Cloud edge collaborative data resource allocation method and system

Abstract

The invention relates to the technical field of data resource allocation, and discloses a cloud-side collaborative data resource allocation method and system, and the method comprises the following steps: 1, firstly, a user submits the information of a calculation task to a cloud-side collaborative resource allocation system in a cloud environment; 2, the system obtains task information; 3, determining how many tasks are processed by the edge node according to the residual resources and task information of the current edge node; 4, resource cost calculation; 5, performing resource allocation on the calculation task according to a decision result of the system task resource decision unit; and step 6, task execution. According to the cloud-edge collaborative data resource allocation method and system, the data processing tasks are intelligently allocated between the cloud and the edge nodes, resource allocation can be dynamically optimized according to the real-time requirements and processing complexity of the tasks, maximum utilization of resources is ensured, resource waste is avoided, and the resource utilization rate is improved. The data transmission delay is low, and the resource utilization rate is high.

Description

Technical Field

[0001] The present invention relates to the technical field of data resource allocation, and specifically to a cloud-edge collaborative data resource allocation method and system. Background Art

[0002] With the rapid development of the Internet of Things, big data, and cloud computing technologies, the demand for data generation, processing, and storage is growing exponentially. In this context, cloud computing provides powerful data processing capabilities, but its efficiency is poor due to data transmission delays and bandwidth limitations. In addition, privacy and data security issues have also prompted the demand for data processing to shift to edge computing, which processes data near where it is generated, thereby reducing latency, improving efficiency, and solving bandwidth bottlenecks.

[0003] However, edge computing resources are relatively limited and cannot handle large-scale data analysis and complex data processing tasks, which requires an effective data distribution method to optimize data processing. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the prior art, the present invention provides a cloud-edge collaborative data resource allocation method and system, which has the advantages of low data transmission delay and high resource utilization, and solves the above-mentioned problems.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a cloud-edge collaborative data resource allocation method, comprising the following steps:

[0008] Step 1: First, the user submits the computing task information to the cloud-edge collaborative resource allocation system in the cloud environment;

[0009] Step 2: The system obtains task information;

[0010] Step 3: According to the remaining resources and task information of the current edge node, determine how many tasks are processed by the edge node;

[0011] Step 4: Resource cost calculation;

[0012] Step 5: Allocate resources to computing tasks according to the decision result of the system task resource decision unit;

[0013] Step 6: Task execution.

[0014] Preferably, the information in step 1 includes the type of task, the required computing resources, and the estimated processing time. The method for generating computing task information submitted by the user to the system is as follows:

[0015] S1. Discretize the duration of the system service cycle into T duration slots;

[0016] S2. In each time slot t, users arrive randomly and submit task information to the edge node to request computing resources.

[0017] Preferably, the step 2 is to receive the task submitted by the user through the cloud-edge collaborative resource allocation system in the cloud environment, and count the remaining resources of the edge nodes in the current system;

[0018] In a private cloud environment, the cloud-edge collaborative computing resource allocation system will obtain task information submitted by dynamic users. This information includes the type of computing task requested by the user, the size of the required resources, and the computing time. The system will make task resource decisions based on the remaining computing capacity of the edge nodes of the current system and the cost of private cloud computing, that is, decide how many tasks are processed by edge nodes and how many tasks are processed by private clouds.

[0019] Preferably, the step three determines how many tasks are processed by the edge node and how many tasks are processed by the private cloud according to the remaining resources and task information of the current edge node, the computing cost of the private cloud in the long slot at this time, and the remaining capacity of the edge node;

[0020] Task resource decision-making is another important part of cloud-edge collaborative computing. It involves how to make the best task allocation decision based on the current system status and task characteristics. The following are the general steps for task resource decision-making:

[0021] S1. Task characteristics analysis;

[0022] S2. System status assessment;

[0023] S3.Decision making;

[0024] S4. Dynamic adjustment.

[0025] Preferably, in step 4, after the resource allocation decision is made, the physical cost of the computing resources provided by the private cloud node to the edge node for the collaborative edge node to perform calculations is calculated.

[0026] Preferably, the step six is ​​that the user obtains the computing resources allocated by the system, performs computational processing on the task, and obtains the task implementation result.

[0027] A cloud-edge collaborative data resource allocation system, comprising:

[0028] User submits task information unit;

[0029] System edge node computing resource statistics unit;

[0030] System task resource decision unit;

[0031] System resource cost calculation unit;

[0032] System resource allocation unit;

[0033] User task execution unit.

[0034] Compared with the prior art, the present invention provides a cloud-edge collaborative data resource allocation method and system, which has the following beneficial effects:

[0035] The present invention intelligently distributes data processing tasks between the cloud and edge nodes, can dynamically optimize resource allocation according to the real-time requirements and processing complexity of the tasks, and can significantly reduce the time required for data generation to processing. This not only improves the efficiency of data processing, but also ensures the maximum utilization of resources, avoids resource waste, and has low data transmission delay and high resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a block diagram of the data resource allocation system of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1 , a cloud-edge collaborative data resource allocation method, comprising the following steps:

[0039] Step 1: First, the user submits the computing task information to the cloud-edge collaborative resource allocation system in the cloud environment;

[0040] The information includes the type of task, the required computing resources, and the estimated processing time. The method for users to submit computing task information to the system is as follows:

[0041] S1. Discretize the duration of the system service cycle into T duration slots;

[0042] S2. In each time slot t, the user randomly arrives and submits task information to the edge node to request computing resources;

[0043] Step 2: The system obtains task information;

[0044] The cloud-edge collaborative resource allocation system in the cloud environment receives tasks submitted by users and counts the remaining resources of edge nodes in the current system. In a private cloud environment, the cloud-edge collaborative computing resource allocation system obtains task information submitted by dynamic users, including the type of computing tasks requested by users, the size of required resources, and computing time. The system makes task resource decisions based on the remaining computing capacity of edge nodes in the current system and the cost of private cloud computing, that is, it decides how many tasks are processed by edge nodes and how many tasks are processed by private clouds.

[0045] Step 3: According to the remaining resources and task information of the current edge node, determine how many tasks are processed by the edge node;

[0046] According to the remaining resources and task information of the current edge node, the computing cost of the private cloud in the current long slot, and the remaining capacity of the edge node, it is decided how many tasks are processed by the edge node and how many tasks are processed by the private cloud. Task resource decision is another important link in cloud-edge collaborative computing. It involves how to make the best task allocation decision based on the current system status and task characteristics. The following are the general steps for task resource decision:

[0047] S1. Task characteristic analysis: Analyze the characteristics of the task, including computing requirements, real-time requirements, priority, etc. These characteristics will affect the task allocation decision;

[0048] S2. System status assessment: assess the current system status, including the remaining computing capacity of edge nodes and the cost of private cloud computing;

[0049] S3. Decision making: Make task resource decisions based on task characteristics and system status. This includes deciding which tasks should be processed at the edge node and which tasks should be processed at the cloud node;

[0050] S4. Dynamic adjustment: During task execution, the system needs to dynamically adjust resource allocation based on real-time feedback and changing system status;

[0051] Step 4: Resource cost calculation;

[0052] After the resource allocation decision is made, the physical cost of the computing resources provided by the private cloud node to the edge node for the collaborative edge node to perform computation is calculated. The purpose of this step is to evaluate the economic efficiency of resource allocation and ensure the rationality of resource allocation;

[0053] Step 5: Allocate resources to computing tasks according to the decision result of the system task resource decision unit;

[0054] Step 6: Task execution;

[0055] The user obtains the computing resources allocated by the system, performs computation on the task, and obtains the task implementation result. That is, the task submitted by the user for allocation of computing resources is completed. This step is the ultimate goal of the entire resource allocation process, ensuring that the user can obtain the calculation results in a timely manner.

[0056] A cloud-edge collaborative data resource allocation system, comprising:

[0057] User submitted task information unit: This unit is responsible for collecting computing task information submitted by users. When users need to perform computing tasks, they submit the task information to the edge node to request computing resources.

[0058] System edge node computing resource statistics unit: This unit is responsible for counting the remaining resource capacity of edge nodes in the current system;

[0059] System task resource decision unit: This unit is the core of resource allocation. It determines which tasks should be processed by edge nodes and which tasks should be processed by private clouds based on the remaining resources and task information of the current edge nodes.

[0060] System resource cost calculation unit: After the resource allocation decision, this unit calculates the physical cost of the computing resources provided by the private cloud node to the edge node for the collaborative edge node to perform computation;

[0061] System resource allocation unit: This unit is responsible for allocating resources to computing tasks based on the decision results of the system task resource decision unit;

[0062] User task execution unit: Once the computing task requested by the user obtains the computing resources allocated by the system, this unit is responsible for executing the task calculation.

[0063] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data resource allocation method for cloud-edge collaboration, characterized in that: The following steps are involved: Step 1: First, the user submits the computing task information to the cloud-edge collaborative resource allocation system in the cloud environment; Step 2: The system obtains task information; Step 3: According to the remaining resources and task information of the current edge node, determine how many tasks are processed by the edge node; Step 4: Resource cost calculation; Step 5: Allocate resources to computing tasks according to the decision result of the system task resource decision unit; Step 6: Task execution.

2. According to claim 1, a cloud-edge collaborative data resource allocation method is characterized by: The information in step 1 includes the type of task, the required computing resources, and the estimated processing time. The method for generating computing task information submitted by the user to the system is as follows: S1. Discretize the duration of the system service cycle into T duration slots; S2. In each time slot t, users arrive randomly and submit task information to the edge node to request computing resources.

3. According to claim 1, a cloud-edge collaborative data resource allocation method is characterized by: The second step is to receive the tasks submitted by the user through the cloud-edge collaborative resource allocation system in the cloud environment, and count the remaining resources of the edge nodes in the current system.

4. The method for allocating data resources for cloud-edge collaboration according to claim 1, characterized in that: The step three determines how many tasks are processed by the edge node and how many tasks are processed by the private cloud based on the remaining resources and task information of the current edge node, the computing cost of the private cloud in the current long slot, and the remaining capacity of the edge node.

5. The method for allocating data resources for cloud-edge collaboration according to claim 1, characterized in that: The step 4 calculates the physical cost of the computing resources provided by the private cloud node to the edge node for the collaborative edge node to perform computing after the resource allocation decision is made.

6. The method for allocating data resources for cloud-edge collaboration according to claim 1, characterized in that: The sixth step is that the user obtains the computing resources allocated by the system, performs computing processing on the task, and obtains the task implementation result.

7. A cloud-edge collaborative data resource allocation system, used to implement a cloud-edge collaborative data resource allocation method according to any one of claims 1 to 6, characterized in that: include: User submits task information unit; System edge node computing resource statistics unit; System task resource decision unit; System resource cost calculation unit; System resource allocation unit; User task execution unit.

Images