948 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 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.

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.

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.

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 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 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 present technology pertains to receiving, by a multi-tenanted cloud-native headend, network traffic from a source that is directed to a destination within a network of a first tenant, where the network traffic is routed using BGP over a multi-tenanted BGP network, where the multi-tenanted BGP network utilizes route isolation to isolate the network of the first tenant from networks of other tenants making use of the multi-tenanted BGP network, mapping the received network traffic based on at least a source IP, source port, and virtual network interface (VNI) to a port associated with the network of the first tenant over the isolated BGP route, where the mapping is a stateful mapping, where the mapping is also potentially based on other information associated with the network traffic, and directing the received network traffic to a local address in the network of the first tenant.

In an example embodiment, a singular test is used to validate a user entity data model for any type of instance in an efficient manner. This results in an Entity-Agnostic test. This approach provides tremendous savings from test implementation, support, data storage, and test triaging perspective, as well as being able to always validate the functional correctness of the data model for any customer / user.

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.

A cloud-based platform for zero trust network access (ZTNA) services provides zero trust network access as a service for multiple customers in a multi-tenant architecture. In this context, the configuration for a new ZTNA application is validated with a service proxy in a sandbox or similar environment before release by the cloud-based platform for access through a public network. As a significant advantage, this approach mitigates inadvertent conflicts or instability in a service proxy that supports other applications and customers.

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.

A computer-implemented method performing an auto expiring multi-tenant multi-entity sliding window event counter includes defining a dynamic key for a key value pair uniquely created for a tenant per day in any key value store. The method also includes storing a hash associated with the dynamic key. The hash includes an entity identification (ID) as a key and an event count as a value, and upon a valid event for an entity of the tenant, identifying a tenant level key for a day and incrementing the value of the entity ID. The method further includes retrieving a sum of counts over the predefined period of time for each entity identified with the entity ID for the tenant, and performing automated key deletion / cleanup after expiration of the predefined period of time set as expiry for the key.

One example method includes receiving, by a confidential container service from a tenant, a request to provision a tenant node, provisioning the tenant node using a multi-tenant trusted execution environment (TEE) attested resource provisioning process, receiving, from the tenant, tenant-specific security information concerning the tenant node, storing the tenant-specific security information in a tenant-specific catalog, implementing security procedures, specified in the tenant-specific security information, in the tenant node, and upon successful authentication of the tenant, using the security procedures, enabling the tenant to access the tenant specific confidential resources from the tenant node(s).

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 discloses a method, a system and equipment for carrying out resource management on a database and a medium. The method comprises the following steps: creating at least one resource group in a database, and defining configuration information of each resource group; based on the binding relation table of each resource group, binding each resource group, and determining the binding type of each resource group; based on the binding type of each resource group, updating the configuration information of each resource group through a resource group operation interface to obtain target configuration information of each resource group; executing a corresponding query operation based on the target configuration information of each resource group and the state of each resource group; monitoring whether the state of each resource group is changed or not, triggering a corresponding resource adjustment strategy when the state of each resource group is changed, and recording change information; according to the method, resources of different tenants are isolated through a resource group mechanism, so that database-level resource isolation and fine management are realized, and the stability of a multi-tenant environment is improved.