5069 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 virtual machine scheduling method in a distributed environment based on deep reinforcement learning, and belongs to the technical field of cloud computing resource scheduling. According to the method, the defects of a traditional method in multi-objective optimization and mixed action space collaborative decision-making are overcome by constructing a mixed action space joint decision-making mechanism. The method specifically comprises the following steps: establishing a mixed action space containing discrete node selection and continuous resource allocation, filtering invalid nodes by adopting a dynamic mask mechanism, and ensuring resource ratio constraint through projection gradient descent; designing a hierarchical reward function to realize multi-target dynamic balancing, and dynamically adjusting the priorities of energy consumption, load balancing and SLA guarantee based on an adaptive weight strategy; a multi-agent collaborative framework is provided, cross-node topological dependence is captured by using a graph attention network, and dynamic fusion of spatio-temporal characteristics is realized through cross attention in combination with LSTM coding time sequence load characteristics; a course learning strategy and a priority experience playback mechanism are introduced to improve training efficiency and strategy robustness.

Disclosed are various embodiments for rate proportional scheduling to reduce packet loss in virtualized network function chains. A congestion monitor executed by a first virtual machine executed by a host computing device can detect congestion in a receive queue associated with a first virtualized network function implemented by a first virtual machine. The congestion monitor can send a pause signal to a rate controller executed by a second virtual machine executed by the host computing device. The rate controller can receive the pause signal. In response, the rate controller can pause the processing of packets by a second virtualized network function implemented by the second virtual machine to reduce congestion in the receive queue of the first virtualized network function.

The invention relates to a feature defense method and system in a network transmission process, and the method comprises the steps: intercepting a network transmission data flow in a migration channel of a virtual machine in real time, and building a bidirectional flow mirror image pipeline; performing deep protocol analysis and recombination on the original transmission message in the mirror image pipeline to obtain a structured traffic log of complete load information and metadata labels; based on a preset traffic feature set, extracting a memory paging transmission time sequence feature and a disk data block check code distribution feature from the structured traffic log, and constructing a first detection sub-vector; according to the invention, a high-precision virtual machine migration security protection system is realized.

The invention relates to a competition management system and method based on cloud computing, and belongs to the technical field of resource allocation. The number of virtual machines, physical resources and competition levels are determined by dynamically acquiring competition information, resource priorities are automatically associated, stable allocation of high-level competition item resources is guaranteed, a scheduling model is constructed through a deep reinforcement learning algorithm, a virtual machine allocation probability matrix is generated, and long-term income is evaluated in combination with a reward function. Autonomous learning optimization of a scheduling strategy is realized; a multi-dimensional server state matrix is constructed, matching tasks and hardware characteristics are quantified, and the task interruption risk is reduced; a dynamic label system is generated in combination with static code analysis and runtime data, and key competition resource allocation is ensured; and staged competition items are staged, and resource demand vectors are defined, so that staged dynamic resource adjustment is realized. By remarkably improving the resource utilization rate and the load balancing capacity, the fairness of competition management, the system stability and the intelligent level are improved.

The invention discloses an address mapping method in DPU local storage virtualization, which comprises the following steps: a virtual machine calls a system call interface provided by an address mapping configuration management module on a host side to perform mapping configuration of a virtual machine physical address and a host physical address, and issues the virtual machine physical address and length to a virtual address mapping module of a DPU; the virtual address mapping module manages global direct memory access address resources, firstly allocates a global direct memory access address on a host side, then maps a virtual machine physical address to the allocated global direct memory access address in a mapping page table of the DPU, and returns the virtual machine physical address to the address mapping configuration management module; and the address mapping configuration management module obtains the distributed global direct memory access address, and maps the distributed global direct memory access address to a host physical address in the IOMMU. According to the method, by optimizing address mapping, the efficient and flexible local storage virtualization service can be realized while the data reliability is ensured.

This invention pertains to a system optimized for reutilizing allocated underutilized or unused allocated DRAM, comprising a first host, a second host, and a resource composer, interconnected via CXL. Both hosts run packaged computing environments (PCEs), which may be containers or virtual machines, and are equipped to handle respective processes, P1 and P2. The resource composer is tasked with receiving data related to P1's memory usage from a kernel module on the first host, identifying underutilized DRAM mapped to P1, and subsequently remapping it to P2's address space on the second host. This process involves the use of CXL.mem commands, which are then translated into appropriate CXL.cache or CXL.io commands for DRAM access based on the mapping.

Event-triggered forensics capture technologies balance security incident data availability against data processing and storage costs. A forensic correlation engine receives basic status data of a monitored computing system. A forensic computing system detects a trigger event in the basic status data, and starts capturing extended status data per a corresponding capture specification. Captured data is submitted to a forensic analysis tool. Different trigger events may cause different data captures. A query specifying which data to capture from a live stream or from virtual machines may operate as a capture trigger start event. Extended status data capture activity may be stopped by a change in the basic status data being received, by a timeout, or by forensic analysis that finds no vulnerability or threat based on captured data. Data transfers and storage may be restricted to comply with privacy regulations or policies.

The invention provides a security verification method for a large model service operation environment, a medium, equipment and a product, and relates to the technical field of computers, large model services are deployed in a container group created in a virtual machine, and the virtual machine is deployed in a hardware-based trusted execution environment, so that the security verification of the large model service operation environment is realized. The method comprises the following steps: acquiring environment measurement corresponding to a running environment of a large model service through a remote attestation service deployed in a hardware-based trusted execution environment, the environment measurement at least comprising a first measurement corresponding to the trusted execution environment in which a virtual machine is located and a container measurement under a container stack dimension corresponding to a container group; the remote attestation service generates a remote attestation report according to the environmental metrics to enable a user to validate the security of the operating environment through the remote attestation report. According to the invention, not only can the attack from the IaaS layer be shielded, but also the credibility verification under the dimension of the virtual machine and the credibility verification under the dimension of the container stack can be provided, so that the credibility of the user to the service provided by the service provider is improved.

The invention belongs to the technical field of virtual machine mirror image static scanning, and particularly relates to a virtual machine mirror image static scanning method and system based on a hyper-fusion platform, and the method comprises the steps: creating a to-be-scanned mirror image storage snapshot, employing an index and retrieval algorithm, obtaining a static virtual machine mirror image file, extracting and recording metadata information of the static virtual machine mirror image file, and then carrying out integrity verification; collecting all-around resource data of the hyper-fusion platform in real time, and distributing corresponding resource quotas for scanning tasks by using a preset algorithm; multi-stage deep scanning is carried out, file fingerprints and feature matching are firstly used for preliminary screening, then misjudgment is eliminated through version intelligent comparison, potential high-risk vulnerabilities are deeply excavated for key complex components by adopting static code analysis, and configuration files in mirror images are comprehensively examined through rapid screening and positioning of known malicious software; and a preset algorithm is used for carrying out deep classification arrangement, correlation analysis and summary statistics on various types of result data, so that virtual machine mirror image scanning in a large-scale hyper-fusion cluster is completed in a relatively short time.

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

The invention discloses a 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.

The invention relates to the technical field of wind power generation. The invention provides a wind field unit comparison type fault diagnosis method, system and device and a storage medium. The method comprises the following steps: synchronously acquiring operation and corresponding environment data of units of the same type in the same wind field, and establishing a multi-dimensional data set; building a normal working condition multi-dimensional parameter reference model by using clustering analysis and updating in real time; performing multi-scale comparison on a target and a reference unit, and extracting feature fusion to generate a comprehensive health index; establishing a double-layer diagnosis model, constructing a virtual unit based on digital twinning in the first layer, comparing residual errors and positioning a potential fault source, detecting outliers by using an improved isolated forest algorithm in the second layer, determining a fault propagation path in combination with an association rule, and establishing a fault mode knowledge base; and triggering an early warning mechanism according to the comprehensive health index and the fault mode classification. The problems that a traditional operation and maintenance mode is difficult in fault prediction, high in cost and lack of a reliable evaluation system, state monitoring is limited to a single parameter, multi-unit comparative analysis means are insufficient, and fault diagnosis is lagged are solved.

The invention relates to the technical field of network security, in particular to a network security situation real-time monitoring method and system. The method comprises the following steps: acquiring virtual machine running state data and physical host deployment data, and performing distributed probe deployment in a cloud computing environment according to the virtual machine running state data and the physical host deployment data to obtain monitoring probe data, wherein the monitoring probe data comprises lightweight virtual machine probe data, physical host monitoring probe data and network flow probe data; according to the monitoring probe data, performing global identification mapping on the corresponding relationship between the virtual machine and the physical host so as to obtain identification mapping data; obtaining tenant security data; and performing streaming hierarchical processing on the tenant security data according to the identifier mapping data to obtain tenant isolation data. Through the design of the distributed probe, a virtual machine, a physical host and network flow are covered, comprehensive data can be collected in real time, and a monitoring blind area is eliminated.

In some embodiments, an automation optimizer is configured to determine whether a provisioning condition is satisfied, for instance according to a current length of a job queue, or according to a current workload of a selected RPA host platform executing a plurality of software robots. When to the provisioning condition is satisfied, some embodiments automatically provision additional VMs onto the respective RPA host platform, and automatically remove VMs when automation demand is low. Exemplary RPA hosts include cloud computing platforms and on-premises servers, among others.

A system includes one or more “BotMagnet” modules that are exposed to infection by malicious code. The BotMagnets may include one or more virtual machines hosting operating systems in which malicious code may be installed and executed without exposing sensitive data or other parts of a network. In particular, outbound traffic may be transmitted to a Sinkhole module that implements a service requested by the outbound traffic and transmits responses to the malicious code executing within the BotMagnet. Credentials for services implemented by a BotSink may be planted in an active directory (AD) server. The BotSink periodically uses the credentials thereby creating log entries indicating use thereof. When an attacker accesses the services using the credentials, the BotSink engages and monitors an attacker system and may generate an alert. Decoy services may be assigned to a domain and associated with names according to a naming convention of the domain.

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.

The invention discloses a network intercommunication method and system, electronic equipment, a storage medium and a program product, and relates to the field of cloud computing, and the method comprises the following steps: for first virtual machine traffic output by a virtual machine in a computing node, performing route matching on the first virtual machine flow through a routing table corresponding to a first virtual route and a forwarding instance in the computing node; and instructing a target switch to send the obtained second virtual machine traffic to the physical server node, so that network intercommunication between the virtual machine and the physical server node is realized, and the target switch is connected to the physical server node, the routing reflector and the computing node. The problem that the network intercommunication effect between the virtual machine and the bare metal node is poor due to the defects of bandwidth forwarding bottleneck and high scheme cost of a soft gateway used between the virtual machine and the bare metal node in the computing node in the cloud computing environment is solved, and the network intercommunication effect between the virtual machine and the bare metal node is improved.

The disclosed system for testing of fleet management software generates virtual assets (e.g., simulations of machinery) that are associated with simulated sensor values. For at least one particular asset in the set, the system generates a simulated application programming interface (API) message definition structured to capture the simulated sensor values. The system binds the set of virtual assets to a simulation scenario record that includes a trigger condition. The system causes a scheduler to detect that the trigger condition is met, fetch the simulation scenario record, and generate simulated API messages for the assets according to the API message definitions.

The invention provides a DPU (Data Processing Unit)-based RDMA (Remote Direct Memory Access) virtualized memory address conversion and data transmission method and a DPU-based RDMA virtualized memory address conversion and data transmission system. The method comprises the following steps that: a user process specifies a client virtual address and a corresponding memory The virtual machine kernel generates a first memory conversion table, and the first memory conversion table records conversion from the client virtual address to the client physical address. When the drive program of the remote direct memory access device obtains the first memory conversion table, a preset instruction is triggered to enable the execution control right to be switched to the virtual machine supervision program from the virtual machine kernel. The customer physical address is converted into the host virtual address in the switching process. The virtual machine supervision program obtains mapping from the host virtual address to the host physical address through the host system information, and a second memory conversion table containing the conversion relation from the client virtual address to the host physical address is obtained. The execution control right is switched back to the kernel of the virtual machine, and the second memory conversion table is configured to the remote direct memory access device DPU. According to the method, address conversion can be simplified, hardware dependence is reduced, and RDMA performance is 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.

Techniques for adaptive sleep virtual machine management are described. A service of a provider network receives a parameter indicating that a first virtual machine can be slept and determines to sleep the first virtual machine based at least in part on the parameter. A state of the first virtual machine is captured, and the first virtual machine is terminated. The service determines to resume the captured state of the first virtual machine based at least in part on an indication. A second virtual machine is launched using the captured state of the first virtual machine and resumes execution of the captured state of the first virtual machine. A proxy server sends traffic to the first virtual machine before the termination of the first virtual machine and to the second virtual machine after the resumption of execution of the captured state of the first virtual machine by the second virtual machine.

The invention provides a credential and credential environment adaptation method and system in combination with a cloud computing platform, and relates to the technical field of credential and credential environment adaptation, and the method comprises the steps: generating adaptation analysis basic features based on static structure information; performing interface adaptation analysis by using the API support database and the adaptation analysis basic features; after a virtual credential environment is established by utilizing the virtual machine, executing the operation record of the target software, and establishing an operation data set; behavior capture of the operation data set is carried out under the guidance of the risk hot area of the compatibility matching graph by utilizing a behavior abnormity identification channel, and clustering analysis is carried out on behavior capture results; uI presentation of the target software is recorded, presentation consistency verification is carried out based on the UI presentation, and a presentation consistency score is established; and establishing an adaptation result according to the adaptation risk score table, the behavior stability score and the presentation consistency score. According to the invention, the technical problem of poor adaptation stability in the prior art can be solved, and the technical effect of improving the adaptation stability is achieved.

The invention discloses a data processing method, device and equipment and a readable storage medium, and the method comprises the steps: running a business application in a virtual machine, and obtaining a business application instruction set; the instruction type of the business application instruction set is an instruction type supported by a processor of the host machine; writing a service application instruction set in a virtual memory of the virtual machine into a shared physical memory in a virtual interaction device corresponding to the virtual machine, and generating writing notification information used for representing a writing completion state of the service application instruction set; the shared physical memory is a physical memory used for being jointly read and written by the virtual machine and the management program in the host machine; and in the management program, obtaining write-in notification information, obtaining the business application instruction set in the shared physical memory through the write-in notification information, and executing the business application instruction set through a processor of the host machine to obtain instruction execution data corresponding to the business application instruction set. By adopting the method and the device, the performance of running the business application by the virtual machine in the host machine can be improved.

The invention discloses a virtual machine computing resource dynamic allocation method and system based on a PCIe card and a storage medium, and belongs to the field of virtualization technology and hardware resource management.The method comprises the steps that the computing unit resource utilization rate of the PCIe card is monitored in real time through a hardware abstraction layer; dynamically generating a resource allocation scheme based on an improved auction algorithm, wherein the algorithm optimizes a resource quota through a time attenuation factor and a resource reuse penalty term; utilizing a programmable register remapping technology to distribute independent VF equipment for the virtual machine and establish a direct access channel; realizing interruption isolation between virtual functions through an interruption redirection controller; and during resource redistribution, atomic migration of a calculation task is completed by adopting a check point migration mechanism, and the atomic migration comprises context preloading and PCIe link layer retraining. According to the method, the problems of hardware resource distribution rigidness, insufficient isolation and migration service interruption in the prior art are solved, and fine-grained control and service lossless switching of the heterogeneous computing units are realized.

According to the big data management-based data labeling method and system provided by the invention, efficient, accurate and intelligent data labeling is realized by integrating multi-virtual machine collaborative labeling, multi-model fusion labeling, adaptive data preprocessing and hierarchical cleaning and a multi-dimensional task scheduling and dynamic load balancing mechanism. The method comprises the steps that self-adaptive preprocessing and layered cleaning are conducted on original big data, noise is removed, missing values are filled up, and format conversion is completed; dividing the preprocessed data into a plurality of subtasks according to data characteristics; distributing the sub-tasks to different virtual machine instances for parallel processing by utilizing a multi-virtual machine technology; labeling the same data fragment on each virtual machine by adopting a multi-model labeling mechanism; adopting a weighted fusion or voting mechanism to generate a comprehensive annotation result for the output results of the multiple models in each virtual machine; and summarizing the labeling results of the virtual machines to form a global data labeling result.

In accordance with example implementations, a process includes associating a secondary computer system with a primary computer system. The secondary computer system includes a first virtual machine, which is hosted by a secondary set of physical computer nodes. The primary computer system has a primary role, and the primary role corresponds to a minimum physical resource allocation. The secondary computer system is associated with a secondary role, and the second computer system is provisioned with a second physical resource allocation, which is less than the minimum physical resource allocation. The process includes, responsive to the primary computer system failing and without rebooting the secondary computer system, preparing the secondary computer system to take over the primary role. The preparation includes adding at least one physical computer node to the secondary set of physical computer nodes to cause the second physical resource allocation to meet or exceed the minimum physical resource allocation. The process includes, responsive to the primary computer system failing and without rebooting the secondary computer system, causing the secondary computer system to take over the primary role.

The present disclosure relates to the technical field of Graphics Processing Units (GPUs), and in particular to a GPU scheduling method and apparatus, and a storage medium. The method comprises: acquiring a migration command, the migration command being used for migrating a workload of a source Virtual Machine (VM) from a source GPU core to a target GPU core; on the basis of the migration command, establishing an association relationship between the source VM and a corresponding hardware identifier on the target GPU core; and on the basis of the association relationship after the migration, using the target GPU core to process the workload from the source VM. According to embodiments of the present application, while the VM is not shut down, the target GPU core is used to process the workload from the source VM, and thus the workload of the source VM is migrated from a high-load source GPU core to an idle target GPU core, so that the thermal migration between multi-core GPU cores can be achieved, the load between a plurality of GPU cores can be balanced, the pressure of a high-load GPU core can be reduced, the response time can be shortened, and the throughput rate can be increased.