2791 results about "Virtual machine" patented technology

An AI orchestration system dynamically manages multiple artificial intelligence (AI) agents within a cloud computing environment to efficiently process user requests. A model orchestration subsystem determines whether a request is handled locally using a domain-specific database or by invoking one or more AI agents. The system maintains AI agents in active and inactive states, provisioning computing resources for inactive agents as needed. Real-time model metrics guide the selection of target AI agents, and if a degrading performance trend is detected, the system preemptively spins up additional AI instances. The system provisions processor cycles, memory, and network bandwidth through a cloud-based resource manager, instantiates containerized execution environments or virtual machines, and performs automated load balancing among AI instances.

The invention discloses a full-scene task autonomous execution method and system based on a large model, belongs to the technical field of computer application and artificial intelligence, and aims to solve the technical problems that an existing RPA technology is lack of autonomous thinking ability, and a browser use technology is limited in application range, is greatly influenced by factors such as webpage change and the like, and is poor in reliability. The Computer use technology is inaccurate in operation, high in development difficulty and has security risks, and the technical scheme comprises the following steps: establishing an MCP service, and compiling a task autonomous execution tool set containing various basic operation instructions including software opening, browser starting, precise clicking and information filling; the MCP service is deployed in the Windows virtual machine environment, and stable operation of the service is ensured; constructing a task autonomous execution workflow agent with task understanding, planning and scheduling capabilities; the user inputs a task target through a natural language, and the agent receives and records the specific demand of the user as a target guide for subsequent task generation and execution.

The invention relates to the technical field of virtualization management, in particular to a virtual machine operation management system based on RISC-V. The system comprises an instruction set mapping module, a resource scheduling optimization module, a virtual machine monitoring integration module, an operation tuning update module and an operation configuration distribution module. According to the method, by analyzing the operation instruction of the virtual machine and combining the RISC-V underlying characteristics, accurate instruction mapping is achieved, the processing efficiency and accuracy are remarkably improved, the performance loss and conversion delay caused by too high abstraction level of a traditional virtual machine on the instruction level are overcome, the optimal allocation scheme is screened, the resource utilization rate and the system response speed are effectively improved, and the system performance is improved. The operation state and performance indexes of the virtual machine are integrated, anomaly detection and resource recovery are embedded, system stability and effective resource reutilization are ensured, basic configuration is continuously and incrementally updated, operation configuration refinement and automatic management and updating are achieved, the overall operation efficiency and management flexibility are improved, and the manual intervention cost is reduced.

A system and method for real-time or near real-time multilingual language transformation in telecommunications environments using distributed artificial intelligence (Al). The system includes at least one processor configured to instantiate a plurality of Al agents, each corresponding to a user in a communication session, and to manage their operation via an Al-agent runtime adapter comprising a microkernel agent manager and a virtual machine layer. A language model abstraction layer enables access to language models and Al algorithm libraries through publish-subscribe interfaces. The system supports contextual transformation of speech based on user profile data such as sentiment, emotional tone, and cultural background. Execution can occur on telecommunication-class switches, edge devices, or general-purpose computing hardware. Al agents operate asynchronously, access shared or private memory, and produce personalized, bidirectional linguistic output. The architecture supports secure, scalable deployment across heterogeneous devices and networks.

The invention relates to a cloud computing extension cluster high availability method based on a dynamic fault domain and intelligent scheduling, and relates to the field of cloud computing extension cluster high availability. According to the method, hardware health data and network performance indexes of physical nodes are collected in real time, and the fault correlation degree between the nodes is calculated based on the hardware health similarity and a network topology attenuation factor; dynamically generating and updating a logic fault domain topological structure according to the fault correlation degree and a preset threshold value; based on the logic fault domain topological structure and a preset SLA strategy library, executing a virtual machine scheduling decision of multi-objective optimization so as to balance business service quality, fault domain risk and resource cost; and in response to the detected fault event, triggering a corresponding hierarchical migration process according to the service priority and the fault level. According to the method, closed-loop linkage of real-time reconstruction and intelligent scheduling of the dynamic fault domain in the cross-region cloud computing extension cluster can be realized, and the disaster tolerance capability and the resource utilization rate of the system are remarkably improved.

A cooperative control optimization method suitable for a near-field fan wall micromodule, comprising the following steps: step 10: obtaining IT system scheduling data and air-conditioning system data of the near-field fan wall micromodule within a preset period of time, and separately establishing a micromodule server real-time power model, an air-conditioning system power model, and a rapid server temperature prediction model; and step 20: performing cooperative control on deployment of virtual machines and an air-conditioning system by using an optimization method of combining real-time joint optimization and timed decoupling optimization based on the micromodule server real-time power model, the air-conditioning system power model, and the rapid server temperature prediction model. The present invention provides a cooperative control optimization method suitable for a near-field fan wall micromodule, to resolve the technical problem that local hot spots and overcooling are prone to occur during energy-saving scheduling of a near-field fan wall micromodule.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant regulation and control method and system for realizing new energy consumption, and the system comprises a multi-source heterogeneous data collection module, an intelligent prediction analysis module, a resource aggregation modeling module, an optimization decision module, a block chain cooperation module, and a digital twinborn evaluation module. Multi-time-scale coupling prediction is carried out on new energy output and load demand through the deep space-time convolutional neural network, fluctuation and intermittency characteristics of new energy can be described, short-term and ultra-short-term prediction precision is improved, wind curtailment and light curtailment rate and load reduction risk are reduced, and the prediction efficiency is improved. According to the method, high matching between a virtual power plant scheduling plan and an actual operation condition is guaranteed, a flexible resource feature matrix is constructed, and distributed energy storage, interruptible load and electric vehicle multi-element resources are subjected to refined modeling and aggregation, so that a virtual unit capable of being efficiently scheduled can be formed, the resource utilization efficiency is improved, and the overall scheduling cost is reduced.

The invention discloses a method for communication between a user program and a virtual machine on a microkernel Hypervisor, the microkernel Hypervisor is provided with two shared memory areas, the shared memory area 1 is accessed by the user program and a root service Rootserver, the shared memory area 2 is accessed by the Rootserver and the virtual machine, the user program writes communication request data with the virtual machine into the shared memory area 1, and the user program writes communication request data with the virtual machine into the shared memory area 2. The method comprises the following steps that a VMM sub-thread is used as a shared memory area 1, a Rootserver is notified through inter-process communication, the Rootserver reads communication request data from the shared memory area 1, the communication request data is written into a shared memory area 2 after being analyzed by the VMM sub-thread, a system calls a syscale to transmit a communication request to a kernel, the kernel injects virtual interrupt into a virtual machine, and the virtual machine sends the communication request to the Rootserver. And an interrupt processing program of the virtual machine processes the communication request and writes a processing result into the shared memory area 2, then the processing result is returned to the Hypervisor through the Hypercall, and a Rootserver of the Hypervisor feeds back the processing result to a user program through the IPC. The method is designed for the microkernel Hypervisor environment, and the overall performance and efficiency of the embedded virtualization system are improved.

The invention provides a real-time monitoring system and method for sorting of a digital twin-driven Delta mechanical arm. According to the system, firstly, a physical experiment platform is built, and a virtual model is built through SolidWorks, 3DMAX and Unity 3D. A communication framework is designed, and wireless communication of the PLC, the sensor, the mechanical arm, the conveying belt and the digital twin system is achieved. Positive and inverse kinematics analysis and Cartesian space trajectory planning are carried out on a Delta robot, and a related algorithm is packaged into an MATLAB function and compiled into a DLL library for C # calling. The system collects joint angle data in real time based on a rotary encoder, transmits the joint angle data to a digital twin system through an OPC UA protocol, and drives a virtual mechanical arm model after resolving, so that high-precision and low-delay virtual-real synchronous pose reconstruction is realized. In addition, the system also develops a collision detection function based on a bounding box algorithm, and performs visual detection by using camera calibration and a YOLO model.

The invention discloses an ebpf-based soft lock detection analysis method and system, a medium and a product, and the method comprises the steps: carrying out the soft lock detection based on the guest mode quitting information of a vCPU in the execution state of the virtual machine vCPU monitored by an ebpf code on a host system during the soft lock of the virtual machine, and if the guest mode quitting information of the vCPU is not found in a plurality of continuous soft lock detection periods, carrying out the soft lock detection. If yes, judging that the result is caused by a host system; if it is found that the quit frequency statistical information of the vCPU quitting the guest mode in multiple continuous soft lock detection periods exceeds a preset threshold value and the quit reason is matched with a preset rule, it is judged that the result is caused by the internal reason of the virtual machine. The method aims at accurately judging the reason for the soft lock problem of the virtual machine under the condition that the kernel of the virtual machine is not changed, and determining whether the soft lock problem of the virtual machine is caused by the resource scheduling of a host system or the internal part of the virtual machine.

The invention provides a hot upgrade method for a virtual machine monitor (VMM), which comprises the following steps of: pausing the operation of a virtual machine, storing state information of an original VMM, and reserving a kernel-based virtual machine KVM extension page table and a memory management structure of the original VMM; reserving a page global directory and a virtual memory area VMA corresponding to a virtual machine RAM area according to the memory management structure, and refreshing a process address space described by the memory management structure into a new VMM execution program so as to complete switching of the original VMM into the new VMM; and executing the new VMM, recovering the state information to the new VMM, and inheriting the KVM extension page table by the new VMM to recover the operation of the virtual machine. The invention further provides computer equipment, a computer readable medium and a computer program product.

Techniques for host fleet management in a cloud provider network are described. Forecast data including a forecasted demand for virtual machines in each capacity pool of a set of capacity pools is obtained. A mathematical optimizer application is executed to generate a first optimal fleet plan, the mathematical optimizer application having an objective function to minimize a number of new host computer systems to add to the set of host computer systems to satisfy the forecasted demand for each capacity pool, the first optimal fleet plan includes an identification of a set of hardware types and, for each hardware type in the set, a quantity of new host computer systems of the hardware type. A plurality of new host computer systems is deployed, based on the first optimal fleet plan, for a hardware type in the set of hardware types into the set of host computer systems.

The invention relates to a video memory management method and device, electronic equipment and a storage medium, the method is applied to a GPU drive in a virtual machine, and the method comprises the steps that when it is detected that the remaining amount of a GPU video memory is lower than a set remaining amount threshold value, cold pages in a GPU page table are recognized; wherein the cold page is a page table item which is not accessed in a recent preset time interval; and under the condition that the cold page is a video memory, replacing a physical address corresponding to the virtual address in the cold page to a physical address in a system memory so as to replace video memory data corresponding to the cold page to the system memory. The embodiment of the invention can improve the overall utilization rate of the video memory.

The invention provides a mechanical arm control system and control method based on an embedded virtual machine architecture. A real-time operating system and a non-real-time operating system are deployed on the same embedded board card; the non-real-time operating system transmits control words of all the drivers to a driver communication module deployed on the real-time operating system through a control queue based on the received control request, and the driver communication module transmits state data of all the drivers to the non-real-time operating system through a state queue; and the motion planning module deployed on the non-real-time operating system transmits the generated position data to the driver communication module through the position queue according to a preset control period, and issues the control word and the position data to the driver through the driver communication module. By the adoption of the method, the problems that a non-real-time operating system is interrupted, communication stability is poor due to multi-task resource preemption, control response is slow, state coverage is caused by jitter of the non-real-time operating system, and a mechanical arm is stuck due to position transmission jitter can be solved.

A secure digital management tool for managing real estate ownership and leases, consisting of: a tamper-proof enclosure containing a secure processing module; a biometric authentication module comprising a fingerprint sensor and an iris scanner, which are operationally coupled with a secure biometric engine to verify a user's physical identity; a document integrity module configured to generate a cryptographic hash of a digital document representing a certificate of ownership or a lease agreement; a smart contract engine configured to compile and execute the logic for legal transactions related to ownership in a Turing-complete virtual machine environment, the logic defining obligations, rights, payment schedules or renewal triggers encoded in the lease or ownership contract; a blockchain interface processing unit operationally connected to the document integrity module and the smart contract engine, wherein the blockchain interface processing unit is configured to anchor cryptographic hashes of the documents and the execution status of smart contracts in a decentralized ledger network; a secure element embedded in the secure processing module and configured to store cryptographic private keys and perform isolated signature and encryption operations without exposing confidential key material to general storage space; a communication subsystem that includes at least one of the following elements: a GSM / 4G module, a Wi-Fi 6 module and a GPS receiver, configured for secure network connectivity and geolocation binding of transactions; a touchscreen interface configured for displaying transaction forms, digital agreements, and interactive contract signing interfaces, the interface also including pen input for capturing digital handwritten signatures; and a secure audit trail subsystem configured to maintain a chronologically ordered, append-only log of all biometric access events, document hash generations, blockchain anchoring events, and smart contract lifecycle transitions, with the audit trail regularly hashed and anchored for forensic integrity in a decentralized blockchain network.

The invention discloses an ARMv8-based virtual machine interrupt through routing method, which comprises the following steps that: a Hypervisor directly routes a physical interrupt to an EL1 through configuration, sets a top page table address of a vCPU (Virtual Central Processing Unit) and a Hypervisor control register at the same time, and sets a vcpudid flag bit for indicating whether the vCPU adopts a virtual interrupt injection mechanism or a physical interrupt through routing mechanism; and judging whether an interrupt through routing condition is met or not so as to determine whether an interrupt through routing mechanism or a virtual interrupt injection mechanism is adopted, periodically judging whether a switching condition is met or not when the interrupt through routing mechanism is adopted, and switching from the interrupt through routing mechanism to the virtual interrupt injection mechanism if the switching condition is met. The method is compatible with two interrupt controller specifications of GICv2 and GICv3, supports the virtual machine and the hypervisor to respectively and independently respond to respective corresponding interrupt requests, and meanwhile, the method has the capability of dynamically switching an interrupt routing mechanism during the system operation period, so that the flexibility of interrupt processing and the expandability of the system are improved.

Techniques for implementing a scalable automated training framework for anomaly and ransomware detection are disclosed. In some embodiments, a computer system performs operations comprising: instantiating a plurality of virtual machines, each one of the virtual machines being loaded with a corresponding file system; simulating user actions and ransomware on the virtual machines, the simulating of user actions and ransomware on the virtual machines causing changes to the corresponding file systems of the virtual machines; for each one of the plurality of virtual machines, generating a corresponding metadata file based on one or more corresponding snapshots of the virtual machine, the one or more corresponding snapshots indicating the changes to the corresponding file system of the virtual machine; and training a ransomware detection model using a machine learning algorithm and training data, the training data being based on the corresponding metadata files of the virtual machines.

A system and a method for responding to an outage of a computing system use a sidecar computing system and an event manager. The computing system is operatively connected to multiple user devices by a first network. The sidecar computing system includes a processor, a non-transitory storage device and multiple virtual machines, wherein computer-readable instructions are stored in the storage device. A second network operatively connected to the sidecar computing system and the sidecar computing system is deactivated when the computing system is operating. The event manager communicates with the computing system through the first network and the sidecar computing system through the second computer network, wherein the event manager activates the sidecar computing system to operate in an outage mode when the computing system has an outage.

The invention relates to the technical field of wind power plant network operation and maintenance, in particular to a method and related equipment for monitoring alarm information, and the method comprises the steps: inputting basic information of a physical machine and a virtual machine, including an equipment model, an IP address, a deployment position and specified field information; a resource binding threshold value is set, and the resource binding threshold value covers the space utilization rate of a CPU, a memory and a disk; monitoring resource use conditions in real time, performing natural language processing on alarm information, and extracting key features; matching the key features with historical data by using a preset classification model, dividing alarm levels and allocating the alarm levels to corresponding processing queues in combination with a resource threshold exceeding degree; and pushing alarm information to the contact person through a multi-mode notification channel according to the alarm level.

The invention discloses a distributed caching method and system and an acceleration method for large model reasoning service. The system comprises a confidential virtual machine cluster, a zero-trust distributed caching system and a FUSE client. The zero-trust distributed cache system comprises a data strategy service, a data engine plug-in, a distributed file system and a trusted access service. And the FUSE client is deployed at the confidential container end, and establishes secure connection with the trusted access service based on the remote proof and the identity legality confirmation proof of the corresponding confidential container so as to provide a plaintext data access service for the corresponding confidential container. According to the scheme, a distributed cache implementation scheme is provided for the confidential computing container cluster, and the problem of performance loss caused by repeatedly reading, encrypting and decrypting the same data from an external storage system is avoided while the data security is guaranteed by using the large-scale confidential container cluster; and the storage access efficiency in scenes such as model training and reasoning is improved.

The invention discloses a virtual machine memory processing method and device, a product, a virtualization server and a medium, and relates to the field of server virtualization. In the method, through a virtual input and output rear-end module arranged on a virtual machine monitor in a host and a virtual input and output front-end module on a virtual machine, it is ensured that the virtual input and output rear-end module can obtain information of a memory allocation event of direct memory access; secondly, after acquiring the information of the memory allocation event of the direct memory access, the virtual input / output rear-end module locks the address related to the memory allocation event, namely, the locked address meets the direct memory access requirement, and the memory of the virtual machine is dynamically locked, so that the memory utilization rate is improved, and the waste of resources is reduced; and only part of the memory of the virtual machine is locked, and only the locked memory part is migrated after the host is replaced, so that the amount of data to be migrated after the host is replaced is reduced.

A method of VM migration includes allocating a designated system memory for a guest VM on a target node and reserving a shared physical memory region on a transfer node that is part of a shared memory pool that is memory coherent and cache coherent with a source node and the target node. The VM system memory of the guest VM is re-mapped from a first physical memory region on a source node to the shared physical memory region on the transfer node and the system memory of the guest VM is copied from the first physical memory region on the source node to the shared physical memory region on the transfer node. The designated system memory on the target node is mapped to the shared physical memory region on the transfer node; the guest VM is stopped on the source node and then resumed on the target node.

Disclosed in the present invention are a memory data swapping method, a memory data swap-out method, a memory data swap-in method, and a memory data swapping system. The memory data swapping method comprises: controlling a host machine to switch from running a host machine operating system to running a virtual machine operating system; during running of the virtual machine operating system on the host machine, acquiring a memory swap request, wherein the memory swap request is used for requesting to swap memory data between a physical memory of the host machine and a back-end storage medium corresponding to the host machine; in response to the memory swap request, determining a task swap category corresponding to the memory data; and for the memory data, executing a swap task, corresponding to the task swap category, between the physical memory of the host machine and the back-end storage medium. The present invention solves the technical problem of great memory swap limitation.

The invention discloses a disaster recovery resource scheduling method and device based on a heterogeneous cloud environment, and the method comprises the steps: constructing a multi-dimensional resource portrait model and a cross-cloud network topological graph in the heterogeneous cloud environment, and generating a standardized resource vector set and a topological affinity scoring matrix; aggregating the local SLA default risk prediction model of each cloud node through federated learning, and performing fine tuning through transfer learning to obtain a global prediction model; calculating load fluctuation entropy based on the SLA constraint template and real-time load data, and inputting the load fluctuation entropy into a global model to predict and obtain an SLA default probability set; constructing and solving a dynamic weight multi-objective optimization function to obtain an optimal takeover cloud node list and a migration strategy identifier set; a target cloud node and a migration strategy are determined through SLA simulation verification, and an adaptive execution adapter is called to complete virtual machine incremental snapshot migration or cross-cloud arrangement deployment of containerized services. According to the invention, intelligent and automatic scheduling of disaster recovery resources is realized, and the fault takeover speed and the cross-cloud resource utilization rate are improved.

The invention discloses a tracking connection number control method and device of a virtual machine, equipment and a medium, and relates to the technical field of virtualization, the method comprises the following steps: determining a demand tracking connection number of a host machine according to historical tracking connection number information of the host machine; determining a virtual machine distributable tracking connection number of the host machine according to the total tracking connection number upper limit value and the demand tracking connection number of the host machine; adjusting the tracking connection number upper limit value of each virtual machine according to the virtual machine distributable tracking connection number and the tracking connection number of each virtual machine on the host machine; according to the method, the total tracking connection number of the host machine is divided into the tracking connection number of the host machine and the tracking connection number of the virtual machine, and the tracking connection number required by the follow-up host machine can be predicted by utilizing the historical tracking connection number information of the host machine, so that the tracking connection number of the host machine can be predicted under the condition of ensuring normal communication of the host machine. The tracking connection number of the virtual machine is conveniently and effectively controlled, and the resource utilization rate and safety of the host machine are improved.

The invention belongs to the field of computer resource management, particularly relates to a computer resource dynamic allocation management method based on cloud computing, and aims to solve the problems of locality of a decision view angle and hysteresis of a time dimension in related technologies. The method comprises the following steps: acquiring the resource utilization rate of each virtual machine in a cloud environment, wherein the resource utilization rate comprises a historical resource utilization rate and a real-time resource utilization rate; according to the resource utilization rate, predicting a resource demand of each virtual machine in a future preset time period; determining a resource competition conflict according to the predicted resource demand, and constructing a resource competition graph model; mapping the resource competition graph model into a non-cooperative game model; and determining a scheduling scheme in the cloud environment based on the non-cooperative game model, and executing a resource allocation operation according to the scheduling scheme. According to the method, potential conflicts can be identified and scheduled before resource competition actually occurs, and performance reduction caused by insufficient resources is reduced.

A file system in a user space partition of virtual memory may be mounted by a computing device that runs a virtual machine which includes a set of storage disks. The file system in user space may then expose one or more virtual files associated with one or more storage disks that correspond to one or more loop devices configured to map files of the virtual machine to the one or more virtual files. The computing device may then receive a request to read a data block stored at the virtual machine and may identify a file and corresponding virtual file that stores the requested data block based on a set of metadata provided by the loop devices. The computing device may then determine the location of the data block stored at the virtual machine, and may read the data block from the determined location.

Implementations are described herein for detecting deepfakes in digital media while preserving the privacy of the source computing device. In various implementations, sensor fingerprints and / or security tokens that signal software-introduced alterations, e.g., introduced by hardware abstraction layers (HALs) or virtual machines (VMs) may be utilized to detect such deepfakes. These signals may be used, separately and / or in combination, for various purposes, such as flagging digital content to a user as being a deepfake, preventing or blocking receipt and / or playback of digital content deemed to be a deepfake, allowing an end user to disable aspect(s) (e.g., layers) of digital content that are determined to be synthetic, etc.

The invention relates to the technical field of information security, in particular to a cluster self-discovery method suitable for a cloud server cipher machine, which comprises the following steps: when physical equipment of the cloud server cipher machine leaves a factory, a certificate management module generates an equipment certificate and a root CA certificate in combination with an enterprise CA system, and stores the equipment certificate and the root CA certificate; after the virtual cipher machine is created and started, the key management service module combines with the certificate management module to generate a virtual machine certificate, and stores the virtual machine certificate, the storage device certificate and the root CA certificate together; the cluster management service module enables the master node of the virtual cipher machine to discover the slave node of the virtual cipher machine in the same network domain through UDP broadcast, and executes three-level verification on the slave node of the virtual cipher machine from three aspects of an equipment certificate, a virtual machine certificate and an authentication signature; and the virtual cipher machine master node synchronizes the cluster master key to the virtual cipher machine slave node passing verification. According to the invention, hierarchical identity isolation and verification of the cloud server cipher machine host and the virtual cipher machine can be realized, and the security of cluster self-discovery and authentication stages is ensured.

The invention discloses a cloud platform capacity expansion and contraction method and device, equipment and a medium, and relates to the technical field of computers, and the method comprises the steps: determining a trend coefficient and a load fluctuation rate according to each target resource data of each virtual machine in a cloud platform and the size of a sliding window; determining a prediction error rate and a service priority, and adjusting the size of the sliding window according to the trend coefficient, the load fluctuation rate and the prediction error rate to obtain the adjusted size of the sliding window; the prediction error rate represents a deviation percentage between a historical prediction value and an actual value of the target resource data; determining each resource demand prediction value according to each target resource data and the adjusted size of the sliding window, and judging a to-be-executed operation of the cloud platform through a preset threshold value determined according to the service priority and each resource demand prediction value; the operation to be executed comprises a capacity expansion operation and a capacity reduction operation; and executing corresponding capacity expansion and shrinkage operation on the cloud platform according to the resource demand predicted value based on the judgment result. Elastic capacity expansion and shrinkage are achieved, and therefore the resource utilization rate is increased.