CN115002799B - 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
- H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
- H04W28/18—Negotiating wireless communication parameters
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
- H04W28/18—Negotiating wireless communication parameters
- H04W28/20—Negotiating bandwidth
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
- H04W28/18—Negotiating wireless communication parameters
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- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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 data uplink transmission tasks by applying a 0-1 integer programming method; calculating local calculation time and energy consumption, uplink transmission time delay and energy consumption, and WiFi base station calculation time and energy consumption; calculating the time delay of the task transmission to the cloud storage; calculating total energy consumption and total time delay; establishing an objective function, and measuring the performance of the three-layer edge computing network architecture system by applying linear combination between time delay and energy consumption according to the requirements of different tasks; and optimizing an unloading strategy by applying a discrete particle swarm algorithm to minimize an objective function. The invention can improve the service quality of users, reduce the transmission delay of links and meet the overall requirement 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 new generation 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 attracting attention. In industrial internet of things, real-time processing of computing tasks is required, and releasing the relationship between a large amount of computing data and ultra-low latency becomes an important bottleneck. In order to solve the problem of limited computing power of mobile devices, computing offloading is a key technology to ease the computing burden thereof. The computing task is offloaded from the mobile device end to the cloud end, and although the remote cloud end has strong computing capability and storage capability, the problems of low bandwidth and service in cloud computing need to be considered, for example, when the mobile device transmits data to a remote cloud server, a large delay is generated. 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 for performing computing tasks, and data is transmitted to the edge server by selecting a proper communication mode so as to meet strict service quality requirements.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a task unloading and resource allocation method for an industrial hybrid network, which can improve the service quality of users, reduce the transmission delay of links and meet the overall demands of a system by optimizing the link selection and resource allocation in mobile edge calculation.
In order to solve the technical problems, the invention adopts the following technical scheme:
a task unloading and resource allocation method for an industrial hybrid network comprises the following steps:
step 1, on the basis of a mobile edge computing system infrastructure, heterogeneous network equipment is used as an edge layer access point to form a three-layer edge computing network architecture system, an unloading strategy is adopted according to the proportion of the data volume generated by user equipment, and different communication links are selected to execute data uplink transmission tasks by applying a 0-1 integer programming method;
step 2, applying an unloading strategy to the data of the user equipment layer, and calculating the local calculation time and energy consumption, the uplink transmission time delay and energy consumption and the WiFi base station calculation time and energy consumption;
step 3, calculating the time delay of the task processed by the WiFi base station to be transmitted to the cloud storage;
step 4, calculating the total energy consumption and the total time delay for completing all subtasks according to the steps 2 and 3;
step 5, establishing an objective function of a linear weighting function based on the total time delay and the total energy consumption, adjusting the optimized deviation between the total time delay and the total energy consumption by using a balance factor, and measuring the performance of the three-layer edge computing network architecture system by applying linear combination between the time delay and the energy consumption according to the requirements of different tasks;
and 6, optimizing an unloading strategy by applying a discrete particle swarm algorithm to minimize an objective 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 applied by selecting the link communication mode 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 is i,j For a binary variable, N represents the user equipment and M represents the edge node if a i,j =0, then communication mode 1 is selected for unloading, a i,j And =1 selects communication mode 2 for unloading.
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 Representing the offloading rate of user equipment transmitting data in different communication modes, (1-x) i,j ) Representing user equipment local computation, L n For the total number of tasks generated by the user device,for CPU cycle frequency of user equipment, F i loc Is the computing power of the user equipment.
The technical scheme of the invention is further improved as follows: in step 2, during the uplink transmission of data, each ue needs to select a corresponding link to perform data transmission, and allocates the data size to be transmitted according to the bit size, where y is i,j Indicating selection of communication mode 1 for direct transmission to a WiFi base station, (1-y) i,j ) Then the communication mode 2 is selected to be transmitted to the WiFi base station in an auxiliary mode through the 5G relay;
a i,j when=0, the transmission rate of direct transmission to the WiFi base station is:
the transmission delay and the energy consumption of the direct transmission 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 =1, the transmission rate for auxiliary transmission through 5G relay and task offloading at WiFi base station is:
auxiliary transmission is carried out through the 5G relay, and the transmission delay and the energy consumption of the task to be unloaded at the WiFi base station are respectively as follows:
after auxiliary transmission is carried out through the 5G relay, the calculation time delay and the energy consumption of the WiFi base station are as follows:
wherein,for bandwidth in direct transmission, +.>P for assisting transmission bandwidth by 5G relay i,j (t) is the transmission power of the user equipment, h w For channel parameters in direct transmission, h g For channel parameters for auxiliary transmission by means of 5G relay, < >>Power for additive white gaussian noise when directly transmitted, +.>For additive white gaussian noise power for auxiliary transmission by means of 5G relay,CPU cycle frequency for direct transfer, +.>For CPU cycle frequency with 5G relay assisted transmission, F 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 of the task processed by the WiFi base station to be transmitted to the cloud storage is as follows:
wherein B is c P is the bandwidth transmitted to the cloud end by the WiFi base station 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:
the maximum delay for completing all subtasks is:
the total delay for completing the subtasks is:
T total (a)＝T RA (a)
for each task, the two links need to be transmitted in parallel through direct transmission and 5G relay auxiliary transmission, and after the two links are transmitted together, the minimum delay can represent the delay required under the 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 is i max For maximum calculation force, B total The total bandwidth transmitted for the entire N sub-tasks.
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 is n Bandwidth for 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 i,j ) N*M
Conversion into vectors
Z＝(Z 1 ,Z 2 ,...Z i )
Wherein Z is i ＝j。
By adopting the technical scheme, the invention has the following technical progress:
1. the invention provides a method for solving the problems of large transmission delay, low service reliability and the like in cloud computing by combining the existing mobile edge computing and unloading technology, and based on the method, different communication modes are selected when a link is transmitted, computing tasks to be transmitted are distributed according to data quantity, the computing tasks to be transmitted are selectively transmitted in the different communication modes, and finally the computing is carried out in a transmission edge server, so that the problem of network blocking in the transmission process can be solved, the data quantity is distributed and transmitted according to the standard, and the aim of minimizing delay in a system is achieved.
2. The invention completes the research of the scheduling and deployment algorithm for low-delay, resource efficient and optimal link communication mode selection based on a three-layer network architecture model. And aiming at sensitive tasks of time delay and reliability, the constraint nonlinear problem optimization of link communication selection and power distribution is established, and compared with the traditional mobile edge computing network, the method can provide collaborative communication and computing tasks, improve the resource utilization rate of a server, effectively reduce the influence caused by transmission tasks and reduce the time delay. Based on 0-1 integer programming, queuing game theory, multi-objective optimization and other theories, the effective balance between 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, calculation resources and energy consumption.
Drawings
FIG. 1 is a flow chart of the system architecture of the present invention;
FIG. 2 is a topology of a communication link of the present invention;
FIG. 3 is a flow chart of the offloading policy of the invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and examples:
as shown in fig. 1, a method for task offloading and resource allocation for an industrial hybrid network includes the following steps:
step 1, on the basis of a mobile edge computing system infrastructure, heterogeneous network equipment is used as an edge layer access point to form a three-layer edge computing network architecture system, an unloading strategy is adopted according to the proportion of the data volume generated by user equipment, and different communication links are selected to execute data uplink transmission tasks by applying a 0-1 integer programming method;
as shown in fig. 2, in the communication link topology diagram, the user equipment i e N, the edge node j e M, 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 at the user equipment layer to divide data into two parts according to bits, wherein one part of data is calculated locally, and the other part of data is transmitted to the WiFi base station of the edge layer server for calculation. In the uplink transmission process of data, the 0-1 integer programming method applied by selecting the link communication mode comprises the following steps:
a∈(a i,j ) N*M
wherein a is i,j For a binary variable, N represents the user equipment and M represents the edge node if a i,j =0, then communication mode 1 is selected for unloading, a i,j And =1 selects communication mode 2 for unloading.
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 transmitting to the WiFi base station with the assistance of a 5G relay, and in either communication mode, the data volume generated by the user equipment is calculated in the edge server WiFi base station.
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 the data of the user equipment layer, and calculating the local calculation time and energy consumption, the uplink transmission time delay and energy consumption and the WiFi base station calculation time and energy consumption;
due to limited computing power of the user equipment, offloading policy is applied to data at the user equipment layer to offload part of computing tasks to nearby edge servicesThe task is completed in the device, and the effect of reducing delay can be achieved to a certain extent, wherein x is i,j Representing the offloading rate of user equipment transmitting data in different communication modes, (1-x) i,j ) Representing the user device local computation.
The local calculation time is as follows:
the local calculation energy consumption is as follows:
wherein L is n For the total number of tasks generated by the user device,for CPU cycle frequency of user equipment, F i loc Is the computing power of the user equipment.
In the uplink transmission process of data, due to the fact that the transmission rates of the two communication modes are greatly different, the proper communication mode is selected, the time required to be consumed in transmission can be relatively reduced, 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 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 a WiFi base station, (1-y) i,j ) Then, the communication mode 2 is selected to carry out 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 of each link are calculated respectively:
a i,j when=0, the transmission rate of direct transmission to the WiFi base station is:
the transmission delay and the energy consumption of the direct transmission 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 =1, the transmission rate for auxiliary transmission through 5G relay and task offloading at WiFi base station is:
auxiliary transmission is carried out through the 5G relay, and the transmission delay and the energy consumption of the task to be unloaded at the WiFi base station are respectively as follows:
after auxiliary transmission is carried out through the 5G relay, the calculation time delay and the energy consumption of the WiFi base station are as follows:
wherein,for bandwidth in direct transmission, +.>P for assisting transmission bandwidth by 5G relay i,j (t) is the transmission power of the user equipment, h w For channel parameters in direct transmission, h g For channel parameters for auxiliary transmission by means of 5G relay, < >>Power for additive white gaussian noise when directly transmitted, +.>For additive white gaussian noise power for auxiliary transmission by means of 5G relay,CPU cycle frequency for direct transfer, +.>For CPU cycle frequency with 5G relay assisted transmission, F i w Is the computing power of the WiFi base station node.
Step 3, calculating the time delay of the task processed by the WiFi base station to be transmitted to the cloud storage;
the data processed by the WiFi base station are finally transmitted to the cloud for storage, and the transmission time delay and energy consumption from the WiFi base station to the cloud are ignored because of the strong computing capacity of the cloud:
its transmission rate is:
the time delay of the task processed by the WiFi base station to be transmitted to the cloud storage is as follows:
wherein B is c P is the bandwidth transmitted to the cloud end by the WiFi base station c And (t) is the transmission power of the WiFi base station.
Step 4, calculating the total energy consumption and the total time delay for completing all subtasks according to the steps 2 and 3;
the delay in selecting different links for the offloading process is expressed as:
the total energy consumption to complete all subtasks is:
the maximum delay for completing all subtasks is:
because the relays work in parallel, the total delay for completing the task is the maximum delay for the relays to complete the respective subtasks, and the total delay for completing the subtasks is:
T total (a)＝T RA (a)
for each task, the two links need to be transmitted in parallel through direct transmission and 5G relay auxiliary transmission, and after the two links are transmitted together, the minimum delay can represent the delay required under the 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 is i max For maximum calculation force, B total The total bandwidth transmitted for the entire N sub-tasks.
Step 5, establishing an objective function of a linear weighting function based on the total time delay and the total energy consumption, adjusting the optimized deviation between the total time delay and the total energy consumption by using a balance factor, and measuring the performance of the three-layer edge computing network architecture system by applying linear combination between the time delay and the energy consumption according to the requirements of different tasks;
in the whole three-layer edge computing network architecture system, total time delay and total energy consumption are two important indexes for measuring the performance of the system, delay needs to be minimized, energy consumption is weighed to optimize the system, in order to improve the system performance, the balance factor is utilized to flexibly adjust the optimization deviation between the total time delay and the total energy consumption, a linear weighting function based on the total time 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 this system, the offloading policy, bandwidth allocation and communication mode selection all affect the weighting cost Φ (a), and the constraints of the optimization process are:
∑B n ＝B total
wherein B is n Bandwidth for nth subtask, B total Is the total bandwidth.
Step 6, optimizing an unloading strategy by applying a discrete particle swarm algorithm to minimize an objective function;
the unloading strategy bit 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 solution is carried out by adopting an discrete particle swarm algorithm. A e (a) i,j ) N*M
Conversion into vectors
Z＝(Z 1 ,Z 2 ,...Z i )
Wherein Z is i ＝j。
p l ＝(p l,1 ,p l,2 ,...p l,N )、v l ＝(v l,1 ,v l,2 ,...v l,N ) Depicted as the position and velocity vector of the particle, respectively. P is p best ＝(p bestl,1 ,p bestl,2 ,...p bestl,N ) G is the best position 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 through repeated iteration updating of 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, calculation resources and energy consumption.
In summary, the present invention introduces constraints of energy consumption and computational effort based on the use of 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 task data size by comparing with the case that a single link is selected for task offloading.
Claims (1)
1. A task unloading and resource allocation method for an industrial hybrid network is characterized in that: the method comprises the following steps:
step 1, on the basis of a mobile edge computing system infrastructure, heterogeneous network equipment is used as an edge layer access point to form a three-layer edge computing network architecture system, an unloading strategy is adopted according to the proportion of the data volume generated by user equipment, and different communication links are selected to execute data uplink transmission tasks by applying a 0-1 integer programming method;
in step 1, in the uplink transmission process of data, the 0-1 integer programming method applied by selecting the link communication mode 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 is i,j For a binary variable, N represents the user equipment, M represents the edge node, if a i,j =0, then communication mode 1 is selected for unloading, a i,j Selecting communication mode 2 for unloading;
step 2, applying an unloading strategy to the data of the user equipment layer, and calculating the local calculation time and energy consumption, the uplink transmission time delay and energy consumption and the WiFi base station calculation time and energy consumption;
in step 2, the local calculation time is:
the local calculation energy consumption is as follows:
wherein x is i,j Representing the offloading rate of user equipment transmitting data in different communication modes, (1-x) i,j ) Representing user equipment local computation, L n For the total number of tasks generated by the user device,for CPU cycle frequency of user equipment, F i loc Computing power for the user equipment;
in the uplink transmission process of data, each user equipment needs to select a corresponding link to perform data transmission, and allocates the data volume to be transmitted according to the bit size, wherein y is as follows i,j Indicating selection of communication mode 1 for direct transmission to a WiFi base station, (1-y) i,j ) Then the communication mode 2 is selected to be transmitted to the WiFi base station in an auxiliary mode through the 5G relay;
a i,j when=0, the transmission rate of direct transmission to the WiFi base station is:
the transmission delay and the energy consumption of the direct transmission 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 =1, the transmission rate for auxiliary transmission through 5G relay and task offloading at WiFi base station is:
auxiliary transmission is carried out through the 5G relay, and the transmission delay and the energy consumption of the task to be unloaded at the WiFi base station are respectively as follows:
after auxiliary transmission is carried out through the 5G relay, the calculation time delay and the energy consumption of the WiFi base station are as follows:
wherein,for bandwidth in direct transmission, +.>P for assisting transmission bandwidth by 5G relay i,j (t) is the transmission power of the user equipment, h w For channel parameters in direct transmission, h g For channel parameters for auxiliary transmission by means of 5G relay, < >>Work for additive white gaussian noise in direct transmissionRate of->For additive white gaussian noise power for auxiliary transmission by means of 5G relay +.>CPU cycle frequency for direct transfer, +.>For CPU cycle frequency with 5G relay assisted transmission, F i w The computing power of the WiFi base station node;
step 3, calculating the time delay of the task processed by the WiFi base station to be transmitted to the cloud storage;
in step 3, the transmission rate is:
the time delay of the task processed by the WiFi base station to be transmitted to the cloud storage is as follows:
wherein B is c P is the bandwidth transmitted to the cloud end by the WiFi base station c (t) is the transmit power of the WiFi base station;
step 4, calculating the total energy consumption and the total time delay for completing all subtasks according to the steps 2 and 3;
in step 4, the total energy consumption for completing all subtasks is:
the maximum delay for completing all subtasks is:
the total delay for completing the subtasks is:
T total (a)＝T RA (a)
for each task, the two links need to be transmitted in parallel through direct transmission and 5G relay auxiliary transmission, and after the two links are transmitted together, the minimum delay can represent the delay required under the 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
wherein F is i max For maximum calculation force, B total The total bandwidth transmitted for the entire N sub-tasks;
step 5, establishing an objective function of a linear weighting function based on the total time delay and the total energy consumption, adjusting the optimized deviation between the total time delay and the total energy consumption by using a balance factor, and measuring the performance of the three-layer edge computing network architecture system by applying linear combination between the time delay and the energy consumption according to the requirements of different tasks;
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 is n Bandwidth for the nth subtask;
and 6, optimizing an unloading strategy by applying a discrete particle swarm algorithm to minimize an objective function.
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| CN114237889A (en) * | 2021-12-17 | 2022-03-25 | 安徽师范大学 | Fog computing resource scheduling method based on improved particle swarm algorithm and neural network |
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