24 results about "Virtual machine scheduling" patented technology

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 provides a sandbox virtual machine scheduling method and device based on a large language model, and relates to the technical field of computers.The method comprises the steps that under the condition that a task request is received, metadata extraction and feature analysis are executed on a target task, and a structured task object corresponding to the target task is obtained; the task object comprises metadata and task features; obtaining a current running state of the system and a text description corresponding to the target task, and performing semantic analysis and reasoning on the current running state, the text description and the task object by using a large language model to generate a target scheduling plan; and scheduling the virtual machine to execute the target task based on the target scheduling plan. According to the sandbox virtual machine scheduling method and device based on the large language model, the virtual machines can be dynamically allocated according to task semantics and risks, and the scheduling strategy can be self-learned and optimized through task execution feedback, so that the resource utilization rate and the scheduling efficiency are improved while the system security is ensured.

The application relates to the technical field of cloud computing, in particular to a computing resource scheduling method and device, equipment and a storage medium, the method comprises the following steps: constructing a self-defined type of resource, and constructing a corresponding controller according to the self-defined type of resource; when the category of the controller is a virtual machine, a container containing the virtual machine is constructed; according to the type corresponding to the self-defined type of resource, corresponding resource configuration is extracted, and the creation instruction of the container containing the virtual machine is rewritten, the virtual machine is scheduled, and thus a single physical computer can support mixed scheduling of virtual machines and containers simultaneously.

This application provides a virtual machine scheduling method, apparatus, electronic device, storage medium, and computer program product. The method includes: generating a first optical signal based on the computing resource data of each of a plurality of computing nodes; processing the first optical signal by calling a diffraction deep neural network model to obtain a second optical signal; and determining a first computing node for creating a virtual machine among the plurality of computing nodes based on the position of the second optical signal projected on a set imaging plane.

The application discloses a kind of edge virtual machine protection method and system of being attacked by invisible DDoS, to resist the fuzzy attack of invisible DDoS attacker to edge virtual machine, considering the uncertainty of edge virtual machine environment, introduce fuzzy number to quantify the state of virtual resource, the uncertain information observed to the virtual resource consumption of invisible attacker is characterized as fuzzy number, fuzzy game model is established, and it is analyzed and handled using fuzzy logic. In the dynamic virtual machine environment attacked by invisible DDoS, since the uncertain game utility is obtained in the observation space, it cannot be directly used for virtual machine resource preemption decision, the application obtains virtual machine scheduling priority by fuzzy reasoning, and then makes virtual machine resource preemption decision, so that a fuzzy learning algorithm with high robustness is proposed.

The application discloses a virtual machine scheduling method, device and equipment and a computer storage medium. The method comprises the following steps: obtaining an IP address of a virtual machine in a first source host; calculating an IP address of a first virtual machine to be migrated in the first source host according to a preset IP number contained in continuous IP and the IP of the virtual machine; obtaining first resource data of the first virtual machine to be migrated according to the IP address of the first virtual machine to be migrated; obtaining second resource data of a first target host; calculating resource utilization rate estimation data of the first target host according to the first resource data and the second resource data; and determining first migration information according to the first resource data and the second resource data when the first source host and the first target host satisfy a first migration rule. According to the embodiment of the application, the problem that multiple virtual machines carrying the same service function are simultaneously faulty due to the failure of a single host can be solved.

The invention discloses a virtual machine scheduling method and device, equipment and a storage medium, and the method comprises the steps: obtaining a target vector related to a to-be-deployed virtual machine and real-time state data of a plurality of candidate host machines, the target vector being constructed based on historical load data, service attributes and software components of the to-be-deployed virtual machine; inputting the target vector and the real-time state data of the plurality of candidate host machines into a preset scheduling model, wherein a strategy function is built in the scheduling model; calculating a scheduling probability of scheduling the virtual machine to be deployed to each candidate host machine in a plurality of candidate host machines based on the strategy function; and based on the scheduling probability, the candidate host machine with the highest scheduling probability is selected from the plurality of candidate host machines as the target host machine, and the target host machine is an output result of the scheduling model, so that reasonable allocation of computing resources can be realized, and the scheduling efficiency of the virtual machine is effectively improved.

The application discloses a lightweight virtualization technology suitable for an automobile embedded system. A Type-2 virtual machine management software is adopted, and multiple virtual machines are allowed to run simultaneously on an ARMv8 embedded development board; the virtual machine system supports common RT-Thread and Linux systems. According to the characteristics of the embedded system, a device named recorder is mounted to each virtual machine, the priority of a process (or thread) in the virtual machine is mapped to a host computer, the host computer participates in scheduling decision-making, and the scheduling of the virtual machine is optimized in a targeted manner. Relying on the excellent real-time performance of the real-time operating system RT-Thread and the hardware support of the ARM architecture in the virtualization aspect, the virtualization extension of the RT-Thread system is realized. In addition, according to the characteristics of specific applications, a fine-grained virtual machine scheduling mode is proposed, so that the virtual machine scheduling mode is more suitable for the automobile embedded system.

This invention discloses a virtual machine scheduling method, apparatus, device, storage medium, and program product. The method includes: acquiring current optimization target preference information and physical host cluster status information; wherein the optimization target preference information is used to indicate the optimization preference ratio of at least two optimization targets; based on the optimization target preference information and the physical host cluster status information, optimizing virtual machine placement on the target physical host cluster to obtain a target virtual machine placement scheme; and scheduling virtual machines on the target physical host cluster based on the target virtual machine placement scheme. This invention optimizes virtual machine placement on the target physical host cluster by considering optimization target preference information, thereby focusing on optimizing the virtual machine placement scheme for the optimization target with the higher optimization preference ratio while considering multiple optimization targets, thus ensuring a globally optimal scheduling scheme and improving the accuracy of virtual machine scheduling.

The invention discloses a virtual machine scheduling method and system. The method comprises the following steps that a scheduling module determines whether a virtual machine needs to be migrated or not; if the scheduling module determines that the virtual machine needs to be migrated, the scheduling module determines the physical host existing in the permission list; determining, by the scheduling module, a destination physical host after the scheduling module determines the physical hosts present in the permission list; after the scheduling module determines the destination physical host, determining, by the scheduling module, an overhead to migrate the virtual machine and the source data to the destination physical host; the virtual machine and the source data are migrated to the second physical host or the third physical host by the scheduling module based on the overhead to migrate the virtual machine and the source data to the destination physical host. The method provided by the invention can help a scheduling algorithm to select a more appropriate physical host, thereby improving the efficiency of the whole system.

The application discloses a virtual machine vcpu thread special-purpose scheduler based on a linux kernel and KVM, and the main body is VMSCH, characterized in that: the VMSCH is a virtual machine vcpu scheduler, the VMSCH priority is between an RT scheduling strategy and a CFS scheduling strategy, the priority is 99, and the RT priority is adjusted from 1-99 to 1-98, the VMSCH only manages the scheduling of vcpu threads and does not manage other threads. In the application, the duty cycle of vcpu exclusive pcpu can be dynamically configured, and it is unnecessary to configure at booting, so that the flexibility of the scheme of isolating pcpu for vcpu use by using isolate is avoided, pcpu resource waste caused by bandwidth limitation and idle state of the RT scheduling strategy is avoided, and the application has CPU architecture independence, and after implementation, any CPU architecture, such as X86, ARM and Loongson, can be naturally supported, so that the application has extremely wide application range.

A virtual machine orchestration scheduling method, an electronic device, and a storage medium are provided. The method includes: in response to a virtual machine secondary scheduling request, creating a first virtual machine planning model according to a deployment attribute feature of an initial virtual machine deployed on a physical machine, and determining a first deployment plan that minimizes a total scheduling cost; determining an initial virtual machine to be migrated out in each type of initial virtual machine in each physical machine; creating a second virtual machine planning model for a target initial virtual machine to be migrated out with an anti-affinity constraint in the initial virtual machine to be migrated out, and determining a second deployment plan that minimizes a scheduling cost of the target initial virtual machine to be migrated out; and performing virtual machine scheduling according to the first deployment plan and the second deployment plan.

The application provides a cloud computing virtual machine scheduling algorithm suitable for railway communication cloud, aiming to solve the deficiency of existing scheduling algorithms in handling high real-time and high security requirements in railway communication systems. The algorithm performs accurate scheduling based on the model and bandwidth attributes of PCI network cards, ensuring that virtual machines can directly access physical network cards to reduce latency and improve data transmission rate. At the same time, the algorithm realizes network isolation between virtual machines through physical switch configuration, ensuring the security of the system. When creating virtual machines in batches, the scheduling algorithm can schedule physical server nodes that meet the topology requirements according to the user-defined network topology structure, ensuring the correct connection between virtual machines and network topology. Through this algorithm, the railway communication system can meet the requirements of low latency, high bandwidth, security isolation and batch scheduling in complex network environments, significantly improving the efficiency and flexibility of virtual machine scheduling.

Embodiments of the present application provide a virtual machine scheduling method, an electronic device and a storage medium. The method comprises: determining specification definition information of a virtual machine; screening a physical machine according to the specification definition information to determine at least one physical machine satisfying a specification condition; screening the at least one physical machine satisfying the specification condition according to an application type of the virtual machine, and deleting a physical machine with an abnormal label matching the application type; and sorting the remaining physical machines according to a set rule, and selecting a target physical machine according to corresponding sorting information. The abnormal physical machine can be filtered out, and the performance of the virtual machine affected by the abnormal physical machine can be reduced.

This application provides a sandbox virtual machine scheduling method and apparatus based on a large language model, relating to the field of computer technology. The method includes: upon receiving a task request, performing metadata extraction and feature analysis on the target task to obtain a structured task object corresponding to the target task; the task object includes: metadata and task features; acquiring the current running state of the system and the text description corresponding to the target task, and using a large language model to perform semantic analysis and reasoning on the current running state, text description, and task object to generate a target scheduling plan; and scheduling virtual machines to execute the target task based on the target scheduling plan. The sandbox virtual machine scheduling method and apparatus based on a large language model provided in this application can not only dynamically allocate virtual machines according to task semantics and risk, but also optimize the scheduling strategy through self-learning via task execution feedback, thereby improving resource utilization and scheduling efficiency while ensuring system security.

The application discloses a virtual machine scheduling method of an OpenStack cloud platform, and comprises the following steps: setting screening information of a corresponding computing node based on received configuration information; wherein the screening information comprises switch information, cabinet information and cabinet column information; performing anti-affinity screening based on a screening rule and information of each node to obtain a target node; and scheduling a virtual machine to the target node. The granularity of the virtual machine scheduling of the OpenStack cloud platform is refined, the effect of the virtual machine scheduling is improved, and the reliability and availability of the virtual machine are further improved. The application also discloses a virtual machine scheduling device of an OpenStack cloud platform, a server and a computer readable storage medium.

The invention discloses a cloud computing virtual machine scheduling method based on dynamic resource requirements and Gamma robust optimization, which comprises a virtual machine scheduling optimization model and a heuristic algorithm, and is characterized in that the Gamma robust optimization model is constructed by introducing Gamma parameters, redistribution of virtual machines is performed through a Gamma robust Firstfit algorithm, and the Gamma robust Firstfit algorithm is used for scheduling the virtual machines. According to the cloud computing virtual machine scheduling method and system, cloud computing virtual machine scheduling based on dynamic resource requirements and gamma robust optimization is achieved, the problems that the cloud computing resource utilization rate is low, the migration cost is high and uncertainty is difficult to deal with are solved, efficient virtual machine integration in a large-scale cloud environment is achieved, the resource utilization rate is effectively increased, host use is reduced, and cost is reduced.

The invention belongs to the technical field of cloud computing and virtualization, and discloses a virtualization platform encrypted volume self-adaptive deployment system based on life cycle management. Through cooperative work of eight core modules including information acquisition, risk assessment, abnormity identification, decision construction, risk prediction, window identification, operation execution and verification, intelligent management of the whole life cycle of the encrypted volume is realized. According to the method, the stage of the encrypted volume can be accurately identified, the existing state and the initialization completion degree can be detected, the abnormities such as equipment suspension or mounting conflicts can be found, the repair decision is constructed based on the standard state machine model, and the repair operation is automatically executed at the optimal time. Through a self-adaptive management mechanism, the operation opportunity can be intelligently judged in the virtual machine scheduling process, and data safety and service continuity are ensured. According to the invention, complex and changeable business requirements can be effectively met, the management cost is reduced, and the resource utilization rate is improved.

This invention belongs to the field of cloud computing and virtualization technology. It discloses an adaptive deployment system for encrypted volumes on a virtualization platform based on lifecycle management. Through the collaborative work of eight core modules—information acquisition, risk assessment, anomaly identification, decision building, risk prediction, window identification, operation execution, and verification—it achieves intelligent management of the entire encrypted volume lifecycle. This invention can accurately identify the stage of the encrypted volume, detect its existing state and initialization completion, identify anomalies such as device hanging or mounting conflicts, construct repair decisions based on a standard state machine model, and automatically execute repair operations at the optimal time. Through its adaptive management mechanism, this invention can intelligently determine the timing of operations during virtual machine scheduling, ensuring data security and business continuity. This invention can effectively cope with complex and ever-changing business needs, reduce management costs, and improve resource utilization.

The invention discloses a virtual machine scheduling method and device, equipment, a storage medium and a product. The method comprises the following steps: determining the resource demand quantity of a virtual machine to be migrated on a plurality of resource dimensions; obtaining multi-dimensional allocable resource information of each physical node in the cluster, and determining a first candidate node list according to a preset resource priority and the multi-dimensional allocable resource information of each physical node; obtaining multi-dimensional available resource information of each physical node in the cluster, and determining a second candidate node list according to a preset resource priority and the multi-dimensional available resource information of each physical node; and determining a target migration physical node corresponding to the virtual machine to be migrated according to the first candidate node list and the second candidate node list. According to the method, the demand quantity of the virtual machine in multiple resource dimensions and the distributable resources and the available resources of each physical node in the corresponding resource dimensions are comprehensively considered, so that the screened target migration physical node is enabled to be more adaptive to the virtual machine.

The application discloses a scheduling method and device of a virtual machine scheduling domain, equipment and a storage medium. First, the number of VCPU required by a to-be-scheduled virtual machine in a target processor is obtained to obtain core requirement number, and a scheduling domain binding strategy is determined based on the core requirement number. A plurality of PCPU corresponding to each L3 Cache in the target processor is obtained, and a topology information table is constructed based on the corresponding relationship between them. Then, the actual available states of each PCPU included in the topology information table are determined, and the PCPU with an idle state as the actual available state is determined as a target core. Then, all target cores are grouped based on the corresponding L3 Cache of each target core in the topology information table to obtain a plurality of candidate core groups, so that the above strategy and the plurality of candidate core groups are used for scheduling domain binding of all VCPU. The application can improve the processor cache hit rate, reduce the data access delay, and further improve the overall running performance of the virtual machine.

Embodiments of the present application disclose a virtual machine scheduling method and device, electronic equipment and storage medium. The method comprises: obtaining a virtual machine to be scheduled; determining a set of available failure domains according to a set of available host computers in a virtualization cluster, determining a target service domain to which the virtual machine to be scheduled belongs according to a service attribute, and determining a target available failure domain according to distribution information of virtual machines corresponding to the target service domain in the set of available failure domains; determining a scheduling weight value of each host computer in the target available failure domain, determining a target host computer according to the scheduling weight value, and scheduling the virtual machine to be scheduled to the target host computer, so that virtual machines in the same service domain have a long-term balanced distribution across failure domains, thereby solving the problem of business continuity affected by virtual machine migration, and solving the problem of the influence range of unpredictable failure on the virtualization cluster.

The disclosure provides a virtual machine scheduling method and device, electronic equipment and readable storage medium, and relates to the technical field of virtual machines. The virtual machine scheduling method based on a non-uniform memory access (NUMA) system comprises: generating a NUMA topology of CPUs based on a NUMA node topology in the NUMA system and a CPU list of the NUMA node; configuring a corresponding local access control group (Cgroup) under a specified directory based on the NUMA topology and the CPU list; in response to scheduling information, determining a virtual machine to be scheduled, and performing a balancing scheduling operation for the virtual machine to be scheduled based on the Cgroup, so that the virtual machine accesses local memory. Through the technical scheme of the disclosure, the virtual machine accesses local memory, thereby facilitating reduction of latency caused by access.

The present disclosure relates to a virtual machine scheduling method, a medium, and an electronic device, and relates to the field of computer technologies. The virtual machine scheduling requirement is converted into a target constraint condition through resource information of a target virtual machine and a target physical machine, and a target virtual machine migration path is determined according to the target constraint condition, the resource information of the target virtual machine and the target physical machine, and a basic scheduling algorithm. The virtual machine scheduling is performed according to the target virtual machine migration path.