the second monitoring indicator of the common device to monitor the second operation data flow, and obtaining a second monitoring result corresponding to the common device; An operation and maintenance management result of the big data center is generated according to the first monitoring result and the second monitoring result.

2. The method according to claim 1, characterized in that Before obtaining the first operation data stream of the proprietary device in the big data center and the second operation data stream of the common device, the method further includes: Obtain the hardware configuration information and task configuration information of each device in the big data center; The device whose hardware configuration information meets the standard hardware configuration information and whose task configuration information is in the task running state is determined as the proprietary device.

3. The method according to claim 1, characterized in that The allocating the first operating data flow to a cache level corresponding to the traffic level according to the traffic level of the first operating data flow includes: Acquire the historical operation data flow of the proprietary device, determine the flow level corresponding to the proprietary device, and the flow level corresponds to multiple flow intervals; Determine the traffic level of the first operation data flow according to the traffic interval in which the real-time traffic of the proprietary device is located; Allocate the first running data flow to a cache level corresponding to the traffic level.

4. The method according to claim 3, characterized in that The obtaining of the historical operation data flow of the proprietary device and determining the flow level corresponding to the proprietary device includes: Obtaining the historical operation data flow of the proprietary device, and determining the average flow value and flow change curve corresponding to the historical operation data flow; A flow level corresponding to the proprietary device is defined according to the average flow value and the flow change curve.

5. The method according to claim 4, characterized in that The flow level includes the number of flow levels and flow intervals, and the flow level corresponding to the proprietary device is defined according to the average flow value and the flow change curve, including: The number of flow levels of the proprietary device is determined according to the average flow value, and the flow interval is determined according to the flow change curve.

6. The method according to claim 1, characterized in that After the second monitoring indicator of the common device is used to monitor the second operation data flow and a second monitoring result corresponding to the common device is obtained, the method further includes: Obtaining a first processing duration corresponding to the first monitoring result and a second processing duration corresponding to the second monitoring result; Determining whether the first processing duration is the same as the second processing duration; If the first processing time is different from the second processing time, the traffic level is adjusted according to a difference between the first processing time and the second processing time.

7. The method according to claim 1, characterized in that After generating the operation and maintenance management result of the big data center according to the first monitoring result and the second monitoring result, the method further includes: The operation and maintenance management results of the big data center are output visually.

8. An operation and maintenance management system for a big data center, characterized in that: The system comprises: A data acquisition module, used to acquire a first operation data stream of a proprietary device and a second operation data stream of a common device in a big data center; A level allocation module, configured to allocate the first operation data flow to a cache level corresponding to the traffic level according to the traffic level of the first operation data flow; A first monitoring module, configured to monitor the data flow at each cache level using a first monitoring indicator of the proprietary device, and obtain a first monitoring result corresponding to the proprietary device; A second monitoring module, configured to monitor the second operation data flow using a second monitoring indicator of the common device to obtain a second monitoring result corresponding to the common device; A result output module is used to generate an operation and maintenance management result of the big data center according to the first monitoring result and the second monitoring result.

9. A computer storage medium, characterized in that The computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory and a transceiver, the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

Images