CN113660696B - Multi-access edge computing node selection method and system based on regional pool networking - Google Patents
Multi-access edge computing node selection method and system based on regional pool networking Download PDFInfo
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- H—ELECTRICITY
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- H04W36/22—Performing reselection for specific purposes for handling the traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/08—Load balancing or load distribution
- H04W28/086—Load balancing or load distribution among access entities
Abstract
The invention discloses a multi-access edge computing node selection method and a system based on regional pool networking, which construct a multi-access edge computing node regional pool; obtaining service processing time delay according to communication time delay between the terminal and the multi-access edge computing node, the multi-access edge computing node computing time delay, the terminal computing time delay and the migration time delay of terminal switching; according to the local calculation energy consumption, the task transmission energy consumption and the multi-access edge calculation node calculation energy consumption, service processing energy consumption is obtained; obtaining service benefit according to service processing cost and income after service processing; and obtaining the optimal multi-access edge computing node for processing the terminal service according to the service processing time delay, the service processing energy consumption, the service benefit and the multi-access edge computing node load. The optimal multi-access edge computing node is selected according to the service processing time delay, the service processing energy consumption, the service benefit and the multi-access edge computing node load, the dimension is more comprehensive, and the service processing efficiency and the service benefit are improved.
Description
Technical Field
The invention relates to the technical field of mobile communication, in particular to a multi-access edge computing node selection method and system based on regional pool networking.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The multi-access edge computing can provide network, computing and storage services at a position close to a user, can realize localized processing of traffic, reduces traffic impact on a transmission network, and can provide a low-delay application running environment. Currently, applications of mobile edge computing will extend to the fields of autopilot, AR/VR, cloud gaming, industrial automation, and the like.
The research on multi-access edge computing is mainly focused on the condition that multi-access edge computing nodes MECs are independently deployed, namely after a user initiates a service, MECs are selected to serve according to service requirements and MEC resource conditions connected with the user. In addition, the patent application "an application service access method and apparatus based on mobile edge computing" proposes the concept of a MEC server group, and considers the MEC server group (in which multiple access edge computing nodes are fixed) as a whole to meet the offloading requirement, but none of the application service access methods and apparatus relate to the MEC server group networking.
However, the inventors found that the prior art has at least the following problems:
(1) When a user moves, the service processing time delay and the cost are increased; for example, the user UE1 offloads the task to the MEC1 for processing through the access point 1, and when the task is not processed, the user UE1 moves to the coverage of another access point 2, but since the access point 2 is not connected with the MEC1, the interworking with the MEC1 cannot be realized through the access point 2, and the MEC1 is required to migrate the unprocessed task to the MEC2 through the core network for further processing; however, since the core network element is generally located in a large data center, the core network element is far away from the user, resulting in a significant increase in service processing delay and processing cost.
(2) In view of the redundancy scenario, MEC utilization is not high.
(3) When the optimal MEC is selected for task offloading, the alternative MEC can only select the MEC connected with the current access point of the user, and the regional optimization cannot be achieved.
Disclosure of Invention
In order to solve the problems, the invention provides a multi-access edge computing node selection method and a system based on regional pool networking, wherein regional pools are introduced to form a multi-access edge computing node regional pool, the selection of the optimal multi-access edge computing node is considered by taking service processing time delay, service processing energy consumption, multi-access edge computing node load and service benefit into consideration, the dimension is considered more comprehensively, the user requirements are met, and the task unloading processing success rate is improved.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides a multi-access edge computing node selection method based on regional pool networking, including:
constructing a multi-access edge computing node area pool;
acquiring terminal service;
obtaining service processing time delay according to communication time delay between the terminal and the multi-access edge computing node, the multi-access edge computing node computing time delay, the terminal computing time delay and the migration time delay of terminal switching;
according to the local calculation energy consumption, the task transmission energy consumption and the multi-access edge calculation node calculation energy consumption, service processing energy consumption is obtained;
obtaining service benefit according to service processing cost and income after service processing;
and obtaining the optimal multi-access edge computing node for processing the terminal service in the multi-access edge computing node area pool according to the service processing time delay, the service processing energy consumption, the service benefit and the multi-access edge computing node load.
As an alternative implementation manner, the communication delay between the terminal and the multi-access edge computing node is:
wherein:for the task quantity x i Unloading to MEC i Is a transmission time of (a);
for the task quantity x i In MEC i Is calculated according to the calculation time of (2);
to calculate result ρx i From MEC i Time of transmission to the mobile user;
is MEC i Processing task amount x i The time required;
wherein: x is x i Offloading to MEC for user planning i Is a task amount of (1);for mobile users and MECs i Is not required, the connection time of (2); />Pre-handoff and MEC for user i An uplink transmission rate; />Pre-handoff and MEC for user i A downlink transmission rate; />For user switching and MEC i An uplink transmission rate; />For user switching and MEC i A downlink transmission rate; v (V) Ci Is MEC i Is calculated according to the calculated rate of (2); ρx i To calculate the result size.
As an alternative embodiment, the multi-access edge computing node computes the delay as:
as an alternative embodiment, the terminal calculates the time delay as:
wherein: w is a taskTotal amount of V Cl The rate is calculated for the user terminal.
In an alternative embodiment, in the multi-access edge computing node area pool, when the terminal is switched, the task is not migrated, and the migration delay of the terminal switching is:wherein: t (T) HO And switching the time delay for the user.
As an alternative embodiment, the service processing delay is:
as an alternative embodiment, the local computing energy consumption is:
wherein: p (P) Cl And calculating the single bit energy consumption for the local.
As an alternative embodiment, the task transmission energy consumption is:
wherein: p (P) Rl And (5) the energy consumption for transmitting/receiving the single bit locally.
As an alternative embodiment, the multi-access edge computing node computes the power consumption as:
wherein: p (P) Ci Is MEC i Energy consumption per unit time.
As an alternative embodiment, the service processing energy consumption is expressed as:
as an alternative embodiment, the service processing cost is:
wherein: alpha, beta, gamma and epsilon are respectively the communication cost per unit time, the calculation cost, the migration cost and the unit energy consumption cost.
As an alternative embodiment, the service revenue is:
wherein: n is the revenue after the business process is completed,the maximum delay requirement is processed for the service.
As an alternative embodiment, the service benefit is:
in a second aspect, the present invention provides a multi-access edge computing node selection system based on regional pool networking, including:
a region pool construction module configured to construct a multi-access edge computing node region pool;
the service acquisition module is configured to acquire terminal services;
the time delay acquisition module is configured to acquire service processing time delay according to the communication time delay of the terminal and the multi-access edge computing node, the multi-access edge computing node computing time delay, the terminal computing time delay and the migration time delay of terminal switching;
the energy consumption acquisition module is configured to obtain service processing energy consumption according to local calculation energy consumption, task transmission energy consumption and multi-access edge calculation node calculation energy consumption;
the benefit obtaining module is configured to obtain service benefits according to the service processing cost and the income after service processing;
the multi-access edge computing node determining module is configured to obtain an optimal multi-access edge computing node for processing terminal services in the multi-access edge computing node area pool according to service processing time delay, service processing energy consumption, service benefit and multi-access edge computing node load.
In a third aspect, the invention provides an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of the first aspect.
In a fourth aspect, the present invention provides a computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the method introduces the concept of the regional pool, builds the multi-access edge computing node regional pool, and in the multi-access edge computing node regional pool, the access points are fully interconnected with the multi-access edge computing nodes, the multi-access edge computing nodes are fully interconnected with the SDN controller, the problem that task processing time delay and cost increase caused by task migration after the user moves are solved, and the task processing success rate of unloading is improved.
According to the invention, all the multi-access edge computing nodes in the regional pool can serve the UE, and the load condition of the multi-access edge computing nodes is considered, so that the load balance of the multi-access edge computing nodes can be better realized on the premise of meeting the service requirement, and the resource utilization rate of the multi-access edge computing nodes is effectively improved.
When user service unloading is carried out, all multi-access edge computing nodes in the regional pool are used as alternative multi-access edge computing nodes, and the optimal multi-access edge computing nodes are selected to serve the multi-access edge computing nodes based on time delay, energy consumption, multi-access edge computing node load and benefit conditions, so that regional optimization is better realized.
According to the invention, the selection of the optimal multi-access edge computing node is considered in terms of service processing time delay, service processing energy consumption, service benefit and multi-access edge computing node load, the dimension is considered more comprehensively, the effect is better, the user demand is better met, the benefit maximization is realized, and the benefit of operators is improved.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a schematic diagram of a current independent deployment architecture of multiple access edge computing nodes according to embodiment 1 of the present invention;
fig. 2 is a task processing schematic diagram of the present multi-access edge computing node provided in embodiment 1 of the present invention in an independent deployment;
fig. 3 is a schematic diagram of a multi-access edge computing node pool networking provided in embodiment 1 of the present invention;
fig. 4 is a flowchart of selecting multiple access edge computing nodes in an area pool according to embodiment 1 of the present invention;
fig. 5 is a schematic view of a scene in MEC POOL mode provided in embodiment 1 of the present invention;
fig. 6 (a) is a schematic diagram of a trigger MEC switching scenario where the task amount provided in embodiment 1 of the present invention is not completely uploaded;
fig. 6 (b) is a schematic diagram of a trigger MEC switching scenario where the task amount provided in embodiment 1 of the present invention is not completely calculated;
fig. 6 (c) is a schematic diagram of a trigger MEC switching scenario when the task amount provided in embodiment 1 is not returned;
fig. 6 (d) is a schematic diagram of a handover-free scenario provided in embodiment 1 of the present invention;
fig. 7 is a schematic diagram of task processing of a multi-access edge computing node in MEC POOL mode according to embodiment 1 of the present invention.
The specific embodiment is as follows:
the invention is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms also are intended to include the plural forms, and furthermore, it is to be understood that the terms "comprises" and "comprising" and any variations thereof are intended to cover non-exclusive inclusions, such as, for example, processes, methods, systems, products or devices that comprise a series of steps or units, are not necessarily limited to those steps or units that are expressly listed, but may include other steps or units that are not expressly listed or inherent to such processes, methods, products or devices.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Example 1
As shown in fig. 1-2, which is a schematic diagram of an independent deployment architecture of a current multi-access edge computing node MEC, after a user initiates a service, according to the service requirement and the multi-access edge computing node resource condition connected with the user, the multi-access edge computing node is selected to serve the user, but the service processing delay and cost are greatly increased under the condition of considering the mobility of the user, as shown in fig. 2, the user UE1 offloads a task to the MEC1 through the access point 1, the task is not processed, the user UE1 moves to the coverage area of another access point 2, but the intercommunication with the MEC1 cannot be realized through the access point 2, the MEC1 is required to migrate the unprocessed task to the MEC2 through a core network for continuous processing, but the core network element is generally located in a large-scale data center and is far away from the user, so that the service processing delay and processing cost are greatly increased;
meanwhile, in the case of considering redundancy, the MEC utilization rate is not high, for example, one multi-access edge computing node is down, the remaining multi-access edge computing nodes take over, and the highest utilization rate of MEC1 and MEC2 connected with access point 1 cannot be higher than 50%.
For this reason, the present embodiment introduces the concept of regional POOL, and builds MEC POOL, as shown in fig. 3, and the built multi-access edge computing node POOL (POOL) is composed of three layers of architecture;
the lowest layer is the access point responsible for accessing the UE, e.g.: a base station of the existing network;
the middle layer is a multi-access edge computing node and is responsible for processing the task of UE unloading;
the uppermost layer is an SDN controller and is responsible for generating a time delay topology in an area POOL by utilizing the minimum spanning tree and collecting the multi-access edge computing node resource condition in the POOL;
and, the POOL internal access point is fully interconnected with the multi-access edge computing node, the multi-access edge computing node is fully interconnected with the SDN controller, and the multi-access edge computing node is fully interconnected.
Based on the multi-access edge computing node pool networking proposed above, the embodiment provides a multi-access edge computing node selection method based on regional pool networking, as shown in fig. 4, which specifically includes the following steps:
(1) Constructing a multi-access edge computing node area POOL by using a minimum spanning tree through an SDN controller, generating a time delay topology in an area POOL POOL, and acquiring multi-access edge computing node resource data in the POOL;
(2) Transmitting the POOL internal time delay topology and MEC resource to an access point;
(3) Acquiring a service initiated by a terminal user;
(4) Selecting an optimal multi-access edge computing node according to time delay, energy consumption and multi-access edge computing node load in POOL and service benefit conditions;
specifically, in the step (4),
(4-1) obtaining service processing time delay according to the communication time delay of the terminal and the multi-access edge computing node, the multi-access edge computing node computing time delay, the terminal computing time delay and the migration time delay of terminal switching;
(4-2) calculating energy consumption according to the local calculation energy consumption, task transmission energy consumption and multi-access edge calculation node to obtain service processing energy consumption;
(4-3) obtaining service benefit according to the service processing cost and the income after service processing;
and (4-4) obtaining the optimal multi-access edge computing node for processing the terminal service in the multi-access edge computing node area POOL according to the service processing time delay, the service processing energy consumption, the multi-access edge computing node load in the POOL and the service benefit.
In step (1), the multi-access edge computing node resource data includes, but is not limited to: load, computing resources, network resources, etc. of the multi-access edge computing node.
In step (4), the time delays include, but are not limited to: uplink and downlink transmission delay of task unloading, calculation task delay of multi-access edge calculation nodes, additional task migration delay caused by user movement and the like;
benefits include, but are not limited to: estimating income/business processing cost after business processing;
costs include, but are not limited to: the computing cost of the multi-access edge computing node and the terminal, the transmission cost of the network, and the energy consumption cost of the terminal and the multi-access edge computing node.
In the MEC POOL mode, the present embodiment provides a calculation process of three indexes of service processing delay, service benefit and service processing energy consumption in a user high-speed mobile scenario, as shown in fig. 5, assuming that N MEC servers serve mobile users, the mobile users need to process a computationally intensive task W, and the task W to be processed can be partially split due to limited computing capacity of the mobile users and can be processed in local equipment and unloaded to MEC for processing at the same time; the business process flow is shown in fig. 6 (a) -6 (d). Where a is the total amount of tasks the user plans to offload to the MEC server.
As shown in fig. 6 (a), for scenario one, after the user uploads the task amount B, the user switches due to the high-speed movement, but based on the multi-access edge computing node area POOL, the user is still connected with the MECi, and the user needs to continuously upload the remaining service (a-B) to the MECi, and calculate and return the result by the MECi.
As shown in fig. 6 (b), for scenario two, after the user uploads the task amount a and calculates the task amount C, the user switches due to the high-speed movement, but based on the multi-access edge calculation node area POOL, the user is still connected with MECi, which will continue to complete the remaining uncomputed task amount and return the result to the user. The two steps 3 (a) and 3 (b) shown in the figure in this scenario are parallel in time.
As shown in fig. 6 (c), for scenario three, after the user uploads, calculates the completion task amount a and returns the calculation result of the completion task amount D, the user switches due to the high-speed movement, but based on the multi-access edge calculation node area POOL, the user is still connected with the MECi, and the MECi continues to return the calculation result of the remaining unreturned (a-D) to the user.
As shown in fig. 6 (d), for scenario four, no user switching occurs during the user upload, MEC calculation backhaul.
Based on the above service flow, in step (4-1), the service processing delay is calculated as follows:
(4-1-1) the communication delay required for the task transfer between the mobile user and the MECi is expressed as:
wherein:for the task quantity x i Unloading to MEC i Is a transmission time of (a);
for the task quantity x i In MEC i Is calculated according to the calculation time of (2);
to calculate result ρx i From MEC i Time of transmission to the mobile user;
is MEC i Processing task amount x i The time required;
wherein: x is x i Offloading to MEC for user planning i Is a task amount of (1);for mobile users and MECs i Is not required, the connection time of (2); />Pre-handoff and MEC for user i An uplink transmission rate; />Pre-handoff and MEC for user i A downlink transmission rate; />For user switching and MEC i An uplink transmission rate; />For user switching and MEC i A downlink transmission rate; v (V) Ci Is MEC i Is calculated according to the calculated rate of (2); ρx i To calculate the result size.
(4-1-2) MECi computation delay expressed as:
(4-1-3) the user terminal calculating time delay is expressed as:
wherein: w is the total task amount, V Cl The rate is calculated for the user terminal.
(4-1-4) calculating migration delay; because MEC group POOL, task migration will not occur, and only user switching occurs, the migration delay is expressed as:
wherein: t (T) HO And switching the time delay for the user.
(4-1-5) the total delay of service processing is expressed as:
wherein: t1 isThe time delay of the processing service is T2, and the time delay of the processing service under other conditions is T2;
specifically:
in the step (4-2), the service processing energy consumption index only considers the energy consumed by the user terminal and the multi-access edge computing node for computing, and ignores the energy consumed by the multi-access edge computing node for transmitting; the service processing energy consumption is obtained according to the local calculation energy consumption, the task transmission energy consumption and the multi-access edge calculation node calculation energy consumption; therefore, the calculation process of the service processing energy consumption specifically comprises the following steps:
(4-2-1) calculating a local calculation energy consumption expressed as:
wherein: p (P) Cl And calculating the single bit energy consumption for the local.
(4-2-2) calculating task transmission energy consumption, the task transmission energy consumption being expressed as:
wherein: p (P) Rl And (5) the energy consumption for transmitting/receiving the single bit locally.
The energy consumption of the terminal side is obtained according to the local calculation energy consumption and the task transmission energy consumption:
(4-2-3) computing Multi-Access edge computing node computing energy consumption, MEC i The calculated energy consumption is expressed as:
wherein: p (P) Ci Is MEC i Energy consumption per unit time.
(4-2-4) calculating service processing energy consumption, wherein the service processing energy consumption is expressed as:
in the step (4-3), obtaining the income of operators according to the service processing cost and the income after service processing; the method comprises the following steps:
(4-3-1) the service processing cost is expressed as:
wherein: alpha, beta, gamma and epsilon are respectively the communication cost per unit time, the calculation cost, the migration cost and the unit energy consumption cost.
(4-3-2) the service revenue is expressed as:
wherein: n is the revenue after the business process is completed,the maximum delay requirement is processed for the service.
(4-3-3) the service benefit is expressed as:
in step (4-4), the process of selecting an optimal multi-access edge computing node based on the metrics includes:
(4-4-1) according to the related strategies, giving different weights for the service processing time delay, the service processing energy consumption, the service benefit, the multi-access edge computing node load and the like;
in this embodiment, each part has a weight between 0 and 1, and the sum of the weights of the parts is 1, and the more important part is the higher the weight given according to the policy.
(4-4-2) evaluating the values of the indexes according to different multi-access edge computing nodes in the POOL, wherein the values are between 0 and 1; for example: under the condition that the time delay meets the requirement, the lower the value is, the higher the value is; the higher the benefit, the higher the value; the lower the load of the computing node is, the higher the value is for the multi-access edge.
(4-4-3) calculating the comprehensive value of each multi-access edge calculation node in the POOL according to the four index weights and the related values.
Specifically, the integrated value=sum (each index weight is the value of each index).
(4-4-4) sorting the schemes according to the total value from large to small, and selecting the multi-access edge computing node with the highest total value as the multi-access edge computing node for service.
In this embodiment, the process of selecting the optimal multi-access edge computing node may also use a genetic algorithm or a related neural network algorithm to obtain the multi-access edge computing node with the highest comprehensive value as the serving multi-access edge computing node.
In this embodiment, based on the constructed multi-access edge computing node area pool, after the user moves, task migration does not need to be generated, as shown in fig. 7, the user UE1 sends the task to the MEC1 through the access point 1 for processing; if the task is not processed, the user UE1 moves to the coverage area of another access point 2, and because the MEC1 is directly connected with the access point 2, the MEC1 can continue to process the incomplete task, without migrating the incomplete task to the MEC2 through the core network, thereby greatly reducing the service processing delay and cost.
In this embodiment, when a user switches in a service processing process, according to the IP address of the destination multi-access edge computing node of task offloading, it is ensured that the destination multi-access edge computing node is unchanged, that is, the connection with the MEC1 is continuously maintained until the service processing is completed;
in addition, although the mec1 node is relatively far away from the access point 2, the simulation results show that the latency is still low compared to the task migration.
Example 2
The embodiment provides a multi-access edge computing node selection system based on regional pool networking, which comprises the following components:
a region pool construction module configured to construct a multi-access edge computing node region pool;
the service acquisition module is configured to acquire terminal services;
the time delay acquisition module is configured to acquire service processing time delay according to the communication time delay of the terminal and the multi-access edge computing node, the multi-access edge computing node computing time delay, the terminal computing time delay and the migration time delay of terminal switching;
the energy consumption acquisition module is configured to obtain service processing energy consumption according to local calculation energy consumption, task transmission energy consumption and multi-access edge calculation node calculation energy consumption;
the benefit obtaining module is configured to obtain service benefits according to the service processing cost and the income after service processing;
the multi-access edge computing node determining module is configured to obtain an optimal multi-access edge computing node for processing terminal services in the multi-access edge computing node area pool according to service processing time delay, service processing energy consumption, service benefit and multi-access edge computing node load.
In this embodiment, the system further includes a storage module configured to store the received related information such as service requirement, user mobility, channel quality, multi-access edge computing node load, and delay topology in POOL; and stores unit computation cost, unit communication cost, unit migration cost, unit energy consumption cost, operator service charge or service income, related evaluation strategy or neural network algorithm.
In this embodiment, the system further comprises an updating unit configured to update the information in the storage module.
It should be noted that the above modules correspond to the steps described in embodiment 1, and the above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 1. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method described in embodiment 1. For brevity, the description is omitted here.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly embodied as a hardware processor executing or executed with a combination of hardware and software modules in the processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
Those of ordinary skill in the art will appreciate that the elements of the various examples described in connection with the present embodiments, i.e., the algorithm steps, can be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (9)
1. The multi-access edge computing node selection method based on the regional pool networking is characterized by comprising the following steps of:
constructing a multi-access edge computing node area pool;
acquiring terminal service;
obtaining service processing time delay according to communication time delay between the terminal and the multi-access edge computing node, the multi-access edge computing node computing time delay, the terminal computing time delay and the migration time delay of terminal switching;
according to the local calculation energy consumption, the task transmission energy consumption and the multi-access edge calculation node calculation energy consumption, service processing energy consumption is obtained;
obtaining service benefit according to service processing cost and income after service processing;
obtaining an optimal multi-access edge computing node for processing terminal service in a multi-access edge computing node area pool according to service processing time delay, service processing energy consumption, service benefit and multi-access edge computing node load;
the total time delay of service processing is expressed as:
wherein: t1 isThe time delay of the processing service is T2, and the time delay of the processing service under other conditions is T2; />For the task quantity x i Unloading to MEC i Is a transmission time of (a); />For mobile users and MECs i Is not required, the connection time of (2); />For the task quantity x i In MEC i Is calculated according to the calculation time of (2); x is x i Offloading to MEC for user planning i Is a task amount of (1);
specifically:
calculating time delay for the multi-access edge calculation node; />Calculating time delay for the terminal;is migration delay; v (V) Ci Is MEC i Is calculated according to the calculated rate of (2); t (T) HO Switching time delay for the user;
the service processing energy consumption is expressed as:
calculating energy consumption for the local area; />Energy consumption is transmitted for the task; />Calculating energy consumption for the multi-access edge calculation node;
the service benefit is expressed as:
the service processing cost is; />Is incomes for business.
2. The multi-access edge computing node selection method based on regional pool networking as claimed in claim 1, wherein the communication delay between the terminal and the multi-access edge computing node is:
wherein:for the task quantity x i Unloading to MEC i Is a transmission time of (a);
for the task quantity x i In MEC i Is calculated according to the calculation time of (2);
to calculate result ρx i From MEC i Time of transmission to the mobile user;
is MEC i Processing task amount x i The time required;
wherein: x is x i Offloading to MEC for user planning i Is a task amount of (1);for mobile users and MECs i Is not required, the connection time of (2); />Pre-handoff and MEC for user i An uplink transmission rate; />Pre-handoff and MEC for user i A downlink transmission rate; />For user switching and MEC i An uplink transmission rate; />For user switching and MEC i A downlink transmission rate; v (V) Ci Is MEC i Is calculated according to the calculated rate of (2); ρx i To calculate the result size.
3. The method for selecting multiple access edge computing nodes based on regional pool networking as claimed in claim 1, wherein the multiple access edge computing nodes calculate time delays as follows:
or, the terminal calculates the time delay as follows:
wherein W is the total task amount, V Cl The rate is calculated for the user terminal.
4. The method for selecting multiple access edge computing nodes based on regional pool networking as claimed in claim 1, wherein in the multiple access edge computing node regional pool, when the terminal is switched, the task is not migrated, and the migration delay of the terminal switching is:wherein: t (T) HO And switching the time delay for the user.
5. The method for selecting a multi-access edge computing node based on regional pool networking as claimed in claim 1, wherein the local computing energy consumption is:
wherein: p (P) Cl Calculating single bit energy consumption for the local; w is the total amount of tasks;
or, the task transmission energy consumption is as follows:
wherein: p (P) Rl The energy consumption is single bit for local transmitting/receiving; ρx i Calculating the size of the result;
or, the multi-access edge computing node calculates the energy consumption as follows:
wherein: p (P) Ci Is MEC i Energy consumption per unit time.
6. The multi-access edge computing node selection method based on regional pool networking as claimed in claim 1, wherein the service processing cost is:
wherein: alpha, beta, gamma and epsilon are respectively the communication cost, the calculation cost, the migration cost and the unit energy consumption cost of unit time;
or, the business income is:
wherein: n is the revenue after the business process is completed,the maximum delay requirement is processed for the service.
7. A multi-access edge computing node selection system based on regional pool networking, comprising:
a region pool construction module configured to construct a multi-access edge computing node region pool;
the service acquisition module is configured to acquire terminal services;
the time delay acquisition module is configured to acquire service processing time delay according to the communication time delay of the terminal and the multi-access edge computing node, the multi-access edge computing node computing time delay, the terminal computing time delay and the migration time delay of terminal switching;
the energy consumption acquisition module is configured to obtain service processing energy consumption according to local calculation energy consumption, task transmission energy consumption and multi-access edge calculation node calculation energy consumption;
the benefit obtaining module is configured to obtain service benefits according to the service processing cost and the income after service processing;
the multi-access edge computing node determining module is configured to obtain an optimal multi-access edge computing node for processing terminal services in the multi-access edge computing node area pool according to service processing time delay, service processing energy consumption, service benefit and multi-access edge computing node load;
the total time delay of service processing is expressed as:
wherein: t1 isThe time delay of the processing service is T2, and the time delay of the processing service under other conditions is T2; />For the task quantity x i Unloading to MEC i Is a transmission time of (a); />For mobile users and MECs i Is not required, the connection time of (2); />For the task quantity x i In MEC i Is calculated according to the calculation time of (2); x is x i Offloading to MEC for user planning i Is a task amount of (1);
specifically:
calculating time delay for the multi-access edge calculation node; />Calculating time delay for the terminal;is migration delay; v (V) Ci Is MEC i Is calculated according to the calculated rate of (2); t (T) HO Switching time delay for the user;
the service processing energy consumption is expressed as:
calculating energy consumption for the local area; />Energy consumption is transmitted for the task; />Calculating energy consumption for the multi-access edge calculation node;
the service benefit is expressed as:
the service processing cost is; />Is incomes for business.
8. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of any one of claims 1-6.
9. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of any of claims 1-6.
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