Computing power resource access path recording method and device of intelligent computing center

Abstract

The invention provides a computing power resource access path recording method and device for an intelligent computing center, and relates to the technical field of intelligent computing centers, and the method comprises the steps: S1, obtaining access data for accessing a computing power resource, the access data comprising resource information of the computing power resource used by a user and authentication information of the user, the authentication information comprises login time, a login identity ID and first request information, the resource information is used for recording response state information, type information and address information of a first computing power resource, and the first computing power resource is a computing power resource requested to be accessed by the first request information in a plurality of computing power resources of the intelligent computing center; and S2, generating an access log based on the access data, wherein the access log comprises an association relationship between the resource information and the authentication information. The computing power resource access path recording of the intelligent computing center is realized, and the accurate computing power service can be provided for the user according to the actual use condition of the computing power resource.

Description

Technical Field

[0001] This invention relates to the technical field of intelligent computing centers, smart computing centers, and computing infrastructure, specifically to a method and apparatus for recording access paths to computing resources in an intelligent computing center. Background Technology

[0002] With the rapid development of artificial intelligence technology, "intelligent computing centers" and "smart computing centers" have emerged.

[0003] An "intelligent computing center" refers to a facility that provides the necessary computing power, data, and algorithms for artificial intelligence applications (such as the development, training, and inference of deep learning models) by utilizing large-scale heterogeneous computing resources, including general-purpose and intelligent computing power. Intelligent computing centers encompass facilities, hardware, and software, and can provide full-stack capabilities from underlying computing power to top-level application enablement.

[0004] "Intelligent computing center" includes, but is not limited to, "intelligent computing center".

[0005] "Intelligent computing center" or artificial intelligence computing center is a type of computing infrastructure that provides computing power services, data services, and algorithm services required for artificial intelligence applications, based on artificial intelligence theory and adopting artificial intelligence computing architecture.

[0006] "Computing power" is the core of "intelligent computing center" and "smart computing center". It is the ability of computer equipment or computing / data center to process information. It is the ability of computer hardware and software to work together to perform a certain computing requirement. It is the computing power to achieve the target output by processing information data. It is a new type of productivity that integrates information computing power, network carrying capacity and data storage capacity. It mainly provides services to society through computing power infrastructure.

[0007] Currently, among computing resources, network resources serve as the bridge connecting various network nodes and external users, and their stable and efficient operation directly impacts the performance of the entire intelligent computing center. Different users and different business applications have varying demands for network resources, making it difficult for intelligent computing centers to provide precise computing services based on the actual usage of computing resources.

[0008] It is evident that since the emergence of intelligent computing centers, how to provide users with accurate computing power services based on the actual usage of computing resources has become an urgent problem to be solved. Summary of the Invention

[0009] This invention provides a method and apparatus for recording the access path of computing resources in an intelligent computing center, in order to solve the problem of how to provide accurate computing services to users based on the actual usage of computing resources since the emergence of intelligent computing centers.

[0010] To solve the above problems, the present invention is implemented as follows:

[0011] In a first aspect, embodiments of the present invention provide a method for recording access paths to computing resources in an intelligent computing center, comprising:

[0012] Step S1: Obtain access data for accessing computing resources. The access data includes resource information of the user's use of computing resources and the user's authentication information. The authentication information includes login time, login ID and first request information. The resource information is used to record response status information, type information and address information of the first computing resource. The first computing resource is the computing resource requested to be accessed by the first request information among multiple computing resources of the intelligent computing center.

[0013] Step S2: Generate an access log based on the access data. The access log includes the association between the resource information and the authentication information.

[0014] In one embodiment, step S2 includes:

[0015] Step S21: Associate at least one of the login time, the login ID, and the first request information with the first computing power resource to obtain a first association relationship;

[0016] Step S22: Associate at least one of the response status information, the type information, and the address information with the first computing power resource to obtain a second association relationship;

[0017] Step S23: Generate the access log based on the first association relationship and the second association relationship;

[0018] The association between the resource information and the authentication information includes the first association and the second association.

[0019] In one embodiment, step S23 includes:

[0020] Step S231: Obtain the traffic data corresponding to the port of the first computing power resource;

[0021] Step S232: Calculate the load index of the first computing power resource based on the traffic data. The load index includes inbound data packet rate, outbound data packet rate and bandwidth utilization.

[0022] Step S233: Generate the access log based on the load metric, the first association relationship, and the second association relationship.

[0023] In one embodiment, prior to step S1, the method further includes:

[0024] Step S3: Preprocess the obtained raw data of accessing the computing resources to obtain the access data. The preprocessing includes removing authentication information corresponding to invalid IDs, deduplication, and missing value repair. The invalid ID is a login ID that has not passed authentication. The deduplication is used to delete duplicate records, and the missing value repair is used to repair missing information.

[0025] In one embodiment, after step S2, the method further includes:

[0026] Step S4: Based on the access log, generate a visualization report. The visualization report includes a network access topology map starting from the login ID and a load trend map of the first computing power resource starting from the login time. The network access topology map is marked with the response status information, type information and address information of the first computing power resource associated with the login ID. The load trend map of the first computing power resource is used to display the change information of the load index of the first computing power resource associated with the login ID.

[0027] In a second aspect, embodiments of the present invention provide a device for recording access paths to computing resources in an intelligent computing center, comprising:

[0028] The acquisition module is used to acquire access data for accessing computing resources. The access data includes resource information of the computing resources used by the user and the authentication information of the user. The authentication information includes login time, login ID and first request information. The resource information is used to record response status information, type information and address information of the first computing resource. The first computing resource is the computing resource requested to be accessed by the first request information among multiple computing resources of the intelligent computing center.

[0029] The first generation module is used to generate an access log based on the access data, wherein the access log includes the association between the resource information and the authentication information.

[0030] In one embodiment, the first generation module is specifically used for:

[0031] Associate at least one of the login time, the login ID, and the first request information with the first computing power resource to obtain a first association relationship;

[0032] Associate at least one of the response status information, the type information, and the address information with the first computing power resource to obtain a second association relationship;

[0033] The access log is generated based on the first association and the second association.

[0034] The association between the resource information and the authentication information includes the first association and the second association.

[0035] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it implements the steps in the method for recording the access path of computing resources in an intelligent computing center as described in the first aspect above.

[0036] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the method for recording the access path of computing resources in an intelligent computing center as described in the first aspect above.

[0037] Fifthly, the present invention also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps in the method for recording the access path to computing resources in an intelligent computing center as described in the first aspect above.

[0038] In this embodiment of the invention, by collecting and analyzing access data, detailed access logs are generated, realizing the recording of the access paths of computing resources in the intelligent computing center. Based on this, resources can be rationally allocated and optimized, improving resource utilization and performance. The intelligent computing center can provide users with accurate computing services based on the actual usage of computing resources. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of a method for recording the access path of computing resources in an intelligent computing center, provided by an embodiment of the present invention;

[0041] Figure 2 This is a structural diagram of a device for recording the access path of computing resources in an intelligent computing center, provided in an embodiment of the present invention.

[0042] Figure 3 This is a schematic diagram of the structure of an electronic device provided in this invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] The “computing power” mentioned in this invention refers to: the ability of computer equipment or computing / data center to process information; the ability of computer hardware and software to work together to perform a certain computing requirement; the computing power to achieve the target result output by processing information data; and a new type of productivity that integrates information computing power, network carrying capacity, and data storage capacity, mainly providing services to society through computing power infrastructure.

[0045] The "computational power" (CP) described in this invention refers to the ability of a data center server to process data and output results. It is a comprehensive indicator of a data center's computing power, encompassing general computing power, supercomputing power, and intelligent computing power. The commonly used unit of measurement is floating-point operations per second (FLOPS, 1 EFLOPS = 10^18 FLOPS), with higher values ​​indicating stronger overall computing power. It is estimated that 1 EFLOPS is approximately the computing power output of 5 Tianhe-2A supercomputers, 500,000 mainstream server CPUs, or 2 million mainstream laptops. The calculation formula is: CP = CP 通用 +CP 智能 +CP 超级 .

[0046] The "Network Power" (NP) mentioned in this invention refers to the performance of data transmission capability of computing facilities, which includes comprehensive capabilities such as network architecture, network bandwidth, transmission latency, intelligent management and scheduling, and involves network transmission within and between data centers. It is a comprehensive indicator for measuring network transmission scheduling capability.

[0047] The "Storage Power" (SP) described in this invention refers to the comprehensive capabilities of a data center in four aspects: data storage capacity, performance, security and reliability, and green and low-carbon operation. It is a comprehensive indicator for measuring the data storage capacity of a data center, including external storage devices such as storage arrays and internal storage devices in servers. The commonly used unit of measurement for storage capacity is exabytes (EB, 1EB = 2^60 bytes), the commonly used unit of measurement for performance is the number of read / write operations per second per unit capacity (IOPS / TB), and the disaster recovery ratio is an important indicator of security and reliability.

[0048] The "computing infrastructure" mentioned in this invention refers to a new type of information infrastructure that integrates information computing power, network carrying capacity, and data storage capacity, enabling centralized computing, storage, transmission, and application of information.

[0049] The "new information infrastructure" mentioned in this invention refers to network infrastructure such as 5G networks, fiber optic broadband networks, backbone networks, international communication networks, and satellite internet; computing infrastructure such as data centers, general computing centers, intelligent computing centers, and supercomputing centers; and new technology facilities such as artificial intelligence, blockchain, and quantum computing.

[0050] The “computing power” mentioned in this invention includes: general computing power, intelligent computing power, and supercomputing power.

[0051] The "general computing power" mentioned in this invention refers to the computing power provided by servers based on CPU (Central Processing Unit) chips, which is used to support basic general computing such as cloud computing and edge computing.

[0052] The "intelligent computing power" mentioned in this invention refers to: a computing platform deployed on a large scale based on dedicated chips such as GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), and ASIC (Application Specific Integrated Circuit) for various artificial intelligence innovative applications, such as natural language processing and machine vision.

[0053] The “supercomputing power” mentioned in this invention refers to the computing power provided by high-performance computing clusters such as supercomputers. It utilizes the centralized computing resources of multiple computer systems working in parallel and uses a dedicated operating system to handle extremely complex or data-intensive problems. It is mainly used for computing in cutting-edge scientific fields, such as planetary simulation, drug molecule design, and gene analysis.

[0054] The "intelligent computing center" described in this invention refers to a facility that, through the use of large-scale heterogeneous computing resources, including general-purpose computing power (CPU) and intelligent computing power (GPU, FPGA, ASIC, etc.), primarily provides the necessary computing power, data, and algorithms for artificial intelligence applications (such as the development, training, and inference of deep learning models). The intelligent computing center encompasses facilities, hardware, and software, and can provide full-stack capabilities from underlying computing power to top-level application enablement.

[0055] The "intelligent computing center" mentioned in this invention includes, but is not limited to, "smart computing center".

[0056] The "intelligent computing center" mentioned in this invention, also known as an artificial intelligence computing center, is a type of computing infrastructure that provides computing power services, data services, and algorithm services required for artificial intelligence applications, based on artificial intelligence theory and adopting an artificial intelligence computing architecture.

[0057] The "computing center" mentioned in this invention refers to a facility that is mainly composed of infrastructure such as wind, thermal, hydro, and electricity, and IT hardware and software equipment, and has computing power, carrying capacity, and storage capacity, including general data centers, intelligent computing centers, supercomputing centers, etc.

[0058] The "supercomputing center" mentioned in this invention refers to a supercomputing data center, which is a data center based on supercomputers or large-scale computing clusters. It can provide large-scale computing, storage and network services and is widely used in aerospace, defense, oil exploration, climate modeling and genome sequencing and other application scenarios.

[0059] The “computing resources” mentioned in this invention refer to the technologies and facilities required for the development of the digital society that have the ability to compute, transmit, store and apply information, including but not limited to computing resources such as CPUs and GPUs, network resources such as switches and routers, storage resources such as storage arrays and distributed storage, security resources such as firewalls and intrusion detection systems, and supporting and guaranteeing resources such as wind, fire, water and electricity.

[0060] The "model" mentioned in this invention includes, but is not limited to, "large language model" and "multimodal large model".

[0061] The "large language model" mentioned in this invention refers to a large-scale language model (LLM), which is a language model with a large number of parameters. It is designed to understand and generate human language, and is trained with a large amount of text data. It can perform a wide range of tasks, including text summarization, translation, and sentiment analysis.

[0062] The “Multimodal Large Models” mentioned in this invention refer to models that combine multimodal information such as text, images, videos, and audio for training, including but not limited to multimodal large language models.

[0063] Please see Figure 1 , Figure 1 This is a flowchart of a method for recording the access path of computing resources in an intelligent computing center, as provided in an embodiment of the present invention. Figure 1 As shown, it includes the following steps:

[0064] Step S1: Obtain access data for accessing computing resources. The access data includes resource information of the user's use of computing resources and the user's authentication information. The authentication information includes login time, login ID and first request information. The resource information is used to record response status information, type information and address information of the first computing resource. The first computing resource is the computing resource requested to be accessed by the first request information among multiple computing resources of the intelligent computing center.

[0065] In this step, authentication information serves as the user's identity credential and basis for requesting access to computing resources. Login time can be used to analyze user habits, such as whether the user uses the service frequently during specific weekday hours or weekends. The login identity (Identity Document, ID) uniquely identifies the user, facilitating the differentiation and statistical analysis of different users' access behaviors. The first request information clarifies the user's specific needs for computing resources, such as the required network type, task scale, and storage requirements. Resource information records relevant details of the first computing resource, which can be the resource requested by the user from among the numerous computing resources in the intelligent computing center. Resource information includes response status information, type information, and address information. The response status information reflects the computing resource's response to the user's request, such as a normal response, a delayed response, or an error response, facilitating subsequent evaluation of the computing resource's availability and performance. The type information specifies the specific type of computing resource, such as computing resources corresponding to inference tasks or training tasks, allowing for reasonable resource allocation based on different task requirements. The address information provides the specific location identifier of the computing resource within the intelligent computing center, aiding in accurate resource location and retrieval. By understanding the actual usage of computing resources, we can provide users with accurate computing services.

[0066] Step S2: Generate an access log based on the access data. The access log includes the association between the resource information and the authentication information.

[0067] After obtaining the access data, step S2 further integrates and correlates the data to generate an access log containing the association between resource information and authentication information. In one example, the login time in the authentication information can be used as a key clue. When a user initiates an access request to computing resources at a specific time (i.e., the first request information), the response status, type, and address information of the first computing resource at that time are searched in the resource information. For example, if a user initiates an access request at 10:00 AM, the relevant information of the first computing resources near that time is correlated to determine whether the resource response is timely and whether the resource type meets the request requirements. In another example, the association can be further strengthened using the login ID and the first request information. The login ID serves as a unique identifier for the user, ensuring the accuracy of the association. The first request information clarifies the specific first computing resource the user requested to access, thus tightly binding the resource's details with the user's authentication information. For example, if a user initiates a deep learning computing request with a specific ID, the relevant information of the first computing resource providing deep learning computing capabilities is correlated with the user's authentication information.

[0068] The associated resource information and authentication information are integrated according to a certain format. Structured data formats, such as JSON or XML, can be used to facilitate storage, retrieval, and analysis.

[0069] It should be noted that the user information used in this technical solution is limited to information for which the user has given individual consent, including but not limited to notifying the user to read the agreement (notification) and sign the agreement (authorization) which includes the authorization of relevant user information before the user uses the function.

[0070] By analyzing the relationships within access logs, administrators at the intelligent computing center can clearly understand each user's usage of different types of computing resources. For example, they can identify which users frequently use specific types of resources and which resources have poor response times. Based on this information, resources can be rationally allocated and optimized to improve resource utilization and performance. Access logs serve as crucial troubleshooting tools when abnormal computing resource usage or user request failures occur. By examining associated authentication and resource information, the problem can be quickly located, determining whether it stems from the user's request, the resource itself, or the interaction between the two. Furthermore, analyzing access logs reveals user needs and usage habits, enabling more personalized services. For instance, based on a user's historical request information, suitable computing resources can be prepared in advance, improving the user experience.

[0071] In this way, by collecting and analyzing access data, detailed access logs are generated, realizing the recording of the access paths of computing resources in the intelligent computing center. Based on this, resources can be rationally allocated and optimized, improving resource utilization and performance. The intelligent computing center can provide users with precise computing services based on the actual usage of computing resources.

[0072] In one embodiment, step S2 includes:

[0073] Step S21: Associate at least one of the login time, the login ID, and the first request information with the first computing power resource to obtain a first association relationship;

[0074] Step S22: Associate at least one of the response status information, the type information, and the address information with the first computing power resource to obtain a second association relationship;

[0075] Step S23: Generate the access log based on the first association relationship and the second association relationship;

[0076] The association between the resource information and the authentication information includes the first association and the second association.

[0077] In this embodiment, at least one of the following is selected from login time, login ID, and first request information to be associated with the first computing power resource. For example, associating with login time can clearly identify which first computing power resource the user accessed at a specific time; associating with login ID can identify which user is using the resource; and associating with first request information can clearly reveal the specific content of the user's request and the corresponding resource. Taking login ID as an example, in the intelligent computing center system, each login ID corresponds to a series of user operations and resource accesses. When a user initiates first request information, the system records the first computing power resource to which the request points, thereby establishing an association between the login ID and the first computing power resource. By connecting the key elements in the user authentication information with the first computing power resource, the correspondence between the user's request and the accessed computing power resource is clarified.

[0078] Then, at least one item from the response status information, type information, and address information is selected and associated with the first computing resource. The response status information reflects the resource's processing status of the user request; the type information indicates the specific type of the resource; and the address information locates the resource's position. Taking the response status information as an example, when the first computing resource receives a user request and responds, the system records the resource's response status (such as normal response, error response, etc.) and associates it with the first computing resource. In this way, by associating the detailed information of the first computing resource (response status information, type information, and address information) with the resource, the system improves the recording of resource usage and provides a basis for providing accurate computing power services based on the actual usage of the computing resources.

[0079] Finally, the information from the first and second association relationships is integrated according to a specific format to generate a complete access log, recording users' access to computing resources. The first association relationship allows us to understand the computing resources used by each user; the second association relationship provides detailed information on the resource status and type. Combining these two approaches helps in the rational allocation and optimization of resources, improving resource utilization. This enables the intelligent computing center to provide users with precise computing services based on the actual usage of computing resources.

[0080] In one embodiment, step S23 includes:

[0081] Step S231: Obtain the traffic data corresponding to the port of the first computing power resource;

[0082] Step S232: Calculate the load index of the first computing power resource based on the traffic data. The load index includes inbound data packet rate, outbound data packet rate and bandwidth utilization.

[0083] Step S233: Generate the access log based on the load metric, the first association relationship, and the second association relationship.

[0084] In this embodiment, port traffic data can be extracted from the service process log of the first computing power resource through step 231. Examples include the number of inbound requests and outbound responses for the HTTP port (e.g., 8080) of the deep learning computing power service; and the byte transfer volume of the RPC port (e.g., 9000) of the distributed computing node. Alternatively, port traffic can be obtained in real time through the monitoring API built into the computing power resource (e.g., the Prometheus metrics interface), such as the cumulative number of inbound data packets and the cumulative number of outbound bytes. Then, the load metric of the first computing power resource is calculated based on the traffic data. The inbound data packet rate refers to the number of data packets entering the port within a given time period, reflecting the frequency of user requests. The calculation formula is: Inbound data packet rate = (Current number of inbound packets - Last recorded number of packets) / Time interval (seconds). For example, if the number of inbound packets increases from 1000 to 1300 within 30 seconds, then the inbound data packet rate = (1300 - 1000) / 30 = 10 packets / second. The outbound data packet rate refers to the number of data packets sent from the port per unit time, reflecting the resource response efficiency. It can be calculated based on the number of outbound packets. Bandwidth utilization refers to the percentage of actual bandwidth used by the port relative to its rated bandwidth, measuring resource load pressure. The calculation formula is: Bandwidth utilization = (Inbound bytes + Outbound bytes) × 8] / (Time interval (seconds) × Port rated bandwidth (bps)). For example, a port with a rated bandwidth of 1Gbps (10... 9 (bps), total bytes transferred within 60 seconds: 10MB (10×10) 6 If the bandwidth utilization is (bytes), then the bandwidth utilization rate is (10 × 10^6 bytes). 6 ×8) / (60×10 9 =0.133%. In this way, the real-time pressure on computing resources is quantified through load indicators, providing resource performance data support for the subsequent step S233.

[0085] Then, according to step S233, the load metrics are deeply integrated with the first association (user-resource) and the second association (resource-attribute) to generate access logs. This binds user authentication information with resource load metrics, enabling end-to-end traceability from "login ID → resource access → resource load". High-frequency users can be identified based on inbound rate, allowing for the allocation of dedicated resource ports or adjustment of QoS priorities. This provides precise data support for application-level path optimization and elastic resource scheduling in the intelligent computing center.

[0086] In one embodiment, prior to step S1, the method further includes:

[0087] Step S3: Preprocess the obtained raw data of accessing the computing resources to obtain the access data. The preprocessing includes removing authentication information corresponding to invalid IDs, deduplication, and missing value repair. The invalid ID is a login ID that has not passed authentication. The deduplication is used to delete duplicate records, and the missing value repair is used to repair missing information.

[0088] In this embodiment, removing authentication information corresponding to invalid IDs can filter out unauthorized access records that have failed authentication, preventing contamination of subsequent analysis. Deduplication eliminates duplicate records in the original data, avoiding redundant calculations and analysis, and ensuring data uniqueness and accuracy. Duplicate records may arise due to network failures, system retry mechanisms, or errors during data acquisition. Missing value repair can correct missing information in the original data, ensuring data integrity and usability. Missing information may affect subsequent analysis and processing, such as the inability to accurately associate resource information and authentication information.

[0089] In this way, invalid login IDs and their corresponding authentication information that have not passed authentication are first removed to ensure that only access records of legitimate users are retained; then, deduplication is performed based on combination keys such as login ID, login time, and first request information to delete duplicate records and avoid data redundancy; finally, for missing key information (such as login time, resource response status, etc.), it is inferred and supplemented by associating with historical data, resource configuration tables, or similar data to obtain accurate, unique and complete access data, laying the foundation for the subsequent generation of access logs.

[0090] In one embodiment, after step S2, the method further includes:

[0091] Step S4: Based on the access log, generate a visualization report. The visualization report includes a network access topology map starting from the login ID and a load trend map of the first computing power resource starting from the login time. The network access topology map is marked with the response status information, type information and address information of the first computing power resource associated with the login ID. The load trend map of the first computing power resource is used to display the change information of the load index of the first computing power resource associated with the login ID.

[0092] In this embodiment, after generating the access log in step S2, step S4 is performed, which generates a visualization report based on the access log. The report consists of two parts: First, starting with the login ID, it connects the user to the accessed first computing resource through a logical path based on the first and second associations in the access log, marking the resource's response status, type, and address information, intuitively presenting the logical access path from the user to the target computing resource and the resource's basic attributes; second, using login time as the horizontal axis, it extracts the load indicators (inbound data packet rate, outbound data packet rate, bandwidth utilization) of the first computing resource associated with the login ID, displaying their changing trends over time through line graphs or area graphs, helping to analyze the fluctuations in resource load during user access. These two types of visualization reports integrate the associated data in the access log, transforming the abstract computing resource access record into a graphical interactive interface, providing intuitive data support for the intelligent computing center to monitor resource usage status, locate response anomalies, and optimize resource scheduling strategies. This, in turn, provides users with precise computing services based on the actual usage of computing resources.

[0093] Please see Figure 2 , Figure 2 This is a structural diagram of a device for recording the access path of computing resources in an intelligent computing center, as provided in an embodiment of the present invention. Figure 2 As shown, the computing resource access path recording device 200 of the intelligent computing center includes:

[0094] The acquisition module 201 is used to acquire access data for accessing computing resources. The access data includes resource information of the computing resources used by the user and the authentication information of the user. The authentication information includes login time, login ID and first request information. The resource information is used to record response status information, type information and address information of the first computing resource. The first computing resource is the computing resource requested to be accessed by the first request information among multiple computing resources of the intelligent computing center.

[0095] The first generation module 202 is used to generate an access log based on the access data, wherein the access log includes the association between the resource information and the authentication information.

[0096] In one embodiment, the first generation module 202 is specifically used for:

[0097] Associate at least one of the login time, the login ID, and the first request information with the first computing power resource to obtain a first association relationship;

[0098] Associate at least one of the response status information, the type information, and the address information with the first computing power resource to obtain a second association relationship;

[0099] The access log is generated based on the first association and the second association.

[0100] The association between the resource information and the authentication information includes the first association and the second association.

[0101] In one embodiment, the first generation module 202 is further configured to:

[0102] Obtain the traffic data corresponding to the port of the first computing power resource;

[0103] The load index of the first computing power resource is calculated based on the traffic data. The load index includes inbound data packet rate, outbound data packet rate and bandwidth utilization.

[0104] The access log is generated based on the load metric, the first association, and the second association.

[0105] In one embodiment, the apparatus further includes:

[0106] The preprocessing module is used to preprocess the acquired raw data of accessing the computing resources to obtain the access data. The preprocessing includes removing authentication information corresponding to invalid IDs, deduplication, and missing value repair. The invalid ID is a login ID that has not passed authentication. The deduplication is used to delete duplicate records, and the missing value repair is used to repair missing information.

[0107] In one embodiment, the apparatus further includes:

[0108] The second generation module is used to generate a visualization report based on the access log. The visualization report includes a network access topology map starting from the login ID and a load trend map of the first computing power resource starting from the login time. The network access topology map is marked with the response status information, type information and address information of the first computing power resource associated with the login ID. The load trend map of the first computing power resource is used to display the change information of the load index of the first computing power resource associated with the login ID.

[0109] The computing resource access path recording device 200 of the intelligent computing center provided in this embodiment of the invention can realize the various processes of the above-mentioned intelligent computing center computing resource access path recording method. The technical features are one-to-one and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0110] It should be noted that the computing resource access path recording device of the intelligent computing center in the embodiments of the present invention can be a device, or it can be a component, integrated circuit or chip in an electronic device.

[0111] This invention also provides an electronic device, see [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device includes a memory 301, a processor 302, and a program or instructions stored in the memory 301 that run on the memory. When the program or instructions are executed by the processor 302, they can achieve the following: Figure 1 The corresponding steps in the method embodiment for accessing computing resources in the intelligent computing center and achieving the same beneficial effects will not be elaborated here.

[0112] The processor 302 can be a CPU, ASIC, FPGA or GPU.

[0113] Those skilled in the art will understand that all or part of the steps of the above-described embodiment of the method for recording the access path of computing resources in an intelligent computing center can be implemented by hardware related to program instructions, and the program can be stored in a readable medium.

[0114] This invention also provides a readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described functions. Figure 1 The corresponding steps in the method embodiment for accessing computing resources in the intelligent computing center can achieve the same technical effect, and therefore will not be repeated here to avoid repetition. The storage medium may include read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0115] The present invention also provides a computer program product, including computer instructions that, when executed by a processor, implement the above-described... Figure 1 The corresponding intelligent computing center's access path to computing resources records each process of the implementation method, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0116] In the embodiments of this invention, the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices. Additionally, the use of "and / or" in this application indicates at least one of the connected objects, such as A and / or B and / or C, representing seven possibilities: A alone, B alone, C alone, both A and B present, both B and C present, both A and C present, and A, B, and C present.

[0117] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0118] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or second terminal device, etc.) to execute the methods of the various embodiments of this application.

[0119] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for recording the access path of computing resources in an intelligent computing center, characterized in that, include: Step S1: Obtain access data for accessing computing resources. The access data includes resource information of the user's use of computing resources and the user's authentication information. The authentication information includes login time, login ID and first request information. The resource information is used to record response status information, type information and address information of the first computing resource. The first computing resource is the computing resource requested to be accessed by the first request information among multiple computing resources of the intelligent computing center. Step S2: Generate an access log based on the access data. The access log includes the association between the resource information and the authentication information.

2. The method as described in claim 1, characterized in that, Step S2 includes: Step S21: Associate at least one of the login time, the login ID, and the first request information with the first computing power resource to obtain a first association relationship; Step S22: Associate at least one of the response status information, the type information, and the address information with the first computing power resource to obtain a second association relationship; Step S23: Generate the access log based on the first association relationship and the second association relationship; The association between the resource information and the authentication information includes the first association and the second association.

3. The method as described in claim 2, characterized in that, Step S23 includes: Step S231: Obtain the traffic data corresponding to the port of the first computing power resource; Step S232: Calculate the load index of the first computing power resource based on the traffic data. The load index includes inbound data packet rate, outbound data packet rate and bandwidth utilization. Step S233: Generate the access log based on the load metric, the first association relationship, and the second association relationship.

4. The method as described in claim 1, characterized in that, Prior to step S1, the method further includes: Step S3: Preprocess the obtained raw data of accessing the computing resources to obtain the access data. The preprocessing includes removing authentication information corresponding to invalid IDs, deduplication, and missing value repair. The invalid ID is a login ID that has not passed authentication. The deduplication is used to delete duplicate records, and the missing value repair is used to repair missing information.

5. The method as described in claim 2, characterized in that, After step S2, the method further includes: Step S4: Based on the access log, generate a visualization report. The visualization report includes a network access topology map starting from the login ID and a load trend map of the first computing power resource starting from the login time. The network access topology map is marked with the response status information, type information and address information of the first computing power resource associated with the login ID. The load trend map of the first computing power resource is used to display the change information of the load index of the first computing power resource associated with the login ID.

6. A device for recording access paths to computing resources in an intelligent computing center, characterized in that, include: The acquisition module is used to acquire access data for accessing computing resources. The access data includes resource information of the computing resources used by the user and the authentication information of the user. The authentication information includes login time, login ID and first request information. The resource information is used to record response status information, type information and address information of the first computing resource. The first computing resource is the computing resource requested to be accessed by the first request information among multiple computing resources of the intelligent computing center. The first generation module is used to generate an access log based on the access data, wherein the access log includes the association between the resource information and the authentication information.

7. The apparatus as claimed in claim 6, characterized in that, The first generation module is specifically used for: Associate at least one of the login time, the login ID, and the first request information with the first computing power resource to obtain a first association relationship; Associate at least one of the response status information, the type information, and the address information with the first computing power resource to obtain a second association relationship; The access log is generated based on the first association and the second association. The association between the resource information and the authentication information includes the first association and the second association.

8. An electronic device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the method for recording the computing resource access path of an intelligent computing center as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for recording the access path to computing resources in an intelligent computing center as described in any one of claims 1 to 5.

10. A computer program product, characterized in that, The method includes computer instructions that, when executed by a processor, implement the steps of the method for recording the access path to computing resources in an intelligent computing center as described in any one of claims 1 to 5.

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