887 results about "Mobile edge computing" patented technology

The invention discloses a joint optimization method for task unloading and resource allocation in a mobile edge computing network, which comprises the following steps of 1, establishing an OFDMA (Orthogonal Frequency Division Multiple Access)-based multi-MEC (Mobile Edge Computing) base station and a multi-user scene model, wherein the MEC base station supports the multi-user access; 2, introducing an unloading decision mechanism; Meanwhile, constructing a local calculation model and a remote calculation model, selecting a user needing to perform calculation unloading, and establishing a calculation task unloading and resource allocation scheme based on minimum energy consumption under the condition of meeting the time delay constraint according to the conditions; 3, carrying out variablefusion on three mutually constrained optimization variables, namely an unloading decision variable, a wireless resource distribution variable and a computing resource distribution variable, so as to simplify the problem; and 4, obtaining an unloading decision and a resource allocation result which enable the total energy consumption of the user in the MEC system to be minimum through a branch andbound algorithm. The method has the advantage that the energy consumption of the system can be effectively reduced on the premise that strict time delay limitation is guaranteed.

The invention discloses a distributed mobile edge computing unloading method in an ultra-dense network architecture, belonging to the technical field of wireless communication network and cloud computing. The method includes the following steps: calculating the interference of mobile equipment, and if unloading is needed, carrying out computing unloading by selecting a strategy that meets the loadlimitation, interference limitation and delay limitation; further, when the energy overhead of the selected strategy is superior to a current computing unloading strategy, sending update request information to a currently-selected base station to request to update the own computing unloading strategy; after the mobile equipment acquires the information that the base station allows to update the computing strategy, notifying other mobile equipment that the current update opportunity has been acquired, and adopting the updated strategy in the next time slot; and if the mobile equipment does notacquire the update opportunity, maintaining the existing strategy in the next time slot. According to the method disclosed by the invention, the energy consumption in a computing unloading process can be effectively reduced under the premise of guaranteeing a certain delay limitation, the purpose of saving the energy consumption can be effectively achieved, and the good leading effect and applicability can be achieved.

The invention provides a computing task unloading method based on edge computing and cloud computing collaboration. The computing task unloading method comprises the steps that variable parameters areset and initialized; Constructing respective time delay models and energy consumption models of the mobile terminal, the edge node and the remote cloud, obtaining a time delay expected value model and a total energy consumption model of the mobile terminal when the current task load is completely executed, and further obtaining a time delay expected value model and a total energy consumption model of all tasks in the total mobile terminal when the tasks are executed; Defining an optimal distribution problem and converting the optimal distribution problem into a convex optimization problem; And introducing a Lagrangian function to solve an optimal solution of task execution amounts of the terminal local machine, the edge node and the far-end cloud under the KKT constraint condition, so that each mobile terminal adjusts and executes according to the task execution amounts of the terminal local machine, the edge node and the far-end cloud, which are obtained by respectively and correspondingly solving the optimal solution. According to the implementation of the invention, the computing power and the power consumption limitation of the mobile terminal, the edge node and the remote cloud are comprehensively considered, and an optimal computing task unloading decision is realized.

Systems and methods for standards compatible Mobile Edge Computing (MEC), including splitting Serving gateways (SGWs) and Packet Data Network gateways (PDN-GWs) to provision sufficient resources to deploy data-plane entity instances locally at a Radio Access Network (RAN) edge with one or more cloudlets. One or more local controller nodes is deployed in one or more operator clouds, a dedicated bearer is leveraged to route traffic from the one or more cloudlets through the split SGWs and PDN-GWs, and the dedicated bearer is configured with a traffic flow template (TFT) including an Internet Protocol (IP) address of the one or more cloudlets. Efficient access to one or more MEC applications at the RAN edge is provided to one or more user devices using the dedicated bearer.

The Internet of vehicles distributes computing tasks between a remote cloud and a local vehicle-mounted terminal to improve vehicle services. In order to further reduce the delay and transmission costof computing unloading, the invention provides a cloud-based MEC (Mobile Edge Computing) unloading framework. MEC brings the computing capacity to a mobile network edge close to intelligent mobile equipment; compared with local computing, the MEC contributes to saving energy, but leads to network load increase and transmission delay. In order to research a balance between the energy consumption and the delay, the invention provides an energy-sensing unloading scheme. The scheme is to co-optimize communication and computing resource distribution under limited energy and sensitive delay. In thetext, a multi-cell MEC network scene is considered. Residual energy of a vehicle battery is introduced into a definition of a weighing factor of the energy consumption and the delay. For an MINLP (Mixed Integer Non-Linearity Problem) of the computing unloading and resource distribution, an original NP (Network Performance) difficult problem is decoupled into problems on seeking power and subcarrier distribution and unloading tasks.

The invention relates to a content caching decision and resource allocation combined optimization method based on mobile edge computing in the Internet of Vehicles, and belongs to the technical fieldof mobile communication. The mobile edge calculation MEC is used as a foreground edge calculation, and has strong calculation capability and storage capability. The MEC server is deployed on the RSU side and can provide storage space and computing resources for vehicle users. Although the MEC provides services similar to cloud computing, the problems of caching, computing resource allocation, spectrum resource allocation and the like still exist. The method aims to maximize the network revenue under the condition of guaranteeing the time delay requirement. According to the method, a content caching decision, MEC server computing resource distribution and spectrum resource distribution are jointly modeled into a Markov decision process (MDP), a deep reinforcement learning method is used forsolving, and an optimal content caching decision, computing resource distribution and spectrum distribution are obtained.

Mobile Edge Computing (MEC) has the advantages of high bandwidth and low latency and has attracted extensive attention in the study of next generation of mobile networks by providing service environment and computing power at the edge of mobile networks. The invention discloses a vehicle task unloading and user mobility prediction and edge cache fusion policy in a vehicle networking environment. An MEC-based unloading framework is built. Tasks can be unloaded to an MEC server for vehicle-to-infrastructure (V2I) communications or can be unloaded to an adjacent vehicle for vehicle-to-vehicle (V2V) communications. Considering task backhaul failure caused by link disconnection caused by user mobility, the Kalman filter algorithm is used to predict link connection estimation, and a user can select a stable service node during task request and task collection. Cache decisions are made before the service node first returns a task to a request vehicle. Access latency, traffic load and networkcost can be significantly reduced through content cache.

The invention discloses a dynamic resource allocation method based on evolutionary game in a mobile edge computing system. The method comprises the following steps that (1) a network is divided into a plurality of areas according to the network coverage condition, accessible service points in the areas are different, and a centralized controller is arranged in the network; (2) terminals with the task unloading need in the same area form a population, and the terminals in the population establish task unloading cost functions; (3) all terminals in each population randomly select accessible SPs in an SP selection strategy set; the evolutionary game is established in each population in the network; (4) the terminals in each population compute task unloading costs and send the SP selection strategies and the cost information to the controller; (5) the population carries out SP selection strategy update according to dynamic copy; and (6) the dynamic copy reaches evolution equilibrium. The method fully utilizes the computing resources and the radio resources of the SPs, aims at the equal task unloading costs of all terminals in the populations and meets the task unloading need of each mobile terminal based on the evolutionary game.

The invention relates to the field of mobile communication, and particularly relates to a mobile edge computing node selection method, device and system. the method comprises: the edge cloud gateway receiving a domain name request forwarded by the user plane function, wherein the domain name request is forwarded when a domain name in the domain name request sent by the terminal is analyzed by a user plane function to be a service acceleration domain name or a destination address is an IP address with global load balance; or forwarding the domain name request when the user plane function determines that the protocol port information in the domain name request sent by the terminal meets the protocol port requirement or the destination address is an IP address with global load balance; and obtaining a corresponding edge application VIP from the global load balance, sending the VIP to the terminal through the user plane function, receiving a service request sent by the terminal forwarded by the user plane function, and distributing the service request to the corresponding MEC processing server. An edge cloud gateway is newly added, DNS and HTTP DNS addressing scenes are compatible, edge computing nodes are selected nearby, and the purpose of edge acceleration is achieved.

The invention relates to a resource allocation method suitable for mobile edge computing scenes. The method is used for realizing optimal task cache and uploading and downloading time allocation and low-complexity suboptimum task cache and uploading and downloading time allocation based on task cache and transmission optimization mechanisms, when the computation result of a to-be-executed task ofa mobile device has been cached by a base station, the mobile device downloads the computation result of the task from a base station, otherwise, the mobile device uploads the task to the base stationfor computation, and then downloads the computation result of the task from the base station, when multiple mobile devices upload the same task to the base station, the base station selects the mobile device with the best channel to achieve the uploading, when the multiple mobile devices download the computation result of the same task, the base station sends the computation result of the task ina multicast mode, and the mobile device having the worst channel just successfully receives the computation result. Compared with the prior art, the resource allocation method has the advantages of jointly optimizing the cache and uploading and downloading times and saving energy.

The present invention discloses a 5G mobile communication method and system based on MEC (Mobile Edge Computing) and a hierarchical SDN (software defined network). The method includes the following steps that: S1, a business request is received and is forwarded to an MEC edge node; S2, a Packet-in message is sent to an MEC node controller through the switch of the MEC edge node; S3, whether the MEC edge node has business consistent with business requested by the business request is judged; S4, a total controller performs network slicing planning and selection according to the business request, and then sends a Packet-out message to a core network controller included in an SDN sub-controller; S5, the core network controller performs resource scheduling processing on a mobile network, and buffers business or services related to the business request to the MEC edge node according to current resource occupation quantity; and S6, a terminal acquires the business or services from the MEC edge node. With the 5G mobile communication method and system provided by the technical schemes of the invention adopted, problems such as delay, congestion and capacity of a network can be solved, and ultimate experience of terminal users can be realized. The method and system have the advantages of high traffic, low time delay, low energy consumption, high reliability and the like.

The invention discloses a mobile edge computing task unloading method in a single-user scene, and relates to the field of treatment of a mobile computing system. The invention aims to reduce the reaction time delay and energy consumption of mobile equipment. construction of single-user scene task unloading model comprises construction of a system overall model and construction of each partial model, wherein the construction of each partial model comprises: a task queue model, a local computing model, a cloud computing model and a computing task loading model; a task unloading strategy: givinga task unloading scheme targeting at minimizing the system overall load K; executing all tasks on local CPU or an MEC server based on binary particle swarm optimization; correspondingly and locally executing a load optimal scheduling strategy, and executing a load optimal scheduling strategy by the MEC server based on pipeline scheduling. Through verification, the task unloading method in a sing-user scene provided by the invention reduces the reaction time delay and energy consumption of the mobile equipment.

The invention discloses a resource allocation and base station service deployment method based on mobile edge computing, and the method comprises the following steps: when a computing task is detectedin a mobile terminal, sending a computing migration request to a smart base station; when a cache unit in the base station is short of computing data needed for the task request, sending the needed task data requirement to a network side; receiving the needed task data returned by the network side; computing a time delay gain and an energy consumption gain according to the received needed task data; obtaining a computing migration decision matrix according to an experienced utility function; and performing computing migration according to the computing migration decision matrix. The base station deployment scheme comprises a cache unit, a computing unit, an acquisition processing unit and a sending unit, and can provide computing ability and data caching ability. Thus, the MEC based resource scheduling method and base station service deployment scheme can realize the computing migration in which terminals have multiple tasks, the base stations are multifunctional and targets are diversified.

The invention relates to a joint task unloading and resource allocation method in a mobile edge computing network, and belongs to the field of wireless communication and mobile edge computing. The method comprises the steps that UE generates a new computing task and sends a task unloading request to an MEC server; the MEC server collects calculation unloading request information sent by all user sides in the time slot; the user calculation task is matched with MEC server resources for the first time to form an initial unloading strategy set, and the value of an initial target function is calculated; and a minimum target function value is solved, and a user with the optimal target function value is obtained. The unloading decisions of all the users are updated, whether the obtained optimaltarget function is not smaller than the target function value of the last time or not is judged, and if yes, the unloading decisions are output, the channel allocation matrix and the optimal computingresources are determined. According to the invention, the cost expenditure of user unloading is reduced, and the total cost of mobile users is saved; and more calculation unloading tasks can be accepted, so that the task execution efficiency of the system is improved.

The invention provides an industrial soft gateway based on multiple access and edge computing and an implementation method. The industrial soft gateway comprises a configuration interaction module, adata collection module, a data edge computing module and a data sending control module, wherein the configuration interaction module comprises a connection configuration module and a data standardization module; the data collection module is used for a multiple access collection method; the data edge computing module is used for carrying out real-time computing on the data collected by the data collection module; and the data sending control module is used for carrying out caching and task scheduling and allocation of external forwarding on all data to be sent. According to the industrial softgateway based on multiple access and edge computing and the implementation method, a visual interface is arranged to provide connection configuration and standardized operation for various communication protocols and databases, and meanwhile, a graphical monitoring interface of a data collection state, an edge computing result and a forwarding state is provided. Data standardization can be realized, the data noise can be eliminated and the data features can be extracted through the edge computing module, the network transmission data volume of a data cloud platform is reduced, and the data transmission efficiency is improved.

The invention discloses a distributed uplink unloading strategy for mobile edge computing. The invention obtains an adaptive computing unloading strategy based on the Lyapunov theory and a proposed deviation degree update decision algorithm DUDA. The strategy comprises two main aspects: first, obtaining an optimal unloading decision set of users in Small Cells based on the Lyapunov theory on the premise of ensuring the system stability and minimized overhead; and secondly, proposing the DUDA to decide an unloading decision update sequence of the Small Cells in each time slot according to the deviation degree; and in the distributed uplink unloading strategy disclosed by the invention, it is considered that a user terminal has partial task unloading capability, that is, the tasks of a single users can be subdivided, a part of tasks are selected for local calculation according to specific application requirements and available resources with the target of minimizing the system overhead,and the remaining tasks are unloaded to an edge server of a Macro Cell in the HetNet scene. According to the distributed uplink unloading strategy disclosed by the invention, the system stability andthe optimal overhead are ensured by determining a drift penalty function, and an optimal unloading strategy set of users in the Small Cell under the condition is obtained.

The invention belongs to the technical field of Internet of Things task unloading, and particularly relates to a computing unloading method based on mobile edge computing in the Internet of Things. The invention relates to a theoretical framework of Internet of Things (Internet-of-Things-of-Things, IoT), Mobile Edge Computing (MEC), mode selection and node matching, dynamic optimization and the like. According to the technical scheme, local unloading is considered; Direct cloud unloading, Device end offload, Four computing unloading modes of equipment relay forwarding unloading are adopted, the influence of the social relation between the equipment on the unloading service level and the long-term dynamic performance of the system are added and considered, a system long-term income functionrelated to time delay and energy consumption is constructed, and a computing unloading scheme based on edge computing in the Internet of Things is obtained through mode selection and node matching. The method has the beneficial effects that better balance is achieved in the aspects of time delay and energy consumption performance, and the reliability and the stability of the system are improved.

The invention aims to provide an unloading delay optimization method in a mobile edge computing scene. The method comprises the following steps: step 1, constructing a system model, wherein the systemmodel comprises 2M users and an MEC server, each user has L tasks and needs to be unloaded to the MEC server for calculation, and it is assumed that only two users are allowed to adopt a mixed NOMA strategy to unload at the same time; step 2, setting each user as an executor, and performing action selection by each executor according to a DQN algorithm, i.e., selecting one user from the rest 2M-1users as the own transmission partner and unloading the transmission partner at the same time; step 3, performing system optimization by using a DQN algorithm: calculating the total unloading time delay of the system, updating a reward value, then training a neural network, and updating a Q function by using the neural network as a function approximator after the selection of all users is completed; and continuously carrying out the iterative optimization on the system until the optimal time delay is found. The problem of high time delay consumption in the existing multi-user MEC scene is solved.

The invention discloses a mobility management method and system for mobile edge computing. The method comprises the following steps that a change of an IP address of UE is acquired to obtain a changeof a bearing path, and a position change of the system UE is determined by analyzing a data packet; after target MEC acquires a bearing change of the system UE, the system UE is guaranteed to continuously use services of an MEC application in a process of moving in different MEC regions; the mobile edge computing application is migrated from system source MEC to system target MEC according to a migration decision; after the system migration is completed, the system target MEC carries out routing update and provides a local mobile edge computing service for the system UE; and the system targetMEC provides services for the system UE and sends a notification to the system source MEC simultaneously, and the system source MEC determines whether to be closed or not according to a use conditionof a system application. According to the method, the energy optimization can be effectively realized on the premise of guaranteeing the customer service quality.