225 results about "Server load" patented technology

The invention discloses a railway intelligent construction site safety penetration type management messenger platform which comprises a multi-modal data fusion processing module, an edge computing node cluster module, a three-dimensional visual penetration type management interface module, an intelligent early warning and emergency response module, a self-adaptive network transmission module and the like. Real-time cleaning, alignment and correlation analysis are realized by using a dynamic data calibration algorithm, and a data island is broken; the edge computing node cluster carries out localization preprocessing and the like on data in a key area, so that the load of a central server is reduced; the three-dimensional visual interface is based on a digital twinborn construction model, supports drilling type viewing and realizes three-dimensional monitoring; the intelligent early warning system adopts a reinforcement learning model to automatically trigger multi-channel early warning; and the adaptive network transmission module dynamically switches communication modes to ensure low-delay transmission of key data. The platform realizes real-time acquisition and integration of construction site data and reduces manual intervention.

The invention belongs to the technical field of data centers, discloses an energy efficiency evaluation method of an artificial intelligence data center, and solves the problem that a static evaluation method is disjointed with the real-time operation state of the data center in the prior art by constructing a dynamic feature library and an AI adaptive weight model. The dynamic feature library collects and associates service scene labels and time-space coupling data in real time, and provides a scene basis for an AI adaptive weight model, so that weight adjustment is converted from passive response index mutation in the prior art to active adaptive service scene change. Nonlinear influences of dynamic factors such as server load fluctuation and environment adjustment on energy efficiency are effectively captured, so that an energy efficiency evaluation result better fits the actual operation state of the data center, energy efficiency factor relevance changes caused by service switching of the AI data center are adapted, scene-based self-adaptive adjustment of weights is achieved through a deep learning model, and energy efficiency evaluation accuracy is improved. The limitation that the fixed weight distribution ignores the difference of different service scenes in the prior art is solved.

The invention discloses a distributed server load balancing system based on multi-modal data fusion. The distributed server load balancing system comprises a multi-modal sensing layer, a fusion analysis layer, an intelligent decision-making layer and an elastic execution layer, the multi-modal sensing layer collects multi-source heterogeneous data; the fusion analysis layer is used for mining an association relationship between data of different dimensions; meanwhile, converting multi-source data into a unified feature vector, and outputting a dynamic load balancing strategy; the intelligent decision-making layer generates an executable load balancing strategy according to the fusion analysis result; and the elastic execution layer converts the strategy generated by the intelligent decision-making layer into an actual action, and deals with load change through an elastic mechanism. According to the method, server performance, network topology and request semantic data are fused, a unified feature tensor is generated by using a cross-modal attention mechanism, and load association is accurately quantified; on the basis of cooperation of a global mixed integer programming model and a local lightweight DQN agent, load migration is completed in a very short time, and the response speed is increased.

The invention provides a component-level collaborative editing control method, which is applied to a multi-user collaborative editing control system, and the system comprises a plurality of front-end engines and a back-end server. A front-end engine obtains user editing operation data and a rear-end real-time version snapshot, component structure difference comparison is carried out, and a component operation list is generated; and performing component conflict judgment based on the operation list, merging and editing operation data according to a conflict judgment result and a preset conflict resolution strategy, generating processed version snapshots, sending the processed version snapshots to a rear end, storing the processed version snapshots by a rear-end server, and sending the processed version snapshots to each front-end engine. Compared with the prior art, the method has the advantages that the data transmission quantity and the load of the rear-end server are reduced by a lightweight architecture through the front-end component level difference comparison and the preset conflict resolution strategy, the rear-end server does not need to be frequently requested for conflict resolution and data merging, the response speed and the collaborative editing efficiency are remarkably improved, and the user experience is improved. And better user experience is brought to the user. The system has the same beneficial effects.

The invention discloses a server temperature monitoring and regulating method and electronic equipment, and relates to the technical field of servers. According to the scheme, the sensor group comprising a plurality of different types of temperature sensors is used for monitoring the temperature of each component in the server, so that the measurement error is obviously reduced; meanwhile, the working temperature interval of the component compared with the measured temperature value is not a fixed interval, but is dynamically adjusted according to the change of the load in the operation process of the server, so that the optimal heat dissipation effect is realized during temperature regulation and control; when it is confirmed that temperature regulation and control need to be carried out, the target cooling liquid flow is determined through a pre-constructed cooling liquid flow prediction model, the model is specifically constructed based on the average temperature of all parts of the server, the heat dissipation power and the server load, the optimal cooling liquid flow can be predicted to achieve energy consumption minimization of a pump set, heat dissipation energy consumption is effectively reduced, and the service life of the pump set is prolonged. And comprehensive optimization of the heat dissipation effect is realized.

The invention discloses a data center low-carbon control method and system, and relates to the technical field of data centers, and the method comprises the steps: collecting the environment parameters and equipment operation data of a data center in a hot-summer and warm-winter region in real time based on a multi-mode sensor network; constructing a climate-adaptive space-time diagram convolutional network, taking a server rack, a cooling tower and a power distribution cabinet as nodes, taking a cold and hot flow path as a dynamic weighted edge, embedding climate feature vectors, and generating a thermal load prediction map; a dynamic refrigeration strategy and a server load migration strategy are generated by taking a thermodynamic equation and a fluid dynamic model as strategy optimization boundary conditions through a physical constraint reinforcement learning framework and combining a thermal load prediction map; and based on the dynamic refrigeration strategy, the air conditioning system is controlled to start a hybrid refrigeration mode according to the real-time climate type, the water cooling main cycle and indirect evaporative cooling standby loop are synchronously operated, and the cold charging and discharging logic of the chilled water storage system is switched according to the electricity price time period.

The invention discloses a state grid cloud-based cloud service deployment method and system, and relates to the field of cloud service deployment, and the method comprises the steps: collecting service types needing to be deployed in a power grid, analyzing the use demands of tenants, and evaluating the server performance demands of the service types; installing a Ceph distributed storage framework on a plurality of storage nodes of the state grid cloud platform; the method comprises the following steps of: disassembling a service category into a plurality of independent micro-services, packaging each micro-service in an independent container through Docker, and performing automatic management on the container by using a Kubernetes container arrangement tool; docker is used for achieving multi-tenant isolation, a dynamic resource scheduling mechanism is introduced, and the number of container instances is adjusted according to changes of server loads and use requirements of the tenants; and testing the multi-tenant micro-service container environment, and performing online deployment on a state grid cloud platform after the test is passed. According to the method, the Docker is adopted to realize multi-tenant isolation, and a dynamic resource scheduling mechanism is introduced, so that efficient, safe and extensible cloud service deployment is realized.

The invention discloses a secure cloud workstation and a resource scheduling method suitable for a high-altitude weak network environment, and the secure cloud workstation comprises a virtualization management platform which is used for carrying out the virtualization processing of core computing components of a plurality of servers contained in a back-end server cluster, and forms a plurality of virtual hosts; the front-end thin client only reserves display and input functions, establishes connection with the virtualization management platform, receives a virtual host human-computer interface pushed by any virtual host in the virtualization computing resource pool, and feeds back a user operation instruction to the virtualization management platform; the transmission protocol optimization module is used for integrating graph, input and audio data transmission channels, monitoring the network bandwidth and the delay state of a high-altitude area in real time by adopting a self-adaptive compression algorithm, and dynamically adjusting the transmission image quality and the frame rate of a virtual host human-computer interface; and the resource scheduling module is used for automatically allocating computing resources to the plurality of virtual hosts based on the load data of the virtualization management platform so as to realize cross-server load balancing.

The invention provides a method for constructing a container cloud agent based on a MiniMax-Text-01 large model, a medium and equipment, belongs to the technical field of automatic operation and maintenance, and can perform fault detection, flow prediction and resource elastic expansion and contraction according to real monitoring data. The intelligent agent constructed by the invention can automatically pre-judge and give an optimal scheme for capacity expansion or migration when finding that the load of the server is abnormal, so that the frequency and the duration of manual intervention are reduced. In a multi-cloud and hybrid cloud architecture, an intelligent agent can cooperatively manage calculation, storage and network resources on a heterogeneous platform, and cross-cloud load balancing and cost optimization are realized. In addition, the intelligent agent can help a data analysis and machine learning team to better utilize a cloud GPU / TPU cluster and automatically select a proper computing power and storage scheme, so that the training and prediction efficiency of mass data is remarkably improved. In the scene of Internet of Things and edge computing, the intelligent agent can link the cloud and the edge nodes, intelligently dispatch the computing power and bandwidth of each node, and find and correct abnormal data streams in time.

The invention discloses a server energy consumption control method, system and device, a storage medium and a program product, and relates to the technical field of server management.The method comprises the steps that system index data of a server is input into a prediction model, and a dynamic first threshold value and a dynamic second threshold value are predicted through the prediction model; if the total load data of the server is smaller than the first threshold value, the first target device is controlled to enter an energy-saving state; and if the total load data is greater than the second threshold, controlling the second target equipment to enter a working state. The problems that the power supply is frequently switched, the fault risk is increased and the energy-saving efficiency is low due to server energy consumption management can be solved. According to the method, the load state of the server is judged according to the two threshold values, whether the energy-saving operation is carried out or not is determined, intelligent optimization of the energy consumption of the server is achieved, and the energy-saving efficiency is improved. In addition, the weight coefficient of the prediction model enables the two threshold values to be automatically adjusted according to the actual operation condition of the server, and the method is suitable for different service load modes and system states.

The invention provides a server load balancing method and device, a medium and a program product, and a specific implementation mode of the application comprises the steps that after a load balancer receives a task request, attributes such as basic types (authentication, authorization and charging), priorities and protocol versions of the request are analyzed firstly; meanwhile, the resource utilization rate, response time, health state and other information of the server cluster are monitored in real time, a dynamic strategy decision tree is constructed based on the data, in addition, the load balancer sends a health check request to the server regularly, whether the server breaks down or not is judged according to the state information returned by the server, and the service life of the server is prolonged. And if a fault exists, removing the fault from the request allocation list to ensure that the request is always allocated to the available server. According to the method, the dynamic adjustment of the load balancing strategy and the update of the request allocation list are realized, and the resource utilization rate of the server is improved so as to cope with the rapidly changed request load and flow mode.

The invention discloses a Web end video monitoring distributed decoding method and system, and belongs to the technical field of network video playing. The method comprises the following steps of: actively initiating WebSocket connection by a browser end through a local loopback address port (127.0. 0.1: 19022), and detecting whether a distributed decoding program is installed in a user terminal or not; calling a local FFmpeg library by a distributed decoding program, decoding a video stream in real time by utilizing a terminal CPU (Central Processing Unit), and converting decoded video key frames into an image format originally supported by a browser frame by frame through OpenCV; the distributed decoding program packages binary data of each frame of image and an address identifier of the binary data into a WebSocket message, the WebSocket message is actively pushed to the browser, and the browser refreshes and displays the WebSocket message in a specified area of a page in real time according to the address identifier; according to the method, zero-plug-in, low-delay, cross-platform, safe and controllable Web end video decoding is realized, the server load and the network delay are remarkably reduced, and the user experience and the system compatibility are improved.

The invention relates to the technical field of edge computing resource management, in particular to an edge server load evaluation method based on analytic hierarchy process and performance loss. According to the method, firstly, edge servers are divided into a common type, a computing type, a memory type and an I / O type according to heterogeneous types; aiming at each type of edge server, constructing a judgment matrix by adopting an analytic hierarchy process and solving a feature vector so as to calculate load weight coefficients of CPU (Central Processing Unit), memory and disk I / O (Input / Output) resources; calculating a real-time load based on the weight coefficient, wherein the real-time load is the weighted sum of the resource utilization rates; the performance loss is further calculated according to the nonlinear relation between the real-time load and the total load ratio; and finally, restraining the real-time load through the load threshold value, and evaluating the performance state of the server in combination with the performance loss. According to the method, the accuracy and reliability of load evaluation of the heterogeneous edge server are effectively improved, the resource utilization rate is optimized, overload of the server is avoided, and the system stability is enhanced.

The invention discloses a double-layer collaborative energy management and control method and system for a cold plate type liquid cooling data center, and belongs to the technical field of data center energy management. The method comprises the steps that upper-layer day-ahead economic dispatching is based on time-of-use electricity price and outdoor environment temperature prediction, and a server load scheme and a cooling system reference flow track are generated with the minimum total operation cost as the target; in lower-layer intraday dynamic regulation and control, model prediction control is adopted to track the trajectory, the load rate of a server and the rotating speed of a secondary pump are adjusted in real time, and it is ensured that the temperature of a chip is within the safety range of 45-70 DEG C; and the lower layer feeds back the actual chip temperature and energy consumption to the upper layer for online correction of dynamic thermodynamic model parameters to form closed-loop optimization. The system comprises a data acquisition module, a dynamic thermodynamic modeling module, an upper and lower layer optimization control module and an information interaction and feedback module. According to the invention, the overall operation cost and the PUE value of the data center are reduced on the premise of ensuring the temperature safety of the chip.

The invention belongs to the field of deep learning prediction, and particularly relates to a deep learning prediction method for patient medical experience. A deep learning prediction method for patient medical experience comprises the following steps: acquiring sample data containing patient medical images and doctor-patient dialogue audio, converting the sample data into scale data in combination with a stored scale, and unifying data types of the scale data; screening the scale data through a preset missing rate threshold value; performing data amplification according to the score of each label in the scale data; a prediction model is constructed based on a multi-task deep learning model, and the bottom layer of the prediction model is composed of a linear layer and a convolution auto-encoder. According to the scheme, through multi-modal data processing, a multi-task model and edge calculation, the recognition accuracy is improved, the load of the server is reduced, and the problem that the economic burden of a hospital is increased due to the high-performance requirement of the server is solved; meanwhile, resource consumption is balanced, and data security is guaranteed.

The invention relates to a method for accelerating file acquisition by using RDMA (Remote Direct Memory Access), and belongs to the technical field of AI (Artificial Intelligence) training, reasoning and file transmission in a large-scale CPU (Central Processing Unit) calculation-intensive scene. Aiming at the problems of IO bottleneck, client memory limitation and high special hardware deployment cost existing in traditional file reading, a client-server architecture is adopted, a control plane and a data plane are separated, a server loads a remote shared file through a large memory and registers as an RDMA memory area, and the remote shared file is stored in the RDMA memory area. And establishing a quick index of the file and the memory area by means of Hash mapping. The client directly accesses the memory of the server based on RDMAreadverbs, data does not need to be forwarded and copied by a kernel, oversized files can be read in blocks, and the system is compatible with a common NFS storage system and an ROCE network card. The method effectively breaks through the memory limitation of the client, reduces the deployment and hardware cost, guarantees the data transmission accuracy, is suitable for a multi-client concurrent access scene, and provides a low-delay and high-bandwidth efficient solution for high-frequency data set access.

The invention relates to a deep neural network segmentation and task unloading joint scheduling method and device, and the method comprises the steps: constructing a dynamic optimization model based on a Lyapunov function, and constructing a Lyapunov optimization framework containing adjustable parameters according to the dynamic optimization model; determining a deep neural network segmentation strategy meeting a preset optimal condition, and dynamically selecting a mixed mode of all-local execution, all-server execution or partial local and partial server execution of the task by using the computing power, server load and network state of any terminal device; and constructing an optimization objective function containing real-time energy consumption and a queue state, and jointly scheduling a deep neural network segmentation and task unloading strategy under the condition that the optimization objective function meets the calculation capability constraint of any server, so as to determine a final deep neural network segmentation and task unloading joint scheduling result. Therefore, the problems of high task processing energy consumption, large delay and the like in an edge computing system are solved.

The invention discloses a precise liquid feeding structure control method applied to a single-phase immersion liquid cooling data center box body, which comprises the following steps: step 1, data acquisition and modeling: acquiring parameters by using a sensor, and simulating a cooling liquid flow path and temperature field distribution through thermal analysis; 2, simulation analysis and problem positioning are carried out, the flow velocity, flow distribution and temperature field of cooling liquid under different loads are simulated, and an unsmooth flow area, a hot spot area and a high-energy-consumption node are positioned; 3, hardware deployment and debugging: adjusting the installation position of a mainboard water pump to optimize the initial axial direction of a cooling liquid, simulating different server loads, and comparing test data with a simulation result to perform fine tuning optimization; 4, dynamically controlling the flow, executing multi-mode control logic based on the temperature parameters, and dynamically adjusting the rotating speed of a main board water pump and the rotating speed of a circulating pump; cooling liquid is forced to flow through the mainboard water pump, flow distribution is optimized in combination with the heat-flow coupling model, and forced flowing of the cooling liquid is achieved.

The invention relates to the technical field of mobile edge computing, and discloses a method for service migration, and the method comprises the steps: predicting the traffic flow of a set road section in a preset time period; planning a moving path of at least one target user terminal according to the traffic flow of the set road section; at least one target user terminal belongs to the same interest alliance; determining a target server of a to-be-migrated service corresponding to the interest alliance according to the moving path of each target user terminal; and migrating the to-be-migrated service to the corresponding target server. According to the method, the moving path of the user terminal is planned through the predicted traffic flow in the future time period, a congested road section can be avoided for the user terminal, and the to-be-migrated service is shunted in advance, so that the probability that the load of the target server is too large can be reduced, the probability of service interruption is reduced, and the user experience is improved. The invention further discloses a service migration device, electronic equipment and a storage medium.

The invention discloses a server load balancing method based on distributed computing. The method comprises the following steps: collecting instantaneous or short-window statistical data of a server at each entrance load balancer to generate a state vector, and dividing service categories; constructing an objective function containing congestion cost, migration cost and risk sensitive cost under an improved distributed average field game model based on the state vector; establishing average field distribution and determining a feasible route set under the constraints of capacity, quota and affinity; each entrance adopts mirror image outer ladder double-step updating based on the optimal response; time attenuation and maximum value protection are introduced in the updating process, temperature decline disturbance and a cold start discharge mechanism are combined, and finally convergence is judged according to the change rate and the default rate, and routing distribution is output. According to the invention, an improved distributed average field game model is introduced, so that efficient and stable load balancing of a service-level-oriented protocol in a large-scale cluster is realized.

The invention discloses a data center dynamic energy-saving scheduling method and system based on AI prediction, electronic equipment and a storage medium, and the method comprises the steps: collecting load data of a data center server and external service flow data in real time to construct an original data set, and processing the original data set through employing a long short-term memory network to obtain a load change trend; according to the load change trend, determining distribution characteristics of the thermal load trend in a future time window, extracting idle computing resource data from the server group based on the distribution characteristics, and computing a computing resource distribution proportion needing to be adjusted; carrying out iterative optimization on the calculation resource distribution proportion by adopting a genetic algorithm to obtain an optimized server load distribution scheme; through a server load distribution scheme, obtaining associated refrigeration system parameter data, determining an adjustment vector of a refrigeration strategy, fusing real-time energy consumption data of a power supply module, and judging a correction demand of power supply scheduling; and obtaining the operation configuration of the overall energy efficiency optimization according to the correction demand.

The invention provides a game server state monitoring method and system based on cloud computing, and the method comprises the steps: obtaining a player interaction event record and a server resource consumption index of a game server cluster in a preset monitoring period, constructing a multi-dimensional data association structure, carrying out the behavior mode analysis of the player interaction event record, and carrying out the analysis of the behavior mode. Generating a virtual economic flow map and scene interaction thermodynamic distribution, and performing feature extraction on server resource consumption indexes in the virtual economic flow map and the scene interaction thermodynamic distribution to obtain fragmentation features of a memory access sequence and response delay features of a network session; inputting the virtual economic flow atlas, the scene interaction thermodynamic distribution and fragmentation features and the response delay features into a pre-constructed cross-dimension correlation model to perform server load state evaluation, generating a load state evaluation result, generating a resource scheduling instruction according to resource bottleneck positioning information in the load state evaluation result, and performing resource scheduling on the server according to the resource scheduling instruction. And adjusting a computing resource allocation strategy of the game server cluster. According to the method and the device, the accuracy and the effectiveness of server resource scheduling are effectively improved.

This disclosure discloses a terminal device control method, apparatus, electronic device, and computer-readable medium. One specific implementation of the method includes: generating a set of associated user groups based on an acquired set of user attribute information; performing time-series information fusion processing on the associated user group set based on acquired user virtual item circulation information to obtain a heterogeneous graph of virtual item circulation among associated nodes; extracting node features from the heterogeneous graph of virtual item circulation among associated nodes using an associated node feature extraction model to obtain a set of associated node feature vectors; reconstructing nodes in the associated node feature vector set to obtain a reconstructed feature vector set of associated nodes; generating an abnormal associated user group set based on the reconstructed feature vector set and the associated node feature vector set; and performing circulation interception control on the user terminal device set corresponding to the abnormal associated user group set. This implementation can reduce server load and minimize system resource waste.

This invention belongs to the field of server load resource optimization and scheduling technology. Specifically, it discloses a server load balancing and resource optimization scheduling system. This system includes: by analyzing the impact of changes and fluctuations in production tasks on the enterprise's production line on server load balancing, it can predict the trend of server load changes in advance. Furthermore, by analyzing the server resource requirements of different types of production task changes, it can allocate server resources more rationally. By analyzing the incremental data interaction between the equipment on the production line and the server due to maintenance operations, it helps the enterprise reserve certain resources for the server in advance during the restart process of the production line, avoiding startup failure or system instability during the startup process due to insufficient resources. This ensures that the server can quickly resume normal operation after restarting, enhancing the server's adaptability to different production scenarios on the enterprise's production line.

The invention discloses an integrated air cooling and liquid cooling data center heat dissipation device and a heat dissipation method thereof, and belongs to the technical field of data center heat dissipation, and the integrated air cooling and liquid cooling data center heat dissipation device comprises an air cooling unit which comprises a fan and cools a data center; the liquid cooling unit comprises a heat exchanger module, a pump set module and a liquid supplementing module; the pump set module and the liquid supplementing module are connected with the heat exchanger module. And the control unit is connected with the air cooling unit and the liquid cooling unit and controls the air cooling unit to cool the data center and the liquid cooling unit to perform liquid cooling heat dissipation on the data center. Through cooperative work of air cooling and liquid cooling heat dissipation, the fan output flow and the pump output flow can be dynamically adjusted according to the load change of the server, the heat dissipation efficiency is optimized, and the overall energy efficiency control of the data center is improved.

A method of noise reduction, including: collecting configuration data with agents from configuration items, transmitting the collected configuration data to a server for processing, blocking receipt of selected configuration data by a server load protection module to protect the server from overload, storing the configuration data at the server in a storage, processing the configuration data with a batch noise detection module that identifies noise in the configuration data; the batch noise detection module deduces rules based on the identified noise and stores the rules in a data knowledge repository, receiving a user query from a user using a front-end module, analyzing the configuration data with an analytical module to transform the configuration data to be aligned with the user query, responsive to the analysis applying a runtime noise detection module to identify and suppress transformed configuration data to remove irrelevant transformed configuration data.

The utility model discloses a dynamic heat dissipation device coping with local temperature mutation of a cabinet server, the horizontal extension direction of a back plate of a cabinet is defined as an X direction, the horizontal extension direction of a side wall of the cabinet is defined as a Y direction, the height direction of the cabinet is defined as a Z direction, and the dynamic heat dissipation device comprises a Z-direction guide column, an X-direction guide rod, a fan and a plurality of temperature sensors; the bottom of the Z-direction guide column is rotationally sleeved with a bottom supporting foot, and the Z-direction guide column supports the X-direction guide rod in a lifting mode. A fan is horizontally supported on the X-direction guide rod along the X direction; and the temperature sensors are arranged at heat dissipation ports of a plurality of servers in the cabinet. According to the heat dissipation device, the heat dissipation module is designed to be of a flexibly movable structure, adjustment can be conducted according to the real-time change of server load distribution, and heat dissipation resources are optimally configured. According to the dynamic heat dissipation system, the heat dissipation efficiency can be remarkably improved, hot spot accumulation is avoided, energy waste can be reduced, and the green and energy-saving purposes are achieved.

Systems, methods, and computer readable medium are disclosed for performance of network connection operations. Performance of network connection operations includes accessing a cloud service via a client device, the cloud service containing real-time network information including at least two of a real-time server load for a plurality of servers, real-time server capacity, a customer preference, or geolocation IP; receiving from the cloud service, identities of a plurality of candidate servers from the servers, wherein, based on the real time information, the candidate servers meet criteria for potential establishment of a connection with the client device; transmitting probe packets from the client device to at least some of the candidate servers; receiving responses to the probe packets; determining from the responses, communication characteristics for at least some of the probe packets; selecting a server based on the communication characteristics; and establishing a connection between the client device and the selected server.

The present invention provides a method, medium and system for establishing a virtualized server load response model, which belongs to the field of virtualized server load response technology. By setting initial test conditions and changing them multiple times, multiple groups of load tests are performed on the virtualized server, and test data under different conditions are obtained to form a load data set. A load response characteristic equation group containing all parameters is established, and the data set is used for fitting. Then, a resource conversion model and a resource competition model are established to reflect the resource mapping relationship between the physical server and the virtual server and the resource competition between the virtual servers. The two models are integrated, and the fitted equation group is solved to obtain a numerical solution matrix. Finally, a virtualized server load response model is constructed based on the matrix to describe the resource usage and performance of the virtual server under different load conditions, which solves the technical problem that the server load response model established by the prior art is not accurate enough.

The application relates to the technical field of artificial intelligence and password security, and discloses a large language model privacy reasoning method and system capable of verifying no-log behavior and output integrity; the method first sends an encrypted question to the server by the client; the server loads a pre-compiled no-log reasoning program in a trusted execution environment, decrypts and reasons; the reasoning is completed, and zero is cleared; a joint hash value is obtained, a custom data field of a remote proof report is embedded, and a program integrity measurement value is obtained; finally, the response and the proof report are returned to the user. The user client compares the local joint hash value with the custom data in the proof, and verifies the program measurement value, so that it can be confirmed that the log function is not enabled in this reasoning, and the received model response is generated by the specified model for the question, and is not tampered with. The application first realizes double-verified guarantee of privacy and output credibility in large model services, and significantly improves the trust degree of the user for the AI system.