59 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 discloses a multi-layer virtual machine scheduling system based on a large language model, and the system comprises the following modules: an initial heuristic design module which is used for designing an initial heuristic function of a virtual machine scheduling problem; the scheduling scene feature analysis module is used for extracting and analyzing features of the cloud scheduling scene; the scene self-adaptive optimization module is used for performing independent optimization according to different scenes; the multi-scene strategy management module is used for managing cross-scene strategy switching; and the performance evaluation module evaluates the optimization effect in real time. By combining the intelligent reasoning capability of the large language model, the system can provide an efficient, flexible and self-adaptive scheduling optimization scheme, and the utilization efficiency of cloud resources is remarkably improved. Through the advantages of the large language model in the aspect of code generation, a heuristic function adaptive to virtual machine scheduling can be intelligently and efficiently generated, so that resource allocation is optimized, the efficiency and performance of a high-dynamic cloud scheduling problem are remarkably improved, and an innovative solution is provided for cloud resource scheduling.

The invention discloses a train fusion control method based on a virtualization method. The train fusion control method comprises the following steps: adopting a configuration mode of combining a universal operating system with virtualization extension; according to the method, virtual extension is carried out on train starting, fault recording and state monitoring processes, fusion control of trains is realized, the method is realized by adopting a chip supporting a virtual machine monitor, the virtual machine monitor is used for providing virtual machine resource allocation, virtual machine scheduling and virtual machine data interaction services, a virtio virtualization method is adopted for virtualization extension, and the virtual machine data interaction service is provided for the virtual machine. The virtio virtualization method is an I / O (Input / Output) para-virtualization solution, provides a set of general I / O equipment virtualization, abstracts a group of general I / O equipment in a para-virtualization virtual machine monitor, and provides a communication framework and a programming interface between an upper-layer application and each virtual machine monitor virtualization equipment.

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 arrangement and scheduling method and equipment, and the method comprises the steps: creating a first virtual machine planning model according to the deployment attribute characteristics of an initial virtual machine deployed on a physical machine in response to a secondary scheduling request of a virtual machine, and determining a first deployment scheme enabling the total scheduling cost to be lowest; comprising the following steps: on the premise of not considering the anti-affinity constraint of the virtual machines, predicting the deployment number of each type of initial virtual machines in each physical machine, and determining to-be-migrated initial virtual machines in each type of initial virtual machines in each physical machine; creating a second virtual machine planning model for the target to-be-emigrated initial virtual machine with the anti-affinity constraint, and determining a second deployment scheme enabling the scheduling cost of the target to-be-emigrated initial virtual machine to be the lowest, including a target physical machine to which each target to-be-emigrated initial virtual machine is to be deployed on the premise of considering the anti-affinity constraint of the virtual machine; and performing virtual machine scheduling. Through a multi-stage scheduling simplified model, the scheduling efficiency and success rate of the virtual machine are improved.

A virtual machine scheduling method, an electronic device and a storage medium. The method includes: acquiring optimal resource distribution information of a plurality of physical machines in a cloud computing system, wherein the optimal resource distribution information includes specifications and numbers of specifications of virtual machine instances expected to be deployed on idle resources of the plurality of physical machines; for a first to-be-deployed virtual machine instance, determining a score of matching degree between each physical machine and the first to-be-deployed virtual machine instance based on the optimal resource distribution information; and determining the physical machine with the highest score of matching degree as a host machine of the first to-be-deployed virtual machine instance, for resource allocation.

The embodiment of the invention provides a method for realizing computing node virtualization based on OpenStack and a cloud platform, and the method comprises the steps: installing a nova-complex service on a first computing node, and configuring an interface address corresponding to a Libvirt connection address of the nova-complex according to the virtualization type of the first computing node; the control node dispatches a second computing node for the to-be-created virtual machine and sends a request for creating the virtual machine to a nova-complex service of the second computing node, and the second computing node is a first computing node meeting the virtualization type of the to-be-created virtual machine; and creating the virtual machine to be created through a Libvirt interface which is connected with the Libvirt connection address of the nova-component of the second computing node by the nova-component service of the second computing node. According to the technical scheme, the computing node meeting the requirement is scheduled according to the virtualization type of the virtual machine, the scheduled computing node creates the virtual machine through the interface connected with the Libvirt connection address of the nova-complex configured according to the virtualization type of the computing node, and therefore creation of the type1 virtual machine is achieved through OpenStack.

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 invention relates to the technical field of cloud computing, and provides a virtual machine scheduling method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring first load data of a target host machine 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 a similarity value of 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 characteristic value corresponding to each alternative cluster according to the second load data; and selecting a target cluster according to the average characteristic value, taking the alternative virtual machine corresponding to the target cluster as a target migration virtual machine, and scheduling the target migration virtual machine to the target host machine. By adopting the method, the migration cost of the virtual machine can be reduced on the basis of meeting the load balancing condition.

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.

The present application provides a task scheduling method, device and electronic device, which relate to the technical field of cloud computing task scheduling. The method comprises: responding to a virtual machine scheduling task requested by a user terminal, obtaining multiple status information corresponding to multiple data centers, determining multiple candidate data centers whose renewable energy meets the energy required by the data center, when the available computing resources of the candidate data center closest to the user terminal meet the computing resources required by the virtual machine scheduling task, determining the first server with the smallest energy consumption increase in the first data center as the target server, and when the renewable energy of the first data center can meet the energy consumption required by the target server to execute the virtual machine scheduling task, allocating the virtual machine scheduling task to the target server.

The invention provides a hybrid bionic algorithm-based task scheduling and resource management method in a cloud environment, which comprises the following steps of: 1, determining a plurality of virtual machines and a plurality of tasks for executing tasks on the basis of acquiring a task request of a user and resource requirements of the tasks; 2, based on a plurality of virtual machines and a plurality of tasks, determining a plurality of virtual machine scheduling schemes by adopting an improved particle swarm optimization algorithm; each particle represents a virtual machine scheduling scheme, the particle swarm represents a search space of virtual machine scheduling, an iteration termination condition of an improved particle swarm optimization algorithm is set, loop iteration is carried out, and the current scheduling scheme is output when the iteration termination condition is met; wherein in the process of determining the scheduling scheme of the multiple virtual machines by adopting the improved particle swarm optimization algorithm based on the multiple virtual machines and the multiple tasks, the computing resources of the virtual machines are managed by adopting the improved particle swarm optimization algorithm. And the overall utilization rate and the system stability of the cloud resources in a dynamic task and heterogeneous environment are remarkably improved.

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.

The present invention discloses a workload-aware hybrid scheduling method and system for containers and virtual machines. The present invention includes collecting resource information of a resource pool, where the resource pool is used to uniformly manage physical resources of multiple nodes; obtaining application resource request information of an application; judging the request type according to the application information included in the application resource request information of the application. If the request type is a request for publishing the application, then analyze the workload characteristics of the application based on the application resource request information and the current resource information of the resource pool, dynamically select the publishing type information as a container or a virtual machine according to the workload characteristics of the application, schedule and generate allocation resource information and publishing node information of the application; and publish the application to the node corresponding to the publishing node information. The present invention aims to solve the problems of resource fragmentation and scheduling complexity in the existing solutions, and improve the flexibility, resource utilization rate and fault recovery ability of the container and virtual machine hybrid scheduling 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 cloud platform GPU virtual machine scheduling method and device, and relates to the technical field of computers. Obtaining the number of target GPU resources required by the new task, and if the total number of the remaining GPU resources in all channels is greater than the number of the target GPU resources and no single channel meets the number of the target GPU resources, sorting all the channels according to the number of the remaining GPU resources of each channel from large to small, and then obtaining the previous preset number of channels to obtain an effective channel set; dynamically migrating the resources in the effective channel set through a preset algorithm to obtain a migration result; and if the migration result is that the target migration channel is generated, the number of the target GPU resources of the new task is all scheduled to the target migration channel. Idle resources are dynamically migrated in a cross-channel mode, resource fragmentation of a single channel is avoided, migration is preferentially carried out from a channel with many residual resources, the utilization rate of GPU resources is increased, the scheduling complexity is reduced, and the requirements of high-performance computing tasks are met.

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 present invention relates to a virtual machine optimization scheduling method for cloud computing. The method takes the multi-objective scheduling optimization result of virtual machines as input parameters. First, the method establishes an original decision matrix through objective transformation, and then combines the system state transition relationship of the resource scheduling process to establish a Markov decision process MDP corresponding to the resource scheduling process. Furthermore, an auxiliary decision matrix with virtual machine migration cost information is obtained through reinforcement learning technology training. Finally, a trade-off decision matrix is constructed using the original decision matrix and the auxiliary decision matrix, and according to the user's preference information for target attributes, a global optimal scheduling scheme is output. Compared with the prior art, the present invention has the advantages that it not only considers the steady-state target information before the execution of the virtual machine scheduling scheme, such as energy consumption, quality of service, resource utilization rate, etc., but also takes into account the potential migration costs that may be caused to subsequent resource integration after the execution of the virtual machine scheduling scheme.

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.