123 results about "Multitenancy" patented technology

Devices, systems, machine-readable media, and methods for web session caching with improved security are provided. A method can include receiving, at the multi-tenant cache and from a web-based client, a first request to perform an operation on web session data in the multi-tenant cache, issuing, by the multi-tenant cache, a validation request to an authorization service, receiving, by the multi-tenant cache, a result of the validation request, responsive to determining the result of the validation request is a valid verification, performing the operation on the multi-tenant cache, and issuing, by the multi-tenant cache and to the web-based client, a response to the first request.

This invention discloses a cloud-native method for dynamic resource scheduling and elastic scaling management of mini-programs, specifically relating to the fields of cloud-native and microservice resource scheduling. The method involves: First, collecting multi-dimensional operational metrics in a non-intrusive bypass manner via a sidecar proxy; second, inputting the filtered and filled metrics into a hybrid predictor to predict resource demand and calculate a burst factor; third, designing a two-layer collaborative scheduler that combines horizontal elastic scaling with vertical dynamic adjustment based on reinforcement learning; and fourth, introducing a multi-tenant fair arbitration mechanism based on resource claims, prioritizing the expansion requests of less-used tenants by sorting them in ascending order of claim value; finally, encapsulating the decision into a custom resource object for execution, and performing incremental model training and closed-loop optimization based on the feedback error sequence.

The application provides a security detection method and system for a large language model key-value cache, relates to the field of artificial intelligence information security, and comprises the following steps: obtaining original request text input by a user, intercepting a prefix from the original request text, taking the prefix as a root node Token, and constructing an initial Token tree; with the aid of a target large language model to be detected and a locally deployed shadow large language model, performing downward iteration generation on the initial Token tree until a stopping condition is met, and obtaining a complete Token tree; selecting a path with the highest security score in the Token tree, comparing the matching degree with the original request text, and quantitatively evaluating the privacy protection strength of the large language model key-value cache; and with the aid of candidate Tokens generated by the shadow large language model and the first character generation delay detected by the target large language model, the security defense capability of the large language model key-value cache in a multi-tenant shared environment is evaluated.

A method and system for switching routes based on conditions in cloud-based multi-tenant systems is disclosed. Routes delivers services to end user devices. The routes are specified for policies. The policies specify residencies for the routes, cloud services, and data storage. An application running on an end user device selects a policy and a cloud service. A route corresponding to the policy, and the cloud service is returned to the application. An access resource server determines traffic rules to change the route, and changes the route based on conditions including a time of day, utilization, latency or a performance threshold

The application discloses a kind of zero job platform multi-tenant authority hierarchical access control processing methods, specifically related to computer network and information security technical field, for solving the difficulty of cross-domain access under multi-tenant cooperation to consider isolation, dynamic authorization and tamper-proof archiving;By responding to the cross-domain collaborative matching event triggered by the first tenant and the second tenant for the target user, the minimum set attribute data is extracted and the target shadow entity is generated in the second tenant, the entity binding message is output to the API gateway layer, and the first permission policy token is generated and issued around the life cycle state vector, the global permission fuse is triggered at the archiving lock node, the identification and rollback cleaning of the to-be-tested cross-domain concurrent write request are identified and rolled back, the pre-archiving cache area is formed and the final state shadow entity is obtained, and then a data digest hash tree is generated based on the cross-domain interaction data, a digital signature is received and a three-party signature consensus verification is performed, and a legal release voucher and a right-confirmation archiving load are output.

A hybrid forecasting system (100) for tiered cloud pricing using ensemble learning, comprising: a data ingestion module configured to continuously ingest and aggregate heterogeneous data from a variety of sources, including historical cloud usage data, real-time resource consumption metrics, customer subscription profiles, service-level agreement parameters, and external demand indicators; a preprocessing engine functionally coupled with the data ingestion module, the preprocessing engine being configured to perform data cleansing, normalization, transformation, feature extraction, and dimensionality reduction to generate structured and model-compatible datasets;a model training unit that is functionally coupled with the preprocessing engine, wherein the model training unit comprises a variety of heterogeneous predictive models, including at least one statistical model, at least one machine learning model, and at least one deep learning model, each configured to process the structured datasets independently to generate predictive results that meet future cloud resource needs, workload variability, and price sensitivity across multiple service tiers;an ensemble aggregation layer that is functionally coupled to the model training unit, wherein the ensemble aggregation layer is configured to receive and combine the forecast results generated by the multitude of forecasting models using ensemble learning techniques, including weighted averaging, stacking or boosting, with the weights assigned to each forecasting model being dynamically adjusted based on predefined performance evaluation metrics to generate a uniform and optimized forecast;a price optimization engine that is functionally connected to the ensemble aggregation layer, wherein the price optimization engine is configured to determine and dynamically adjust tiered price structures based on the unified forecast, taking into account parameters such as forecasted demand, user segmentation, demand elasticity, infrastructure capacity constraints, and predefined optimization goals such as revenue maximization and resource utilization efficiency;and a feedback adjustment module that is functionally coupled with the price optimization engine and the model training unit, wherein the feedback adjustment module is configured to monitor system performance in real time, user response to price adjustments and resource utilization results, and iteratively updates model parameters and pricing strategies using adaptive learning mechanisms, including reinforcement learning, wherein the system (100) is configured to operate in a multi-tenant cloud environment, supports real-time data processing and decision-making, and enables automated, scalable, and adaptive optimization of tiered cloud pricing.

The present invention relates to the field of digital information transmission technology, specifically a multi-tenant store patrol monitoring third-party platform device management system based on a compatibility layer. The specific implementation process includes: obtaining the original IoT message stream and importing it into the protocol parsing array, reconstructing it into a monitoring semantic packet and sending it into the wide-area distribution link; injecting tenant information tags and placing them in the mimetic transfer space, automatically adsorbing them to the virtual isolation container, and triggering a degradation mechanism when it is detected that the information tags do not match the container environment; when signaling congestion occurs, using an elastic echo proxy to synthesize a spatio-temporal mirror copy, feeding back the mimetic information flow to the terminal while activating the reverse signaling blocking pulse. The present invention realizes the parsing and encapsulation of multi-source heterogeneous protocols through protocol distillation and semantic capsules, and uses virtual isolation containers to ensure the security isolation of multi-tenant concurrent access. By using an elastic echo proxy to answer repeated polls, it effectively suppresses the network bandwidth consumption caused by high-frequency direct connections and improves the congestion problem of core information nodes.

This invention provides a consumer finance O2O promotion system, method, and apparatus. The system includes a mobile front-end module, a server-side module, and a data layer module. The mobile front-end module is equipped with an SDK containing a behavior acquisition engine, an encrypted cache manager, and a secure transmitter. The server-side module adopts a multi-tenant architecture, assigning independent tenant identifiers and risk levels to terminals in different offline scenarios. The data layer module is responsible for the encrypted storage of promotion rules, risk control thresholds, and end-to-end data corresponding to each tenant. The server-side module acquires terminal data through a communication submodule, uses a rule engine to match differentiated promotion rules based on tenant identifiers and risk levels, and uses a risk control linkage engine to perform pre-risk control prediction based on offline scenario behavior data, dynamically adjusting risk control thresholds and promotion strategies to achieve secure financial product promotion.

This invention discloses a method and device for detecting and reassigning violations in multi-tenant chauffeur services, relating to the field of intelligent transportation and shared mobility service technology. The method includes: real-time collection and storage of driver endpoint location data; synchronization of channel order and multi-tenant capacity data, calculating grid supply-demand heat value and channel capacity balance value; responding to order status changes, extracting location data reported by the driver's end bound to the current order within a preset time period before and after the order status change; quantifying multiple risk factors, dynamically adjusting factor weights based on supply-demand heat value and channel capacity balance value, calculating a weighted risk score and determining the risk level; obtaining the real-time capacity status of the grid, and deciding on the final reassignment action based on the risk level. This invention, through a hierarchical reassignment mechanism that integrates multi-dimensional factors, dynamic weights, and capacity linkage, accurately identifies violations such as drivers quickly canceling or picking and choosing orders, improving completion rates and capacity efficiency, and reducing misjudgments and complaints.

The application discloses a kind of edge virtual network function system framework and network architecture, its system framework includes: portal control center, service ability empowerment and dispatching middle station and multiple POP clusters;Wherein, portal control center is used to face user, operation and maintenance, operation and service provider provide unified entrance, include portal management, operation and maintenance alarm system, operation work order system, order and delivery management system and unified identity authentication system;Service ability empowerment and dispatching middle station are responsible for connection control module, API gateway, tenant management, service guarantee, identity authentication, multiple cluster resource scheduling strategy issue and third party interface unification;POP cluster is used to be responsible for service chain arrangement and the operation of NFV network function pool, in combination with monitoring and operation, resource scheduling and high-performance forwarding surface complete tenant business load.The application realizes controllable flow arrangement under the multi-tenant scenario, resource elastic allocation and performance guarantee, improves the flexibility, scalability and user experience of NaaS platform.

A secure, modular multi-tenant machine learning platform is configured to: receive untrusted code supplied by a first tenant; perform a security scan of the untrusted code to determine whether the untrusted code satisfies a set of one or more security requirements; responsive to determining that the untrusted code satisfies the security requirement(s): deploy the untrusted code to a runtime execution environment; deploy a machine learning model associated with the first tenant to the runtime execution environment, the untrusted code being configured to perform one or more functions using the machine learning model; receive a set of untrusted code supplied by a second tenant; perform a security scan of the untrusted code to determine whether the untrusted code satisfies the security requirement(s); and responsive to determining that the untrusted code does not satisfy the security requirement(s): refraining from deploying the untrusted code to a runtime execution environment.

The application discloses a kind of electronic bank dynamic flow control method and system based on multidimensional data fusion, belong to data processing technical field.The method includes: the multiple-source data in electronic bank system is collected and unified structured processing is carried out;Dynamic baseline is generated based on historical transaction data using sliding window algorithm, and dynamic baseline is corrected in combination with real-time cluster load state;Business priority and multi-tenant priority are calculated, and after dynamically adjusting weight coefficient according to load factor, weighted summation is obtained to obtain comprehensive priority;According to the comprehensive priority, generate token bucket parameters, execute service level throttling and interface level throttling, and dispatch requests according to queue weight proportion;The execution result is collected and returned to update dynamic baseline and calculation parameters.The application realizes adaptive adjustment and differentiated resource guarantee of throttling parameters through the synergistic effect of dynamic baseline calculation, comprehensive priority model, hierarchical throttling execution and closed-loop feedback.

Receiving, by a multi-tenant platform, a segment query, wherein the segment query includes static attributes and dynamic attributes, wherein the dynamic attributes include interaction data. Receiving, by the multi-tenant platform in response to the execution of the segment query, a segment result set, wherein the segment result set comprises a segment of a population of a tenant of the multi-tenant platform. Automatically synchronizing, by the multi-tenant platform in response to receiving the segment result set, the segment result set with one or more third-party systems. Triggering, in response to the automatic synchronization, the one or more third-party systems to perform one or more campaign actions of a campaign using the segment included in the segment result set. Storing the segment query. Receiving updated data for the static attributes and the dynamic attributes. Re-executing, either periodically or on-demand, the stored segment query using the updated data for the static attributes and the dynamic attributes.

This invention discloses a GPU memory virtualization method and apparatus based on deep parsing of the ioctl protocol, belonging to the field of electronic information technology. The method includes: intercepting ioctl system call requests initiated by the target container to obtain ioctl system call requests for GPU memory management; parsing its parameter blocks and extracting memory-related instructions to obtain memory-related instructions for subsequent auditing; constructing a shadow object tree and intelligently auditing memory allocation and mapping operations to obtain a precise memory usage ledger; determining whether the quota has been exceeded and executing interception during the physical admission control phase to obtain a control result on whether instruction pass-through is allowed; implementing view virtualization and returning virtualized memory information to the application in the target container to achieve transparent application adaptation, thereby completing precise virtualization isolation and multi-tenant secure sharing of GPU memory. This invention combines kernel-level control strength with user-space deployment flexibility.

The multi-tenant system includes one or more hardware processors that obtain a query that includes statements defining one or more operations to be performed on a subset of constituent records stored in a source database. The hardware processors parse the query to decipher a type of the one or more operations, the subset of the constituent records referred to, and one or more other conditions of the operations. The hardware processors obtain the subset of the constituent records from the parsed query from the source database. The hardware processors execute the one or more operations, to generate a materialized view that denormalizes the data. The hardware processors detect any changes in the subset of the constituent records, synchronize the materialized view with the any changes, store the synchronized materialized view within a data platform, and transmit the synchronized materialized view to a target database.

The application discloses a multi-tenant cache method based on a distributed heterogeneous storage architecture, on one hand, the tenant priority and the cache structure are combined, the read-write performance of the cache structure is fully used, and different priority tenants can be better provided with differential services. On the other hand, considering the boundary condition of cache penetration, the idle load of the storage service cluster is utilized, the cache space is maximally utilized, and the cache avalanche is avoided as much as possible; meanwhile, the application is oriented to the distributed heterogeneous storage architecture, compared with the conventional distributed storage system, the problems are more complex, and the application is more in line with the real scene.

This application discloses an adaptive inventory management method and apparatus for multi-tenant environments. The method includes: receiving an inventory operation request from a target tenant, the request carrying a target tenant identifier and an operation type; obtaining a target control mode corresponding to the target tenant identifier, including a forced source mode, a weak source mode, and a source-and-no-source coexistence mode. The forced source mode allows the creation of documents associated with upstream orders, the weak source mode allows the creation of multi-source documents and automatically adjusts process nodes, and the source-and-no-source coexistence mode allows both source-based and source-less operations; adjusting the operation permissions of preset business process nodes according to the target control mode to obtain the target business process node adapted to the request; and updating the inventory status based on the operation type and the target business process node to obtain the inventory operation execution result. Using this application, the needs of medium-sized enterprises of different sizes and business models can be adapted to the differentiated requirements of two types of documents under multi-tenant environments.

This invention provides a method and related equipment for monitoring the operational status of an AI server. The method includes: collecting operational status data from the host side and the accelerated computing side of the AI ​​server to obtain a raw operational status dataset; performing time correlation processing on the data from both sides based on time information to obtain a cross-component correlated dataset; performing status analysis on the cross-component correlated dataset to identify abnormal operational status data; and performing correlation parsing on the abnormal operational status data to determine the type of resource interference and obtain monitoring results. By establishing cross-layer data correlation between the host side and the accelerated computing side, this invention can locate the source of host-side resource interference causing abnormal accelerated computing performance, improving the accuracy and efficiency of root cause diagnosis and effectively supporting the operation and maintenance management and performance optimization of multi-tenant GPU infrastructure.

This application discloses a multi-tenant task scheduling method and related apparatus, relating to the field of cloud computing technology. It receives tasks to be scheduled and their metadata, determines dynamic priority information, and sends the task to a target priority queue if the tenant corresponding to the task has an effective quota. When a tenant has a concurrent consumption token in the target priority queue, it acquires the token, executes the task, and then releases the token. This application sets different dynamic priorities for tasks to be scheduled, ensuring that tasks are distributed to resource-isolated independent queues, guaranteeing priority processing of each task, and improving resource utilization. Before task publication, sliding window rate limiting is implemented, and tenant-level concurrent consumption tokens are distributed during task consumption, isolating tasks between tenants. In complex multi-tenant, high-concurrency production scenarios, this enables intelligent task adjustment, significantly improving resource utilization and scheduling efficiency.

The application provides an information processing method, device and equipment and a storage medium, the method comprising: receiving a plurality of task processing requests sent by a plurality of target objects, each task processing request carrying at least identification information of a corresponding target object; determining target task processing requests belonging to the same target object based on the identification information of the plurality of target objects; when the target task processing request is processed by a work thread, obtaining a target information storage block corresponding to the identification information carried in the target task processing request from a resource storage pool, the resource storage pool being determined based on serialization attribute information of services rented by the plurality of target objects; and performing a corresponding task operation in the target information storage block. In a serialization task processing scenario, the application allocates available resource quotas to each tenant according to the serialization capability of each tenant, ensures that each tenant uses at most one work thread for transaction processing, and effectively isolates the resources of multiple tenants.

The subject matter herein provides a platform and mechanism to enable dynamic control over data signal collection telemetry in a bot detection-based access control system executing, for example, in associated with a multi-tenant shared network infrastructure. The approach herein leverages the ability of a native SDK running in a mobile device application to launch and use a webview that controls the data collection process. The techniques make it harder for bad actors to send spoofed telemetry from their mobile devices to an overlay network edge platform having an associated bot detection system back-end.

This invention discloses a multi-tenant end-to-end data security isolation and control system and method, belonging to the field of vehicle network data security. It aims to solve the problems of easy data crosstalk, discontinuous isolation, unreliable trust benchmarks, and difficulty in compliance traceability in multi-tenant scenarios. The system generates a tenant root identifier (TID) for vehicle enterprise tenants, which, together with the device sub-identifier (DID), forms a tenant identifier chain. This chain is synchronized with a trusted authentication center to establish a vehicle-cloud hardware-level trust root. When the vehicle terminal accesses the system, it completes two-way identity authentication and allocates a dedicated message queue telemetry transmission MQTT topic partition and access permissions according to the TID. The terminal collects time-series vehicle control data and generates encrypted data frames with intrinsic identifiers and checksums for uploading. The access gateway performs dual isolation verification before transparent transmission. Data is forwarded to an independent logical storage partition for isolated storage according to the TID. This invention achieves strong hardware-level isolation, real-time verification, and full traceability throughout the entire end-to-cloud chain, effectively preventing cross-tenant data leakage.

This application provides a component-centric isolation and sharing device and its operating method based on containerized encapsulation. The device includes a component encapsulation and registration center for building and registering container images; a containerized runtime scheduling engine for isolating environment instantiation of components and managing their lifecycle; a centralized access proxy gateway for verifying requests and routing them; and a unified configuration and policy management center for storing and managing various policies and providing query and update interfaces. The advantages of this application are: containerized encapsulation achieves environment decoupling and consistency, eliminating dependency conflicts; strong isolation policies ensure multi-tenant data security and system stability; centralized management enables fine-grained access control; intelligent elastic scaling optimizes resource utilization; and it supports centralized management of the entire component lifecycle, improving reusability, maintainability, and collaborative efficiency.

A disclosed computer-implemented method may include providing, by a processor, an artificial intelligence model (“predictive model”) pre-configured to infer, via a plurality of features included in the predictive model trained using data representative of computing resource requirements of current tenants of a multitenant computing platform, computing resource requirements for potential tenants of the multitenant computing platform. The method may also include receiving, by the processor, at least one query comprising data that describes an attribute of a potential tenant of the multitenant computing platform, and determining, by the processor, based on an inference of the predictive model generated based on the at least one query, an output comprising an estimated computing resource requirement of the potential tenant. Various other methods, systems, and computer-readable media are also disclosed.

The application relates to a method, system, device and medium for realizing multi-tenancy based on directory partitioning. In the multi-tenancy mode, a first target root directory with a true tenant root directory attribute is created, and a target first-level subdirectory with a true target working directory attribute of a tenant is created under the first target root directory according to the tenant; the target first-level subdirectory is only visible and accessible to the corresponding tenant; a target second-level subdirectory with a true target namespace working directory attribute of a user under the corresponding tenant is created under the target first-level subdirectory, and the target second-level subdirectory is only visible and accessible to the corresponding user to realize resource isolation between different users; and the user can create a subdirectory with a true ordinary directory attribute under the corresponding target second-level subdirectory to realize file management index. The application realizes resource isolation between tenants and users by using directory partitioning of a file system directory, so as to meet the multi-tenancy and multi-user demand.