Disclosure of Invention
In view of the foregoing, it is desirable to provide an edge computing task allocation method, apparatus, computer device, computer readable storage medium, and computer program product that can ensure fairness and consistency in dynamic and unreliable network environments.
In a first aspect, the application provides a method for distributing edge computing tasks, which is applied to an edge cluster, wherein the edge cluster comprises an audit node, a leader node and a plurality of edge nodes, and the method comprises the following steps:
The auditing node acquires registration information sent by each edge node;
The auditing node registers all edge nodes added into the edge cluster according to the registration information;
the leader node splits a task into a plurality of subtasks and distributes the subtasks to at least two edge nodes which finish registration;
the leader node performs correctness checking on the execution result of the subtasks, and integrates all subtasks with correct checking results to obtain an integrated task result;
And the leading node sends the integrated task result to a user side.
In one embodiment, the audit node obtains registration information sent by each edge node, including:
When the edge node joins the edge cluster, the edge node sends registration information to the audit node, wherein the registration information comprises the geographic position of the edge node, computing resources and types of the computing resources;
And the auditing node receives the registration information.
In one embodiment, the leader node splits a task into a plurality of subtasks and assigns the subtasks to at least two of the edge nodes that complete registration, including:
The leader node splits a task initiated by a user terminal to obtain a plurality of subtasks;
the leader node distributes the subtasks to at least two edge nodes which complete registration, wherein the same subtask is distributed to at least two edge nodes for processing.
In one embodiment, the method further comprises:
The audit node obtains a hash value of the log of the leader node in a preset time period;
and if the hash value of the log changes, reelecting the leader node, and punishing and deducting the original leader node.
In one embodiment, the method further comprises:
the audit node receives a leader node election request initiated by the edge node;
The audit node judges whether a re-election condition is met according to the leader node election request;
if the re-election condition is met, the election of the leader node is allowed;
and if the re-election condition is not met, punishing and withholding the edge node which initiates the leader node election request, wherein the re-election condition comprises the following steps:
The audit node is out of connection with the original leader node, and/or
The audit node is not in decoupling with the original leader node, and the edge node exceeding 1/3 initiates the leader node election request.
In one embodiment, if the audit node allows election of the leader node, the method further comprises:
Determining the score of each edge node according to the front score and punishment deduction of the edge node, wherein the front score comprises a user score and a score between the nodes, the user score comprises a score of the task execution accuracy of the execution node and a score of the task execution time of the execution node when the edge node is taken as the execution node, and the score between the nodes comprises a score of the execution node on the leader node when the edge node is taken as the leader node, a score of the task execution efficiency and a score of the task execution accuracy when the edge node is taken as the execution node;
Each edge node sends the integral of each edge node to other edge nodes in a broadcasting mode, and the audit node monitors the integral broadcasted by each edge node;
each edge node performs voting according to the received integral, and feeds back the voting result to the auditing node;
the audit node obtains voting results of all edge nodes, and carries out weighting treatment on the voting results according to the weights corresponding to the edge nodes to obtain weighted voting results, wherein the higher the integral is, the higher the weight corresponding to the edge nodes is;
The audit node determines the edge node with the highest vote number as a new leading node according to the weighted voting result;
if the number of the edge nodes with the highest ticket number is larger than 1, selecting one of the edge nodes as a new leading node in a random mode.
In a second aspect, the present application further provides an edge computing task allocation device, which is applied to an edge cluster, and the device includes:
the acquisition module is used for acquiring registration information sent by each edge node;
the registration module is used for registering all edge nodes joining the edge cluster according to the registration information;
The task allocation module is used for splitting a task into a plurality of subtasks and allocating the subtasks to at least two edge nodes which finish registration;
The verification module is used for verifying the correctness of the execution result of the subtasks and integrating all subtasks with correct verification results to obtain an integrated task result;
and the sending module is used for sending the integrated task result to the user side.
In a third aspect, the present application also provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
Acquiring registration information sent by each edge node;
registering all edge nodes added into the edge cluster according to the registration information;
splitting a task into a plurality of subtasks, and distributing the subtasks to at least two edge nodes which finish registration;
performing correctness checking on the execution result of the subtasks, and integrating all subtasks with correct checking results to obtain an integrated task result;
and sending the integrated task result to a user side.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Acquiring registration information sent by each edge node;
registering all edge nodes added into the edge cluster according to the registration information;
splitting a task into a plurality of subtasks, and distributing the subtasks to at least two edge nodes which finish registration;
performing correctness checking on the execution result of the subtasks, and integrating all subtasks with correct checking results to obtain an integrated task result;
and sending the integrated task result to a user side.
In a fifth aspect, the application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
Acquiring registration information sent by each edge node;
registering all edge nodes added into the edge cluster according to the registration information;
splitting a task into a plurality of subtasks, and distributing the subtasks to at least two edge nodes which finish registration;
performing correctness checking on the execution result of the subtasks, and integrating all subtasks with correct checking results to obtain an integrated task result;
and sending the integrated task result to a user side.
The edge computing task distribution method, the device, the system, the storage medium and the product are applied to an edge cluster, the edge cluster comprises an audit node, a leading node and a plurality of edge nodes, registration information sent by each edge node is obtained through the audit node, the audit node registers all the edge nodes added into the edge cluster according to the registration information, and therefore the audit node can register the information of each edge node, and cluster management is facilitated. The leader node splits the task into a plurality of subtasks and distributes the subtasks to at least two edge nodes which finish registration, so that the task can be split, the task execution is refined, and the computing resources of each edge node are fully utilized. The leader node performs correctness checking on the execution results of the subtasks, integrates all subtasks with correct checking results to obtain integrated task results, and sends the integrated task results to the user side. Therefore, a plurality of edge nodes can be used as a cooperative whole to work, so that the edge cluster can adapt to dynamic node change, whether the edge nodes exit or join, the agreement can be rapidly achieved, and the fairness and consistency of task allocation under dynamic and unreliable network environments are ensured.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The edge computing task allocation method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein, the user terminal 101 communicates with the edge cluster 102 through a network. Edge cluster 102 includes a plurality of edge nodes (i.e., edge computing devices), and according to functionality, a leader node may be elected among the edge nodes, and an audit node may be further provided to monitor the leader node and the edge nodes. The auditing node acquires registration information sent by each edge node, registers all edge nodes added into the edge cluster according to the registration information, splits a task into a plurality of subtasks and distributes the subtasks to at least two edge nodes which finish registration, performs correctness checking on execution results of the subtasks, integrates all subtasks with correct checking results to obtain integrated task results, and sends the integrated task results to a user side. The edge nodes in the edge cluster 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart vehicle devices, projection devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The head-mounted device may be a Virtual Reality (VR) device, an augmented Reality (Augmented Reality, AR) device, smart glasses, or the like.
In an exemplary embodiment, as shown in fig. 2, an edge computing task allocation method is provided, and an example of application of the method to the edge cluster in fig. 1 is described, which includes the following steps 201 to 205. Wherein:
in step 201, the audit node obtains registration information sent by each edge node.
Wherein the audit node may be a computing-capable device that can oversee edge nodes in the edge cluster. Optionally, at least one edge node can be selected from the original edge cluster and is specially used as an audit node.
For example, in an area, there may be a large number of edge devices, such as edge cameras and sensors, that work cooperatively to perform complex environmental detection and data processing tasks. Assuming that there are multiple edge nodes in a certain edge cluster, the edge node set may be denoted as n= { N1,n2,...,nk }, where the number of edge nodes shared by the edge cluster is k, and Ni is the ith edge node in the edge node set.
In this embodiment, to effectively allocate tasks, coordinate the cooperation between devices, and verify the calculation result, raft consensus protocol may be introduced. The Raft consensus protocol consistency algorithm can enable a plurality of nodes to work cooperatively as a whole, and the whole cluster can rapidly agree to continue to provide service when part of edge nodes in the cluster join or exit.
The edge node sends registration information to the audit node when joining the edge cluster, wherein the registration information comprises the geographic position of the edge node, computing resources and types of the computing resources, and the audit node receives the registration information.
And 202, the auditing node registers all edge nodes added into the edge cluster according to the registration information.
In this embodiment, the audit node registers and registers all edge nodes, so that node management and supervision in the cluster can be facilitated. When the edge node is used as a leader node, the audit node is used for inspecting the leader behavior of the leader node.
In step 203, the leader node splits the task into a plurality of subtasks and assigns the subtasks to at least two edge nodes that complete registration.
The leader node distributes the subtasks to at least two edge nodes which finish registration, wherein the same subtask is distributed to at least two edge nodes for processing.
In this embodiment, a redundancy mechanism is used to distribute a subtask to multiple subtasks for execution, and finally, result comparison is performed, so that the correctness of the result of each subtask can be ensured.
In step 204, the leader node performs correctness checking on the execution result of the subtasks, and integrates all subtasks with correct checking results to obtain an integrated task result.
In this embodiment, the leader node further performs correctness checking on the execution result of each subtask. For example, when two or more execution nodes execute the same sub-task and the feedback execution results are the same, the execution results are generally considered to be correct. If two or more executing nodes execute the same subtask and the feedback execution results are different, the executing nodes are considered to have errors. Further, the execution results corresponding to the split subtasks are integrated, and the execution results corresponding to the original complete tasks are obtained.
In step 205, the leader node sends the integrated task result to the client.
In this embodiment, the leader node sends the final integrated task result to the user terminal that initiates the task.
In the embodiment, task allocation is performed through the leading order and the execution result is verified, so that the rationality and accuracy of task allocation can be ensured, and the malicious node is prevented from damaging the availability of the edge cluster.
The edge computing task allocation method is applied to an edge cluster, and the edge cluster comprises an audit node, a leading node and a plurality of edge nodes, registration information sent by each edge node is obtained through the audit node, the audit node registers all the edge nodes added into the edge cluster according to the registration information, and therefore the audit node can register the information of each edge node, and cluster management is facilitated. The leader node splits the task into a plurality of subtasks and distributes the subtasks to at least two edge nodes which finish registration, so that the task can be split, the task execution is refined, and the computing resources of each edge node are fully utilized. The leader node performs correctness checking on the execution results of the subtasks, integrates all subtasks with correct checking results to obtain integrated task results, and sends the integrated task results to the user side. Therefore, a plurality of edge nodes can be used as a cooperative whole to work, so that the edge cluster can adapt to dynamic node change, whether the edge nodes exit or join, the agreement can be rapidly achieved, and the fairness and consistency of task allocation under dynamic and unreliable network environments are ensured.
In another exemplary embodiment, as shown in fig. 3, an edge computing task allocation method is provided, and an example of application of the method to the edge cluster in fig. 1 is described, which includes the following steps 301 to 307. Wherein:
In step 301, the audit node obtains registration information sent by each edge node.
In step 302, the audit node registers all edge nodes joining the edge cluster according to the registration information.
In step 303, the leader node splits the task into a plurality of subtasks and assigns the subtasks to at least two edge nodes that complete registration.
And step 304, the leader node performs correctness checking on the execution result of the subtasks, and integrates all subtasks with correct checking results to obtain an integrated task result.
In step 305, the leader node sends the integrated task result to the client.
In this embodiment, the specific implementation process and technical effects of steps 301 to 305 are described in the related description of steps 201 to 205 in the method embodiment shown in fig. 2, and are not repeated here.
And step 306, the audit node acquires the hash value of the log of the leader node in a preset time period.
In the embodiment, in order to prevent malicious nodes from modifying the log, the audit node needs to patrol and monitor the leader node, and the audit node records the hash value of the log and randomly checks the hash value of the log of the leader node for a certain period of time.
Step 307, if the hash value of the log changes, reselecting the leader node, and punishing and deducting the original leader node.
In this embodiment, if the hash value varies, the leader node may be reselected, and punishment deduction may be performed on the original leader node. So that the original leader node forms a penalty score record.
In this embodiment, the audit node performs regular inspection and supervision on the behavior of the leader node, and if it is found that the hash value of the log of the leader node changes, a reelection of the leader node is initiated. Therefore, fairness of task allocation of the leader node can be guaranteed.
In yet another exemplary embodiment, as shown in fig. 4, an edge computing task allocation method is provided, and an example of application of the method to the edge cluster in fig. 1 is described, which includes the following steps 401 to 409. Wherein:
in step 401, the audit node obtains registration information sent by each edge node.
And step 402, the auditing node registers all edge nodes added into the edge cluster according to the registration information.
In step 403, the leader node splits the task into a plurality of subtasks and assigns the subtasks to at least two edge nodes that complete registration.
And step 404, the leader node performs correctness checking on the execution result of the subtasks, and integrates all subtasks with correct checking results to obtain an integrated task result.
In step 405, the leader node sends the integrated task result to the client.
In this embodiment, the specific implementation process and technical effects of step 401 to step 405 are described in the related description of step 201 to step 205 in the method embodiment shown in fig. 2, and are not repeated here.
In step 406, the audit node receives a leader election request initiated by the edge node.
In this embodiment, in order to prevent the edge node from maliciously initiating election, the audit node checks for each leader node selection request initiated by the edge node. For example, an audit log of an edge node selected by an initiating leader node may be checked.
Step 407, the audit node judges whether the re-election condition is satisfied according to the leader node election request, if yes, step 408 is executed, and if not, step 409 is executed.
In this embodiment, the re-election conditions include:
and the audit node is not connected with the original leader node, and/or the audit node is not connected with the original leader node, and the leader node election request is initiated by the edge node exceeding 1/3.
Illustratively, if the audit node and the original leader node are out of connection, the reelect leader node is agreed. If the audit node and the previous leader are not in disconnection and less than 1/3 of the nodes initiate reelection information, punishment deduction is carried out on the reelected nodes. If the audit node and the original leader node are not in disconnection, but have information of initiating reelection by more than 1/3 node, reelecting the leader node is carried out again.
Step 408, allowing election of the leader node.
Illustratively, the implementation of step 408 described above, as shown in fig. 5, may include the following steps:
Step 4081, determining the points of the edge nodes based on the face score and penalty points of each edge node.
The front scoring comprises a user scoring and a scoring between nodes, wherein the user scoring comprises a scoring of task execution accuracy of an execution node and a scoring of task execution time of a user when the edge node is used as the execution node, and the scoring between the nodes comprises a scoring of the execution node to the leader node when the edge node is used as the leader node, a scoring of task execution efficiency and a scoring of task execution accuracy when the edge node is used as the execution node.
In this embodiment, when the Raft consensus protocol is introduced in connection with the description of the embodiment shown in fig. 2, in order to ensure fairness, a suitable leader needs to be selected when voting to select the leader node. Thus, this embodiment introduces an integral weighted voting election. This way, a malicious leader can be prevented from tampering with the task log.
Illustratively, the front score of each node consists of two dimensions, one is the score of a user and one is the score between the nodes, the user submits a task to the edge cluster, one task is divided into a plurality of subtasks, each subtask is distributed to different edge nodes for execution, the score of a user on a certain subtask of the edge cluster is actually the score of the leader node which dominates the task from distribution, execution and integration, the score mainly represents the satisfaction of the user and is influenced by the distribution, execution efficiency, execution accuracy and the like of the whole task, the score of the user on the node ni is represented by Gi for the task J submitted to the edge cluster for execution,Wherein the method comprises the steps ofIs the satisfaction of the user with the accuracy of task J execution,Is the user's satisfaction with the task J execution time.
Illustratively, the scores between nodes can be divided into two types, namely, one is the evaluation of task allocation of the leader node by other executing nodes when the node is used as the leader node, and the other is the efficiency and accuracy of task execution when the node is used as the executing node.
In the embodiment, the evaluation of the leader node by the execution node comprises several aspects, namely, on one hand, the allocation of the subtasks is reasonable, the tasks are divided into computationally intensive, IO intensive and data intensive, whether the allocated subtask types are matched with the node computation types, on the other hand, the network condition of the leader node and the log behavior condition that whether the leader node is forced to synchronize and does not agree are realized, and the scores of all the execution nodes in the edge computation on the leader node ni are expressed asWhere P is the score of the executing node m against the nodes and k is the total number of nodes in the edge cluster.
In this embodiment, for the node ni, the accuracy of task execution isWherein Ri is all execution tasks allocated to the node ni, RTi is the correct number of execution results of the node ni tasks, the efficiency of the node execution tasks is obtained by comprehensively comparing the average execution time of the node execution tasks with the average execution time of other nodes, the lower the time spent by the node execution of the subtasks is in the average execution time, the faster the execution speed is indicated, the subtasks allocated to the node ni are J, the execution efficiency of the node ni isWhere TJi is the time that node ni performs task J,Is the average execution time of all nodes assigned to the task.
Illustratively, when the node ni is used as an execution node, the task execution efficiency and accuracy rate are scored as followsWhere β is the accuracy factor of node ni.
The nodes comprise punishment deduction items besides scoring, wherein the deduction items comprise two aspects, one is used for supervising log conditions of a leader node and the other is used for supervising reinitiation election conditions in a cluster, each node performance is deducted according to the conditions in daily work of an audit node, and punishment deduction records of the audit node to a node ni are Dpi.
In this embodiment, the final election score for node ni consists of a forward score and a penalty score: Delta is a positive impact factor, gamma is a penalty impact factor,Is a user impact factor, wherein Gi represents the score of the user on the node ni, Li is the evaluation of the node ni by the execution node when the node ni is the leader node, Ei is the efficiency and accuracy score of task execution when the node ni is the execution node, and Dpi is the penalty deduction of the audit node on the node ni.
In step 4082, each edge node sends its own score to other edge nodes in a broadcast manner, and the audit node monitors the scores broadcast by the edge nodes.
In the embodiment, the audit node is introduced to monitor the election leader node, so that part of nodes can be prevented from initiating malicious or unnecessary leader election.
In step 4083, each edge node votes according to the received points and feeds the voting result back to the audit node.
In this embodiment, the edge node may directly perform weighting processing on the votes according to its own weight, and then directly feed back the weighted voting result to the audit node.
Step 4084, the audit node obtains the voting results of all the edge nodes, and weights the voting results according to the weights corresponding to the edge nodes to obtain weighted voting results.
Wherein the higher the integral, the higher the weight corresponding to the edge node.
In step 4085, the audit node determines, according to the weighted voting result, the edge node with the highest vote number as the new leader node.
For example, if the number of edge nodes with the highest ticket number is greater than 1, one of the edge nodes is selected as a new leader node in a random manner.
In this embodiment, nodes cast votes to one or more high-score candidate nodes, determine supported objects based on the number of weighted votes, and set a minimum score threshold at which only nodes that reach that score qualify as candidates. The auditing node collects the voting results of all nodes and counts the weighted ballots, and the node which has high integral and obtains the support of a plurality of weighted ballots becomes a leader.
Step 409, punishment deduction is performed on the edge node that initiates the leader node election request.
In this embodiment, if the leader node election request initiated by the edge node does not satisfy the re-election condition, the edge nodes are considered malicious, and punishment deduction is performed on the edge nodes. Penalty points affect the integral of the corresponding edge node, thereby creating a closed loop constraint.
In the embodiment, integral weighted voting election is introduced, so that Raft algorithm is ensured to select a proper leader when the leader node is selected in voting, and the election is supervised through the audit node, thereby preventing part of nodes from initiating malicious or unnecessary leader election, ensuring the reasonability of the election, and realizing the effective allocation and execution of the edge computing task.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an edge computing task allocation device for realizing the above related edge computing task allocation method. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in the embodiments of the edge computing task allocation apparatus provided below may be referred to the limitation of the edge computing task allocation method hereinabove, and will not be repeated herein.
In one exemplary embodiment, as shown in fig. 6, there is provided an edge computing task allocation apparatus, including an acquisition module 601, a registration module 602, a task allocation module 603, a verification module 604, and a transmission module 605, wherein:
An obtaining module 601, configured to obtain registration information sent by each edge node;
A registration module 602, configured to register all edge nodes joining the edge cluster according to the registration information;
A task allocation module 603, configured to split a task into a plurality of subtasks, and allocate the subtasks to at least two edge nodes that complete registration;
The verification module 604 is configured to perform correctness verification on the execution result of the subtasks, and integrate all subtasks whose verification results are correct to obtain an integrated task result;
and the sending module 605 is configured to send the integrated task result to a user side.
The obtaining module 601 is specifically configured to send registration information to an audit node when the edge node joins the edge cluster, where the registration information includes a geographic location of the edge node itself, a computing resource, and a type of the computing resource, and the audit node receives the registration information.
The task allocation module 603 is specifically configured to split a task initiated by a user terminal through a leader node to obtain a plurality of subtasks, and allocate the subtasks to at least two edge nodes that complete registration, where the same subtask is allocated to at least two edge nodes for processing.
The obtaining module 601 is further configured to obtain a hash value of the log of the leader node in a preset time period, and if the hash value of the log changes, resell the leader node, and punish and deduct the original leader node.
In another exemplary embodiment, as shown in fig. 7, an edge computing task allocation device is provided, and on the basis of the device shown in fig. 6, the device in this embodiment may further include:
The election request judging module 606 is configured to receive a leader node election request initiated by the edge node, judge whether a re-election condition is met according to the leader node election request, allow the election of the leader node if the re-election condition is met, and punish the edge node initiating the leader node election request if the re-election condition is not met, where the re-election condition includes:
The audit node is out of connection with the original leader node, and/or
The audit node is not in decoupling with the original leader node, and the edge node exceeding 1/3 initiates the leader node election request.
The method further comprises an election module 607, wherein the election module is used for determining points of the edge nodes according to the front score and penalty deduction of each edge node, the front score comprises a user score and a score among the nodes, the user score comprises a score of the task execution accuracy of the executing node and a score of the task execution time of the executing node when the edge node is used as a leading node, the score among the nodes comprises a score of the leading node when the executing node is used as a leading node, a score of the task execution efficiency and a score of the task execution accuracy when the edge node is used as a executing node, each edge node sends own score to other edge nodes in a broadcasting mode, the audit node monitors the points broadcasted by each edge node, each edge node votes according to the received points and feeds the voting result back to the audit node, the nodes acquire the voting results of all the edge nodes, weight corresponding to the edge nodes is used for weighting the voting results according to the weight corresponding to the edge nodes, the weighted voting results are obtained, the edge nodes are higher in the corresponding weight, the edge nodes are higher than the leading node, the latest edge node is selected as one of the most random edge nodes, and the most recent edge nodes are selected as the leading edge nodes.
The respective modules in the above-described edge computing task allocation device may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 8. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of edge computing tasking.
It will be appreciated by those skilled in the art that the structure shown in FIG. 8 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor performing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are both information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile memory and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (RESISTIVE RANDOM ACCESS MEMORY, reRAM), magneto-resistive Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computation, an artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) processor, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the present application.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.