40 results about "Virtual machine scheduling" patented technology

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.

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 embodiment of the invention provides a virtual machine scheduling method and device and computing equipment. The method comprises the steps that the number of virtual machines and the positions and the number of data corresponding to the virtual machines are obtained; under the condition that the data position corresponding to each virtual machine in the at least one virtual machine is not changed, traversing the position arrangement of all the virtual machines; in each group of position arrangement of all the virtual machines, based on the position and the number of the data corresponding to each virtual machine in the at least one virtual machine, calculating the sum of logic distances of each virtual machine and the data corresponding to each virtual machine; based on the sum of the logic distances, position arrangement of all the virtual machines in at least one group is determined, and the sum of the logic distances is the minimum value; and determining the virtual machine needing to be scheduled based on the position arrangement. Therefore, the number of the local copies of the virtual machines in the server cluster is maximized, and under the condition that the cost is not increased, the performance optimization of the cluster can be realized through relatively small modification, that is, only by scheduling the virtual machines.

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.

The present application provides a virtual machine scheduling method, apparatus, device and storage medium, which relates to the field of virtualization technology. The method includes: obtaining a scheduling request, the scheduling request is used to request that the i-th virtual machine among N virtual machines be scheduled to the target host corresponding to the i-th virtual machine and the disk capacity of the i-th virtual machine be scheduled to the target storage associated with the target host, the scheduling request includes the performance label of the i-th virtual machine and the performance parameters of the i-th virtual machine; based on the performance label of the i-th virtual machine, respectively, in the first host cluster and the first storage cluster included in the multiple data centers, determine the second host cluster and the second storage cluster; based on the performance parameters of the i-th virtual machine, in the second host cluster and the second storage cluster, determine the target host and the target storage. This method implements the scheduling of virtual machines across data centers, which not only improves the user experience, but also helps to improve the resource utilization of multiple data centers and achieve load balancing.

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 invention discloses a virtual machine scheduling method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring target data related to a virtual machine in a customer relationship management system; for any virtual machine cluster, determining an anti-affinity mean absolute error of the virtual machine cluster according to a first number of virtual machines actually borne by each host machine in the target data, a total number of virtual machines and a second number of virtual machines theoretically borne by each host machine; according to the anti-affinity mean absolute error, determining whether the virtual machine cluster meets an anti-affinity index, and determining an anti-affinity adjustment index of each target virtual machine cluster; and scheduling the number of the virtual machines borne by the host machine in the target virtual machine cluster according to the sequence of the anti-affinity adjustment indexes from large to small. According to the method and the device, the technical problem that the virtual machine which possibly has the affinity hidden danger cannot be quickly and accurately scheduled in related technologies is solved.

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 present invention discloses a containerized and virtualized convolutional neural network layered training method and system, belonging to the field of machine learning technology, including a segmented machine learning framework, an abstract modular template, POD, virtual machine creation scheduling, and a monitoring platform; the segmented machine learning framework is used to provide a machine learning program with communication capabilities and generate complete machine learning model code based on the template; the abstract modular template, based on the layered characteristics of the convolutional neural network structure, provides standard abstract templates for each module of the convolutional neural network, and uses a declarative definition method to define the structure of each layer of the convolutional neural network; POD, virtual machine creation scheduling includes machine learning code generation, POD, virtual machine creation, and POD, virtual machine scheduling. The present invention can fully utilize the performance of computing equipment, reduce the cost of model training for model designers, improve the reuse rate of cloud service providers' equipment, and simplify the design process of convolutional neural network models.

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 present application relates to the field of cloud computing technology, and provides a virtual machine scheduling method, apparatus, computer device, computer-readable storage medium, and computer program product. The method includes: obtaining first load data of a target host and second load data of a plurality of alternative virtual machines; processing the first load data and the second load data through a scheduling analysis model, calculating similarity values between every two alternative virtual machines, and clustering the plurality of alternative virtual machines according to the similarity values to obtain a plurality of alternative clusters; wherein the scheduling analysis model is constructed according to a load balancing objective function and a migration loss objective function; obtaining an average eigenvalue corresponding to each alternative cluster according to the second load data; selecting a target cluster according to the average eigenvalue, and using the alternative virtual machines corresponding to the target cluster as target migration virtual machines to be scheduled to the target host. By adopting this method, the virtual machine migration cost can be reduced on the basis of meeting the load balancing condition.

The embodiment of the invention provides a virtual machine scheduling method, electronic equipment, a computer storage medium and a computer program product. The virtual machine scheduling method comprises the following steps: determining tenant business characteristics of each virtual machine in a plurality of virtual machines to be scheduled; based on the tenant business characteristics of each virtual machine, determining the demand degree of the virtual machine for various host machine resources; the plurality of virtual machines to be scheduled are scheduled to at least one host machine, so that the demand degrees of any two virtual machines in the same host machine for each kind of host machine resources are in a demand complementary state, and the difference of the demand degrees of the two virtual machines in the demand complementary state exceeds a preset threshold value. According to the scheme of the embodiment of the invention, the performance stability of the business of the tenant can be ensured, the resource selling rate of the host machine can be ensured, and the operation cost of a cloud service provider is reduced.

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 present invention discloses a virtual machine scheduling heuristic function optimization method based on a large language model, which realizes optimization and efficiency improvement in online virtual machine scheduling by integrating the two ideas of discovery strategy and discovery reflection. Specifically, it includes: designing and constructing an online virtual machine scheduling simulator including a virtual environment engine and a scheduling allocation system, which is used to simulate the virtual machine scheduling process in a cloud computing environment; through a policy mining module, the generation and reasoning capabilities of the large language model are used to generate an initial scheduling strategy, and the strategy is optimized through a code space reinforcement learning method to improve scheduling efficiency; combined with the optimization history, the reflection mining module is used to analyze the previous optimization path, extract effective strategy parameters and generate optimization guidance to avoid repeated invalid exploration, thereby optimizing the global scheduling strategy. The present invention shows higher adaptability, flexibility and global optimal strategy search capabilities in complex cloud computing environments, and has broad application prospects.

A virtual machine deployment method and apparatus, a device, and a readable storage medium. The method uses a firefly algorithm to determine deployment locations of virtual machines, and takes an average performance score of all hosts to be selected as a target function. Locations of respective fireflies are locations of the respective hosts. An iterative optimization process of the firefly algorithm involves finding a host capable of maximizing operation performance of all hosts in a cloud platform after deployment of virtual machines is completed. Since the target function is the average performance score of all hosts after deployment of the virtual machines, selection of a host corresponding to the maximum target function value enables average performance of all hosts to be maximized after the virtual machines have been deployed to a destination host, thereby maximizing operation performance of all hosts in the cloud platform while scheduling the virtual machines.

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.

The invention discloses a load balancing method, device and equipment and a storage medium, and relates to the technical field of load balancing, and the method comprises the steps: generating and storing a load balancing strategy according to cluster service distribution and cluster operation data or according to user configuration parameters; analyzing the stored load balancing strategy to obtain a load index and an affinity constraint; monitoring hosts in the cluster, and screening out to-be-migrated hosts exceeding a load index threshold; traversing hosts in the cluster and checking whether virtual machines running on the hosts meet affinity constraints or not, and if not, marking the virtual machines as non-affinity virtual machines; and according to the load index and the affinity constraint, determining the virtual machines to be migrated from the virtual machines deployed on the host to be migrated and the non-affinity virtual machines, and sequentially migrating the virtual machines to be migrated. According to the method, automatic generation and user configuration can be combined, the load balancing strategy is dynamically adjusted, and the virtual machine is flexibly scheduled, so that the overall performance of the cluster is improved, and the problems of inflexible scheduling and poor adaptability of the virtual machine in related technologies are solved.

The present application provides a virtual machine scheduling method and device, which includes: obtaining the power consumption type of a target virtual machine of a cloud computing platform, wherein the cloud computing platform includes multiple physical servers; determining a target physical server corresponding to the target virtual machine from the multiple physical servers according to the power consumption type of the target virtual machine and the power consumption type of each of the physical servers, so as to schedule the target virtual machine to the corresponding target physical server, so that the target physical server provides computing resources for the target virtual machine, thereby implementing a virtual machine scheduling method based on power consumption matching, improving the degree of power consumption matching between the virtual machine and the assigned physical server, thereby improving the power consumption utilization rate of the physical server, and reducing the risk of overload of the rack where the server is located.

Embodiments of the present disclosure provide a virtual machine scheduling method, an electronic device, a computer storage medium, and a computer program product. The virtual machine scheduling method comprises: determining tenant service characteristics of each virtual machine among a plurality of virtual machines to be scheduled; on the basis of the tenant service characteristics of each virtual machine, determining a demand level of each virtual machine for various host machine resources; and scheduling the plurality of virtual machines onto at least one host machine, so that any two virtual machines in the same host machine are in a demand complementary state with respect to the demand level for each host machine resource, wherein the difference in demand levels between the two virtual machines in a demand complementary state exceeds a preset threshold.

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 embodiment of the invention relates to the technical field of computer communication, and discloses a method and system for measuring the running time of an MCU Hypervisor system and computer equipment, and the method comprises the steps: adding a corresponding time sequence event monitoring interface at a time sequence event needing to be monitored in the MCU Hypervisor system; the MCU Hypervisor system calls a corresponding time sequence event monitoring interface at a position where a time sequence event occurs in a virtual machine scheduling process and a virtual machine communication process, and inputs a corresponding event type parameter; recording a time sequence event log through a time sequence event monitoring interface, and recording the time sequence event log record into the MCU Hypervisor system for caching; and analyzing and processing the time sequence event log record to obtain occurrence cycle data of the event of the corresponding type. According to the method for measuring the running time of the MCU Hypervisor system, time sequence events of all dimensions are measured at a time, then time sequence event measurement results of different dimensions are presented according to actual performance measurement requirements, and the method is very flexible and efficient to use.

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 present invention discloses a cloud platform GPU virtual machine scheduling method and device, which relates to the field of computer technology. The method obtains the target number of GPU resources required for a new task. If the total number of GPU resources remaining in all channels is greater than the target number of GPU resources, and no single channel meets the target number of GPU resources, all channels are sorted from most to least by the number of remaining GPU resources of each channel, and then a preset number of channels are obtained to obtain an effective channel set. The resources in the effective channel set are dynamically migrated by a preset algorithm to obtain a migration result. If the migration result is to generate a target migration channel, the target number of GPU resources of the new task is all scheduled to the target migration channel. By dynamically migrating idle resources across channels, the fragmentation of single channel resources is avoided, and migration from channels with more remaining resources is prioritized, thereby improving the utilization rate of GPU resources, reducing scheduling complexity, and meeting the needs of high-performance computing tasks.