1083 results about "Multitenancy" patented technology

The invention relates to the technical field of communication, in particular to a micro-segmentation-based multi-tenant zero-trust security system, which comprises a tenant network topology sensing module for acquiring network structures and service dependencies of tenants and generating a tenant-level network topology graph and a communication graph; a strategy rule automatic generation module generates a micro-segment access control strategy; the context-aware risk assessment module collects a user identity, an equipment state and a behavior track, and generates an access risk score; the strategy dynamic adjustment and application module updates the access control strategy; and the tenant isolation and zero-trust interaction module deploys a security sandbox to complete trust verification and isolation protection of cross-tenant requests. According to the method, the micro-segmentation strategy is generated by automatically identifying the service dependency boundary, the access control rule is dynamically adjusted based on the context, and refined isolation and risk-driven defense in a multi-tenant environment are realized in combination with the security sandbox and the zero-trust mechanism, so that the security and controllability of the system are remarkably improved.

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 data space multi-tenant isolation method and system based on a zero-trust architecture, particularly relates to the technical field of multi-tenant authority control, and is used for solving the problems of cross-tenant authority conflict and insufficient resource isolation caused by frequent strategy adjustment in the prior art. The method comprises the following steps: constructing a strategy dependence topological structure by acquiring a dynamic access strategy change request in real time so as to model a dependency relationship among a tenant permission node, a resource access path node and a time sequence connection edge; dynamically marking a resource sensitivity weight based on a historical access behavior, and analyzing and screening high-sensitivity conflict nodes in combination with a logic coupling degree; separating an independent resource access chain which directly depends on through reverse backtracking, and generating a hierarchical permission correction rule; finally, rules are injected, and blocking conditions are triggered to execute hierarchical verification and risk marking on the cross-tenant operation requests; the influence of strategy execution on a global system is reduced, and the permission boundary clearness and resource security of a multi-tenant data space are ensured.

The invention relates to the technical field of distributed computing and cloud computing, in particular to a multi-service and multi-tenant-oriented micro-service method and system, and the method comprises the steps: constructing a dynamic modular service framework based on a pluggable service component, a cross-tenant service module based on resource sharing, and a multi-service and multi-tenant service module based on resource sharing; modeling the calling relationship between the micro-services and calculating each resource demand weight coefficient of the micro-services; and scheduling the resource-shared cross-tenant service module by using the dynamic resource allocator. According to the method, the dynamic modular service framework based on the pluggable service component is constructed, so that accurate loading and unloading of each service module of the micro-service can be completed within hundreds of milliseconds, and the agility of the system is remarkably improved. According to the method, calling relation modeling is carried out on the cross-tenant service module, the resource demand weight coefficient is calculated, basic data is provided for resource scheduling, load changes are considered, optimal configuration of resources is achieved, and the overall performance and the response speed of the system are improved.

The invention relates to a Kubernetes cluster automatic deployment system based on modularization, and the system comprises a modularized cluster mirror image system which is used for providing a mirror image containing all assemblies of cluster operation and the optimal practice configuration of each assembly, and is used for flexibly customizing the assembly combination of a cluster according to different demands, and rapidly generating a customized cluster mirror image, the standardization of the delivery of the Kubernetes cluster is realized; the automatic deployment engine is used for defining a topological structure and configuration parameters of the cluster by adopting a declarative API (Application Program Interface), completing full-life-cycle automatic management through an architecture mode of a controller, and providing a monitoring alarm function which is ready to use after box opening; the application management system is used for providing application store based on Web, templated application configuration management and application real-time monitoring management; and the multi-tenant management system is used for providing resource isolation management, tenant authority management and security isolation management. According to the invention, the complexity of cluster deployment and management is greatly reduced, and the application efficiency and reliability of the cloud native technology are improved.

Methods and apparatus for application aware memory patrol scrubbing techniques. The method may be performed on a computing system including one or more memory devices and running multiple applications with associated processes. The computer system may be implemented in a multi-tenant environment, where virtual instances of physical resources provided by the system are allocated to separate tenants, such as through virtualization schemes employing virtual machines or containers. Quality of Service (QoS) scrubbing logic and novel interfaces are provided to enable memory scrubbing QoS policies to be applied at the tenant, application, and / or process level. This QoS policies may include memory ranges for which specific policies are applied, as well as bandwidth allocations for performing scrubbing operations. A pattern generator is also provided for generating scrubbing patterns based on observed or predicted memory access patterns and / or predefined patterns.

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.

Techniques for managing cloud computing resources hosting burstable performance instances are described. A host computer system of a provider network executes burstable performance compute instances. Compute capacity usage data is obtained from the host computer system, the compute capacity usage data including a first indication of a first compute capacity used by a first burstable performance compute instance. A first weight for the first burstable performance compute instance is calculated, the first weight being inversely related to the first compute capacity. A scheduler of the host computer system is updated with process prioritization weights, the process prioritization weights including a first process prioritization weight that is based at least in part on the first weight. The scheduler allocates compute capacity based on the process prioritization weights.

The invention discloses an operation and maintenance knowledge intelligent question and answer interaction method, device and equipment based on an LLM large model. According to the method, questions of operation and maintenance personnel are received through a natural language interaction interface, semantics are analyzed through a pre-trained LLM, candidate answers are generated, the accuracy of the answers is verified in combination with a dynamic operation and maintenance knowledge graph (including equipment model and fault code mapping, a historical case library and a real-time updating knowledge base), and finally a verification result is returned. The device comprises an input module, a processing module, a verification module and an output module, the processing module integrates domain knowledge fine tuning and real-time data integration functions, and the verification module supports multi-source data fusion and confidence evaluation. The platform adopts a multi-tenant architecture, supports visual diagnosis, automatic equipment operation and knowledge use report generation, and ensures that data cannot be tampered through a block chain technology.

Methods, systems and apparatuses for sharing video data across tenants of a multi-tenant security system. The methods may include maintaining a first tenant account of a security server and a second tenant account of the security server. The first tenant account is unrelated to the second tenant account. The second tenant account is configured to include partner account privileges for the first tenant account. A request from the second tenant account to access a resource associated with video data included in a resource registry of the first tenant account is accessed. The resource registry is made accessible to the second tenant account based on the partner account privileges. The request may be approved or denied. The video data is stored in a tenant database in association with the second tenant account based on the request being approved. The request may be processed by an image processing device.

The invention provides a multi-tenant large model reasoning service architecture and an operation method thereof, and the architecture comprises a multi-level cache module which is used for managing the storage of a LoRA model in a video memory, an internal memory, a disk and a remote memory according to the priority; the route registration module is used for recording the model cache position and the load state of each server; the dynamic routing module is used for allocating requests according to the tenant identifiers and the server states; and the parameter fusion module is used for superposing the LoRA model and the basic model to generate a personalized inference model. According to the method, efficient resource utilization and low-delay service are realized through multi-stage cache management, a dynamic routing strategy and a load balancing mechanism. The framework supports on-demand loading and parameter fusion of the tenant personalized LoRA model, and the reasoning service efficiency in a multi-tenant scene is remarkably 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 relates to the technical field of data center high availability, in particular to a cross-data center fault isolation and switching method and system in a multi-tenant environment. The resource mapping table is constructed by taking the tenants as the minimum control units, the affected tenants are accurately identified, logic isolation is executed, the problem of waste of full-tenant service migration resources caused by node-level or cluster-level switching in the prior art is avoided, and the fault influence range is minimized. The optimal data center is dynamically selected by combining the multi-factor objective function with the tenant SLA level, the defects that an existing scheduling strategy is opaque and tenant priorities cannot be distinguished can be overcome, and it is ensured that key tenant services are preferentially recovered. And finally, through combination of a gray takeover mechanism and health feedback confirmation and multi-dimensional recovery verification after migration, the condition that an existing recovery mechanism is extensive can be changed, the fault processing precision and the resource scheduling efficiency of the data center in a multi-tenant environment are remarkably enhanced, and the service continuity is guaranteed.

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.

Techniques are disclosed herein for onboarding users from a single tenant cloud environment to a multi-tenant cloud environment. In one aspect, a method is provided that includes in response to an eligibility status check indicating that a first cloud service instance running a first version of a cloud service in a first cloud environment is eligible for the upgrade, exporting a copy of data from the first cloud service instance to a common storage device, provisioning a second cloud service instance running on the second version of the cloud service in a second cloud environment, importing the first copy of the data from the common storage device to the second cloud service instance, and activating the second cloud service instance to run the second version of the cloud service. During the exporting, provisioning, and importing the first cloud service instance continues to run the first version of the cloud service.

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.

The invention relates to the field of front-end technology, can be applied to the field of digital medical / financial science and technology, and discloses a front-end multi-tenant management method and device, electronic equipment and a storage medium. The method comprises the following steps: returning a security access token corresponding to a target tenant by using an authentication mechanism based on a token or third-party authorization according to a tenant registration request; when registration succeeds, role configuration data and permission configuration data of the target tenant are generated; when it is monitored that login is successful by using the security access token, creating a micro-front-end application and different micro-front-end modules of the target tenant according to the role configuration data and the authority configuration data; starting a micro-front-end application, and dynamically loading a micro-front-end module according to the role configuration data and the authority configuration data; and executing mixed mode rendering on the front-end module at the server side and the tenant side through the integrated front-end framework to obtain a front-end rendering page of the target tenant. According to the method, multi-tenant security isolation is realized, resources are loaded as required, and system performance and expansibility are guaranteed.

The invention relates to an intelligent IT asset management system, and the system comprises a configuration asset management module which is used for automatically collecting IT asset information in a network, building a complete configuration item relation model, and supporting the dependency relation mapping and impact analysis between configuration items; the service desk management module is used for establishing a service catalog, defining a processing flow, response time and a service level protocol, automatically carrying out classification and priority evaluation on the received service requests through an intelligent classification engine, and distributing the service requests to proper processing personnel according to a preset rule; the contract supplier management module is used for full-life-cycle management of suppliers and contracts; the IT service financial module is used for multi-dimensional budget management and fine management of IT cost; and the multi-tenant management module is used for realizing entity separation and authority control. The problems of a traditional IT asset management system in the aspects of asset tracking, service management, cost control and the like are solved, and a set of complete IT service management solution is provided for enterprises.

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.

A system for a multi-tenant API framework in scalable cloud-based database solutions, which includes the following: an API layer module that enables access to database services for multiple tenants; a tenant management module configured to handle tenant authentication, authorization, and role-based access control; a data partitioning module for partitioning tenant-specific data into isolated database structures; a resource allocation module for the dynamic allocation of computing resources to individual tenants based on usage patterns; a security module configured to protect tenant data through encryption and secure data transmission; A monitoring and analysis module for real-time monitoring of tenant activities and system performance.

A service provider may deploy a security threat detection and mitigation platform in a multi-tenant virtualization environment that includes pluggable data collection, data analysis, and response components. The data analysis components may apply machine learning techniques to generate (based on training data sets) and refine (based on subsequently received data sets and feedback about the resulting classifications) predictors configured to detect particular types of security threats, such as denial of service attacks, botnets, scans, or remote desktop attacks. A data collection layer may collect, filter, organize, and curate network packet traffic data, network packet header data, or other information emitted by computing instances or applications executing on them, and provide the curated data as streams to the analysis layer. A response layer may automatically take action in response to threat detections (which may be overridden by an administrator) and may store classification data for subsequent analysis, feedback, and predictor refinement.

The invention discloses an AI-driven multi-tenant cloud computing resource optimal allocation method and system, and belongs to the field of artificial intelligence, and the method comprises the steps: constructing a tenant resource dynamic portrait, and compressing the tenant resource dynamic portrait into a tensor containing a resource type, a time window and disturbance density through a three-dimensional residual attention network based on a resource request, a response time delay and a load jitter sequence; extracting resource competition conflicts among tenants by using a gating map neural encoder, and outputting a conflict risk map; a hierarchical scheduling space is constructed based on the atlas, a throughput maximization and return optimal strategy is trained, and a periodic switching controller is adopted to realize exploration and convergence balance; carrying out robustness screening on scheduling actions, and eliminating sensitive actions by utilizing a self-adaptive annealing mechanism to generate a stable strategy set; and executing resource allocation and continuously collecting microcosmic utilization data to guide subsequent strategy fine adjustment. The method has the beneficial effects that efficient and stable allocation of resource scheduling is realized, and the resource utilization rate and the system robustness in a multi-tenant cloud environment are improved.

The invention discloses a data security isolation and sharing framework implementation method for multiple tenants. The method comprises the following steps of performing multi-tenant data isolation design; the method comprises the steps of S1, role-based access control and dynamic authority management design, S3, data sandbox and sharing mechanism design and S4, performance optimization and resource management. S5, a unified security management and monitoring platform, and relates to the technical field of computers.According to the method, multi-tenant data isolation design is adopted, and effective isolation of tenant data is achieved through combination of logic isolation and physical isolation; controlling access of the tenants to the shared data through access control and dynamic authority management of roles; through a data sandbox and a sharing mechanism, the safe operation of the shared data by the tenant is realized; the resource utilization rate is improved through a cache mechanism, load balancing, a resource recycling mechanism and asynchronous processing, and performance optimization and resource management are achieved.

The invention relates to an electric power personalized master data management system combined with a multi-tenant management mode. The electric power personalized master data management system comprises a tenant management layer, an intelligent master data service layer, an intelligent storage layer and an interface layer, the tenant management layer is used for registering, distributing and authenticating tenants, providing unique identifiers of the tenants, and supporting tenant-level permission and personalized configuration management; the intelligent master data service layer comprises an intelligent data cleaning module, a master data intelligent modeling module and an anomaly detection module; the intelligent storage layer is used for storing main data by adopting a relational database MYSQL; the MongoDB is used for storing unstructured or semi-structured data; and the interface layer provides a standardized API (Application Program Interface) to support integration of multiple systems, ensures identity verification and authorization of all requests through a zero-trust method, and minimizes a trust radius. Efficient storage, cleaning, analysis and application of the master data are achieved, and the master data management quality and efficiency are effectively improved.

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.

A method is provided for controlling secure access to data in hybrid-cloud environments. A trusted authorisation server controls access to data served by a resource server to client devices in an untrusted environment, to ensure client devices are only able to access data that they are permitted to access. In the present techniques, the trusted authorisation server checks the validity of data access requests received from client devices prior to any such requests being received by the resource server or an authorisation server associated with the resource server. This is advantageous because in many existing hybrid-cloud systems, the resource server and authorisation server are unable to check the individual validity of requests received from client devices. This is particularly problematic for multi-tenant resource servers. The present techniques provide a more secure way of controlling access to data stored and served by cloud-based, off-premises / third party resource servers.

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 belongs to the technical field of network security, and particularly relates to a security management and control method for using bank and enterprise USB key equipment based on a multi-tenant system, which comprises the following steps: performing micro-isolation deployment on the multi-tenant system, and building an independent micro-isolation area in a server cluster network layer in combination with the flow intelligent scheduling and precise strategy control capability of an SDN controller; after the connection between the tenant server and the USB key equipment is established, continuous identity verification and authorization implementation are carried out, dynamic access control implementation is carried out in real time, and the USB key equipment communicates with the tenant terminal through an encryption tunnel; when the system runs, real-time behavior monitoring is carried out on connection behaviors between tenants and the USB key equipment, behavior modeling is carried out, real-time monitoring analysis and anomaly detection are carried out on the connection behaviors of the tenants, if the connection behaviors are abnormal, a blocking mechanism is triggered, and network nodes are controlled to block and prohibit abnormal connection. According to the invention, illegal connection in the multi-tenant system can be effectively supervised and blocked.

The invention relates to the technical field of information processing, in particular to a user-oriented multi-tenant security enhancement service isolation system, comprising: at least one tenant, each tenant having an independent namespace, at least one corresponding service, at least one corresponding node, and a network storage space; the public node pool comprises a plurality of public nodes, and firewalls are arranged between the public nodes and the nodes corresponding to the tenants; and the resource controller is used for dynamically adjusting the resources of different tenants. A safe isolation environment of computing resources, storage resources and network resources is constructed in a mode of combining soft isolation and hard isolation, safe and efficient resource intensive management of a multi-tenant environment is realized, a resource adjustment mode of each tenant is determined according to index data of each tenant, tenant resources are automatically and dynamically allocated, and the resource utilization rate is improved. And the business isolation requirements of the tenants are met on the premise of ensuring the system stability and security.