51 results about "Computation offloading" patented technology

Provided is a traffic-aware lightweight layered offloading framework for an adaptive slicing-enabled space-air-ground integrated network (SAGIN), where the adaptive slicing-enabled SAGIN is divided into a communication access platform (CAP) and a computation offloading platform (COP), and resources on each of the CAP and the COP are managed by network slicing; an edge service provider (ESP) provides computation offloading while performing resource allocation; for the resource allocation, a dynamic traffic change is captured by using ProbSparse self-attention, and adaptive network slicing is executed in accordance with predicted traffic and a system load; and for the computation offloading, a communication process and a computation process are separated to allocate a sub-channel as required in accordance with a channel state, then a virtual machine is allocated to a task through a lightweight computation offloading algorithm, and a converged policy is extracted as a lightweight neural network for online inference.

A server (200) includes a userland APL (230) that cooperates with an accelerator (212) while bypassing an OS (220). The userland APL (230) includes an ACC-NIC common data parsing part (232) that parses reception data in which an input data format of an ACC utilizing function and an NIC reception data format are made common.

The invention discloses a production line batching conveying control method based on the Internet of Things, which comprises the following steps: deploying a hyperspectral camera at a key position of a batching production line of a glass electric melting furnace, and identifying the types and positions of pollutants in materials in real time; environment sensors are arranged in the workshops, and digital twin bodies equal to the physical workshops in proportion are constructed; when pollutants are detected, the digital twin starts a simulation engine, a pollution source diffusion path is predicted, and a feature library is established; the system tracks the pollution track in real time, accurately calculates the shunting time, and automatically triggers the shunting valve to guide the polluted material into the waste pool; meanwhile, the air pressure gradient and the cleaning time of the clean room are dynamically adjusted to form an airflow barrier to block diffusion; and after cleaning, executing secondary monitoring and generating a traceability report. The method realizes full-process automatic management and control of pollution, improves production safety and efficiency, and reduces material loss.

This invention discloses a task offloading and resource allocation method under D2D-assisted MEC, belonging to the field of wireless communication and computing resource management technology. The method introduces service-oriented devices assisted by terminal direct transmission communication technology and rationally selects task processing modes, achieving efficient resource utilization and avoiding resource waste. By jointly optimizing computation offloading, D2D selection, computing resource allocation, and spectrum resource allocation, it can both guarantee service quality and reduce network congestion, thereby improving user experience. The two-stage iterative algorithm of this invention combines block coordinate descent, reconstruction linearization technology, and convex optimization methods, effectively solving non-convex optimization problems, and has a fast convergence speed and low computational complexity. Furthermore, the D2D-assisted MEC architecture and optimization method are applicable to different types of intelligent devices and diverse application scenarios, possessing broad applicability and flexibility, and can meet the needs of future mobile edge computing development.

This invention discloses a neighborhood-constrained edge computing method for low-Earth orbit (LEO) satellites. The method includes: acquiring user task attributes, satellite-to-ground visibility relationships, inter-satellite topology relationships, and satellite computing resource status in the LEO satellite network; constructing a candidate access satellite set and a one-hop neighborhood set, and generating a local observation state vector; constructing a topology mask; constructing a multi-branch dual-depth Q-network to output user association decisions, offloading decisions, and resource allocation decisions; constructing a reward function based on task transmission delay, inter-satellite forwarding delay, computation delay, and system energy consumption, and training the multi-branch dual-depth Q-network; and using the trained model to perform collaborative computation offloading in LEO satellite edge computing. This method can reduce the dimensionality of collaborative offloading decisions, suppress Q-value overestimation, and improve the task success rate and energy consumption optimization performance in dynamic satellite networks.

This invention provides a trajectory optimization and computational unloading method and system based on hierarchical reinforcement learning, comprising: acquiring the position information of each UE and the UAV at the current time; employing a sub-target-based HRL framework, dividing the agent into an upper-layer network and a lower-layer network, wherein the upper-layer network generates k cluster centers through a clustering algorithm, and uses the k cluster centers as a sub-target space, representing the expected location of the UAV; the upper-layer network selects a sub-target non-repeatingly every time slot, using the state space and action features as input; based on the sub-targets selected by the upper-layer network, the lower-layer network adjusts the flight trajectory of the UAV in real time in each time slot to achieve rapid response and optimization to the dynamic environment, guiding the UAV to the sub-target; after the UAV moves for a preset duration in each time slot and remains stationary, the lower-layer network first determines the priority of each UE based on the distance between each UE and the UAV, and then makes a computational unloading decision. This invention can perform trajectory optimization and computational unloading.

The present invention discloses a multi-task domain adaptation method for computational offloading. The method comprises: constructing an objective function based on the scenario in which the user processes the target task on their own or offloads it to an edge server for processing, obtaining multiple sets of source domain data, each set of source domain data including sample data and labels of multiple users, the labels being initial classification values ​​and initial regression values; inputting the multiple sets of source domain data into a feedforward neural network model to obtain a trained feedforward neural network model; obtaining multiple sets of target domain data, enhancing the multiple sets of target domain data to obtain multiple sets of enhanced target domain data; wherein each set of target domain data includes sample data of multiple users. The present invention solves the technical problems of the existing task offloading model in different domain environments, such as low generalization ability, high privacy risk, large computational load, and long computational time.

The application provides a kind of based on NOMA's perception data acquisition and computing offloading method, by establishing the optimization problem model based on NOMA's perception data acquisition and computing offloading with the goal of maximizing offloading data volume;Simplify optimization problem model;The simplified optimization problem model is converted into two-layer optimization problem of inner and outer nesting;Get terminal scheduling variables and acquisition and offloading time allocation variables;According to terminal scheduling variables, schedule acquisition terminals in turn according to acquisition and offloading time allocation variables within the specified time based on NOMA for data offloading, and the acquisition terminals that have not been scheduled continue to carry out data acquisition;Base station calculates data according to the calculation resource allocation within the set time;Compared with the existing method, the method can effectively improve the data acquisition and computing efficiency, can maximize the offloading data volume, and the performance improvement is more obvious when the total computing resource is larger.

This invention discloses a joint optimization method and system for task offloading and resource allocation based on SAGIN. It collects task and scheduling information through a SAG-IoT system; utilizes the task data and scheduling information to achieve optimal association between UAVs and IoT devices based on a matching game algorithm, and makes optimal decisions for task offloading to maximize the computational performance of the SAG-IoT system; based on optimal association, optimal task offloading decisions, and maximizing the computational performance of the SAG-IoT system, it constructs an online offloading framework based on deep reinforcement learning, constructing a path from input X(t) to optimal action x. * The low-complexity mapping strategy (t) is based on repeated interactions between different modules and a random environment, iterating and running sequentially to achieve joint optimization of task offloading and resource allocation. This invention achieves optimal computational performance while stabilizing the system queue. In addition to utilizing binary computation offloading, the optimization framework can also be extended to online partial computation offloading strategies consisting of multiple independent subtasks.

The invention discloses a calculation unloading method based on deep reinforcement learning in a cellular-free large-scale MIMO system, and the method comprises the following steps: inputting environment information into a task transmission MADDPG network in a time slot t, and outputting a current access point connection strategy through minimizing the task transmission energy consumption of a user; for the kth user side, all access points connected with the kth user side are clustered according to the access point connection strategy of the kth user side, a plurality of access point clusters are obtained, each access point cluster unloads the MADDPG network through calculation, energy consumption is calculated through minimization of tasks, and the access point cluster unloads the MADDPG network through calculation; selecting a main access point and allocating computing resources to perform computing unloading on a task of the kth user side, returning computing result data to the kth user side after computing unloading is completed, and ending the time slot; repeating the above process to realize calculation unloading; according to the method, the distributed cooperation characteristic of the access points in the non-cellular large-scale MIMO is fully utilized, and the total energy consumption and algorithm complexity of the system are reduced while the communication reliability requirement is met.

The application discloses a user scheduling-oriented calculation unloading method for an edge cell-free network, and relates to the technical field of edge computing (MEC) and cell-free network. After modeling an MEC-assisted cell-free communication model and a parallel calculation unloading model to construct an uplink network simulation environment, a joint optimization calculation unloading, user scheduling and calculation resource allocation strategy (JCSCS) model considering limited orthogonal pilot resources and edge calculation resources is constructed, deep reinforcement learning training based on a proximal policy optimization (PPO) is carried out in the uplink network simulation environment, and finally, the best calculation unloading strategy, user scheduling strategy and calculation resource allocation strategy are obtained through the PPO. The uplink channel is divided into multiple orthogonal sub-channels in the application, user equipment is dynamically scheduled to the sub-channels for unloading, and the influence of multi-user interference and pilot contamination on unloading delay can be significantly reduced.

A kind of computing offloading and resource allocation method based on deep reinforcement learning, aiming at the problem of high task computing delay in multi-user multi-edge server scenario, with the goal of minimizing the total task processing delay of users. By selecting the deep reinforcement learning method based on value function, the D3QN algorithm combining Double-DQN algorithm and Dueling-DQN algorithm based on DQN algorithm with GRU network is proposed for edge computing computing offloading and resource allocation processing method, so as to improve the resource utilization of edge server and effectively alleviate the overestimation and Q value uniqueness problem in DQN algorithm, and obtain a better offloading scheme. Finally, the algorithm is simulated and compared with various baseline algorithms, which proves that the proposed algorithm can achieve the purpose of optimizing the total task processing delay of users and reducing the task drop rate.

The invention belongs to the technical field of Internet of Vehicles, and particularly relates to a task-driven vehicle calculation unloading method. The method comprises the following steps: firstly, training a precision neural network estimation model; establishing a time delay model and a calculation cost model of the vehicle target detection task in different unloading modes; secondly, according to the estimation model, estimating the precision of image acquisition of the vehicle end and the road end, according to the time delay model and the calculation cost model, calculating the time delay and the calculation cost corresponding to the service, and according to the estimation precision, the task completion time delay and the task completion cost, constructing an optimization problem; solving the optimization problem through a genetic algorithm to obtain an optimal solution; and finally, according to the obtained optimal solution, completing selection decision, resource allocation and calculation unloading of the vehicle-mounted service. According to the invention, a precision neural network prediction model is trained, and on the basis, a task-driven vehicle calculation unloading method is provided, so that the perception precision is maximized in an acceptable time delay and calculation cost range.

The application discloses a kind of unmanned plane assisted computing unloading method and device, belong to communication technical field, this method includes: constructing unmanned plane assisted computing unloading method corresponding unloading platform;Unmanned plane is constructed as leader, operator is follower, and spectrum transaction model based on Stackelberg game;Respectively, unmanned plane and operator optimization problem analysis is carried out;According to the analysis result, generate comprehensive optimization problem, find out game equilibrium point, obtain the optimal computing unloading method of computing unloading request user in the communication coverage range of unmanned plane, outside the communication coverage range of unmanned plane, request user passes through D2D relay technology, utilize idle user in the coverage range of unmanned plane as relay object, and the computing task is unloaded to unmanned plane and handled.The application can solve the problem that terminal in communication edge or blind area cannot directly communicate with unmanned plane, leading to computing task difficult to unload or local computing delay too long.

Systems and methods for offloading computation operations from a device to the network are provided. One or more network functions and corresponding connections can be configured in accordance with a request from the device for modifying a current data session between the device and network function, that is in communication with a data network that hosts an application service, for offloading computation operations associated with the data session to the network. Another network function is configured according to the request and has resources for processing the computation operations. Further systems and methods for releasing offloading computation operations and resources in the network associated therewith, associated with a data session between the device and the other network function, are provided.

This invention provides a multi-satellite-assisted AI task computation offloading method and system for ocean-going vessels, belonging to the field of satellite communication and satellite computing technology. The method includes: before initiating an AI task computation offloading application, the vessel selects assisting satellites from a satellite system based on its environmental parameters and AI task requirements; after selecting an assisting satellite, if it determines that a satellite is currently assisting in executing another AI task and is not the selected assisting satellite, it enters a satellite switching process after the current task is completed; otherwise, it directly enters the access process for the selected assisting satellite; the vessel submits a computational task application to the satellite system to access the assisting satellite, and the satellite system generates a computation offloading plan based on the current satellite resource status and the vessel's AI task requirements. In the assisting satellite selection process, satellites with sufficient computing power are matched based on the vessel's own environment and AI task requirements, achieving efficient and accurate docking of satellite resources and vessel tasks, thus improving the efficiency of computation offloading.

This invention proposes a method for computing offloading and resource allocation in the Internet of Vehicles (IoV) based on an improved Black Kite optimization algorithm. The method comprises the following steps: obtaining the computational latency and computational energy consumption of tasks in various computing scenarios based on multiple computing scenarios; calculating the total computational latency and total computational energy consumption of the tasks based on the computational latency and computational energy consumption of the tasks in various computing scenarios; defining a system utility function based on the total computational latency and total computational energy consumption of the tasks; constructing a computational latency and energy consumption model for mobile edge computing scenarios with the goal of minimizing the system utility function; designing a Black Kite optimization algorithm based on an elite reverse learning strategy, a Gompertz model, and a Gaussian mutation strategy; and using the designed Black Kite optimization algorithm to solve the computational latency and energy consumption model for mobile edge computing scenarios to obtain a final computation offloading and resource optimization allocation solution. This invention can accelerate the convergence of the algorithm and prevent the algorithm from prematurely falling into a local optimal solution.

This invention relates to a satellite-ground collaborative edge computing method for 6G space-ground integrated networks, comprising: constructing a system model for a mobile edge computing scenario; based on the system model, constructing a computation offloading model, a low Earth orbit satellite communication time model, and a channel model; based on the computation offloading model, the low Earth orbit satellite communication time model, and the channel model, constructing an offloading optimization problem with the goal of minimizing the weighted sum of total system latency and energy consumption; and iteratively solving the offloading optimization problem using a block coordinate descent algorithm, wherein the offloading optimization problem is decomposed into two sub-problems: task offloading optimization and resource allocation, which are solved separately. This invention solves the task allocation problem between satellites and base stations for multiple users by establishing a refined satellite-ground geometric model and resource scheduling model, minimizing the total system overhead while satisfying task deadlines and satellite service window constraints.

The present disclosure relates to a method, a device, a system, and a computer program for processing-in-memory computation offloading for improving the inference performance of an artificial intelligence model. More specifically, the present disclosure provides a method for performing processing-in-memory (PIM) offloading by using a computing device, the method including: collecting information about a first computation to be processed, the first computation including an operator and at least one operand; determining the usefulness of offloading the first computation, based on the information about the first computation and the optimal operand size of a processing-in-memory (PIM); and offloading the first computation, based on the determination.

This invention discloses a collaborative method for edge sensing and task offloading based on movable antennas, belonging to the field of wireless communication and sensing integration technology. The method is applied to base stations equipped with movable antenna arrays, enabling concurrent downlink environmental sensing and uplink multi-user computation offloading via the movable antenna arrays. It constructs a joint optimization problem centered on optimizing sensing performance while also considering communication constraints. An alternating optimization framework is employed to iteratively optimize receive beamforming, user transmit power, and transmit and receive antenna positions to achieve dynamic collaborative allocation of sensing and communication computing resources. This invention fully leverages spatial freedom, improves sensing accuracy and communication efficiency, and effectively supports the collaborative requirements of high-precision sensing and concurrent multi-task offloading in edge intelligence scenarios.

The invention discloses a task age-based unmanned aerial vehicle networking calculation unloading method and device. The method comprises the steps of obtaining a calculation task generated by a user; generating task freshness of the user according to the calculation task; taking a one-to-one corresponding service relationship between the unmanned aerial vehicle and the user, a transmitting power peak value of the unmanned aerial vehicle, a transmitting power peak value of the user, a CPU frequency upper limit of the user and a total energy consumption upper limit of the centralized control architecture as constraints, and establishing a target function with minimum task freshness of all users in the centralized control architecture; iteratively solving the objective function; according to the method, the task freshness of the user is generated according to the calculation task information generated by the user, and then the target function is generated through the task freshness, so that the operation of a centralized control framework can be realized through the task freshness, the data timeliness is improved, and wrong decisions caused by expiration results are avoided.

This invention relates to the field of edge computing technology, specifically to a multi-agent deep deterministic gradient policy computation offloading method. Specifically, it involves establishing a terminal device task offloading decision model in a collaborative application scenario involving multiple IoT terminal devices and multiple edge computing server nodes. By analyzing the competitive relationship between edge nodes and IoT terminal devices, a Stackelberg game model is constructed, followed by the design of a corresponding multi-agent Markov decision process. Finally, the designed multi-agent training method is used to solve the Nash equilibrium of the game model. Compared with existing technologies, this method effectively reduces the cost of edge computing systems and solves the problem of traditional deep reinforcement learning algorithms getting trapped in local optima due to the long dimension of the action space. It achieves dynamic computation offloading decision-making in collaborative emergency scenarios involving multiple terminals and multiple edge nodes, improving the timeliness of sensing information transmission in emergency scenarios while reducing offloading costs.

The invention discloses a transaction processing method and device, a chip and equipment, and relates to the technical field of chips. The method comprises the following steps: receiving a transaction processing request, and analyzing a to-be-processed transaction from the transaction processing request; executing a shunting operation on the to-be-processed transaction, and determining a shunted target transaction; processing the target transaction to obtain a write-in result of the host memory; and sending the write-in result to a transmission controller, so that the transmission controller writes the write-in result into the host memory. According to the method, the to-be-processed transaction is shunted, and the write-in result corresponding to the shunted target transaction is calculated. According to the method, the shunted target transaction does not need to be sent to a transmission controller (such as a PCIe controller) for calculation of the corresponding write-in result, so that relatively large time delay caused by multiple PCIe bus round-trip between the main equipment and the PCIe controller is avoided, and the transaction processing efficiency is improved.

A computation offloading approach in blockchain-enabled MCS systems is provided to reach a lower total cost in computation offloading. Firstly, building a consortium blockchain-based framework to guarantee secure transactions in MCS systems. Secondly, designing a novel credit-based proof-of-work (C-PoW) algorithm instead of PoW to confirm transactions and add new blocks to the chain, thereby relieving the complexity of POW while keeping the reliability of blockchain. Thirdly, using a scalable deep reinforcement learning based computation offloading (DRCO) method to handle the computation-intensive tasks of C-PoW; by integrating PPO and DNC, the DRCO executes differentiable read-write operations on structured external memories by following an objective-oriented way; the DRCO uses a clipped surrogate objective to control the update of offloading policy, in order to improve the decision-making efficiency; the DRCO uses the DNNs to address the problem of high-dimensional state space.

This invention provides a drone-assisted computation offloading method, system, and computer storage medium, belonging to the field of drone-based edge computing. The method includes: establishing a drone-assisted computation offloading model, the model comprising an IoT device layer, an edge layer, and a cloud layer; in the drone-assisted computation offloading model, using a variable to indicate whether drone u provides services to IoT device k within time slot t; establishing a local IoT device computing scenario, a drone-assisted edge computing scenario, and a cloud-edge-device computing scenario; aiming to minimize the weighted sum of latency and energy consumption of all tasks, obtaining the most suitable drone for serving IoT devices, and having the drone calculate the optimal computation offloading scenario; and completing computation offloading at the optimal offloading scenario. This invention reduces the energy consumption rate during network computing task offloading while ensuring that the latency requirements of related computing tasks are effectively met.

This invention provides an adaptive computation offloading method and system for cooperative MEC networks based on UAV swarms. The method includes: constructing a multi-layer, multi-agent deep reinforcement learning framework; training the agents in a preset simulation environment until the policy function of each agent converges; and deploying the trained agents to output optimized solutions based on the current environmental state. This invention solves the technical problems of large offloading decision-making action space, unstable training, and difficulty in convergence in dynamic network environments.

The invention discloses a multi-unmanned aerial vehicle time window constrained cooperative computing unloading method in an air-space-ground network, and belongs to the technical field of wireless communication and edge computing. According to the method, an unmanned aerial vehicle and high-altitude platform collaborative edge computing system model is constructed, and a mixed integer nonlinear programming problem is established with the purpose of minimizing the comprehensive cost of the unmanned aerial vehicle scheduling number and energy consumption; an outer layer adopts a covariance matrix adaptive evolutionary algorithm to adaptively search an optimal task unloading proportion, an inner layer solves a multi-unmanned aerial vehicle path planning sub-problem with a time window and energy constraint based on a branch pricing algorithm, and joint optimization is realized through iteration interaction of the inner layer and the outer layer. Simulation results show that the number of used unmanned aerial vehicles and system energy consumption can be remarkably reduced, and the resource utilization rate and task execution efficiency of an air-space cooperative computing system are effectively improved.

This invention belongs to the field of edge computing technology and discloses a beamforming and computation offloading method for a wireless power-co-located edge computing network. It optimizes the downlink beamforming design and wireless power supply duration optimization sub-problems of the base station using a centralized deep reinforcement learning algorithm, optimizes the offloading decision and energy consumption optimization sub-problems of mobile devices using a hybrid action multi-agent deep reinforcement learning algorithm, and optimizes the uplink beamforming design optimization sub-problem of the base station using a convex optimization method, thus reducing the complexity of problem solving. The hybrid action multi-agent deep reinforcement learning algorithm, where each mobile device acts as an independent agent for distributed decision-making, avoids the slow training speed and poor performance issues caused by a large action space.

The application discloses an OHT walking shunting and converging control method, comprising a communication module, a data storage module and a ZCU controller; the communication module is used for interacting with OHT equipment driving on an air track, a barcode is arranged on the air track, and after the OHT equipment reads the barcode information, the communication module acquires and reports the barcode information to the ZCU control module; the data storage module is used for user data storage, and the user data comprises basic data and real-time data; the ZCU control module is used for calculating shunting and converging area traffic conditions, the ZCU control module acquires real-time state information of the OHT equipment through the communication module, and compares and schedules OHT equipment operation according to the OHT real-time state information and the basic data in the data storage module. The application avoids the shortcomings of hardware traffic control, and can also work cooperatively with hardware converging management, so that double protection is achieved to avoid the occurrence of OHT collision.

The invention relates to the technical field of edge computing, in particular to an airborne user computing unloading method in a high-dynamic civil aviation scene. The method comprises the following steps: firstly, constructing a mobile edge computing system model comprising a satellite, an aviation ad hoc network and an airborne user, and designing a communication model, a computing model and a switching model; then providing an optimization problem of maximizing system time delay and energy consumption; thirdly, describing an optimization problem as a Markov decision process and solving the optimization problem by using an improved depth deterministic strategy gradient algorithm; and finally, designing a domain-based parameter transfer strategy training method. According to the method, the problems of high mobility and resource limitation of airborne users in a civil aviation scene are comprehensively considered, and the method is designed by taking low-time-delay and low-energy-consumption task unloading and resource allocation as targets. When the method is used for dealing with a high-dynamic network environment, by designing a switching evaluation function and a task pre-unloading module based on a time evolution graph, the switching frequency and network congestion are remarkably reduced, and meanwhile the resource utilization rate and the algorithm convergence speed are improved; when complex resource scheduling is dealt with, efficient execution of tasks and load balance of the system are ensured through dynamic calculation unloading and resource allocation strategies.