9 results about "Resource pricing" patented technology

The invention belongs to the field of mobile edge computing, and particularly relates to a resource pricing problem based on a Stackelberg game in mobile edge computing. The method comprises the following steps: constructing a mobile edge computing model, wherein the mobile edge computing model comprises a server base station, task equipment and cooperative equipment; in a task execution period, for a server base station profit maximization problem of resource pricing based on a Stackelberg game in mobile edge computing, delay brought by task unloading is fully considered in problem modeling, and the delay requirement of a task is met to the maximum extent; analyzing and establishing a dynamic punishment and redistribution mechanism for task unloading failure of the cooperative equipment, wherein the mechanism comprises a punishment cost calculation module, a task value reevaluation module and a game strategy updating module; and obtaining an unloading strategy of the task equipment, a task allocation strategy of the server base station and a resource pricing strategy after the server base station and the cooperative equipment carry out the Stackelberg game by utilizing a greedy strategy, thereby obtaining a final task allocation scheme for maximizing the profit of the server base station. According to the method, the optimal task execution and resource allocation strategy of the server base station is formulated, and the relationship among the task value, the resource cost and the additional resource purchase cost is balanced as required, so that more flexible and efficient task scheduling service can be provided.

The invention provides a vehicle-mounted semantic communication task unloading method based on a multi-agent game, and the method comprises the steps: constructing a multi-leader-single follower Stackelber game model for semantic communication task resource transaction between a vehicle and edge calculation, designing an optimal resource pricing strategy and a distribution excitation strategy under multiple constraint conditions, and carrying out the optimal resource pricing strategy and the distribution excitation strategy; the utility functions of the vehicle and the roadside unit are respectively optimized, so that the benefit balance of the two parties and the efficient utilization of computing resources are realized; under the condition that the optimal resource allocation strategy is determined, a multi-agent deep reinforcement learning mode is adopted, a resource pricing strategy is adaptively learned, semantic communication resource transaction is optimized, and efficient and reliable interaction is realized. According to the method, efficient and self-adaptive semantic task transaction can be realized in a vehicle-mounted semantic communication environment with incomplete information, and economic benefit maximization of independent vehicle users and edge nodes can be guaranteed by determining resource pricing and allocation strategies under game equilibrium.

The invention discloses a slice resource pricing and virtual power plant frequency modulation income optimization method based on a Stackelberg game, and the method comprises the steps: building a Stackelberg game framework between a communication operator and a VPP, wherein the upper operator determines 5G bandwidth resource allocation according to a slice pricing strategy; and the lower VPP optimizes the slice demand and the frequency modulation response under the condition of considering the communication delay, the reliability and the AGC service score. The communication effectiveness is modeled by introducing an effective capacity (EC) theory and is coupled with a frequency modulation revenue function, and a slice-communication performance-frequency modulation revenue closed-loop optimization model is established. According to the method, the problem that communication and frequency modulation decoupling and resource allocation lack an economic mechanism in a traditional scheme can be avoided, balanced improvement of operator income and VPP income is realized, the frequency modulation performance and economy are remarkably improved, and the method has wide application value.

The application discloses an idle user resource auxiliary pricing and distribution method for an Internet of Things edge cloud continuum. First, a multi-agent resource coordination architecture composed of idle users, edge / cloud server resource providers (ECSP) and sensor devices is constructed to cope with the challenges of limited computing resources and prominent supply-demand contradiction in the edge environment. Second, a three-stage evolutionary game model is established to simulate the strategy evolution process of the three types of participating agents in resource pricing and procurement decision-making, thereby realizing dynamic adjustment of pricing and reasonable allocation of resources. In order to improve the rationality of strategy evolution and system stability, the application introduces a reputation incentive mechanism to reward reasonable pricing or resource purchase behavior and punish irrational behavior, and promotes the system evolution to converge to a stable and efficient strategy configuration. By constructing a replicator dynamic equation and combining a stability analysis method, the evolutionary stable strategy (ESS) of the system is identified to ensure the utility optimization of each participant and the maximization of the overall social welfare of the system in a complex dynamic environment.

The application discloses a mobile user task unloading and resource pricing method and system in edge computing, constructs the problem of how the edge server sets the price of resources and how the mobile user unloads tasks to maximize their own long-term benefits into a Markov decision process, and then designs the edge server resource pricing strategy and the mobile user task unloading strategy under dynamic pricing based on the DDPG algorithm and the DQN algorithm. When the edge server is not in the full load state, it will receive the task request, and then the mobile user will send the data required for processing the task to the edge server. After the edge server completes the computing task, the computing result will be immediately returned to the mobile user. The application can take into account the benefits of both the mobile user and the edge server, and can maximize the long-term benefits of both as much as possible, improve the user experience, and also make the edge server obtain more profits.

The invention relates to the technical field of data resource pricing and computers, and discloses a small sample data resource pricing method based on a meta-learning Reptile algorithm, and the method comprises the steps: crawling data resource transaction information from a big data transaction platform through a crawler, and forming an initial training set; performing data preprocessing on the initial training set, and dividing the initial training set into a training set and a test set; using an MLP model as a basic data resource pricing model, and training the MLP model by using the training set and the test set and based on a meta-learning Reptile algorithm to obtain a target data resource pricing model; and performing overall performance evaluation on the target data resource pricing model by calculating a mean absolute error MAE, a mean square error MSE and the goodness of fit R. The problems that small sample data pricing is difficult, pricing standards are fuzzy, transaction rules are missing and the like in an existing data resource pricing method are solved, and the problem that a traditional machine learning pricing method is not suitable for small sample data resource pricing with small sample size and sparse features is solved.

This invention relates to an information scene perception multi-task federated learning incentive method and system based on game theory and reinforcement learning. It acquires the utility of clients and servers, determines the type of information scene, and in open information scenes, employs a game theory-based incentive mechanism to model the client-server utility optimization problem as a resource pricing and purchasing game, determining the client's pricing strategy and the server's purchasing strategy. In closed information scenes, it employs a multi-agent reinforcement learning-based incentive mechanism to model the client-server utility optimization problem as a Markov decision process, determining the client's pricing strategy and the server's purchasing strategy. Formal federated learning training is then performed based on the client's pricing strategy and the server's purchasing strategy until a predetermined number of training rounds are reached. Compared with existing technologies, this invention significantly improves the adaptability and overall utility of the federated learning system in different information environments.

The application provides a service offloading method and system based on a multi-level offloading network, and the method comprises the following steps: a resource pricing scheme of a device is proposed, a multi-level task offloading algorithm MOS is designed to maximize the income of a user completing a task; the multi-level task offloading algorithm MOS takes the user as the center and takes the maximum completion delay of the task as the constraint to construct a multi-level task offloading network, and through layer-by-layer pricing, the user can determine the final task offloading scheme. The application solves the technical problems of resource waste, low utilization rate of auxiliary computing devices in the community and low income of task requesters.