79 results about "Server allocation" patented technology

The invention discloses a load balancing method and a related device, and relates to the field of computers, and the method comprises the following steps: obtaining hash ring data; wherein the Hash ring data reflect the mapping relation between the server and the virtual nodes located on the Hash ring; adjusting the number of virtual nodes of the server based on the current load value of the server and a weight coefficient corresponding to a service scene to which the current service request processed by the server belongs; wherein the current service request is distributed to the server based on the Hash ring data; on the basis of the adjusted number of virtual nodes, Hash ring data are updated; and based on the updated Hash ring data, performing server allocation on the new service request, and sending the new service request to the allocated server for processing. According to the invention, the operation response is more timely while the load balancing is realized.

A method comprising receiving, via a server, a distress signal from a first drone determining among a plurality of drones performing mission tasks, is experiencing distress and has entered a mitigation procedure responsive to the mitigation procedure to include the content received from the first drone; and assigning, via the server, a new drone to replace the first drone as a follower drone or a leader drone.

According to the offshore edge computing task unloading and resource allocation method based on air-sea cooperation, the task queuing model is established, the optimization target model with the purpose of maximizing the average throughput of the system is established, and whether the unmanned aerial vehicle unloads the task to the server or not is determined after solving; when the unmanned aerial vehicle does not distribute the task to any server, the optimal computing resource distribution amount of the unmanned aerial vehicle end is obtained; when the unmanned aerial vehicle allocates the task to the server, the optimal transmission power of the unmanned aerial vehicle unloading task and the calculation resource proportion allocated to the unmanned aerial vehicle by the server are obtained, and then task unloading and resource allocation of the offshore edge system based on air-sea cooperation are completed. According to the invention, through accurate scheduling and efficient cooperation of resources of the unmanned aerial vehicle and the server side, inherent limitations of marine equipment on data acquisition breadth and calculation processing depth can be broken, maximization of the average throughput of the system is realized, and meanwhile, a firm and flexible technical support is provided for efficient processing of various marine tasks.

The embodiment of the invention discloses a file cross-network transmission method and device and a computer readable medium. A specific embodiment of the method comprises the steps of determining file structure information of a to-be-transmitted file in response to the fact that the to-be-transmitted file passes compliance check; determining whether a target file template exists or not according to the file structure information; performing file content separation on the to-be-transmitted file to obtain a target file; generating a chained invisible watermark according to a template identifier corresponding to the target file template, meta-file information corresponding to the to-be-transmitted file and the target file; embedding the chain type invisible watermark into the file to be transmitted to obtain a watermark embedded file; according to the meta-file information, a ferry server matched with the to-be-transmitted file is distributed through a distribution server; and according to the ferry mode, the file embedded with the watermark is transmitted to an external storage space in a cross-network manner through the ferry server. According to the embodiment, efficient and safe cross-network data transmission is realized.

Described herein are systems, methods, and other techniques for assigning high-performance workloads to compute nodes in a compute infrastructure. The workloads are received at a workload scheduler. Each workload of the workloads includes a workload specification indicating requested resources to run the workload. Server statuses indicating resource usages and availabilities at the compute nodes are generated by a set of server agents running on a set of servers containing the compute nodes. The server statuses are sent from the set of server agents to the workload scheduler. Server assignments for the workloads are generated by the workload scheduler based on the requested resources and the resource usages and availabilities. Compute node assignments for the workloads are generated by one or more of the set of server agents based on the requested resources and the resource usages and availabilities.

The application provides a server distribution method, system and device, a server and a storage medium, and relates to the technical field of computers. The method comprises the following steps: receiving a game distribution request of a first game client sent by a role server; determining game servers with an online player number not reaching a preset player number threshold as multiple idle game servers from multiple game servers; determining a game server with the largest online player number as a target game server from the multiple idle game servers according to the online player numbers of the multiple idle game servers; sending an identifier of the target game server to the role server; the identifier of the target game server is used to instruct the role server to inform the first game client to establish a communication connection with the target game server according to the identifier, so that the target game server executes game logic corresponding to the first game client. The application can improve the game experience of players by improving the aggregation degree of players.

Technologies and techniques for providing specific connectivity quality of service along a route of a vehicle comprises, for at least one vehicle: transmitting route information and a definition of a minimum required connectivity quality of service to a service management server by user equipment of the vehicle; managing and reserving the required connectivity quality of service along the route by allocating communication resources using the service management server, which interfaces with a plurality of mobile network operators; and providing feedback to the user equipment regarding the availability of the required connectivity quality of service. The system includes a service management server and user equipment configured to execute the method. This system ensures specific connectivity quality of service, enabling advanced applications, such as remote driving, by dynamically managing resources based on vehicle routes and operator availability.

The invention discloses a resource adaptive federated learning-based multi-modal knee osteoarthritis auxiliary diagnosis method, which comprises the following steps that: a server initializes a knee osteoarthritis diagnosis-oriented multi-modal model, and pre-deploys a low-rank adaptive plug-in in a trainable layer; after the client reports local hardware resources, the server allocates a resource echelon and issues a two-dimensional cutting proportion; the client performs depth and width cutting on the model based on the cutting proportion, activates a LoRA module of a specified layer, loads local knee joint X-ray images and clinical text data to perform multi-modal training, and establishes semantic mapping of image features and clinical description; the client only uploads and activates the LoRA parameter update quantity, and the server optimizes the global model through weighted average aggregation and key layer compensation; and in combination with real-time resource monitoring and knowledge distillation, the learning ability of the model for key discriminant features of knee osteoarthritis is enhanced. According to the method, heterogeneous computing power equipment is covered through a resource adaptive mechanism, and the participation efficiency of basic medical institutions is improved.

The application designs a self-adaptive mobile edge computing unloading and resource allocation method, mainly including the following steps: a user sends a task unloading request; a mobile edge computing system calculates the best unloading strategy of the task according to the data size of the task, the wireless channel information where the user is located and the available computing resources of each edge server in the mobile edge computing system and notifies the user; the user connects a base station according to the notified unloading strategy and sends a task; after the base station receives the task, the base station sends the task to an edge server connected to the base station, the edge server allocates resources to process the task; the edge server sends the processed result to the base station, and the base station returns the processed result to the user. The application adopts a deep reinforcement learning method, obtains the best task unloading scheme at the current time according to historical system information, and reasonably allocates resources to process the task under the limitation of task delay, so that the consumption of resources is reduced and the utilization rate of the edge server is improved.

The invention discloses a clustering federated learning method based on a neural network model structure, and belongs to the technical field of federated learning. The method aims to solve the problem of model performance reduction caused by data isomerism, system isomerism and data drift in federated learning. According to the technical scheme, the method comprises the steps that S1, a server initializes the same initial model for each client; s2, the client receives the model distributed by the server, after pre-training, pruning is performed on the model by using a model pruning technology, and a pruned model and a model structure mask are generated; s3, the client continues to train the pruned model, and uploads the local model and the mask to the server; s4, the server receives the model and the mask uploaded by the client, detects data drift of the client, and performs clustering through a similarity algorithm or determines whether re-clustering is needed or not according to the consistency of a model structure; the method has the advantages that the accuracy of the model can be remarkably improved in a non-independent identically distributed data scene, and meanwhile, the clustering calculation overhead can be reduced.

The embodiment of the application provides a game server distribution method, system and storage medium, and relates to the technical field of computers, which can be executed by an agent server and comprises the following steps: receiving a distribution request of a GS sent by a target PaaS server, the distribution request of the GS being triggered when a login request of a target client is detected, the target client being any client in at least one client; then checking whether there is idle server information of the GS in a queue, the queue storing the idle server information of the GS; if there is the idle server information of the GS in the queue, obtaining the server information of the target GS from the queue and storing the server information in a preset storage space, so that the target client establishes a connection with the target GS based on the server information of the target GS. The game server distribution method, system and storage medium provided by the embodiment of the application can reduce the waiting time of the client, thereby improving the user experience.

The embodiment of the invention provides a communication method and device, and relates to the technical field of networks, and the method comprises the steps: first network equipment and other network equipment except the first network equipment in a local area network carry out main equipment election; if the first network device is elected as a main device, the first network device is configured with a DHCP server function, and a DHCP server backoff function is started, the first network device detects other DHCP servers; after other DHCP servers are detected, the dynamic IP addresses distributed by the other DHCP servers take effect, and the DHCP server function is closed; and if the other network devices are elected as slave devices, the other network devices are configured with the DHCP server function, and the DHCP server backoff function is started, the DHCP server function is closed, and the DHCP server backoff function is closed. According to the scheme, automatic networking during startup under the condition of multiple gateways can be realized, and zero-configuration opening of a user can be realized.

The invention provides a dyeing method for flow detection, a flow detection method, equipment and a medium, and relates to the technical field of communication. The dyeing method for stream detection comprises the following steps: generating a target data message according to acquired dyeing information and a to-be-detected data message; wherein the dyeing information comprises a data stream field, the data stream field is filled with an equipment identifier of the first detection node and a stream identifier allocated to the first detection node by the server, and the stream identifier is used for identifying a service stream corresponding to the to-be-detected data message processed by the first detection node; and sending the target data message to the second detection node. According to the embodiment of the invention, the method can guarantee the compatibility with an original detection system, does not need to increase extra system overhead, and improves the flow detection efficiency.

The application provides a network proxy system and method divided by regions, the method divides a server into multiple VPS, acquires proxy server IP of different regions, and stores to a database, further provides an interface, when a calling party accesses a site across regions, through fixed interface parameters, acquires the region number of the calling party accessing the site across regions, extracts any one proxy IP corresponding to the region number of the calling party accessing the site across regions from the database as a forwarding server IP, and assigns the forwarding server to the calling party, so that the calling party forwards messages through the forwarding IP. The system and method support the calling party to report invalid proxy IP to trigger a dialing mechanism, can acquire a new proxy IP, well guarantees the operation stability of the calling party, and can bind a fixed proxy IP for the calling party, so that the calling party can reduce the database query when acquiring the forwarding server again, and reduces the occupation of system resources.

The invention relates to a semantic-driven collaborative edge computing double-time-domain caching and computing power collaboration method and system, and belongs to the technical field of mobile communication and edge computing crossing. Aiming at the problems of black box of semantic fidelity, insufficient space coupling among multi-edge servers, difficulty in dual-time scale resource collaboration and the like in the collaborative multi-antenna semantic perception MEC system, the method comprises the following steps of: establishing an uplink MEC system model, and defining semantic perception QoE indexes of coupling semantic similarity and end-to-end delay; according to the method, an optimization problem taking long-term reward maximization as a target is constructed, an SCALE framework is designed, long-term cache placement is optimized by combining a DDPG agent enhanced by a long short-term memory (LSTM) network, and short-term semantic compression, task unloading, server allocation and joint power computing resource optimization are processed by a real-time PPO integrated decision RAPID algorithm. Simulation results show that the method is superior to an existing baseline scheme in the aspects of convergence speed, long-term reward, average QoE and cache switching overhead, and the system performance and the resource utilization rate are improved.

The present disclosure relates to a method and device for implementing a headless browser cluster. The method comprises: assigning a corresponding accessible IP address to each server in a plurality of servers; wherein the plurality of servers form a cluster; installing a headless browser in each server; after receiving a headless browser starting instruction, starting a preset number of headless browser instances, the started headless browser instances forming a headless browser instance pool; after detecting that a headless browser instance acquisition instruction is received, acquiring a target headless browser instance available from the headless browser instance pool; acquiring a network socket endpoint address of the target headless browser instance; and sending the network socket endpoint address of the target headless browser instance to a client.

A System and method for producing K-Net devices and improving a K-Net communication network on which the devices run to provide electronic services. A divided Net Extender extends the wireless network. K-Net devices utilize a modem card as an internal or external modem over Wi-Fi, cellular, millimeter wave and microwave bands. A processor on a circuit board coupled to a G chip or traffic controller chip embedded with a network control protocol and coupled to a dedicated cache memory manages networking of mobile devices and autonomous vehicles from a Local Area Network (LAN) manager device. A geographical internet protocol (Geo IP) enhances security. The modem connects to a wireless router device that runs a card control protocol, wherein the router plugs into the LAN manager device, connecting to internal and external DNS servers, GIP allocation and authentication servers. Devices are self-powered by alternating current generated from a direct current.

The application provides a dynamic load balancing method based on a long connection, relates to the technical field of micro services, and periodically receives a machine running index set uploaded by a service end cluster according to a running index receiving period through a coordinator; the coordinator receives a service end address request of a client, and activates a strategy decision maker in the coordinator according to the service end connection request; a load balancing strategy is obtained by calling in a load balancing strategy set; K groups of machine running indexes in the machine running index set are analyzed and compared to determine a link service end address, and a client and a service end based on a long connection network protocol are fed back to perform RPC calling. The technical problem that in the prior art, when a client server is distributed, the consideration dimension of a load balancing strategy is less, the flexibility of server distribution is weak, and the utilization rate of server resources is not high is solved. The technical effect that while the load balancing of a server cluster is borne, the utilization rate of server resources is better improved is realized.

The application discloses a resource self-adaptive federated learning multi-modal knee osteoarthritis auxiliary diagnosis method, which comprises the following steps: a server initializes a multi-modal model for knee osteoarthritis diagnosis, and pre-deploys a low-rank adaptive plug-in in a trainable layer; after a client reports local hardware resources, the server allocates a resource echelon and issues a two-dimensional clipping ratio; the client clips the model in depth and width based on the clipping ratio, activates a LoRA module of a specified layer, loads local knee osteoarthritis X-ray images and clinical text data for multi-modal training, and establishes semantic mapping of image features and clinical descriptions; the client only uploads an activated LoRA parameter update amount, the server aggregates and optimizes a global model through weighted average aggregation and key layer compensation; and the learning ability of the model to key discriminative features of knee osteoarthritis is strengthened in combination with real-time resource monitoring and knowledge distillation. The application covers heterogeneous computing power devices through a resource self-adaptive mechanism, and improves the participation efficiency of primary medical institutions.

The invention provides a network attack defense method and device, equipment, a storage medium and a program product, and the method comprises the steps: determining at least one target server from a server cluster, wherein the target server is a server bearing a network attack; a plurality of target servers which can form an optimal server combination with the target servers are determined from a server cluster, and the optimal server combination is a server combination which is composed of the servers in the server cluster and has the highest computing power in all server combinations with the target servers; and distributing the plurality of target servers to the target servers to form an optimal server combination, and taking the optimal server combination as a defense server combination when the server cluster is attacked by the network. According to the embodiment of the invention, the optimal server combination can be accurately determined, and the optimal server combination is taken as a defense server combination when the server cluster is attacked by the network, so that the defense capability on the network attack is improved.

The embodiment of the application provides a kind of server updating method, service execution method and related equipment, it is related to communication, internet technology field;It can be applied to system upgrade maintenance, game service and the like scene.Server updating method includes: for the server of authentication version, version update operation is cyclically executed, until all the server of current version is updated to the server of target version;Version update operation includes: in the server cluster of current version, configure the server of authentication version;When receiving the server allocation request based on target service is initiated and determining that there are different versions of servers, the server of high version is determined as target server, and the target server is allocated to execute target service;If the target server is the server of authentication version and the response operation to target service meets the preset requirement, it is determined that the target server passes authentication and is updated to the server of target version.The server is updated without stopping service in the application.

The application provides a multi-MEC dynamic load balancing method based on an SDN controller, comprising the following steps: S1, constructing a network framework and building a coverage layer of the SDN controller; S2, when a vehicle is connected to a road test unit, transmitting an offloading task to a MEC server through the road test unit; S3, uploading characteristic information of the MEC server and the offloading task to the SDN controller; S4, performing offloading task distribution ratio calculation based on the uploaded characteristic information of the MEC server and the offloading task; S5, determining a decision result of the distribution ratio of each MEC server after the distribution ratio calculation is completed; S6, controlling each MEC server to perform offloading and calculation based on the decision result; and S7, dynamically adjusting the distribution ratio of each MEC server in the offloading and calculation process, so that the multi-MEC dynamic load balancing is achieved. The application solves the problem of unbalanced load among MEC servers.

The invention relates to the technical field of multi-task scheduling, in particular to a task collaborative management method and system based on templated workflows, and the method comprises the steps: constructing a target task collaborative template according to a plurality of test workflows, receiving a task collaborative management instruction, determining a target work task set based on the task collaborative management instruction, and constructing a target workflow and workflow data based on the target task collaboration template and the target work task set, performing multiple segmentation on the target workflow based on the workflow data to obtain a target work substream set, and performing server allocation on each target work substream in the target work substream set to obtain a task allocation server set. According to the method, the multi-task execution efficiency and stability can be improved, and resource consumption caused by unreasonable task scheduling is reduced.

This application provides a method and system for energy efficiency perception and load allocation of distributed network acceleration nodes, relating to the field of communication technology. The system inputs the server performance profile characteristics of each service server into an energy efficiency prediction model to determine the Nth estimated energy efficiency of each service server under different loads. The Nth estimated energy efficiency, Nth total service access volume, and Nth existing load of each service server under different loads are then input into a service access volume allocation model to allocate service sub-access volumes to each service server. Based on the Nth actual energy efficiency of multiple service servers, the Nth average actual energy efficiency of the multiple service servers is determined. If the Nth average energy efficiency is less than the (N-1)th average energy efficiency, and the difference between the Nth average energy efficiency and the (N-1)th average energy efficiency is greater than a set difference threshold, a reinforcement learning model is used to update the network parameters of the service access volume allocation model.

The embodiment of the invention provides a resource allocation method and device for terminal equipment, the equipment, a medium and a product. The method comprises the following steps: determining residual available resources in available resources of a server to obtain residual available resources of the server; determining a basic resource priority of the terminal device serving as the server device according to the residual available resources of the server; according to the network state of the terminal device, the variable quantity of the device connection quantity in a preset first time range, the variable quantity of data transmission in a preset second time range, the variable quantity of the number of times of terminal device connection in a preset third time range and a preset weight, the basic resource priority is adjusted, and the service end resource allocation priority is obtained; and reallocating server available resources and client available resources in the terminal equipment according to the server resource allocation priority. According to the resource allocation scheme of the terminal equipment provided by the embodiment of the invention, the resource utilization rate and the efficiency when the terminal equipment realizes own functions can be improved.

The present invention discloses a method for quasi-static deployment of digital twins based on the local characteristics of vehicle mobility. The method includes the following steps: obtaining a gridded map of the area to be deployed, performing a first clustering of the grids in the gridded map to obtain K final first clusters; constructing a target model to obtain an optimization target; selecting one final first cluster as the final second cluster; performing a second clustering of the grids in the final second cluster to obtain multiple final third clusters; generating a server allocation plan corresponding to each final third cluster using simulated annealing and particle swarm optimization algorithms based on the optimization target; selecting another final first cluster as the final second cluster; repeating until all final first clusters are selected; and integrating all server allocation plans to obtain a final server allocation plan. This method can effectively deploy digital twins with low resource overhead and configuration cost.

This application provides a page display method, apparatus, electronic device, and computer storage medium, belonging to the field of cloud security technology. In the embodiments of this application, in response to an operation triggered by a target object to access a target server, the client obtains a target verification identifier corresponding to the target server; wherein, the target verification identifier is obtained by executing an identifier acquisition script file issued by an attack protection device to access a third-party verification server; a first access request containing the target verification identifier is sent to the attack protection device, and after determining that the target verification identifier has passed verification, the attack protection device forwards the first access request to the target server; the response data of the first access request returned by the target server is received, and the corresponding display page is displayed according to the response data. The third-party verification server assigns a unique verification identifier, making it impossible for malicious software tools to forge the unique verification identifier configured by the third-party verification server, thereby further improving the security of accessing the server.

The invention discloses a server scheduling method and system based on feature analysis of sequencing data. The method comprises the following steps: acquiring cfDNA sequencing data to be subjected to cancer prediction and corresponding patient parameters; based on a feature analysis algorithm, analyzing data feature parameters corresponding to the cfDNA sequencing data; predicting the prediction processing difficulty corresponding to the cfDNA sequencing data according to the patient parameters and the data characteristic parameters; determining a target server for executing a prediction task from a plurality of candidate prediction servers according to the prediction processing difficulty; and the target server is used for receiving the cfDNA sequencing data and executing a cancer prediction task based on a built-in algorithm model. Therefore, accurate server allocation based on feature analysis and processing difficulty evaluation can be realized, the calculation efficiency and prediction accuracy of a cancer prediction task are improved, and the risk of prediction delay or resource waste caused by improper server selection is reduced.

The application provides a load balancing method and system for an Internet of Things edge terminal. The load balancing method comprises the following steps: acquiring all devices for uploading data in the Internet of Things; classifying the data uploaded by the devices in units of devices to obtain data categories; summarizing the data generated by all devices in units of data categories, and acquiring the data volume corresponding to each data category and a data category set; distributing servers according to the data volume and the data categories to obtain the servers corresponding to each data category; and sending data to the corresponding servers according to the corresponding relationship between the data categories and the servers. The system comprises modules corresponding to the method steps.

The embodiments of the present disclosure disclose a method, device, and computer-readable medium for transferring files across networks. A specific implementation of the method includes: in response to a file to be transferred passing a compliance check, determining the file structure information of the file to be transferred; determining whether a target file template exists based on the file structure information; separating the file content of the file to be transferred to obtain a target file; generating a chain invisible watermark based on the template identifier corresponding to the target file template, the metafile information corresponding to the file to be transferred, and the target file; embedding the chain invisible watermark into the file to be transferred to obtain a watermark-embedded file; allocating a ferry server that matches the file to be transferred through an allocation server based on the metafile information; and transmitting the watermark-embedded file across the network to an external storage space through the ferry server based on the ferry mode. This implementation achieves efficient and secure cross-network data transmission.