648 results about "Multitenancy" patented technology

Techniques for leveraging a large language model (LLM) in software development are described. A description of an error message associated with a software system hosted by the multi-tenant provider network is received. An LLM is prompted with an analysis prompt to generate an analysis of the error message, the analysis prompt including the description of the error message, and the analysis of the error message is received from the LLM. The LLM is prompted with a suggested resolution prompt to generate a suggested resolution to a cause of the error message, the suggested resolution prompt including the description of the error message and the analysis of the error message, and the suggested resolution is received from the LLM. A change to resolve the cause of the error message is sent to an originator of the received description, the change based at least in part on the suggested resolution.

The invention discloses a computing power analysis method used in a cloud computing environment, a matching method and a computer device. The computing power analysis method used in the cloud computing environment comprises the steps that historical task operation data of different types of computing power nodes in a cloud computing platform are acquired, associated computing power portraits corresponding to the computing power nodes are generated through preprocessing, and then a target model is trained in combination with the historical task operation data to obtain a task-computing power mapping model; performing multi-dimensional dynamic and static analysis on a to-be-executed user calculation task to obtain task feature information; and analyzing the task feature information based on the task-computing power mapping model to obtain analysis information of each type of computing power nodes. According to the method, the required computing power resources and the analysis information of each type of computing power nodes can be accurately predicted according to the dynamic and static analysis feature information of the task before the task is executed, so that accurate computing power identification and intelligent matching are realized, the reasonability and efficiency of task allocation are improved, the resource waste rate is effectively reduced, and the adaptation accuracy in a multi-tenant environment is improved.

Systems and methods are described for providing multitenancy in an artificial intelligence (AI) platform. The platform can provide a user interface (UI) that allows for creation of an organization, tenants within that organization, and groups within tenants. This hierarchy can dictate resource permissions that impact which AI pipelines and AI pipeline objects are available to a user. The different tenants can have segregated data and utilize different pipeline objects, such as different prompts, datasets, or models. A marketplace UI can prioritize additional marketplace pipelines for approval and inclusion with the tenant.

The invention provides a multi-tenant data pushing method, and relates to the technical field of computer software and information, and the method comprises the steps: achieving unified identity authentication through single sign-on, and dynamically distributing a pushing authority based on an authority strategy; defining conversion rules and tasks by utilizing a visual interface of the low-code configuration center, and generating a standardized data packet; and centralized scheduling is carried out in a multi-tenant management and control center, and the stability of the system is guaranteed by combining a current limiting and dynamic retry mechanism. Source system data are collected in real time, and format conversion is completed through a rule engine after grammar, permission and event state verification. And pushing in an asynchronous mode, and recording full-link information including a tracking ID, a timestamp, a data fingerprint and a response state. And when pushing fails, a compensation mechanism is triggered, degradation or retry is carried out according to a preset rule, final consistency of data is ensured, and the reliability and maintainability of the system are improved.

The invention provides a multi-tenant-based centralized authentication and authorization method, system, device and medium, and belongs to the technical field of security and identity management, and the system comprises a unified portal layer, an authentication layer, an authorization layer, a strategy center and an audit monitoring layer. The unified portal layer receives a user request, identifies and injects a tenant identifier, and executes WAF rule verification, JWT signature verification and Token blacklist check; the authentication layer performs user or client credential verification on different accessed identity sources, generates and issues a JWT, and monitors the life cycle of a token; the authorization layer extracts an authority statement in the JWT and a locally cached strategy snapshot; the strategy center provides centralized storage, version management and visual editing of multi-tenant strategies; through cooperative work of each layer, accurate identification, unified authentication, centralized strategy management and comprehensive audit monitoring of tenants are realized. And the security, the expandability and the management efficiency of the system are effectively improved.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

The invention provides a data physical node isolation scheduling method and system, and belongs to the technical field of distributed storage and cloud computing. The method comprises the steps of collecting attribute information of tenants and establishing a tenant demand model; monitoring in real time and predicting the resource load of the physical node by using machine learning, and generating a resource evaluation report; formulating differentiated isolation strategies according to the data security levels of the tenants, adopting physical isolation for the tenants with high security levels, and distributing independent physical nodes for the tenants; the scheduling priority is allocated based on the business emergency degree and the resource demand of the tenant, the node load condition is combined, the high-priority tenant is preferentially scheduled to the node with the load lower than the preset threshold value, and meanwhile, the strategy is dynamically adjusted according to real-time information. The corresponding system comprises a tenant management module, a resource monitoring module, an isolation control module and a scheduling decision module. Unification of data security isolation and efficient and dynamic resource scheduling in a multi-tenant environment is achieved, data security and service quality are effectively guaranteed, and the resource utilization rate is increased.

The invention provides a containerized resource elastic scheduling method and system in a cloud computing environment, and relates to the technical field of containerized resource elastic scheduling, and the method comprises the steps: obtaining various resource contention events, and generating data which records a resource contention relationship between tenants in detail; identifying a preset high-frequency conflict mode by deeply analyzing the data, and determining a specific tenant group with a strong resource mutual exclusion effect; dynamically estimating a probability level of resource conflict between the new container and the specific tenant group in combination with historical contention information and a real-time resource load state; generating a set of dynamic isolation scheduling strategies according to the conflict risk indicated by the probability level; an independent conflict buffer resource pool is configured and adjusted for a specified key tenant, a tenant resource conflict mode can be identified, dynamic isolation scheduling and buffer resource configuration can be implemented, and the resource utilization efficiency and the system stability of a multi-tenant container in a cloud computing environment are improved.

The invention discloses a computing power collaborative management method and system based on a cross-architecture state sensing engine, and relates to the technical field of GPU computing power pooling scheduling. The method comprises the following steps: establishing a communication link set between a plurality of GPU instances and a plurality of CPU controllers; collecting and analyzing indexes such as data throughput rate, queue length and communication time delay of each link in real time, and dynamically identifying a system bottleneck link; aiming at the detected bottleneck link, the fine granularity of the bottleneck link is split into a plurality of sub-links, and each sub-link is allocated with an independent cache queue and a flow control strategy, so that fine management and isolation of link resources are realized; furthermore, the scheduling priority is calculated according to the real-time state index of each sub-link, and the optimal sub-link is preferentially selected for task scheduling during task allocation. Therefore, the resource utilization rate and the task scheduling efficiency in the multi-tenant heterogeneous computing power pool environment are effectively improved, and the conditions of local link congestion and false idle / false busy of the computing power pool are remarkably relieved.

The invention discloses a fine-grained QoS isolation method and device for NVMe-oF storage, relates to the technical field of data transmission, and mainly aims to solve the problem of tail delay amplification caused by cross-tenant I / O interference in a multi-tenant environment. The method comprises the following steps: acquiring system telemetry data and a multi-tenant I / O request stream in a multi-tenant scene; performing micro-slice processing on the multi-tenant I / O request stream, and importing each obtained micro-slice into a corresponding virtual queue; determining a two-dimensional credit vector for controlling each virtual queue based on the system telemetry data; and on the basis of the two-dimensional credit vector of each virtual queue, performing scheduling and gating release on the micro-slices in each virtual queue on a preset time grid. According to the invention, the micro burst is effectively inhibited at the RNIC near end, the delay amplification path is cut off, the I / O tail delay is reduced, and the service quality predictability in the shared storage environment is improved.

The invention discloses a multi-tenant adaptive cooperative defense method and system in a hybrid cloud scene, and the method comprises the following steps: obtaining a remote credible proof of hardware, verifying the credibility of a tenant agent and the integrity of a code based on the remote credible proof, granting a mark, and detecting cloud information to generate a machine readable portrait containing key parameters; collecting local multi-mode log data coding embedding vectors of tenants, and after privacy processing, calculating that the abnormal confidence exceeds a threshold value by a local model to trigger current limiting or blocking; and constructing a hierarchical federated architecture containing local nodes of tenants, a regional cloud and a global control plane, encrypting aggregation parameters to generate a cross-tenant attack recognition global model and issuing the cross-tenant attack recognition global model to each tenant on the premise that log data is not out of a domain. According to the invention, integrity verification is carried out on the tenant side security agent through the trusted access mechanism, and the tenant portrait is constructed based on the trusted mark, so that the real and verifiable cooperative defense capability among a plurality of tenants is realized, and the cooperative process has dependency.

The invention discloses a multi-tenant management and control system and method for a big data platform, and relates to the technical field of data management, and the system comprises a data basic management and control module layer and a data processing collaborative management and control layer. The data basic management and control module layer comprises a big data component management module, a tenant management module, a unified login authentication module, a project management module, a data resource authority management module and a computing resource management module; the data processing collaborative management and control layer comprises a component health monitoring sub-module, a resource prediction distribution sub-module, a dynamic desensitization encryption sub-module, a task collaborative management sub-module and a data quality management and control sub-module; the problems of weak tenant basic management and control, low resource allocation efficiency and insufficient component reliability and data security management and control in the existing multi-tenant management and control technology are solved, and the defects of lack of cross-tenant cooperative capability, lack of task and data quality management and control and insufficient data security management and control caused by the problems are overcome.

The invention provides a multi-tenant multi-user login method based on enhanced OAuth2, which relates to the technical field of network security, and comprises the following steps: obtaining a key threshold through a tenant identifier, and executing OAuth2 authentication to obtain an access token; tenant fingerprint information is generated through zero-knowledge proof and homomorphic encryption, and a tenant verification token is generated through secure multi-party calculation; constructing a reinforcement learning access controller in combination with the historical access data, and generating and verifying an authority list; and finally forming a session identifier and storing the session identifier in a distributed cache. According to the invention, the authentication security is improved, cross-tenant fine-grained authority management is realized, and unauthorized access is effectively prevented.

The invention provides a multi-tenant visual large model reasoning resource dynamic allocation and isolation method. An integrated system of a multi-tenant environment, visual large model reasoning, dynamic resource allocation and isolation guarantee is constructed. The system controls resources such as model copy number, video memory quota, batch size, queue weight, bandwidth and the like at tenant level fine granularity, and adjusts priority and quota based on closed-loop feedback of real-time indexes (such as queuing length, delay and throughput). Interference suppression among tasks is realized through priority grading, a hard / soft isolation strategy and a tenant-model copy mapping mechanism in combination with GPU MIG, video memory partitioning, queue scheduling and other technologies. Aiming at the characteristics of high video memory, large input and the like of a visual large model, model loading, batch processing and video memory multiplexing strategies are optimized, and differential resource allocation of heterogeneous models is supported. According to the overall scheme, on the premise that the service quality and isolation are guaranteed, the GPU resource utilization rate is remarkably increased, and the operation cost is reduced.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

The invention provides a method and a device for generating a customized report of an SAAS (Software as a Service) system based on dynamic template configuration. The method comprises the following steps: acquiring a customized visual report template for realizing business data decoupling; editing the report template based on a preset editor, and generating a template configuration file; analyzing a structured marker in the template configuration file to generate a metadata description file; querying a business database through a unified data interface according to a data requirement defined by the metadata description file, and obtaining and assembling customized business data corresponding to the tenant ID; and at least based on the customized business data, controlling a rendering process through a dynamic position calculation algorithm, and dynamically filling the data to a corresponding position of the report template to generate a customized report. According to the method, the problems of code redundancy and difficulty in maintenance of the SAAS multi-tenant report are solved, zero code configuration is realized, and report generation efficiency and tenant personalized adaptation capability are improved.

The invention discloses a localized cloud platform security optimization method based on a trusted execution environment, which is suitable for a multi-tenant computing and key business data security protection scene of a localized cloud platform. Comprising the following steps of executing security startup and measurement verification on a domestic chip, constructing a hardware root trust chain, loading a trusted execution environment, performing sensitivity evaluation and grading on tenant tasks, and dynamically scheduling the tasks related to privacy or key data to a TEE (Trusted Execution Environment) for execution; and performing task data encryption and decryption and dynamic key management, and performing multi-level permission verification on the access request. According to the method, hardware-level security isolation and data protection during operation of the localized cloud platform in a multi-tenant operation environment are realized, and the credibility and protection capability of the platform in the links of task scheduling, encryption calculation and remote verification are remarkably improved.

The invention discloses a dynamic hierarchical encryption system for multi-tenant e-commerce data, and relates to the technical field of multi-tenant data security, the system comprises an encryption management center, the encryption management center is in communication connection with the following modules: a data sensing and grading module, which is used for collecting multi-source context data of an e-commerce platform, and sending the multi-source context data to the encryption management center; performing basic sensitivity level division on the context data; and the multi-tenant knowledge graph construction module is used for constructing a multi-tenant knowledge graph based on the tenant-data-authority association network in combination with the multi-source context data. According to the method, dynamic self-adaptive adjustment of encryption strength is realized through cooperation of the fruit fly algorithm and the knowledge graph, the system dynamically optimizes combination of key length and the encryption algorithm based on data grading labels and real-time context data, and the fruit fly algorithm searches an optimal solution through iteration, so that the system has high encryption efficiency while ensuring compliance. And encryption delay is controlled within a real-time requirement range, so that the user experience in a multi-tenant scene is remarkably improved.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

The invention relates to the technical field of cloud computing and artificial intelligence, in particular to an agent collaborative management system and method based on multi-tenant isolation and dynamic role permission, and the system comprises the following modules: a tenant authorization agent module, a role authorization agent module, a user personalized management agent module, and a cross-tenant collaborative security mechanism. A dynamic permission effective process; the method has the beneficial effects that the first-step distribution of the agents is realized through the tenant authorization agent module, the second-step distribution of the agents is realized through the role authorization agent module, and finally the capability of individually loading and using the agents by each user is realized through the user personalized management agent module.

The invention relates to the technical field of enterprise service digitalization and intelligent management, in particular to an enterprise management method and management platform based on dynamic portraits. The method comprises the following steps: S1, accessing and standardizing multi-source heterogeneous data, calculating a source-level quality score and generating an evidence chain; s2, constructing a portrait map and a time axis taking an enterprise as a main node; s3, the rule engine and the LLM-RAG output numbers in parallel; s4, consistent arbitration and belief propagation; s5, event-driven dependency subgraph increment recalculation is carried out; s6, carrying out portrait-strategy matching and task linkage; s7, carrying out closed-loop write-back and auditable playback; and S8, multi-tenant fine-grained permission. According to the scheme, the problems of data fragmentation and insufficient timeliness and traceability are solved, and the linkage of portrait credibility, interpretability and real-time decision is realized.

The invention relates to the technical field of e-commerce safety supervision, and discloses a multi-tenant shopping mall safety supervision method and system based on big data analysis, and the method comprises the steps: carrying out the hierarchical heterogeneous data adaption, and constructing unified data representation; constructing an identity association graph based on probabilistic reasoning; carrying out distributed feature learning by adopting a federal map neural network; hierarchical threat detection and risk assessment are realized; constructing a safety response mechanism of differential privacy protection; deploying a cloud native architecture and a self-healing mechanism; according to the method, the problems of heterogeneous data processing, cross-platform identity association, privacy protection and safety supervision balance, system reliability and the like in a multi-tenant shopping mall environment are effectively solved, the accuracy, comprehensiveness and reliability of safety supervision are improved, meanwhile, user data privacy is protected, and comprehensive technical support is provided for safety operation of a shopping mall.

The present invention provides a computer system and a computer-implemented method for adaptive quality management in artificial intelligence (AI) processing. The system comprises a memory unit storing machine-readable instructions and a processor configured to execute the instructions to store, in a persistent correction memory, records of prior corrections to AI outputs; retrieve a correction for a current input based on similarity between the input and stored records; generate, by an audit-trail module, a tamper-evident record of the correction and its application; schedule, by a predictive resource manager, AI workloads in dependence on service-level constraints, carbon-intensity data, and quality metrics; and update, by a feedback controller, retrieval and scheduling parameters based on effectiveness information. The processor thereby enables the persistent correction memory, audit-trail module, predictive resource manager, and feedback controller to operate as a closed-loop adaptive system that reduces recurrence of AI errors, ensures verifiable auditability, and optimizes computational efficiency.

The invention discloses a multi-tenant resource demand prediction method based on an LSTM-XGBoost fusion model. The multi-tenant resource demand prediction method comprises the following steps: constructing multi-dimensional input characteristics including a tenant historical resource usage amount, a task priority, an SLA constraint coefficient and a time period load mode; respectively inputting the input features into an LSTM (Long Short Term Memory) time sequence channel and an XGBoost attribute channel for feature extraction; splicing the feature vectors output by the two channels, and inputting the spliced feature vectors to a fusion layer for joint modeling; and outputting the resource demand prediction vector in the future time period through a regression device. By introducing a dual-channel modeling structure, structured information such as historical resource usage sequences and task priorities of tenants, SLA constraint coefficients and load modes is processed respectively, and accurate prediction of multi-dimensional virtual resource demands is realized. By combining a fusion layer and a regression output mechanism, the prediction precision before resource scheduling is improved, efficient utilization and fair distribution of platform resources are realized, and differentiated service guarantee requirements in a multi-tenant environment are met.

The invention discloses an intelligent cloud service system construction method based on quantum security, and relates to the technical field of information security, and the method comprises the following steps: in a multi-tenant cloud environment, distributing an independent quantum key generation module for each tenant, ensuring that the key generation processes of the tenants are mutually isolated, and preventing the cross use of keys. According to the invention, the independent quantum key generation module is distributed for each tenant, so that the problems of cross use and leakage of keys in a multi-tenant cloud environment are solved. And a quantum key updating mechanism and accurate synchronous control are introduced, so that the keys are periodically updated and kept consistent, and the security risk caused by key aging is prevented. By combining the physical principles of quantum inseparable and superimposed state characteristics and the like and quantum channel optimization, the system realizes safe and efficient key transmission, and timely responds to potential security problems through real-time monitoring and anomaly detection, so that the resistance and overall security of the system to quantum computing attacks are improved.

The invention discloses a multi-tenant API key authentication and resource isolation system in a containerized environment, and relates to the field of containerized multi-tenant authentication. Comprising an authentication authorization layer, a resource management and control layer and a security isolation layer. The authentication authorization layer comprises a multi-tenant authentication engine, an API key verification sub-module, a tenant identity recognition sub-module, an authority cascade verification sub-module and a dynamic authority calculation sub-module. And the resource management and control layer comprises a resource quota manager, a dynamic quota allocation sub-module, a real-time use monitoring sub-module, a threshold alarm control sub-module and an elastic capacity expansion and contraction sub-module. The security isolation layer comprises a multi-dimensional isolation engine, a container network isolation sub-module, a storage space isolation sub-module, a process permission isolation sub-module and a system call filtering sub-module; deep fusion of API authentication and container resource control is realized, a dynamic resource quota management mechanism based on tenant identities is established, and fine-grained API authority control and resource use limitation are provided.

The invention provides an online interactive programming education system based on a micro-service architecture, and the system comprises an infrastructure layer which serves as a bottom layer computing power and provides an elastic computing power through a cross-cloud deployment technology and a dynamic resource scheduling technology; the platform core layer constructs a stateless micro-service cluster through a service registration discovery module, a load balancing module and a fusing mechanism module depending on elastic resources of the infrastructure layer, and the infrastructure layer provides physical resource guarantee for operation of the micro-service cluster of the platform core layer; the platform support layer is used for integrating an authority management component, a workflow engine component and a general service component based on a stateless micro-service cluster constructed on the platform core layer, and providing resource isolation and high-concurrency task scheduling support in a multi-tenant environment for the programming education system; the platform core layer is combined with distributed transaction management to provide stable service scheduling and communication capability for an authority management component, a workflow engine component and a general service component of the platform support layer; and the platform application layer combines service scheduling and communication capabilities, realizes teaching functions such as multi-language development, code debugging visualization, AI personalized suggestion and collaborative learning, and directly serves programming learning of students and teaching management requirements of teachers.

The invention discloses an NVMe storage array virtualization device based on an FPGA (Field Programmable Gate Array), which is characterized in that single I / O (Input / Output) virtualization (SR-IOV) is realized in the FPGA, and a plurality of NVMe virtual functions which can be directly accessed by a virtual machine are externally provided; mapping from a virtual volume logic address to a physical storage address is completed in hardware, striping management and RAID verification calculation are carried out on a plurality of NVMe solid state disks at the rear end, and storage resource pooling is achieved; the partial reconfigurable characteristic of the FPGA is utilized, I / O scheduling, data compression and prefetching strategies are dynamically adjusted, and the adaptability and performance efficiency of the system are improved. Hardware unloading of the whole I / O path is completed in the FPGA, intervention of a host CPU is not needed, delay is remarkably reduced, the throughput capacity is improved, service quality isolation and elastic expansion in a multi-tenant environment are supported, and an effective technical path is provided for constructing a next-generation high-performance and low-delay storage infrastructure.

The embodiment of the invention discloses a cluster tenant resource scheduling method and device, electronic equipment and a storage medium, relates to a cloud computing technology and can be used in the field of financial science and technology, and the method comprises the following steps: predicting a future resource demand of a target tenant namespace based on historical resource use data of the target tenant namespace in a cluster, the target tenant namespace comprises a tenant namespace bearing a key service; determining a real-time resource quota of the target tenant namespace based on the available resources of the shared pool and the future resource demand predicted value, the service level and the comprehensive priority of the target tenant namespace; and performing real-time resource scheduling on the target tenant namespace based on the real-time resource quota. According to the embodiment of the invention, the service quality of key services in a multi-tenant mixed deployment scene can be improved, and the resource utilization rate can be effectively improved.

The invention relates to a resource quota dynamic management and control method for multi-tenant sub-accounts in a cloud communication platform, and belongs to the technical field of cloud computing and software. The method comprises the steps of obtaining sub-account data and a tenant grading system and a standardization protocol preset by a platform, performing verification and format regulation, and generating a standardization data set; secondly, based on a grading system, binding a permission boundary, extracting resource and service features for verification, constructing an associated weight distribution mechanism, and forming a grading quota evaluation benchmark; various thresholds and rules are preset, a reference quota is generated in combination with real-time monitoring data and a service load prediction result, and a differentiated adjustment scheme is formed by constructing an elastic adjustment mechanism and dynamically adapting to a resource quota. And finally, quota allocation is executed, indexes such as use saturation and response time delay are monitored through a two-dimensional verification system, a full-link management and control log is generated, and a resource dynamic management and control closed loop is formed. Refined and adaptive management and efficient utilization of resource quotas are realized.