Complex road condition non-uniform speed vehicle task offloading method based on vnf instance sharing

Abstract

The application claims a complex road condition non-uniform speed vehicle task offloading method based on VNF instance sharing, belonging to the field of communication technology. In view of the problem that the high time-varying nature of vehicle speed and direction in large traffic hubs, complex interchanges and other scenes may cause instability or frequent interruption of inter-vehicle links, resulting in task offloading failure, a non-uniform speed vehicle task offloading method based on VNF instance sharing is proposed. According to the offloading demand of each sub-task in the vehicle task request and the change trend of the vehicle driving track, the method selects the candidate offloading vehicle and constructs the corresponding offloading path set, optimizes the resource utilization rate and constructs the optimal offloading decision according to the available computing resources of the candidate offloading vehicle and the VNF instance distribution, thereby effectively improving the system task completion rate and reducing the unit task offloading delay.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication networks, and in particular relates to a method for offloading tasks of vehicles with non-uniform speeds under complex road conditions based on VNF instance sharing. Background Art

[0002] With the advancement of wireless communication technology, vehicles are no longer simply means of transportation; they have become intelligent, connected vehicles with powerful communication and computing capabilities. These vehicles are factory-equipped with advanced sensing, wireless communication, and onboard computing modules. They also enable vehicle-to-vehicle (V2V) communication through technologies such as dedicated short-range communications based on the IEEE 802.11p protocol. Network function virtualization, by software-based and modularizing network functions, enables the implementation of diverse virtual network functions (VNFs) on top of physical networks.

[0003] Existing research on vehicle network task offloading primarily focuses on scenarios where vehicles have fixed directions and speeds, which is difficult to meet the needs of real-world applications. For example, in large transportation hubs and complex interchanges, vehicle directions and speeds adjust in real time with changing road conditions, leading to rapid and chaotic changes in network connectivity. This can cause inter-vehicle communication links to be interrupted, task offloading to fail, and ultimately reduce network resource utilization and degrade the user experience.

[0004] To address these issues, the present invention proposes a method for offloading tasks to vehicles with non-uniform speeds under complex road conditions based on VNF instance sharing. Based on the maximum offloading latency constraints, VNF type requirements, and vehicle trajectory trends in the vehicle offloading request, candidate offloading vehicles are selected for each subtask and a corresponding offloading path is constructed. Based on the remaining available computing resources of the candidate offloading vehicles and the distribution of VNF instances, a particle swarm algorithm is used to optimize task offloading decisions, finding the optimal offloading vehicle and offloading path for each subtask, thereby maximizing the system's task completion rate and minimizing the system's unit task offloading latency. Summary of the Invention

[0005] The present invention aims to solve the above problems in the prior art. It proposes a method for offloading tasks to vehicles with non-uniform speeds in complex road conditions based on VNF instance sharing. The technical solution of the present invention is as follows:

[0006] A method for offloading tasks of vehicles with non-uniform speed in complex road conditions based on VNF instance sharing, comprising the following steps:

[0007] 101. Using particle swarm algorithm to establish vehicle network task offloading optimization model, suppose task vehicle i *The offloading request J = {j}, where j is a parallel subtask, the particle swarm is the set N = {n}, and the offloading decision vector, migration vector and optimal fitness of each particle n in N are ψ n =(ψ n,j | j∈J ),s n =(s n,j | j∈J )and Among them, ψ n,j is the offloading path of subtask j in particle n, s n,j is ψ n,j The transformation step size is η, and the maximum number of iterations of the initialized particle swarm is η max , iteration count variable η=0, temporary set J′=J;

[0008] 102. If Take the first element j from the set J′, and according to the data amount a of subtask j j , the required virtual network function VNF type v j , computational complexity c j , establish a candidate offloading path set P for subtask j j ={p j,k}, where p j,k If it is a candidate offloading path coded as k for subtask j, jump to step 102; otherwise, jump to step 103;

[0009] 103. For each particle n in N, from each set P j | j∈J Choose any path p j,k Initialize the offload decision vector From the set J, each j corresponds to the numerical interval [0,|P j |) randomly selects an integer to initialize the migration vector s n Each component s in n,j | j∈J , calculate the particle fitness t n , initialize the optimal fitness of particle n Optimal unloading decision for particle n

[0010] 104. Let the global optimal unloading decision of particle swarm N be Iteration count variable η=η+1, if η≤η max , let the temporary set N′=N, jump to step 105, otherwise, jump to step 107;

[0011] 105. If Take the first element n from the set N′ and update the unloading decision vector ψn and the migration vector s n , calculate the particle fitness t n , jump to step 106, otherwise, jump to step 104;

[0012] 106. If Let the optimal fitness of particle n be Optimal unloading decision for particle n Jump to step 105, otherwise, jump to step 105;

[0013] 107. Output the global optimal offloading decision ψ best , the step ends.

[0014] Furthermore, in step 102, a candidate offloading path set P is established for subtask j. j ={p j,k}, specifically including the following steps:

[0015] 1) Initialize the set of all vehicles I = {i}, where i is any vehicle, and the set of candidate unloading vehicles The set of candidate offloading paths for subtask j Maximum tolerable delay of task offloading t max ;

[0016] 2) If Take the first element i from the set I, according to the amount of data a of subtask j j , Required virtual network function VNF type v j , computational complexity c j , calculate the minimum computing resources that vehicle i needs to allocate for subtask j Jump to step 3), otherwise, jump to step 4);

[0017] 3) If the remaining available computing resources f of vehicle i i r Greater than or equal to Add vehicle i to the candidate unloading vehicle set I′ and jump to step 2). Otherwise, jump to step 2);

[0018] 4) If Take the first element i from the set I′ and use the depth-first search algorithm to construct the task vehicle i * The set P′ of all feasible paths to candidate unloading vehicle i j ={p j,k}, where p j,k If there is a feasible offloading path coded as k for subtask j, jump to step 5); otherwise, jump to step 9);

[0019] 5) If According to the data volume a of subtask j j , Required virtual network function VNF type v j , computational complexity c j and the remaining available computing resources f of candidate unloading vehicle i i r , calculate the minimum computational delay of candidate unloading vehicle i to process subtask j Jump to step 6), otherwise, jump to step 4);

[0020] 6) For P′ j Each path p in j,k , let subtask j be along p j,k The upload delay to candidate unloading vehicle i is Candidate unloading vehicle i along p j,k The return delay of the original return result is expected to be Among them, r l is the transmission rate of the physical link l, σ j is the ratio of the uploaded and returned data volumes of subtask j, r min is the minimum transmission rate between vehicles, calculate the path p j,k Duration Delay the completion time of the offloading of subtask j Greater than All p j,k From the set P′ j Delete in;

[0021] 7) If For P′ j Each path p in j,k , calculated in The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time Calculate the computing resources f that candidate unloading vehicle i needs to allocate for subtask j under fair allocation of on-board resources Fr , jump to step 8), otherwise, jump to step 4);

[0022] 8) From P′ j Choose to satisfy A path p j,k Add candidate offloading path set P j , skip to step 4);

[0023] 9) Output candidate offloading path set P j , the step ends.

[0024] Furthermore, in step 2), the minimum computing resources that vehicle i needs to allocate for subtask j are The calculation method is shown in formula (1):

[0025]

[0026] In formula (1), a j represents the amount of data for subtask j, c j represents the computational complexity of subtask j, t max represents the maximum tolerated delay of task offloading, f0 represents the basic computing resources required to instantiate VNF, and λ i,j is a binary variable indicating whether vehicle i has a shareable VNF instance. If not, the corresponding VNF needs to be instantiated for subtask j. Let λ i,j =1, otherwise, let λ i,j =0, the calculation method is shown in formula (2):

[0027]

[0028] In formula (2), v j Indicates the VNF type required for subtask j, V i Represents the set of VNF types contained in vehicle i.

[0029] Furthermore, the minimum computational delay of the candidate unloading vehicle i processing subtask j in step 5) is The calculation method is shown in formula (3):

[0030]

[0031] In formula (3), f i r represents the remaining available computing resources of candidate unloading vehicle i.

[0032] Furthermore, in step 6), the path p j,k Duration The calculation method is shown in formula (4):

[0033]

[0034] In formula (4), τ l represents the link duration of the physical link l between vehicle i′ and vehicle i″, where vehicle i′ and vehicle i″ are two vehicles that can communicate directly, τ l The value of is calculated by formula (5):

[0035]

[0036] In formula (5), D represents the effective communication range of the vehicle, (x i′ ,y i′ )、(xi″ ,y i″ ) represent the current position coordinates of vehicle i′ and vehicle i″, μ max represents the maximum speed of the vehicle, γ i′ , γ i″ They represent the angles between the driving directions of vehicle i′ and vehicle i″ and the positive direction of the x-axis after the direction change, respectively. The calculation method is shown in formula (6):

[0037]

[0038] In formula (6), α i′ , α i″ represents the angle between the current driving direction of vehicle i′ and vehicle i″ and the positive direction of the x-axis, and β represents the angle change when vehicle i′ and vehicle i″ change from the current driving direction to the opposite direction. The calculation method is shown in formula (7):

[0039]

[0040] In formula (7), θ represents the instantaneous maximum steering angle of the vehicle.

[0041] Further, in the step 7) The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time The computing resources f that candidate unloading vehicle i needs to allocate for subtask j under fair allocation of on-board resources Fr The calculation methods are shown in formula (8) and formula (9):

[0042]

[0043] In formula (9), f i represents the total computing resources of vehicle i, |I * | represents the total number of mission vehicles, |J| max represents the maximum number of subtasks contained in each unloading request, and |I| represents the total number of vehicles.

[0044] Furthermore, the fitness t of particle n in step 103 is n The calculation method is shown in formula (10):

[0045]

[0046] In formula (10), Represents subtask j along path p j,k The unloading completion delay to vehicle i is calculated as shown in formula (11): is a binary variable, indicating whether subtask j follows path p for particle n.j,k Uninstall to p j,k The last node vehicle i, if yes, let Otherwise, let The calculation method is shown in formula (12):

[0047]

[0048] Furthermore, in step 105, the unloading decision vector ψ of each particle n is updated. n , that is, update ψ n Each component ψ n,j , the calculation method is shown in formula (13):

[0049]

[0050] Update the migration vector s of each particle n n The calculation method of is shown in formula (14):

[0051]

[0052] In formula (14), round() indicates rounding, ω indicates the inertia factor, ε1 and ε2 indicate the learning factors, and rand() indicates a random number in the numerical interval (0, 1).

[0053] The advantages and beneficial effects of the present invention are as follows:

[0054] The present invention discloses a method for offloading tasks of non-uniform vehicles in complex road conditions based on VNF instance sharing. Existing research on the problem of vehicle network task offloading usually only considers uniformly traveling vehicles on single / two-way lanes, and does not consider the real-time changing characteristics of vehicle speed and direction in large transportation hubs or complex interchange environments. In response to the above problems, the present invention selects an unloading vehicle for each subtask and constructs an unloading path based on the maximum tolerated delay constraint of the task in the vehicle unloading request, the VNF type requirement, the shareability of the VNF instance, and the changing trend of the vehicle's driving trajectory, so as to ensure the availability of computing resources and the reliability of the unloading path. The unloading decision vector is optimized by the particle swarm algorithm to maximize resource utilization, thereby improving the system task completion rate and reducing the system unit task unloading delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flowchart of a method for unloading tasks of non-uniform-speed vehicles in complex road conditions based on VNF instance sharing, according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0056] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and in detail. The described embodiments are only some of the embodiments of the present application.

[0057] The technical solution of the present application to solve the above technical problems is:

[0058] The concepts and models involved in the present application are as follows:

[0059] The present application assumes that in a complex road vehicle network, the driving direction and speed of each vehicle dynamically changes. Based on the effective communication distance constraint between vehicles, V2V direct link or V2V multi-hop link can be used to forward tasks. Each vehicle is equipped with a server with certain computing power, and can run multiple VNF instances simultaneously. The vehicle task is composed of several different types of subtasks, which can be offloaded to local or other idle vehicles in parallel. Among them, different subtasks of the same VNF type can share the VNF instance on the same vehicle node, and the same task can be offloaded to at most one vehicle.

[0060] The technical solutions of the present application are described as follows:

[0061] 1. The minimum computing resource required by vehicle i for subtask j

[0062] The calculation method is shown in formula (1):

[0063]

[0064] In formula (1), a j represents the data volume of subtask j, c j represents the computing complexity of subtask j, t max represents the maximum offload tolerance delay of the task, f0 represents the basic computing resource required for instantiating VNF, and λ i,j is a binary variable, indicating whether vehicle i has a shareable VNF instance, if not, the corresponding VNF needs to be instantiated for subtask j, λ i,j =1, otherwise, λ i,j =0, the calculation method is shown in formula (2):

[0065]

[0066] In formula (2), v j represents the VNF type required by subtask j, V i represents the VNF type set contained in vehicle i.

[0067] 2. The minimum computing delay of candidate offload vehicle i processing subtask j

[0068] The calculation method is shown in formula (3):

[0069]

[0070] In formula (3), f i r represents the remaining available computing resources of candidate unloading vehicle i;

[0071] 3. Pathway p j,k Duration

[0072] The calculation method is shown in formula (4):

[0073]

[0074] In formula (4), τ l represents the link duration of the physical link l between vehicle i′ and vehicle i″, where vehicle i′ and vehicle i″ are two vehicles that can communicate directly, τ l The value of is calculated by formula (5):

[0075]

[0076] In formula (5), D represents the effective communication range of the vehicle, (x i′ ,y i′ )、(x i″ ,y i″ ) represent the current position coordinates of vehicle i′ and vehicle i″, μ max represents the maximum speed of the vehicle, γ i′ , γ i″ They represent the angles between the driving directions of vehicle i′ and vehicle i″ and the positive direction of the x-axis after the direction change, respectively. The calculation method is shown in formula (6):

[0077]

[0078] In formula (6), α i′ , α i″ represents the angle between the current driving direction of vehicle i′ and vehicle i″ and the positive direction of the x-axis, and β represents the angle change when vehicle i′ and vehicle i″ change from the current driving direction to the opposite direction. The calculation method is shown in formula (7):

[0079]

[0080] In formula (7), θ represents the instantaneous maximum steering angle of the vehicle;

[0081] 4. The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time The computing resources f that candidate unloading vehicle i needs to allocate for subtask j under fair allocation of on-board resources Fr

[0082] The calculation method is shown in formula (8) and formula (9):

[0083]

[0084] In formula (8), Indicates that subtask j is along p j,k Upload delay to candidate unloading vehicle i, Denotes candidate unloading vehicle i along p j,k The return delay of the original return result, in formula (9), f i represents the total computing resources of vehicle i, |I * | represents the total number of mission vehicles, |J| max represents the maximum number of subtasks contained in each unloading request, and |I| represents the total number of vehicles;

[0085] 5. Fitness t of particle n n

[0086] The calculation method is shown in formula (10):

[0087]

[0088] In formula (10), Represents subtask j along path p j,k The unloading completion delay to vehicle i is calculated as shown in formula (11): is a binary variable, indicating whether subtask j follows path p for particle n. j,k Uninstall to p j,k The last node vehicle i, if yes, let Otherwise, let The calculation method is shown in formula (12):

[0089]

[0090] In formula (12), ψ n,j represents the offloading path of subtask j in particle n;

[0091] 6. Unloading decision vector ψ for each particle n n The component ψ n,j and the migration vector s of each particle n n

[0092] The calculation method is shown in formula (13) and formula (14):

[0093]

[0094] In formula (13), s n,j Represents ψ n,j The transformation step size, |P j | represents the total number of candidate offloading paths for subtask j. In formula (14), round() represents rounding, ω represents the inertia factor, ε1 and ε2 represent the learning factors, and rand() represents a random number in the numerical interval (0,1). represents the optimal unloading decision of particle n, ψ best represents the global optimal unloading decision of particle swarm N;

[0095] 7. Sub-algorithm 1: Establish a set of candidate offloading paths P for subtask j j ={p j,k}

[0096] Step 1: Initialize the set of all vehicles I = {i}, where i is any vehicle, and the set of candidate unloading vehicles The set of candidate offloading paths for subtask j Maximum tolerable delay of task offloading t max ;

[0097] Step 2: If Take the first element i from the set I, according to the amount of data a of subtask j j , Required virtual network function VNF type v j , computational complexity c j , calculate the minimum computing resources that vehicle i needs to allocate for subtask j Jump to step 3, otherwise, jump to step 4;

[0098] Step 3: If the remaining available computing resources f of vehicle i i r Greater than or equal to Add vehicle i to the candidate unloading vehicle set I′ and jump to step 2. Otherwise, jump to step 2;

[0099] Step 4: If Take the first element i from the set I′ and use the depth-first search algorithm to construct the task vehicle i * The set P′ of all feasible paths to candidate unloading vehicle i j ={p j,k}, where p j,k If there is a feasible offloading path coded as k for subtask j, jump to step 5; otherwise, jump to step 9;

[0100] Step 5: If According to the data volume a of subtask jj , Required virtual network function VNF type v j , computational complexity c j and the remaining available computing resources f of candidate unloading vehicle i i r , calculate the minimum computational delay of candidate unloading vehicle i to process subtask j Jump to step 6, otherwise, jump to step 4;

[0101] Step 6: P′ j Each path p in j,k , let subtask j be along p j,k The upload delay to candidate unloading vehicle i is Candidate unloading vehicle i along p j,k The return delay of the original return result is expected to be Among them, r l is the transmission rate of the physical link l, σ j is the ratio of the uploaded and returned data volumes of subtask j, r min is the minimum transmission rate between vehicles, calculate the path p j,k Duration Delay the completion time of the offloading of subtask j Greater than All p j,k From the set P′ j Delete in;

[0102] Step 7: If For P′ j Each path p in j,k , calculated in The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time Calculate the computing resources f that candidate unloading vehicle i needs to allocate for subtask j under fair allocation of on-board resources Fr , jump to step 8, otherwise, jump to step 4;

[0103] Step 8: From P′ j Choose to satisfy A path p j,k Add candidate offloading path set P j , jump to step 4;

[0104] Step 9: Output candidate offloading path set P j , the step ends.

[0105] A method for offloading tasks from vehicles with non-uniform speeds in complex road conditions based on VNF instance sharing specifically includes the following steps:

[0106] Step 1: Use particle swarm optimization to establish a vehicle network task offloading optimization model. Suppose task vehicle i * The offloading request J = {j}, where j is a parallel subtask, the particle swarm is the set N = {n}, and the offloading decision vector, migration vector and optimal fitness of each particle n in N are ψ n =(ψ n,j | j∈J ),s n =(s n,j | j∈J )and Among them, ψ n,j is the offloading path of subtask j in particle n, s n,j is ψ n,j The transformation step size is η, and the maximum number of iterations of the initialized particle swarm is η max , iteration count variable η=0, temporary set J′=J;

[0107] Step 2: If Take the first element j from the set J′, and according to the data amount a of subtask j j , the required virtual network function VNF type v j , computational complexity c j , call sub-algorithm 1 to establish a candidate offloading path set P for subtask j j ={p j,k}, where p j,k If there is a candidate offloading path coded as k for subtask j, jump to step 2; otherwise, jump to step 3;

[0108] Step 3: For each particle n in N, from each set P j | j∈J Choose any path p j,k Initialize the offload decision vector From the set J, each j corresponds to the numerical interval [0,|P j |) randomly selects an integer to initialize the migration vector s n Each component s in n,j | j∈J , calculate the particle fitness t n , initialize the optimal fitness of particle n Optimal unloading decision for particle n

[0109] Step 4: Let the global optimal unloading decision of particle swarm N be Iteration count variable η=η+1, if η≤η max , let the temporary set N′=N, jump to step 5, otherwise, jump to step 7;

[0110] Step 5: If Take the first element n from the set N′ and update the unloading decision vector ψ n and the migration vector s n , calculate the particle fitness t n , jump to step 6, otherwise, jump to step 4;

[0111] Step 6: If Let the optimal fitness of particle n be Optimal unloading decision for particle n Jump to step 5, otherwise, jump to step 5;

[0112] Step 7: Output the global optimal offloading decision ψ best , the step ends.

[0113] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

[0114] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0115] The above embodiments should be understood as merely illustrating the present invention and not as limiting the scope of protection of the present invention. After reading the contents of the present invention, technicians may make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A method for offloading tasks of vehicles with non-uniform speed in complex road conditions based on VNF instance sharing, characterized in that: The following steps are involved:

101. Using particle swarm algorithm to establish vehicle network task offloading optimization model, suppose task vehicle i * The unloading request J = {j}, where j is a parallel subtask, the particle swarm set N = {n}, and the unloading decision vector, migration vector and optimal fitness of each particle n in N are ψ n =(ψ n,j | j∈J ),s n =(s n,j | j∈J )and Among them, ψ n,j is the offloading path of subtask j in particle n, s n,j is ψ n,j The transformation step size is η, and the maximum number of iterations of the initialized particle swarm is η max , iteration count variable η=0, temporary set J′=J; 102. If Take the first element j from the set J′, and according to the data amount a of subtask j j , the required virtual network function VNF type v j , computational complexity c j , establish a candidate offloading path set P for subtask j j ={p j,k }, where p j,k If it is a candidate offloading path coded as k for subtask j, jump to step 102; otherwise, jump to step 103; 103. For each particle n in N, from each set P j | j∈J Choose any path p j,k Initialize the offload decision vector From the set J, each j corresponds to the numerical interval [0,|P j |) randomly selects an integer to initialize the migration vector s n Each component s in n,j | j∈J , calculate the particle fitness t n , initialize the optimal fitness of particle n Optimal unloading decision for particle n 104. Let the global optimal unloading decision of particle swarm N be Iteration count variable η=η+1, if η≤η max , let the temporary set N′=N, jump to step 105, otherwise, jump to step 107; 105. If Take the first element n from the set N′ and update the unloading decision vector ψ n and the migration vector s n , calculate the particle fitness t n , jump to step 106, otherwise, jump to step 104; 106. If Let the optimal fitness of particle n be Optimal unloading decision for particle n Jump to step 105, otherwise, jump to step 105; 107. Output the global optimal offloading decision ψ best , the step ends.

2. The method for offloading tasks of vehicles with non-uniform speed under complex road conditions based on VNF instance sharing according to claim 1, characterized in that: In step 102, a candidate offloading path set P is established for subtask j. j ={p j,k }, specifically including the following steps: 1) Initialize the set of all vehicles I = {i}, where i is any vehicle, and the set of candidate unloading vehicles The set of candidate offloading paths for subtask j Maximum tolerable delay of task offloading t max ; 2) If Take the first element i from the set I, according to the amount of data a of subtask j j , Required virtual network function VNF type v j , computational complexity c j , calculate the minimum computing resources that vehicle i needs to allocate for subtask j Jump to step 3), otherwise, jump to step 4); 3) If the remaining available computing resources f of vehicle i i r Greater than or equal to Add vehicle i to the candidate unloading vehicle set I′ and jump to step 2). Otherwise, jump to step 2); 4) If Take the first element i from the set I′ and use the depth-first search algorithm to construct the task vehicle i * The set P′ of all feasible paths to candidate unloading vehicle i j ={p j,k }, where p j,k If there is a feasible offloading path coded as k for subtask j, jump to step 5); otherwise, jump to step 9); 5) If According to the data volume a of subtask j j , Required virtual network function VNF type v j , computational complexity c j and the remaining available computing resources f of candidate unloading vehicle i i r , calculate the minimum computational delay of candidate unloading vehicle i to process subtask j Jump to step 6), otherwise, jump to step 4); 6) For P′ j Each path p in j,k , let subtask j be along p j,k The upload delay to candidate unloading vehicle i is Candidate unloading vehicle i along p j,k The return delay of the original return result is expected to be Among them, r l is the transmission rate of the physical link l, σ j is the ratio of the uploaded and returned data volumes of subtask j, r min is the minimum transmission rate between vehicles, calculate the path p j,k Duration Delay the completion time of the offloading of subtask j Greater than All p j,k From the set P j ' is deleted; 7) If P j Each path p in ′ j,k , calculated in The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time Calculate the computing resources f that candidate unloading vehicle i needs to allocate for subtask j under fair allocation of on-board resources Fr , jump to step 8), otherwise, jump to step 4); 8) From P′ j Choose to satisfy A path p j,k Add candidate offloading path set P j , skip to step 4); 9) Output candidate offloading path set P j , the step ends.

3. The method for offloading tasks of vehicles with non-uniform speed under complex road conditions based on VNF instance sharing according to claim 2, characterized in that: The minimum computing resources that vehicle i needs to allocate for subtask j in step 2) The calculation method is shown in formula (1): In formula (1), a j represents the amount of data for subtask j, c j represents the computational complexity of subtask j, t max represents the maximum tolerated delay of task offloading, f0 represents the basic computing resources required to instantiate VNF, and λ i,j is a binary variable indicating whether vehicle i has a shareable VNF instance. If not, the corresponding VNF needs to be instantiated for subtask j. Let λ i,j =1, otherwise, let λ i,j =0, the calculation method is shown in formula (2): In formula (2), v j Indicates the VNF type required for subtask j, V i Represents the set of VNF types contained in vehicle i.

4. The method for offloading tasks of vehicles with non-uniform speed under complex road conditions based on VNF instance sharing according to claim 3 is characterized in that: The minimum computational delay of processing subtask j by candidate unloading vehicle i in step 5) The calculation method is shown in formula (3): In formula (3), f i r represents the remaining available computing resources of candidate unloading vehicle i.

5. The method for offloading tasks of vehicles with non-uniform speed under complex road conditions based on VNF instance sharing according to claim 4 is characterized in that: In step 6), the path p j,k Duration The calculation method is shown in formula (4): In formula (4), τ l represents the link duration of the physical link l between vehicle i′ and vehicle i″, where vehicle i′ and vehicle i″ are two vehicles that can communicate directly, τ l The value of is calculated by formula (5): In formula (5), D represents the effective communication range of the vehicle, (x i′ ,y i′ )、(x i″ ,y i″ ) represent the current position coordinates of vehicle i′ and vehicle i″, μ max represents the maximum speed of the vehicle, γ i′ , γ i″ They represent the angles between the driving directions of vehicle i′ and vehicle i″ and the positive direction of the x-axis after the direction change, respectively. The calculation method is shown in formula (6): In formula (6), α i′ , α i″ represents the angle between the current driving direction of vehicle i′ and vehicle i″ and the positive direction of the x-axis, and β represents the angle change when vehicle i′ and vehicle i″ change from the current driving direction to the opposite direction. The calculation method is shown in formula (7): In formula (7), θ represents the instantaneous maximum steering angle of the vehicle.

6. The method for offloading tasks of vehicles with non-uniform speed in complex road conditions based on VNF instance sharing according to claim 5 is characterized in that: In the step 7) The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time The computing resources f that candidate unloading vehicle i needs to allocate for subtask j under fair allocation of on-board resources Fr The calculation methods are shown in formula (8) and formula (9): In formula (9), f i represents the total computing resources of vehicle i, |I * | represents the total number of mission vehicles, |J| max represents the maximum number of subtasks contained in each unloading request, and |I| represents the total number of vehicles.

7. The method for offloading tasks of vehicles with non-uniform speed under complex road conditions based on VNF instance sharing according to claim 6, characterized in that: The fitness t of particle n in step 103 n The calculation method is shown in formula (10): In formula (10), Represents subtask j along path p j,k The unloading completion delay to vehicle i is calculated as shown in formula (11): is a binary variable, indicating whether subtask j follows path p for particle n. j,k Uninstall to p j,k The last node vehicle i, if yes, let Otherwise, let The calculation method is shown in formula (12):

8. The method for offloading tasks of vehicles with non-uniform speed in complex road conditions based on VNF instance sharing according to claim 7 is characterized in that: In step 105, the unloading decision vector ψ of each particle n is updated. n , that is, update ψ n Each component ψ n,j , the calculation method is shown in formula (13): Update the migration vector s of each particle n n The calculation method of is shown in formula (14): In formula (14), round() indicates rounding, ω indicates the inertia factor, ε1 and ε2 indicate the learning factors, and rand() indicates a random number in the numerical interval (0, 1).

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