123 results about "Multi server" patented technology

The invention discloses an unloading strategy and resource scheduling combined method based on the Internet of Vehicles. The method comprises the following steps: constructing a multi-server and multi-user vehicle edge computing system; constructing a communication model according to a vehicle edge computing system; constructing a calculation model; establishing a joint optimization problem model by taking balance of time delay and energy consumption and maximization of task success rate as targets; and solving the joint optimization problem model by using a grey wolf optimization algorithm based on chaotic mapping and differential mutation and a resource allocation algorithm based on delay balance to obtain a task unloading strategy set and a resource allocation strategy set. According to the unloading strategy and resource scheduling combined method based on the Internet of Vehicles, the problems that in the prior art, the task success rate is low due to uneven resource allocation in a multi-server environment, and single target optimization in a dynamic environment is difficult to meet the real-time performance and energy efficiency requirements at the same time are solved.

The invention relates to the technical field of computers, in particular to a GPU cross-server interconnection system which comprises M servers and N switches, the mth server am comprises Q GPU chips, the Q GPU chips contained in any server are fixedly connected, the nth switch bn is connected with the s (n) servers, the s (n) servers are connected with the M servers, and the N switches are connected with the M servers. The s (n) servers having the connection relation with the bn form a server group corresponding to the preset topological relation. According to the system, when the server or the GPU chip breaks down, only the fault server needs to be processed, the maintenance cost is reduced, the interconnection relation of the GPU chips among multiple servers can be configured through the switch, high-bandwidth interconnection server groups with different capacities are formed, the service requirements are flexibly adapted, the flexibility of a GPU interconnection architecture is improved, and the system is suitable for large-scale popularization and application. While the high interconnection bandwidth is maintained, the switch cost and interconnection lines are saved, and the complexity of switch routing and design is reduced.

The invention discloses a GPU Box hardware decoupling system based on a Retimer card and a PCIe Switch chip, and belongs to the technical field of computer hardware architecture and high-speed interconnection. According to the system, a Retimer card and a PCIe Switch chip are integrated in an independent GPU Box, and a decoupling link of a CPU server and a GPU acceleration card is constructed; the Retimer card realizes 30-meter long-distance PCIe signal transmission and breaks through physical distance limitation; the PCIe Switch chip pools GPU resources through a dynamic routing and MRIOV technology, supports flexible allocation of computing power by multiple servers, and realizes Peer-to-Peer direct connection communication between GPUs. Aiming at a large model reasoning scene, the system optimizes KV cache bandwidth allocation and video memory and memory cooperative scheduling, so that the 100B parameter model reasoning throughput is greatly improved; and meanwhile, the usability of the system is greatly improved through fault isolation and hot plug design. According to the method, the problems of physical binding of the CPU and the GPU, limited transmission distance, rigid resource allocation and the like in a traditional architecture are solved, and the method is suitable for large-scale AI calculation and distributed GPU cluster deployment.

The invention relates to the technical field of liquid cooling flow distribution, and discloses a multi-GPU server cluster liquid cooling flow distribution method based on reinforcement learning, and the method comprises the steps: S1, collecting the operation state parameters of each GPU in a multi-GPU server, enabling the operation state parameters to comprise core temperature, voltage, current and load utilization rate, carrying out the thermal behavior modeling through a sliding time window, generating a thermal dynamic feature vector representing the GPU short-time thermal trend; and S2, collecting the flow velocity, the temperature difference of inlet and outlet water and the thermal resistance of the cold plate of each channel of the liquid cooling system in real time, and representing the liquid cooling assembly as a graph structure. According to the method, the technical scheme of joint modeling based on the graph neural network and reinforcement learning is adopted, fusion coding is performed on the GPU thermal dynamic characteristics and the topological state of the liquid cooling system, and an Actor-Critic architecture is introduced to realize self-adaptive regulation and control of the liquid cooling flow, so that the technical effect of improving the heat dissipation efficiency and the energy consumption balance control capability of the multi-GPU server cluster is achieved.

The invention discloses an artificial intelligence content generation task multi-server collaborative optimization method based on deep reinforcement learning, and relates to the crossing field of edge computing and artificial intelligence. According to the method, a user equipment-base station-edge server-core network four-layer architecture is constructed, distributed decision is realized through a deep Q network model, and the method comprises the following steps: defining a task and time delay model; a deep reinforcement learning framework is designed, a decision model is constructed through a state space, an action space and a reward function, and training stability is improved by adopting experience playback and a target network slow update mechanism; and providing an adaptive multi-server selection and load distribution strategy, and solving an optimal distribution proportion based on a time delay equality principle. According to the method, the average unloading time delay of the AIGC tasks is remarkably reduced, the task failure rate is reduced, the dynamic environment adaptability and the extreme scene robustness are improved, and the method is suitable for scenes with strict requirements for time delay and reliability.

The invention provides a satellite-borne computing cluster design scheme with an automatic operating system repairing function. According to the scheme, the heartbeat information and serial port output data of each computing node are synchronously monitored, the operation state of the operation system is accurately judged, node faults are diagnosed, meanwhile, an automatic operation system reinstallation mechanism is configured in the scheme, once the faults are detected, the control node automatically triggers the operation system reinstallation process of the fault computing node, and the operation system reinstallation efficiency is improved. In addition, in order to ensure the reliability of the repair operation, the scheme adopts a multi-PXE server redundancy mechanism, and a plurality of PXE servers are deployed to ensure that the system can still quickly acquire a boot file and a mirror image file from other servers even if part of the servers fail, so that a computing node can accurately acquire required resources, and the reliability of the repair operation is improved. And a solid foundation is laid for stable operation of a satellite-borne computing cluster.

A system for recommendation and optimization of information technology (IT) server resources is disclosed. The system includes a server comprising at least one processor configured to access input datasets associated with an IT workload, determine types and counts of server resources based on predefined criteria and a server consolidation configuration, and access a multi-server performance database. The processor utilizes a trained deep learning model to predict infrastructure requirements and generate recommendations for an optimal server configuration by balancing performance, power consumption, and resource utilization. The processor automatically allocates server resources from multiple manufacturers based on the recommendations and generates data for display on a user interface dashboard. The dashboard presents server utilization patterns, recommended configurations, and real-time performance metrics of allocated resources. The system enables intelligent consolidation and efficient server management within a datacenter environment.

The invention provides a federal learning method and device based on zero knowledge proof, and the method comprises the steps: obtaining a training task from a task publishing server through a communication address, updating the model parameters of a to-be-trained model according to local privacy data, and obtaining a first target parameter, according to the first target parameter and an updating model parameter of a matching training server, performing aggregation through a task publishing server, and performing repeated iteration until an aggregated model is converged; receiving a random round set, screening model parameters, and performing consistency verification through a task publishing server; meanwhile, local privacy data and initial model parameters are screened, correctness of data traceability, parameter iteration and model training processes is verified through a task publishing server based on zero-knowledge proof, and training is determined to be completed under the condition that verification is passed. According to the method, the problems of relatively low model training efficiency and slow updating caused by multi-server federated learning based on zero knowledge proof in the prior art in order to ensure data security are solved.

A user equipment (UE) for wireless communication includes one or more non-transitory computer-readable media having computer-executable instructions embodied thereon, at least one processor coupled to the one or more non-transitory computer-readable media, and configured to execute the computer-executable instructions to: detect, by a multiple-universal subscriber identity module (multi-USIM) application of the UE at a Mobile Terminal (MT) level, a presence of a first USIM belonging to a first core network and a second USIM belonging to a second core network, and send an Internet Protocol (IP)-based multi-USIM registration message to an IP-based multi-USIM server or Application Function (AF), where the IP-based multi-USIM registration message comprises at least one of an indication of the presence of the first and second USIMs, information pertaining to identification, connectivity and preference information of the UE, and capabilities associated with the first and the second USIMs.

The invention relates to a server configuration management system based on a Redfish protocol, and the system comprises an API interface layer which is used for providing an access interface for the outside; the Redfish equipment layer is used for realizing remote management of a server through a Redfish protocol, and the Redfish equipment layer is connected with the API interface layer; and the management control layer is used for carrying out comprehensive management on configuration, faults, compatibility and firmware conditions of the multi-server cluster, and the management control layer is connected with the Redfish equipment layer. Through Redfish API, AI intelligent analysis, automatic management, security authentication and other technical means, a set of efficient, intelligent and safe server remote management scheme is constructed, the operation and maintenance efficiency is effectively improved, and the management cost is reduced.

The invention provides an electric power system monitoring data cloud unified access and resource intelligent prediction system and method based on cloud edge collaboration. The system is provided with a unified access module, an alarm rule configuration module, a real-time alarm module, a real-time monitoring display module and an intelligent trend prediction and health degree evaluation and maintenance module. According to the system and method, Java language development is adopted, the system and method serve as a central platform to receive and manage monitoring data from a plurality of lower-level power platforms in a unified mode, and standardized data unified access, real-time multi-dimensional monitoring, intelligent trend prediction, dynamic alarm management and system health degree evaluation and maintenance of a CPU, a memory, a hard disk, network resources and middleware services are achieved; the method is very suitable for a power system, can also be widely applied to other industries needing high reliability and real-time performance, and effectively solves the technical problems that monitoring data of multiple servers cannot be managed in a unified mode, the resource prediction precision is low, and system fault response lags behind.

The invention discloses a processing method for exporting partition table data, which is used for solving the technical problems of low cross-partition query efficiency, inconsistent file size and lack of concurrent processing capability in the prior art. The method comprises the following steps: firstly, reading configuration information from a task detail table to generate an export task, counting the data volume of each partition, and distributing data to an output file with a fixed line number according to a sequential filling strategy; an innovative coding scheme is adopted to record a distribution result, different partition data containing conditions are represented through characters'-'and'. 'and numbers, and compressed coding is carried out on continuous empty partitions; sending a distribution result to a message queue to realize multi-server concurrent processing; the consumer decodes the distribution information and then executes restrictive query according to a partition sequence, and data is exported concurrently by using streaming query in cooperation with multiple threads; and a perfect state monitoring and exception recovery mechanism is established, and an MD5 verification file is generated to ensure data integrity. According to the method, the processing efficiency of partition table data export and the system reliability are effectively improved.

The application provides a computer system and a multi-server shared storage system, which can be applied to the technical field of server storage. The computer system comprises: a computer system, a non-volatile memory and a plurality of servers which are electrically connected; an arbitration module is used for determining a target server according to a plurality of access signals of the plurality of servers, and sending an authorization signal to the target server and a bus buffer module; the bus buffer module is used for connecting a transmission bus between the target server and the non-volatile memory according to the authorization signal; in the case that a chip select signal in a flash signal is at an effective level, an access address is obtained from the flash signal, the access address is configured by using a configuration partition base address to obtain a target access address, signal conversion is performed on the target access address, a target flash signal is obtained and sent to the non-volatile memory, so that the non-volatile memory manages data stored in a storage partition corresponding to the target access address.

The disclosure relates to a method of applying a heat sink to a semiconductor package, a method of maintaining a processor module in a server, and a data center system. One or more sensors are integrated into a processor module that includes a semiconductor package and a heat sink. There are multiple processor modules in the server, and data from each processor module is sent to a digital integration monitoring system, which may be part of a data center hardware monitoring system. The server may be one of a number of servers located in the data center. The data may also be sent to a remote data center maintenance center that simultaneously monitors multiple data centers. When compared to different criteria, the data may be used to determine in real time whether a given processor module needs maintenance.

A multi-server secure aggregation system and method based on homomorphic chameleon hashing utilizes a bulletin board system maintained by a trusted third party to output, download, and collect data. Public-key encryption and secret sharing ensure data security. Homomorphic chameleon hashing is used to verify aggregation and ensure integrity. This creates a non-interactive secure aggregation model that eliminates the need for secure channels between clients and multiple servers. This system boasts a simple, easy-to-implement architecture, is secure and verifiable, and further improves communication efficiency.

The invention relates to a voice control method and system applied to multiple servers and a storage medium, and the method comprises the steps: U1, enabling a user to carry out voice input, obtaining the data information of voice sequences of the users of multiple groups of servers in real time based on a voice receiving sensor of the multiple servers, carrying out the preprocessing and feature extraction, and obtaining a voice sequence of the user; obtaining data information of the feature vectors of the voice sequences of the users of the multiple groups of servers; and U2, based on the data information of the feature vectors of the voice sequences of the users of the multiple groups of servers, optimizing the feature vectors of the voice sequences of the users by adopting an improved whale optimization algorithm to obtain the optimized data information of the feature vectors of the voice sequences of the users of the multiple groups of servers. According to the invention, the input voice can be accurately processed, the quality of the output voice is improved, the voice received by multiple servers can be processed at the same time, and the operation efficiency of the system is improved.

The application discloses a kind of unmanned plane identity anonymization and safe data outsourcing method and system, method is based on integrated supervision platform and unmanned plane, including system initialization stage, integrated supervision platform generates main private key and public parameter;User anonymous and private key generation stage, unmanned plane registers anonymous identity and obtains user private key;Data preprocessing and outsourcing stage, unmanned plane divides data into block and sub-block and encrypts, generates coding vector using erasure code coding, generates verification label for each coding data block, constructs random distribution table according to the number of cloud server, distributes coding vector and label to multiple cloud server storage.In this way, the unmanned plane identity anonymization and safe data outsourcing method described reduces storage redundancy, ensures that data can be reconstructed when multiple servers fail, and guarantees data availability and robust recovery.

The invention provides an SM9-based client-multi-server rapid self-adaptive key negotiation method. The SM9-based client-multi-server rapid self-adaptive key negotiation method mainly comprises the following steps: an initialization stage, a private key extraction stage, a broadcasting stage, an offline preparation stage, an online response stage and a session key generation stage. The method has the following characteristics: 1, the method is suitable for a client-multi-server scene, a client initiates a communication request, a plurality of servers are automatically adapted to communicate with the client according to states such as self load and communication delay, and a key negotiation process only needs three times of communication; 2, aiming at a client-server communication mode, computing load optimization is carried out; different from the traditional P2P key negotiation with the same calculation amount of both parties, the method migrates the calculation overhead to the server side as much as possible; and 3, the client does not need to carry out complex bilinear pairing operation, and particularly, the calculated amount in an online response stage can be almost ignored. And meanwhile, an online / offline optimization technology is adopted, so that the client can carry out pre-calculation before communication, and the online response efficiency is improved.

The present invention discloses an optimization method for fixed-path AGV task offloading in the industrial Internet of Things environment. Based on the traditional model-free reinforcement learning method with value function update, the present invention optimizes the task offloading scheduling problem in the scenario of AGV assisting edge computing. On this basis, a load balancing algorithm and an improved DQN algorithm are combined. Finally, under the constraints of task processing time sensitivity, path, efficiency, etc., the problem of the shortest optimal offloading processing time of multiple AGVs and multiple server nodes in the Internet of Things environment is realized. The method of the present invention does not require too much prior knowledge, and the task offloading transmission in the short distance meets the requirements of data security. Moreover, the present invention has good reusability in similar application scenarios, and the practical value of the invention is relatively strong.

The present invention discloses a multi-server data transmission method applicable to vehicle networking to support user identity hiding, which is characterized by the following steps: 1) defining the data transmission system structure; 2) system initialization; 3) data processing; 4) credibility judgment of the sender; 5) traffic obfuscation and path obfuscation. This method can prevent third parties from conducting traffic analysis, injection attacks, and timing attacks, reduce data transmission errors caused by network reasons when re-establishing a new link in case of data link congestion, improve the stability during the transmission process, and prevent malicious sending during the communication process.

The invention provides a privacy protection method and device for preventing collusion of multiple servers, equipment and a medium, and the method comprises the steps: determining a first privacy protection conversion strategy corresponding to a data type according to the data type of disturbance output under a privacy protection disturbance mechanism; obtaining a first noise value corresponding to the first privacy budget according to the original data and conversion information corresponding to the first privacy budget; obtaining a second noise value corresponding to a second privacy budget according to the first noise value and a first privacy protection conversion strategy; determining a second privacy protection conversion strategy corresponding to the data type according to the disturbance output data type; according to the original data, the second noise value and the second privacy protection conversion strategy, a third noise value corresponding to the first privacy budget is obtained, the purpose that noise data corresponding to the claimed privacy budget can be obtained by achieving data collection of any server through data multiplexing is achieved, and therefore extra privacy budget consumption is avoided.

The present invention discloses a multi-server-based traffic planning statistics data linkage retrieval system and method, which relates to the technical field of traffic planning management. The system includes a collection client, a collection service cluster, a linkage distribution server, a storage service cluster, a search service cluster and a retrieval terminal. By separating the storage and retrieval of big data and constructing a composite word segmentation of traffic planning statistics data, the performance and speed of big data range search and accurate search are improved; by linking the distribution server dimension extraction, spatiotemporal convergence and control forwarding, data linkage is achieved between the two, and the characteristics of the two types of databases are fully combined, which greatly improves the system retrieval performance and enhances the user experience. The present invention constructs a connection relationship between multiple hardware servers, that is, constructs a hardware carrier of a statistical data linkage retrieval system, so that the hardware carrier assists the relevant software function to achieve the purpose of rapid retrieval, providing convenience for relevant staff.

The utility model discloses a computing power server, which comprises a mainboard, a display card and a case, the mainboard is horizontally arranged at the bottom of the case, the display card is vertically fixed on the upper surface of the mainboard, the plane of the display card is vertical to the plane of the mainboard, the case is of an L-shaped structure, the mainboard is accommodated at the bottom of the L-shaped structure of the case, and the mainboard is fixed on the upper surface of the case. The vertical part of the L-shaped structure accommodates a display card, and the two cases form an integrated unit when stacked; according to the server structure, the display cards are vertically arranged on the main board and form the L-shaped case in appearance instead of being arranged in a traditional parallel stacking mode, the stacking mode is adopted, the overall width of the server module is remarkably reduced, and compared with a traditional square module layout, the structure allows more server modules to be contained, and the cost of a machine room is reduced.

The present invention relates to a distributed audio and video scheduling method, which includes the following: pre-establishing an audio and video scheduling network constructed by multiple servers, with each server serving as a distributed node of the audio and video scheduling network; obtaining the audio and video tasks of the user, and parsing the task content in the audio and video tasks; determining the distributed nodes that can perform the tasks in the audio and video scheduling network based on the task content, recorded as available distributed nodes; calculating the server load status of the available distributed nodes; selecting the preferred distributed nodes from the available distributed nodes based on the server load status; and performing the tasks through the preferred distributed nodes to complete the audio and video tasks. The present invention dynamically evaluates the load conditions of the distributed servers in combination with the program occupancy rate of the central processing unit and the amount of audio and video data processed, and then distributes the tasks of audio and video generation to multiple computing nodes or servers for parallel processing, thereby achieving more efficient and flexible audio and video generation.

The invention discloses a power management method which is executed by a BMC (baseboard management controller) built in a main server in a multi-server system, the multi-server system comprises a plurality of servers and a power management subsystem connected with the servers, the power management subsystem comprises a plurality of power management units connected in parallel, and a selective switch is arranged on a parallel branch where the power management units are located. The multi-server system comprises a master server and a plurality of slave servers, and the method comprises the steps of appointing each acquisition time point for acquiring data after determining that the master server and a BMC (Baseboard Management Controller) in each slave server complete time standard unification; and receiving the real-time power consumption information of each slave server acquired by the BMC in each slave server, performing information grouping on the real-time power consumption information of each slave server according to the acquisition time point, and performing on-off control on the selection switch on the parallel branch where each power management unit is located according to the grouping condition of the real-time power consumption information. And the flexibility, the reliability and the intelligent level of power supply management of the multi-server system are improved.

Techniques are described for managing access of executing programs to non-local block data storage. In some situations, a block data storage service uses multiple server storage systems to reliably store copies of network-accessible block data storage volumes that may be used by programs executing on other physical computing systems, and snapshot copies of some volumes may also be stored (e.g., on remote archival storage systems). A group of multiple server block data storage systems that store block data volumes may in some situations be co-located at a data center, and programs that use volumes stored there may execute on other computing systems at that data center, while the archival storage systems may be located outside the data center. The snapshot copies of volumes may be used in various ways, including to allow users to obtain their own copies of other users' volumes (e.g., for a fee).

This application discloses an authentication method and system under a multi-server architecture. When a user registers with a registration server, the registration server generates a temporary identity identifier (TID) for the user. U When the user needs the service provided by the application server, enter the TID on the client. U The authentication user identity information γ0 can be calculated, and the client sends the set M0 containing γ0 to the application server. The application server generates relevant authentication information and sends the authentication information and M0 to the registration server. The registration server completes the identity authentication of the user and the application server. The authentication of the user and the application server is completed on the registration server. Each application server does not need to store any information about the user, which reduces the resource burden of the application server. In addition, the user only needs to register with the registration server to obtain a temporary identity to complete the authentication and key exchange. There is no need to register with each application server in the multi-server architecture, which saves time and cost.

A multi-server parallel computing static encrypted graph shortest path search method comprises a preprocessing algorithm, a graph encryption algorithm and a shortest distance / path query algorithm. An encrypted graph adjacency linked list is stored in a data encryption algorithm, an application-driven graph segmentation algorithm is adopted for the designed encryption algorithm, a logarithmic factor is added for a graph segmentation device according to theoretical analysis of the algorithm to further improve the segmentation device, overhead parameters are obtained by using deep learning, and the graph adjacency linked list is obtained. And finally, a higher-efficiency segmentation scheme for the graph under the shortest path is realized. The method has the beneficial effects that a Dijkstra loss function in a ciphertext state is obtained through analysis by using a deep neural network and a graph calculation theory, and the function is used for carrying out hybrid segmentation processing on a graph; by utilizing a multi-server parallel technology, the execution speed of the shortest distance query on the encrypted graph is improved, and the pre-calculation overhead is reduced compared with the prior art while the efficient query is ensured.

The application discloses a kind of operation and maintenance task automatic running method and system;Script operation and maintenance environment is dependent on risk high and complex task scheduling inefficient, the present application obtains low code arrangement feature, Playbook structure feature and host load parameter is merged and fused to obtain automatic operation and maintenance feature, and obtains server certificate execution method, utilizes automatic operation and maintenance feature and server certificate execution method to build certificate execution operation and maintenance automation model.The present application considers low code arrangement feature, Playbook structure feature and host load parameter to build multi-parameter correction neural network model, more Playbook structure feature data is generated by reinforcement learning method, so that certificate execution operation and maintenance automation model is more accurate and intelligent to obtain server certificate intelligent execution method, while task runs in container sandbox to isolate risk, combined with the self-recovery mechanism that drag-and-drop flowchart parameter generates executable script, applied to the intelligent batch execution of multiple server certificate update and avoids fault node.

The invention discloses a high-throughput split federated learning method based on an activated cache. The method comprises the following steps: carrying out system initialization and network structure variable definition; performing client parameter synchronization, local feature extraction and independent parameter updating; performing activation feature drift asynchronous calculation and maximum old-age window updating; performing double-branch independent training of the server side based on hybrid retrieval; global federal aggregation based on data volume weighting is carried out; and performing a multi-server integrated reasoning test of the global model. Through an innovative drift sensing activation reuse mechanism and a multi-GPU communication-free integrated training strategy, the throughput performance of the split federated learning system in heterogeneous equipment and a non-independent identically distributed data environment is remarkably improved.