CN115002799A - Task unloading and resource allocation method for industrial hybrid network - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
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Abstract
The invention discloses a task unloading and resource allocation method for an industrial hybrid network, which belongs to the technical field of industrial Internet of things communication and comprises the following steps: forming a three-layer edge computing network architecture system, adopting an unloading strategy, and selecting different communication links to execute a data uplink transmission task by applying a 0-1 integer programming method; calculating local calculation time and energy consumption, time delay and energy consumption of uplink transmission and calculation time and energy consumption of the WiFi base station; calculating the time delay of the task transmitted to the cloud storage; calculating total energy consumption and total time delay; establishing an objective function, and applying linear combination between time delay and energy consumption according to the requirements of different tasks to measure the performance of a three-layer edge computing network architecture system; and optimizing an unloading strategy by using a discrete particle swarm algorithm to minimize an objective function. The invention can improve the user service quality, reduce the link transmission delay and meet the overall requirements of the system by optimizing the link selection and the resource allocation in the mobile edge calculation.
Description
Technical Field
The invention relates to the technical field of industrial Internet of things communication, in particular to a task unloading and resource allocation method for an industrial hybrid network.
Background
With the rapid development of a new generation of wireless transmission technology, more and more electronic devices and intelligent devices accompany production and life, so that the technology of the internet of things is continuously concerned. In the industrial internet of things, real-time processing of computing tasks is required, and the relationship between releasing a large amount of computing data and ultra-low delay becomes an important bottleneck. To address the limited computing power of mobile devices, computing offloading has become a key technology to relieve the computing burden. The computing task is offloaded from the mobile device side to the cloud side, and although the remote cloud side has strong computing capability and storage capability, the problems of low bandwidth and serviceability in cloud computing need to be considered, for example, a large delay is generated when the mobile device transmits data to a remote cloud server. As a new architecture, mobile edge computing was introduced to address the shortcomings of cloud computing, which can reduce latency and increase computing power. In a mobile edge system, an edge node is closer to a mobile device than a remote cloud server and is used to perform computing tasks by selecting an appropriate communication mode to transmit data to the edge server to meet stringent quality of service requirements.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a task unloading and resource allocation method facing an industrial hybrid network, which can improve the user service quality, reduce the link transmission delay and meet the overall requirements of the system by optimizing the link selection and resource allocation in the mobile edge calculation.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a task unloading and resource allocation method facing an industrial hybrid network comprises the following steps:
step 1, on the basis of a basic framework of a mobile edge computing system, heterogeneous network equipment is used as an edge layer access point to form a three-layer edge computing network framework system, an unloading strategy is adopted according to the proportion of the data volume generated by user equipment, and different communication links are selected by applying a 0-1 integer programming method to execute a data uplink transmission task;
step 2, applying an unloading strategy to data of a user equipment layer, and calculating local calculation time and energy consumption, uplink transmission time delay and energy consumption and WiFi base station calculation time and energy consumption;
step 3, calculating the time delay of the task processed by the WiFi base station from transmission to cloud storage;
step 4, calculating the total energy consumption and the total time delay for completing all the subtasks according to the steps 2 and 3;
step 5, establishing a target function of a linear weighting function based on the total time delay and the total energy consumption, adjusting the optimization deviation between the total time delay and the total energy consumption by using a balance factor, and applying linear combination between the time delay and the energy consumption according to the requirements of different tasks to measure the performance of the three-layer edge computing network architecture system;
and 6, optimizing an unloading strategy by using a discrete particle swarm algorithm to minimize the target function.
The technical scheme of the invention is further improved as follows: in step 1, in the uplink transmission process of data, the 0-1 integer programming method for selecting the link communication mode application is as follows:
a∈(a i,j ) N*M
the edge server access point selection decision constraints are:
a i,j ∈{0,1},i∈N,j∈M
∑a i,j ＝1,i∈N,j∈M
wherein, a i,j Is a binary variable, N represents user equipment, M represents edge node if a i,j If equal to 0, the communication mode 1 is selected for offloading, a i,j The communication mode 2 is selected to offload as 1.
The technical scheme of the invention is further improved as follows: in step 2, the local calculation time is:
the local calculation energy consumption is as follows:
wherein x is i,j Indicating the offloading rate of data transmission of the user equipment in different communication modes, (1-x) i,j ) Representing local calculation of the user equipment, L n The total number of tasks generated for the user equipment,
is the CPU cycle frequency, F, of the user equipment i loc Is the computing power of the user device.
The technical scheme of the invention is further improved as follows: in step 2, in the process of data uplink transmission, each user equipment needs to select a corresponding link for data transmission, and the data volume needing to be transmitted is distributed according to the size of bits, wherein y i,j Indicating selection of communication mode 1 for direct transmission to WiFi base station, (1-y) i,j ) The communication mode 2 is selected to carry out auxiliary transmission to the WiFi base station through the 5G relay;
a i,j when 0, the transmission rate directly transmitted to the WiFi base station is:
the transmission delay and energy consumption for direct transmission to the WiFi base station are respectively:
the calculation time delay and energy consumption when directly transmitting to the WiFi base station for calculation are as follows:
a i,j the transmission rate for assisting transmission and offloading the task to the WiFi base station through the 5G relay is 1:
the transmission delay and the energy consumption for performing auxiliary transmission and unloading tasks to the WiFi base station through the 5G relay are respectively:
after auxiliary transmission is performed through a 5G relay, the calculation time delay and energy consumption of the WiFi base station are as follows:
wherein, the first and the second end of the pipe are connected with each other,
is the bandwidth in the case of direct transmission,
bandwidth for transmission assisted by 5G relays, P i,j (t) is the transmission power of the user equipment, h w For the channel parameters in direct transmission, h g To assist the channel parameters of the transmission by means of the 5G relay,
the power of additive white gaussian noise in direct transmission,
to aid the additive white gaussian noise power of the transmission by the 5G relay,
for the directly transmitted CPU cycle frequency,
CPU cycle frequency, F, for transmission assisted by 5G relays i w Is the computing power of the WiFi base station node.
The technical scheme of the invention is further improved as follows: in step 3, the time delay from the task processed by the WiFi base station to the cloud storage is:
wherein, B c Bandwidth, P, for WiFi base station transmission to cloud c And (t) is the transmission power of the WiFi base station.
The technical scheme of the invention is further improved as follows: in step 4, the total energy consumption for completing all subtasks is as follows:
the maximum time delay for completing all subtasks is:
the total time delay for completing the subtasks is:
T total (a)＝T RA (a)
for each task, it needs to be done by direct transmission and 5G relay auxiliary transmission in parallel, and after the two links are transmitted together, the minimum delay can represent the delay required in this system, which is expressed by the following formula:
s.t.T RA ＞1
0≤F i loc ≤F i w ≤F i max
0≤x i,j ≤1
0≤y i,j ≤1
wherein, F i max To calculate the maximum power, B total The total bandwidth transmitted for the entire N subtasks.
The technical scheme of the invention is further improved as follows: in step 5, the objective function is:
Φ(a)＝λT total (a)+(1-λ)E total (a)
the constraint conditions of the optimization process are as follows:
∑B n ＝B total
wherein, B n Is the bandwidth of the nth subtask, B total Is the total bandwidth.
The technical scheme of the invention is further improved as follows: in the step 6, a discrete particle swarm algorithm is adopted to solve, and a e (a) in the unloading strategy is obtained i,j ) N*M
Conversion into vectors
Z＝(Z 1 ,Z 2 ,...Z i )
Wherein Z is i ＝j。
Due to the adoption of the technical scheme, the invention has the technical progress that:
1. the invention combines the existing mobile edge computing unloading technology, can solve the problems of large transmission delay, low service reliability and the like in cloud computing, introduces different communication modes during link transmission on the basis, distributes computing tasks needing to be transmitted according to data quantity, performs selective transmission in different communication modes, and finally performs computing in a transmission edge server, can solve the problem of network blockage in the transmission process, and distributes and transmits the data quantity according to standards to achieve the aim of minimizing delay in a system.
2. Based on a three-layer network architecture model, the invention completes the arrangement and deployment algorithm research oriented to low time delay, high resource efficiency and optimal link communication mode selection. The method is oriented to delay and reliability sensitive tasks, and constraint nonlinear problem optimization of link communication selection and power distribution is established. Based on the theories of 0-1 integer programming, queuing game theory, multi-objective optimization and the like, the effective balance of the service request acceptance rate and the resource utilization rate is realized by cooperatively optimizing the link delay and the resource efficiency, and finally, the time delay of the whole system is minimized under the constraints of communication, computing resources and energy consumption.
Drawings
FIG. 1 is a flow chart of the system architecture of the present invention;
FIG. 2 is a communication link topology of the present invention;
fig. 3 is a flow chart of the offloading policy of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following figures and examples:
as shown in fig. 1, a task offloading and resource allocation method for an industrial hybrid network includes the following steps:
step 1, on the basis of a basic framework of a mobile edge computing system, heterogeneous network equipment is used as an edge layer access point to form a three-layer edge computing network framework system, an unloading strategy is adopted according to the proportion of the data volume generated by user equipment, and different communication links are selected by applying a 0-1 integer programming method to execute a data uplink transmission task;
as shown in fig. 2, in the communication link topology, the user equipment i belongs to N, the edge node j belongs to M, and the user equipment generates data as follows:
wherein I represents the total number of CPU cycles, L represents the task data amount, and E represents the energy consumption constraint.
As shown in fig. 3, an offloading policy is adopted in the user equipment layer, and data is divided into two parts according to bits, wherein one part of data is locally calculated, and the other part of data is uplink-transmitted to the WiFi base station of the edge layer server for calculation. In the process of data uplink transmission, a 0-1 integer programming method for selecting a link communication mode application comprises the following steps:
a∈(a i,j ) N*M
wherein, a i,j Is a binary variable, N represents user equipment, M represents edge node if a i,j When the communication mode is 0, the communication mode 1 is selected for unloading, a i,j The communication mode 2 is selected to offload as 1.
In the example process, the communication mode 1 adopts a mode of directly transmitting to the WiFi base station, the communication mode 2 adopts a mode of assisting to transmit to the WiFi base station by means of 5G relay, and under any communication mode, the data volume finally generated by the user equipment is calculated in the WiFi base station of the edge server.
The edge server access point selection decision constraint is:
a i,j ∈{0,1},i∈N,j∈M
∑a i,j ＝1,i∈N,j∈M
step 2, applying an unloading strategy to data of a user equipment layer, and calculating local calculation time and energy consumption, uplink transmission time delay and energy consumption and WiFi base station calculation time and energy consumption;
due to the limited computing capacity of the user equipment, the data in the user equipment layer is subjected to an unloading strategy, and partial computing tasks are unloaded to a nearby edge server to complete the tasks, so that the effect of reducing delay can be achieved to a certain extent, wherein x i,j Indicating the offloading rate of the user equipment transmitting data in different communication modes, (1-x) i,j ) Representing user equipment local computations.
The local computation time is:
the local calculation energy consumption is as follows:
wherein L is n The total number of tasks generated for the user equipment,
is the CPU cycle frequency, F, of the user equipment i loc Is the computing power of the user device.
Data is transmitted in the uplinkBecause the transmission rates of the two communication modes are greatly different, a proper communication mode is selected, so that the time consumed during transmission is relatively less, and the overall time delay of the system is correspondingly reduced; each user equipment needs to select a corresponding link for data transmission, and the data quantity needing to be transmitted is distributed according to the bit size, wherein y i,j Indicating selection of communication mode 1 for direct transmission to WiFi base station, (1-y) i,j ) Then, the communication mode 2 is selected to perform auxiliary transmission to the WiFi base station through the 5G relay, and the transmission time and the calculation time of each link and the energy consumption thereof are respectively calculated:
a i,j when 0, the transmission rate directly transmitted to the WiFi base station is:
the transmission delay and energy consumption for directly transmitting to the WiFi base station are respectively as follows:
the calculation time delay and energy consumption when directly transmitting to the WiFi base station for calculation are as follows:
a i,j the transmission rate for assisting transmission and offloading the task to the WiFi base station through the 5G relay is 1:
the transmission delay and the energy consumption for performing auxiliary transmission and unloading tasks to the WiFi base station through the 5G relay are respectively:
after auxiliary transmission is performed through a 5G relay, the calculation time delay and energy consumption of the WiFi base station are as follows:
wherein the content of the first and second substances,
is the bandwidth in the case of direct transmission,
bandwidth for transmission assisted by 5G relays, P i,j (t) is the transmission power of the user equipment, h w For channel parameters in direct transmission, h g To assist the channel parameters of the transmission by means of the 5G relay,
the power of additive white gaussian noise in direct transmission,
to aid the additive white gaussian noise power of the transmission by the 5G relay,
for the directly transmitted CPU cycle frequency,
CPU cycle frequency, F, for transmission assisted by 5G relays i w Is the computing power of the WiFi base station node.
Step 3, calculating the time delay of task transmission to cloud storage processed by the WiFi base station;
data handled by the wiFi basic station will be transmitted to the high in the clouds storage finally, and the wiFi basic station is to the transmission delay and the energy consumption in high in the clouds, because the computing power of high in the clouds is strong, will ignore its energy consumption:
then its transmission rate is:
the time delay from the task processed by the WiFi base station to the cloud storage is as follows:
wherein, B c Bandwidth, P, for WiFi base station transmission to cloud c And (t) is the transmission power of the WiFi base station.
Step 4, calculating total energy consumption and total time delay for completing all subtasks according to the steps 2 and 3;
the time delay for selecting different links to perform the offloading process is represented as:
the total energy consumption to complete all subtasks is:
the maximum time delay for completing all subtasks is:
because the relays work in parallel, the total time delay for completing the tasks is the maximum time delay for the relays to complete the respective subtasks, and the total time delay for completing the subtasks is as follows:
T total (a)＝T RA (a)
for each task, it needs to be done by direct transmission and 5G relay auxiliary transmission in parallel, and after the two links are transmitted together, the minimum delay can represent the delay required in this system, which is expressed by the following formula:
s.t.T RA ＞1
0≤F i loc ≤F i w ≤F i max
0≤x i,j ≤1
0≤y i,j ≤1
wherein, F i max To calculate the maximum power, B total The total bandwidth transmitted for the entire N subtasks.
Step 5, establishing a target function of a linear weighting function based on the total time delay and the total energy consumption, adjusting the optimization deviation between the total time delay and the total energy consumption by using a balance factor, and applying linear combination between the time delay and the energy consumption according to the requirements of different tasks to measure the performance of the three-layer edge computing network architecture system;
in the whole three-layer edge computing network architecture system, total delay and total energy consumption are two important indexes for measuring the performance of the system, the system needs to be optimized by minimizing delay and balancing energy consumption, in order to improve the system performance, the optimization deviation between the total delay and the total energy consumption is flexibly adjusted by utilizing a balance factor, a linear weighting function based on the total delay and the total energy consumption is provided, and an objective function is established:
Φ(a)＝λT total (a)+(1-λ)E total (a)
after the objective function is established, the system is optimized by minimizing Φ (a). In the system, the weighted cost Φ (a) is affected by the offloading policy, the bandwidth allocation and the communication mode selection, and the constraint conditions of the optimization process are as follows:
∑B n ＝B total
wherein, B n Is the bandwidth of the nth subtask, B total Is the total bandwidth.
Step 6, optimizing an unloading strategy by applying a discrete particle swarm algorithm to minimize a target function;
the unloading strategy is a 0-1 integer programming problem, and the convex optimization problem is difficult to solve by adopting a conventional optimization method, so that the discrete particle swarm algorithm is adopted for solving. It needs to make a e (a) in the unloading strategy i,j ) N*M
Conversion into vectors
Z＝(Z 1 ,Z 2 ,...Z i )
Wherein, Z i ＝j。
p l ＝(p l,1 ,p l,2 ,...p l,N )、v l ＝(v l,1 ,v l,2 ,...v l,N ) Respectively, as the position and velocity vectors of the/particle. p is a radical of best ＝(p bestl,1 ,p bestl,2 ,...p bestl,N ) To the optimum position, g best ＝(g bestl,1 ,g bestl,2 ,...g bestl,N ) Is the optimal particle.
And finally, obtaining an accurate particle value, namely an optimal unloading strategy, by repeatedly updating the speed and the position, so that the objective function is minimized, and finally, the time delay of the whole system is minimized under the constraints of communication, computing resources and energy consumption.
In summary, the present invention introduces constraints of energy consumption and computational power on the basis of using a given offloading policy and bandwidth allocation, and finally proves the effectiveness of optimizing the system delay when task offloading is performed by selecting different links according to the size of task data by comparing with the case of selecting a single link for task offloading.
Claims (8)
1. A task unloading and resource allocation method facing an industrial hybrid network is characterized in that: the method comprises the following steps:
step 1, on the basis of a basic framework of a mobile edge computing system, heterogeneous network equipment is used as an edge layer access point to form a three-layer edge computing network framework system, an unloading strategy is adopted according to the proportion of the data volume generated by user equipment, and different communication links are selected by applying a 0-1 integer programming method to execute a data uplink transmission task;
step 2, applying an unloading strategy to data of a user equipment layer, and calculating local calculation time and energy consumption, uplink transmission time delay and energy consumption and WiFi base station calculation time and energy consumption;
step 3, calculating the time delay of the task processed by the WiFi base station from transmission to cloud storage;
step 4, calculating total energy consumption and total time delay for completing all subtasks according to the steps 2 and 3;
step 5, establishing a target function of a linear weighting function based on the total time delay and the total energy consumption, adjusting the optimization deviation between the total time delay and the total energy consumption by using a balance factor, and applying linear combination between the time delay and the energy consumption according to the requirements of different tasks to measure the performance of the three-layer edge computing network architecture system;
and 6, optimizing an unloading strategy by using a discrete particle swarm algorithm to minimize the target function.
2. The industrial hybrid network-oriented task offloading and resource allocation method according to claim 1, wherein: in step 1, in the uplink transmission process of data, the 0-1 integer programming method for selecting the link communication mode application is as follows:
a∈(a i,j ) N*M
the edge server access point selection decision constraint is:
a i,j ∈{0,1},i∈N,j∈M
∑a i,j ＝1,i∈N,j∈M
wherein, a i,j Is a binary variable, N represents user equipment, M represents edge node if a i,j When the communication mode is 0, the communication mode 1 is selected for unloading, a i,j The communication mode 2 is selected to offload as 1.
3. The industrial hybrid network-oriented task offloading and resource allocation method according to claim 1, wherein: in step 2, the local calculation time is:
the local calculation energy consumption is as follows:
wherein x is i,j Indicating the offloading rate of the user equipment transmitting data in different communication modes, (1-x) i,j ) Representing local calculation of the user equipment, L n The total number of tasks generated for the user equipment,
is the CPU cycle frequency, F, of the user equipment i loc Is the computing power of the user device.
4. The industrial hybrid network-oriented task offloading and resource allocation method according to claim 1, wherein: in step 2, in the uplink transmission process of data, each user equipment needs to select a corresponding link for data transmission, and the data volume needing to be transmitted is distributed according to the size of bits, wherein y i,j Indicating selection of communication mode 1 for direct transmission to WiFi base station, (1-y) i,j ) The communication mode 2 is selected to carry out auxiliary transmission to the WiFi base station through the 5G relay;
a i,j when 0, the transmission rate directly transmitted to the WiFi base station is:
the transmission delay and energy consumption for directly transmitting to the WiFi base station are respectively as follows:
the calculation time delay and energy consumption when directly transmitting to the WiFi base station for calculation are as follows:
a i,j auxiliary transport and offloading of tasks to Wi over 5G relay 1The transmission rate of the Fi base station is as follows:
the transmission delay and the energy consumption for performing auxiliary transmission through a 5G relay and unloading tasks to a WiFi base station are respectively as follows:
after auxiliary transmission is performed through a 5G relay, the calculation time delay and energy consumption of the WiFi base station are as follows:
wherein the content of the first and second substances,
is the bandwidth in the case of direct transmission,
bandwidth for transmission assisted by 5G relays, P i,j (t) is the transmission power of the user equipment, h w For the channel parameters in direct transmission, h g To assist the channel parameters of the transmission by means of the 5G relay,
for additive in direct transmissionThe power of the white gaussian noise is,
to aid the additive white gaussian noise power of the transmission by the 5G relay,
for the directly transmitted CPU cycle frequency,
CPU cycle frequency, F, for transmission assisted by 5G relays i w Is the computing power of the WiFi base station node.
5. The industrial hybrid network-oriented task offloading and resource allocation method according to claim 1, wherein: in step 3, the time delay from the task processed by the WiFi base station to the cloud storage is:
wherein, B c Bandwidth, P, for WiFi base station transmission to cloud c And (t) is the transmission power of the WiFi base station.
6. The industrial hybrid network-oriented task offloading and resource allocation method according to claim 1, wherein: in step 4, the total energy consumption for completing all subtasks is as follows:
the maximum time delay for completing all subtasks is:
the total time delay for completing the subtasks is:
T total (a)＝T RA (a)
for each task, it needs to be done by direct transmission and 5G relay auxiliary transmission in parallel, and after the two links are transmitted together, the minimum delay can represent the delay required in this system, which is expressed by the following formula:
s.t.T RA ＞1
0≤x i,j ≤1
0≤y i,j ≤1
wherein, F i max To calculate the maximum power, B total The total bandwidth transmitted for the entire N subtasks.
7. The industrial hybrid network-oriented task offloading and resource allocation method according to claim 1, wherein: in step 5, the objective function is:
Φ(a)＝λT total (a)+(1-λ)E total (a)
the constraint conditions of the optimization process are as follows:
∑B n ＝B total
wherein, B n Is the bandwidth of the nth subtask, B total Is the total bandwidth.
8. The industrial hybrid network-oriented task offloading and resource allocation method according to claim 1, wherein: in the step 6, a discrete particle swarm algorithm is adopted to solve, and the unloading strategy is
a∈(a i,j ) N*M
Conversion into vectors
Z＝(Z 1 ,Z 2 ,...Z i )
Wherein Z is i ＝j。
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