CN113114738B - SDN-based optimization method for internet of vehicles task unloading - Google Patents

SDN-based optimization method for internet of vehicles task unloading Download PDF

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CN113114738B
CN113114738B CN202110330542.5A CN202110330542A CN113114738B CN 113114738 B CN113114738 B CN 113114738B CN 202110330542 A CN202110330542 A CN 202110330542A CN 113114738 B CN113114738 B CN 113114738B
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CN113114738A (en
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胡斌杰
杨小淞
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South China University of Technology SCUT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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    • H—ELECTRICITY
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Abstract

The invention discloses an SDN-based optimization method for vehicle networking task unloading, which comprises the following steps: s1, establishing a vehicle networking communication scene comprising a Software Defined Network (SDN), a mobile vehicle and an edge server; s2, decomposing a task initiated by a specific vehicle in a scene into a plurality of subtasks, and transmitting and calculating the subtasks to other edge nodes in parallel in a multicast mode; s3, normalizing the systematic risks of task transmission and calculation into Poisson distribution, and calculating the occurrence probability of the failure of the edge node in processing the tasks; s4, establishing a task reprocessing mechanism and constructing a time delay constraint condition; s5, establishing an optimization problem model by combining constraint conditions with the aim of maximizing the reliability of the Internet of vehicles system; and solving the solution by using a particle swarm algorithm to obtain a subtask allocation scheme for cooperative processing of the vehicle and other edge nodes in the vehicle networking system. The method solves the optimization problem of the SDN-based vehicle networking task unloading with higher reliability.

Description

SDN-based optimization method for internet of vehicles task unloading
Technical Field
The invention relates to the technical field of task unloading of vehicle networking mobile edge computing, in particular to an SDN-based optimization method for task unloading of vehicle networking.
Background
Vehicle interconnection (IoVs) technology has attracted considerable attention in recent years. To improve safety, efficiency and comfort of driving, IoVs have emerged in a variety of applications. Cloud computing is a popular technology to support latency tolerant entertainment applications. However, for highly delay sensitive applications (e.g., automated/assisted driving, emergency fault management), cloud computing may result in excessive latency. Multi-Access Edge (MEC) Computing expands Computing and storage capabilities to the Edge of the network, becoming an attractive technology.
The vehicle networking system is reasonably planned, each subtask is unloaded to other resource nodes for processing, frequency band resources and computing resources in the vehicle networking system are reasonably distributed, and the total reliability of task processing of the vehicle networking system can be the highest on the premise of ensuring specific time delay constraint of the tasks.
In the prior art, the computing capability of a cellular network is enhanced by combining a Device to Device (D2D) technology of an MEC, transmission delay and computation delay in a multicast communication mode are computed through node parameters, and then an optimization problem of task offloading reliability maximization is established according to a reliability model and a task reprocessing model, so as to obtain an optimal task offloading scheme. In the research process of the technical scheme of the invention, at least, the defect of the prior art is that the data packet is transmitted to other edge nodes in a unicast mode, which can cause interference between channels and unnecessary data retransmission under the condition of task processing failure.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides an SDN-based optimization method for unloading tasks of the Internet of vehicles. Specifically, the requesting vehicle divides the task into a plurality of subtasks by using the unloading ratio calculated by the SDN controller; the cluster head vehicle unloads each subtask to a nearby edge service node, and obtains the time delay of transmission and calculation of each subtask; calculating the reliability of transmission and calculation of each subtask and the reliability of the whole system; establishing a task reprocessing mechanism, recalculating the time delay of task transmission and calculation, and establishing an optimization target and a time delay constraint condition; on the basis, the method aims to maximize the total reliability of task transmission and calculation of the vehicle networking system, combines constraint conditions, establishes an optimal problem model, and ensures that the total reliability of task transmission and calculation of the vehicle networking system is maximum.
The purpose of the invention can be achieved by adopting the following technical scheme:
an optimization method for vehicle networking task unloading based on an SDN (software defined network), wherein the task unloading method comprises the following steps:
s1, constructing an Internet of vehicles application scene with a deployed SDN controller, a base station, an MEC server and vehicles, wherein the vehicles have V2V multicast and V2I communication capabilities, cluster head vehicles initiate task unloading requests to the SDN controller in the base station, the SDN controller discovers a V2V multicast link and a V2I link through a south-oriented interface, the SDN controller centrally controls computing resources of the network through a north-oriented interface, the SDN controller calls underlying computing resources in a software programming mode and divides an original task into a plurality of unknown subtasks, the SDN represents a software defined network, V2V represents vehicle-to-vehicle, V2I represents vehicle-to-infrastructure, and the MEC represents multi-access edge computing;
s2, in the application scene of the Internet of vehicles, the cluster head vehicle unloads partial subtasks to nearby mobile vehicles in a D2D multicast communication mode, or unloads partial subtasks to MEC servers in nearby infrastructure, and obtains the time delay of transmission and processing of each partial subtask, wherein D2D represents terminal through connection;
s3, aiming at the systematic risk of the car networking system, normalizing the failed cases into a Poisson distribution driven by time delay and fault rate, and calculating the reliability of transmission and processing of each part of subtasks and the occurrence probability P of faults existing in the systemδ(ζ，ζ′)；
S4, considering the possibility of task transmission or failure in the Internet of vehicles system, establishing a task reprocessing mechanism according to the worst condition of the system, namely, when the task transmission and processing are finished, a cluster head vehicle is required to perform task retransmission after the subtask of the edge server part fails, one node with the largest computing power in the non-failure vehicles is required to be selected as a reprocessing vehicle node after the subtask of the cluster head vehicle part fails, the reprocessing is only required once after the subtask of the non-failure vehicles fails, finally, the time delay of the task transmission and processing is recalculated, and an optimization target R (psi, theta) and a time delay constraint condition are established;
s5, aiming at maximizing reliability R (psi, theta) of task transmission and processing of the Internet of vehicles system, establishing an optimization problem model by combining constraint conditions, solving by adopting a particle swarm algorithm to obtain an optimal solution of a task unloading proportion of a cluster head vehicle so as to ensure that the reliability of the task transmission and processing of the Internet of vehicles system is maximum, transmitting the optimal task unloading proportion back to the cluster head vehicle by the SDN controller, and executing a task unloading scheme with the maximum reliability by the cluster head vehicle.
Further, in step S1, a specific unloading ratio is defined according to an attribute of a task initiated by a cluster head vehicle in an application scenario of the internet of vehicles and a channel capacity between the cluster head vehicle and an edge service node, and a process is as follows:
in the application scene of the Internet of vehicles, the attribute of initiating the task by the cluster head vehicle is psim＝{Cm，Dm，Tm}，CmIndicates the total number of CPU cycles, D, required to complete the taskmIndicating the size of the input data, TmRepresenting the time delay constraint of completing the task, and defining the computational complexity of the task as alphamThe expression is:
Cm＝αmDm (1)
the task unloading proportion obtained by cluster head vehicles in the scene is defined by theta, and the unloading proportion distributed to the cluster head vehicles is thetasThe assigned offload ratio of the edge server is θjN mobile vehicle nodes are assigned an unloading ratio of θ1，θ2，...θnThe expression between the unloading ratios is:
θs+θj+θ1+θ2+…+θn＝1 (2)。
further, the step S2 is as follows:
s2.1, globally controlling the positions, directions, speeds and network states of computing resources and edge nodes in the application scene of the Internet of vehicles by the SDN controller, and defining the coordinates of cluster head vehicles as (x)s，ys) The coordinates of base station j are (x)j，yj) The coordinates of the other moving vehicles i are (x)i，yi) (ii) a Assuming that the moving direction and speed of all moving vehicles are constant, the distance d between the cluster head vehicle and the base station j is setsjDistance d between the cluster vehicle and other moving vehicles isiExpressed as:
Figure GDA0003462186000000041
Figure GDA0003462186000000042
s2.2, connecting cluster head vehicle with base station jUpstream rate between edge servers CUL(s, j) is represented by:
Figure GDA0003462186000000043
the uplink rate C between the cluster head vehicle and other moving vehicles iUL(s, i) is expressed as:
Figure GDA0003462186000000044
in the above formula, W is the uplink channel bandwidth of the cluster head vehicle and other edge nodes, ω is the transmission gain of the cluster head vehicle and other edge nodes, α is the path loss exponent, h0Is the complex Gaussian channel coefficient, N0Is additive white gaussian noise;
s2.3, calculating the time delay of the cluster head vehicle for unloading each subtask to other edge nodes according to the calculation result of the transmission rate in the step S2.2, and unloading the time delay of the cluster head vehicle to the base station j
Figure GDA0003462186000000045
Expressed as:
Figure GDA0003462186000000046
time delay for offloading tasks from cluster head vehicle to other mobile vehicle i
Figure GDA0003462186000000047
Expressed as:
Figure GDA0003462186000000048
s2.4, unloading cluster head vehicles to the time delay of the edge server in the base station j for processing the subtasks of the edge server
Figure GDA0003462186000000049
Expressed as:
Figure GDA0003462186000000051
time delay for offloading cluster head vehicles to other mobile vehicles i to process their subtasks
Figure GDA0003462186000000052
Expressed as:
Figure GDA0003462186000000053
time delay T of subtask processed locally by cluster head vehicleLocalExpressed as:
Figure GDA0003462186000000054
s2.5, obtaining the total time delay of the Internet of vehicles system when no fault occurs according to the calculation result in the step S2.4, wherein the expression is as follows:
Figure GDA0003462186000000055
further, the step S3 is as follows:
s3.1, according to the calculation results of the steps S2.3 and S2.4, reliability of partial subtasks of unloading cluster head vehicles to the edge server in the base station j is represented as RjThe failure rate is expressed as Fj：
Figure GDA0003462186000000056
Wherein λ isjIndicates the failure rate, μ, of the edge server to perform the computationjIndicating a failure rate of a link offloaded by the cluster head vehicle to the base station;
to be clusteredThe reliability of the partial subtasks in which the head vehicle is unloaded to the other moving vehicles i is denoted RiThe failure rate is expressed as Fi：
Figure GDA0003462186000000057
Wherein λ isiIndicating the failure rate, μ, of other vehicles to perform the calculationsiIndicating a failure rate of a link where cluster head vehicles offload to other vehicles;
the reliability of the partial subtasks of the cluster head vehicle unloading the computation performed by itself is denoted as RLocalThe failure rate is expressed as FLocal：
Figure GDA0003462186000000061
Wherein λ isLocalIndicating a failure rate of the other vehicle to perform the calculation;
thus, task ΨmThe reliability of the subtask is determined by the reliability of the subtask distribution to the edge server, the reliability of the moving vehicle, and the reliability of the cluster head vehicle, and the expression is:
Figure GDA0003462186000000062
s3.2, discussing all possible conditions and occurrence probabilities in the scene of the vehicle networking system according to the calculation result of the step S3.1, so that for n vehicles, 1 edge server and 1 cluster head vehicle, 2 are available according to whether the subtasks are completed or notn+2In one case, considering a particular event δ, the set of vehicles that successfully completed each subtask is represented by ζ, and the set of vehicles that did not successfully complete each subtask is represented by ζ'; additionally, if the cluster head vehicle successfully completes the subtasks, K local1, otherwise Klocal0; if the edge server successfully completes the subtasks, K j1, otherwise, Kj0; if other moving vehiclesIf the subtask is successfully completed, K i1, otherwise, Ki0; a specific task in the context of the application of the Internet of vehicles, its transmission and the calculated total reliability PδThe expression of (ζ, ζ') is:
Figure GDA0003462186000000063
further, the step S4 is as follows:
s4.1, assuming that the cluster head vehicle does uniform motion along the x-axis direction, wherein the velocity is vsDistance d 'between cluster head vehicle and base station'sjRecalculating, wherein the expression is as follows:
Figure GDA0003462186000000071
in the task re-processing mechanism, according to the calculation results of step 2.3 and step 2.4, the edge server in the base station recalculates the time delay of the partial subtasks after the subtasks possibly fail
Figure GDA0003462186000000072
The formula is as follows:
Figure GDA0003462186000000073
other moving vehicles recalculate the time delay of the part of subtasks after each subtask possibly fails
Figure GDA0003462186000000074
The formula is as follows:
Figure GDA0003462186000000075
recalculating the time delay of the partial subtasks of the cluster head vehicle after the subtasks possibly fail
Figure GDA0003462186000000076
The formula is as follows:
Figure GDA0003462186000000077
in a task reprocessing mechanism, the transmission and computation delays of an internet of vehicles system are recalculated in the possible case of a task failure
Figure GDA0003462186000000078
The formula is as follows:
Figure GDA0003462186000000079
s4.2, according to the calculation result of the step S4.1, defining a binary variable O for reliably finishing transmission and calculation of the calculation task within the time delay constraint thereofδThe expression is as follows:
Figure GDA00034621860000000710
thus, the optimization problem of maximizing reliability under the delay constraint considering the reprocessing mechanism can be modeled as follows
Figure GDA0003462186000000081
Further, the step S5 is realized by converting into the following calculation formula:
Figure GDA0003462186000000082
C1:θs+θj+θ1+θ2+…+θn＝1
Figure GDA0003462186000000083
Figure GDA0003462186000000084
Figure GDA0003462186000000085
in the above constraint conditions, C1 indicates that each subtask can be completely combined into the original task, C2 indicates that the time delay after the task is reprocessed is not greater than the time delay constraint of the task, C3 indicates that the computing power of other edge nodes and cluster head vehicles can meet the computing power required by the task, and C4 indicates that the sum of the data size of each subtask is equal to the original task size.
Compared with the prior art, the invention has the following advantages and effects:
1) in the optimization method for the vehicle networking task unloading based on the SDN, the V2V link transmits the data packet by multicast, so that the waste of communication resources and the interference of a communication link are avoided, the retransmission time delay when the task fails is avoided, and the reliability of the system is improved;
2) according to the optimization method for the vehicle networking task unloading based on the SDN, the V2V link uses D2D communication, data packet forwarding is not performed through a base station, and the hop count and time delay of data transmission packet transmission are reduced.
Drawings
Figure 1 is a flowchart of a method for optimizing SDN-based vehicle networking task offloading as disclosed in an embodiment of the present invention;
FIG. 2 is a schematic view of a scene disclosed in an embodiment of the present invention;
fig. 3 is a schematic diagram of a simulation result of the optimization method for SDN-based vehicle networking task offloading in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
As shown in fig. 1, the present embodiment discloses an optimization method for SDN-based task offloading in car networking, and the present embodiment divides the optimization method for SDN-based task offloading in three main steps, namely a task transmission and computation model, a reliability model and a task re-processing mechanism. By comprehensively considering factors such as channel capacities of cluster head vehicles, edge servers and all mobile vehicles in the Internet of vehicles system, computing resources of the edge servers and all the mobile vehicles and the like, an unloading scheme which enables the total task unloading reliability to be highest is determined. The method comprises the following specific steps:
s1, constructing an application scene of the vehicle networking system, wherein the application scene is provided with a base station, an MEC server and a plurality of vehicles N {1, 2, … …, N }, the vehicles have V2V and V2I communication capabilities, the vehicles and the MEC server are provided with limited channels and computing resources, a cluster head vehicle firstly initiates a task unloading request to an SDN controller, the SDN controller discovers a V2V multicast link and a V2I link through a south-oriented interface, the SDN controller centrally controls the computing resources of the network through a north-oriented interface, and the SDN controller calls the underlying computing resources in a software programming mode, wherein the vehicles making the task unloading request are called cluster head vehicles, and other vehicles and the MEC server are called other edge nodes.
According to the attribute of a task initiated by a cluster head vehicle and the channel capacity between the cluster head vehicle and an edge service node in an application scene of the Internet of vehicles system, a specific unloading proportion is specified, and the process is as follows:
in the application scene of the vehicle networking system, the attribute psi of the task initiated by the cluster head vehiclem＝{Cm，Dm，Tm}，Cm(unit: megacycle) indicates completion of task ΨmTotal number of CPU cycles required, Dm(unit: KB) represents input data ΨmSize of (D), TmThe unit is second, which represents the time delay constraint for completing the task, and the computational complexity of the task is defined as alpham(unit: cycles/byte), the expression is:
Cm＝αmDm (1)
the task unloading proportion obtained by cluster head vehicles in a scene is defined by theta (theta is more than or equal to 0 and less than or equal to 1), theta is a parameter to be solved and comprises the following three parts: the cluster head vehicle is assigned an unloading ratio thetasThe assigned offload ratio of the edge server is θjThe unloading proportion distributed to the ith mobile vehicle node is thetaiThe unloading ratio of the last moving vehicle is thetanThe expression between the unloading ratios is:
θs+θj+θ1+θ2+…+θn＝1 (2)
s2, unloading each subtask to a nearby edge service node according to the specific unloading proportion of the cluster head vehicle in the scene, and obtaining the transmission and calculation time delay of each subtask, wherein the specific steps are as follows:
s2.1, the SDN controller keeps global state information up to date, including location, direction, speed, network state, etc. of the edge nodes. Without loss of generality, we consider the coordinates of the cluster head vehicle as (x)s，ys) The coordinates of the base station are (x)j，yj) The coordinates of the other moving vehicles are (x)i，yi) (ii) a The direction and speed of movement of all moving vehicles is assumed to be constant. Then the distance of the cluster head vehicle from the base station and the distance from other moving vehicles are respectively expressed as:
Figure GDA0003462186000000101
Figure GDA0003462186000000102
s2.2, the uplink rate between the cluster head vehicle and the edge server in the base station is represented as:
Figure GDA0003462186000000103
the upstream rate between the cluster head vehicle and the other moving vehicles is expressed as:
Figure GDA0003462186000000111
in the above two formulas, W (unit: MHz) is the uplink channel bandwidth of the cluster head vehicle and other edge nodes, ω (unit: dBm) is the transmission gain of the cluster head vehicle and other edge nodes, α is the path loss exponent subject to (2, 5) uniform distribution, and h is the path loss exponent subject to (2, 5) uniform distribution0Is a complex Gaussian channel coefficient, N, following a (0, 1) complex normal distribution0(in dBm) is additive white Gaussian noise.
And S2.3, calculating the time delay of the cluster head vehicle for unloading each subtask to other edge nodes according to the calculation result of the transmission rate in the step S2.2. The time delay expression of the task unloading from the cluster head vehicle to the base station is as follows:
Figure GDA0003462186000000112
the time delay expression of unloading tasks from the cluster head vehicle to other mobile vehicles is as follows:
Figure GDA0003462186000000113
s2.4, the expression of the time delay of the edge server for unloading the cluster head vehicle to the base station to process the subtasks is as follows:
Figure GDA0003462186000000114
wherein Zj(in GHz) as edge serverThe value of the CPU frequency of (1) is 6.25 GHz.
The expression of the time delay for unloading the cluster head vehicle to other mobile vehicles to process the subtasks thereof is as follows:
Figure GDA0003462186000000115
wherein Zi(in: GHz) is the CPU frequency of the edge server, subject to a uniform distribution of (1, 3) GHz.
The expression of the time delay of the subtasks processed locally by the cluster head vehicle is as follows:
Figure GDA0003462186000000121
wherein ZsThe unit is GHz, which is the CPU frequency of the edge server, and the value is 2.2 GHz.
S2.5, according to the calculation result in the step S2.4, the total time delay of the system when no fault occurs can be obtained, and the expression is as follows:
Figure GDA0003462186000000122
s3, aiming at the systematic risk of the car networking system, normalizing the failed cases into a Poisson distribution driven by the failure rate, and calculating the reliability of transmission and calculation of each subtask and the reliability P of the whole systemδ(ζ, ζ') as follows:
s3.1, according to the calculation results of the steps S2.3 and S2.4, the reliability of partial subtasks of the cluster head vehicle unloaded to the edge server in the base station is expressed as Rj(0≤Rj1) or less), the failure rate is expressed as Fj(0≤Fj≤1)：
Figure GDA0003462186000000123
Wherein λ isjRepresenting edgesFailure rate of the edge server to perform the calculations, subject to a uniform distribution of (0, 0.03); mu.sjThe failure rate of the link indicating the unloading of cluster head vehicles to the base station, obeys a uniform distribution of (0, 0.03).
The reliability of the partial subtasks unloaded by the cluster head vehicle to the other moving vehicles is denoted Ri(0≤Ri1) or less), the failure rate is expressed as Fi(0≤Fi≤1)：
Figure GDA0003462186000000124
Wherein λ isiRepresents the failure rate of other vehicles to perform the calculations, subject to a uniform distribution of (0, 0.03); mu.siThe failure rate of the link representing the unloading of the cluster head vehicle to other vehicles obeys a uniform distribution of (0, 0.03).
The reliability of the cluster head vehicle to unload part of the subtasks of the computation performed by itself is denoted RLocal(0≤RLocal1) or less), the failure rate is expressed as FLocal(0≤FLocal≤1)：
Figure GDA0003462186000000131
Wherein λ isLocalAnd a failure rate indicating that other vehicles performed the calculation, subject to a uniform distribution of (0, 0.03).
Thus, task ΨmThe reliability of the sub-tasks and the unloading factor theta depend on the reliability of the sub-tasks assigned to the edge server, the reliability of the mobile vehicle and the reliability of the cluster head vehicle, and the expression is as follows:
Figure GDA0003462186000000132
s3.2, discussing all possible situations and occurrence probabilities in the scene of the Internet of vehicles system according to the calculation result of the step S3.1. Thus, for n vehicles, 1 edge server and 1 cluster head vehicle, depending on whether the subtasks are completeBecome, has 2n+2And (3) a situation. Representing the set of vehicles that successfully completed each subtask by ζ and the set of vehicles that did not successfully complete each subtask by ζ' in view of the particular event δ; additionally, if the cluster head vehicle successfully completes the subtasks, K local1, otherwise Klocal0; if the edge server successfully completes the subtasks, K j1, otherwise, Kj0; if the other mobile vehicles successfully complete the subtasks, K i1, otherwise, Ki0. Then, for a specific task in the scene of the car networking system, the expression of the occurrence probability of the system failure is as follows:
Figure GDA0003462186000000133
s4, considering the possibility of task transmission or failure in the Internet of vehicles system, establishing a task re-processing mechanism, re-calculating the time delay of task transmission and calculation, and constructing an optimization target R (psi, theta) and a time delay constraint condition, wherein the specific steps are as follows.
S4.1, assuming that the cluster head vehicle does uniform motion along the x-axis direction, wherein the velocity is vsThen, the distance between the cluster head vehicle and the base station needs to be recalculated, and the expression is:
Figure GDA0003462186000000141
in the task re-processing mechanism, according to the calculation results of step 2.3 and step 2.4, after a sub-task may fail, the expression of the time delay for re-processing the sub-task in the edge server in the base station is as follows:
Figure GDA0003462186000000142
after each subtask of other moving vehicles possibly fails, the expression of the time delay for reprocessing the part of subtasks is as follows:
Figure GDA0003462186000000143
after a subtask of a cluster head vehicle possibly fails, an expression of time delay for reprocessing the partial subtask is as follows:
Figure GDA0003462186000000144
in the task re-processing mechanism, under the condition that a task failure possibly exists, the transmission and calculation time delay of the system is recalculated, and the expression is as follows:
Figure GDA0003462186000000145
s4.2, according to the calculation result of the step S4.1, defining a binary variable O for reliably finishing transmission and calculation of the calculation task within the time delay constraint thereofδThe expression is as follows:
Figure GDA0003462186000000151
thus, the optimization problem of maximizing reliability under the delay constraint considering the reprocessing mechanism can be modeled as follows:
Figure GDA0003462186000000152
s5, the SDN-based vehicle networking task offloading optimization method according to claim 1, wherein the step S5.2 is performed to maximize total reliability P of task transmission and calculation in the vehicle networking systemδ(ζ, ζ') for the goal, the optimization problem model was built as follows:
Figure GDA0003462186000000153
C1：θs+θj+θ1+θ2+…+θn＝1
Figure GDA0003462186000000154
Figure GDA0003462186000000155
Figure GDA0003462186000000156
in the above constraint conditions, C1 indicates that each subtask can be completely combined into the original task, C2 indicates that the time delay after the task is reprocessed is not greater than the time delay constraint of the task, C3 indicates that the computing power of other edge nodes and cluster head vehicles can meet the computing power required by the task, and C4 indicates that the sum of the data size of each subtask is equal to the original task size.
Due to the dual parameters of equation (23), the optimization problem P1 is non-convex and is not easily transformed into a convex problem. Furthermore, this problem is also the NP-hard problem. In the embodiment, a Particle Swarm Optimization (PSO) algorithm is adopted to solve the problem, and a global suboptimal solution is obtained with low complexity. The specific solving process is as follows:
for a population of particles with u, when the algorithm proceeds to iteration I, the position of each particle I is recorded
Figure GDA0003462186000000157
Figure GDA0003462186000000158
Figure GDA0003462186000000161
Representing an unloading scheme, the velocity of each particle i is
Figure GDA0003462186000000162
Figure GDA0003462186000000163
The optimum position of each particle i is
Figure GDA0003462186000000164
Figure GDA0003462186000000165
The optimal position of the particle group is expressed as
Figure GDA0003462186000000166
Figure GDA0003462186000000167
The velocity update formula for the particles is as follows:
Figure GDA0003462186000000168
where ε is the inertial weight, β1And beta2Is a learning factor, γ1And gamma2Is [0, 1 ]]The random number in χ is the puncturing factor, which is expressed as follows:
Figure GDA0003462186000000169
wherein
Figure GDA00034621860000001610
The shrinkage factor ensures the convergence of the particle swarm and prevents the explosion of the particle speed.
The particle position update formula is as follows:
Xi(I+1)＝Vi(I+1)+Xi(I) (28)
the fitness function for particle i is as follows:
Figure GDA00034621860000001611
in the above formula, R is a penalty factor, H and N are a feasible region and an infeasible region, respectively, Rq(X) is the constraint violation of the q-th constraint of the infeasible solution, Φ (X, I) is an additional heuristic for accelerated convergence of the PSO algorithm, RqThe expression (X) is as follows:
Figure GDA00034621860000001612
the Φ (X, I) expression is as follows:
Figure GDA0003462186000000171
wor (I) records the maximum fitness value of the feasible particles after the iteration of the generation I, and the expression is as follows:
Figure GDA0003462186000000172
the PSO algorithm comprises the following specific steps:
initializing the position X of each particlei(0) And velocity Vi(0) Setting the current position P of the particle ii(0) Is the best position.
And selecting the position of the particle with the best fitness in all the particles u as the global optimal position Gbest (0).
The iteration number s starts from 0, and is performed according to the following steps for each particle from 1 to u until s is 99, namely the iteration number reaches the maximum iteration number 100;
for each particle i, executing a velocity update formula (26) and a position update formula (27);
for each event delta, the probability of occurrence of a system fault is calculated using equation (17), the time delay for task reprocessing is calculated using equation (22), and whether the event delta is reliable is evaluated using equation (23).
And (5) evaluating the fitness function of the particle i by using the formula (24) and the formulas (29-32).
If theta (X)i(I+1))＞Θ(Pi(I) Let P) theni(I+1)＝Xi(I+1)。
If theta (X)i(I +1)) > theta (Gtest (I)), let Gtest (I +1) ═ Xi(I+1)。
Finally, output Gtest and theta (Gtest)
The simulation result of the embodiment is shown in fig. 3, which is a comparison graph of the simulation result obtained by directly dividing a task by a cluster head vehicle and unloading the divided subtasks to other mobile vehicles or edge servers in a base station in a multicast manner for task processing, and the result obtained by unloading the divided subtasks to other edge nodes in a unicast manner in the same simulation environment. Numerically, it can be seen that when the computational complexity is gradually increased from 1000 to 10000, the method provided by the present invention achieves higher system reliability than the unicast method.
In summary, to support these compute-intensive and delay-sensitive IoVs applications, the present invention integrates mobile edge compute nodes and fixed edge compute nodes to provide low-latency computing services in a cooperative manner. In order to better utilize the heterogeneous edge computing resources, the invention provides a concept of software-defined networking (SDN) and MEC assisted IoVs, and by offloading tasks to other resource nodes for processing in a multicast mode, the sharing of surrounding idle computing resources is realized, so that the resources in the network can be fully utilized. Furthermore, in complex and dynamic IoVs environments, interruptions in processing nodes and communication links are inevitable, possibly leading to life-threatening consequences. In order to ensure high reliability completion of delay-sensitive IoVs services, the invention introduces partial computation offloading and reliable task allocation with a reprocessing mechanism into the system, ensuring reliable transmission and computation of tasks to a certain extent.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (1)

1. An optimization method for vehicle networking task unloading based on an SDN (software defined network), which is characterized by comprising the following steps:
s1, constructing an Internet of vehicles application scene with a deployed SDN controller, a base station, an MEC server and vehicles, wherein the vehicles have V2V multicast and V2I communication capabilities, cluster head vehicles initiate task unloading requests to the SDN controller in the base station, the SDN controller discovers a V2V multicast link and a V2I link through a south-oriented interface, the SDN controller centrally controls computing resources of the network through a north-oriented interface, the SDN controller calls underlying computing resources in a software programming mode and divides an original task into a plurality of unknown subtasks, the SDN represents a software defined network, V2V represents vehicle-to-vehicle, V2I represents vehicle-to-infrastructure, and the MEC represents multi-access edge computing;
s2, in the application scene of the Internet of vehicles, the cluster head vehicle unloads partial subtasks to nearby mobile vehicles in a D2D multicast communication mode, or unloads partial subtasks to MEC servers in nearby infrastructure, and obtains the time delay of transmission and processing of each partial subtask, wherein D2D represents terminal through connection;
s3, aiming at the systematic risk of the car networking system, normalizing the failed cases into a Poisson distribution driven by time delay and fault rate, and calculating the reliability of transmission and processing of each part of subtasks and the occurrence probability P of faults existing in the systemδ(ζ,ζ′)；
S4, considering the possibility of task transmission or failure in the Internet of vehicles system, establishing a task reprocessing mechanism according to the worst condition of the system, namely, when the task transmission and processing are finished, a cluster head vehicle is required to perform task retransmission after the subtask of the edge server part fails, one node with the largest computing power in the non-failure vehicles is required to be selected as a reprocessing vehicle node after the subtask of the cluster head vehicle part fails, the reprocessing is only required once after the subtask of the non-failure vehicles fails, finally, the time delay of the task transmission and processing is recalculated, and an optimization target R (psi, theta) and a time delay constraint condition are established;
s5, aiming at maximizing reliability R (psi, theta) of task transmission and processing of the Internet of vehicles system, establishing an optimization problem model by combining constraint conditions, solving by adopting a particle swarm algorithm to obtain an optimal solution of a task unloading proportion of a cluster head vehicle so as to ensure that the reliability of the task transmission and processing of the Internet of vehicles system is maximum, transmitting the optimal task unloading proportion back to the cluster head vehicle by the SDN controller, and executing a task unloading scheme with the maximum reliability by the cluster head vehicle;
in step S1, a specific unloading ratio is defined according to an attribute of a task initiated by a cluster head vehicle in an application scenario of the internet of vehicles and a channel capacity between the cluster head vehicle and an edge service node, and a process is as follows:
in the application scene of the Internet of vehicles, the attribute of initiating the task by the cluster head vehicle is psim＝{Cm,Dm,Tm}，CmIndicates the total number of CPU cycles, D, required to complete the taskmIndicating the size of the input data, TmRepresenting the time delay constraint of completing the task, and defining the computational complexity of the task as alphamThe expression is:
Cm＝αmDm (1)
the task unloading proportion obtained by cluster head vehicles in the scene is defined by theta, and the unloading proportion distributed to the cluster head vehicles is thetasThe assigned offload ratio of the edge server is θjThe 1 st, 2 nd and … th mobile vehicle node is distributed with the unloading proportion theta1,θ2,…θnThe expression between the unloading ratios is:
θs+θj+θ1+θ2+…+θn＝1 (2)；
wherein, the step S2 is as follows:
s2.1, globally controlling the positions, directions, speeds and network states of computing resources and edge nodes in the application scene of the Internet of vehicles by the SDN controller, and defining the coordinates of cluster head vehicles as (x)s,ys) The coordinates of base station j are (x)j,yj) The coordinates of the other moving vehicles i are (x)i,yi) (ii) a Assuming the moving directions of all moving vehiclesKeeping the speed constant, and keeping the distance d between the cluster head vehicle and the base station j constantsjDistance d between the cluster vehicle and other moving vehicles isiExpressed as:
Figure FDA0003462185990000021
Figure FDA0003462185990000022
s2.2, enabling the uplink rate C between the cluster head vehicle and the edge server in the base station jUL(s, j) is represented by:
Figure FDA0003462185990000031
the uplink rate C between the cluster head vehicle and other moving vehicles iUL(s, i) is expressed as:
Figure FDA0003462185990000032
in the above formula, W is the uplink channel bandwidth of the cluster head vehicle and other edge nodes, ω is the transmission gain of the cluster head vehicle and other edge nodes, α is the path loss exponent, h0Is the complex Gaussian channel coefficient, N0Is additive white gaussian noise;
s2.3, calculating the time delay of the cluster head vehicle for unloading each subtask to other edge nodes according to the calculation result of the transmission rate in the step S2.2, and unloading the time delay of the cluster head vehicle to the base station j
Figure FDA0003462185990000033
Expressed as:
Figure FDA0003462185990000034
time delay for offloading tasks from cluster head vehicle to other mobile vehicle i
Figure FDA0003462185990000035
Expressed as:
Figure FDA0003462185990000036
s2.4, unloading cluster head vehicles to the time delay of the edge server in the base station j for processing the subtasks of the edge server
Figure FDA0003462185990000037
Expressed as:
Figure FDA0003462185990000038
time delay for offloading cluster head vehicles to other mobile vehicles i to process their subtasks
Figure FDA0003462185990000039
Expressed as:
Figure FDA00034621859900000310
time delay T of subtask processed locally by cluster head vehicleLocalExpressed as:
Figure FDA00034621859900000311
s2.5, obtaining the total time delay of the Internet of vehicles system when no fault occurs according to the calculation result in the step S2.4, wherein the expression is as follows:
Figure FDA0003462185990000041
wherein, the step S3 is as follows:
s3.1, according to the calculation results of the steps S2.3 and S2.4, reliability of partial subtasks of unloading cluster head vehicles to the edge server in the base station j is represented as RjThe failure rate is expressed as Fj：
Figure FDA0003462185990000042
Wherein λ isjIndicates the failure rate, μ, of the edge server to perform the computationjIndicating a failure rate of a link offloaded by the cluster head vehicle to the base station;
the reliability of the partial subtasks for offloading the cluster head vehicle to other moving vehicles i is denoted as RiThe failure rate is expressed as Fi：
Figure FDA0003462185990000043
Wherein λ isiIndicating the failure rate, μ, of other vehicles to perform the calculationsiIndicating a failure rate of a link where cluster head vehicles offload to other vehicles;
the reliability of the partial subtasks of the cluster head vehicle unloading the computation performed by itself is denoted as RLocalThe failure rate is expressed as FLocal：
Figure FDA0003462185990000044
Wherein λ isLocalIndicating a failure rate of the other vehicle to perform the calculation;
thus, task ΨmThe reliability of the subtask is determined by the reliability of the subtask distribution to the edge server, the reliability of the moving vehicle, and the reliability of the cluster head vehicle, and the expression is:
Figure FDA0003462185990000045
s3.2, discussing all possible conditions and occurrence probabilities in the scene of the vehicle networking system according to the calculation result of the step S3.1, so that for n vehicles, 1 edge server and 1 cluster head vehicle, 2 are available according to whether the subtasks are completed or notn+2In one case, considering a particular event δ, the set of vehicles that successfully completed each subtask is represented by ζ, and the set of vehicles that did not successfully complete each subtask is represented by ζ'; additionally, if the cluster head vehicle successfully completes the subtasks, Klocal1, otherwise Klocal0; if the edge server successfully completes the subtasks, Kj1, otherwise, Kj0; if the other mobile vehicles successfully complete the subtasks, Ki1, otherwise, Ki0; a specific task in the context of the application of the Internet of vehicles, its transmission and the calculated total reliability PδThe expression of (ζ, ζ') is:
Figure FDA0003462185990000051
wherein, the step S4 is as follows:
s4.1, assuming that the cluster head vehicle does uniform motion along the x-axis direction, wherein the velocity is vsDistance d 'between cluster head vehicle and base station'sjRecalculating, wherein the expression is as follows:
Figure FDA0003462185990000052
in the task re-processing mechanism, according to the calculation results of step 2.3 and step 2.4, the edge server in the base station recalculates the time delay of the partial subtasks after the subtasks possibly fail
Figure FDA0003462185990000053
The formula is as follows:
Figure FDA0003462185990000061
other moving vehicles recalculate the time delay of the part of subtasks after each subtask possibly fails
Figure FDA0003462185990000062
The formula is as follows:
Figure FDA0003462185990000063
recalculating the time delay of the partial subtasks of the cluster head vehicle after the subtasks possibly fail
Figure FDA0003462185990000064
The formula is as follows:
Figure FDA0003462185990000065
in a task reprocessing mechanism, the transmission and computation delays of an internet of vehicles system are recalculated in the possible case of a task failure
Figure FDA0003462185990000066
The formula is as follows:
Figure FDA0003462185990000067
s4.2, according to the calculation result of the step S4.1, defining a binary variable O for reliably finishing transmission and calculation of the calculation task within the time delay constraint thereofδThe expression is as follows:
Figure FDA0003462185990000068
thus, the optimization problem of maximizing reliability under the delay constraint considering the reprocessing mechanism can be modeled as follows
Figure FDA0003462185990000069
Wherein, the step S5 is realized by converting into the following calculation formula:
Figure FDA0003462185990000071
C1：θs+θj+θ1+θ2+…+θn＝1
C2：
Figure FDA0003462185990000072
C3：
Figure FDA0003462185990000073
C4：
Figure FDA0003462185990000074
in the above constraint conditions, C1 indicates that each subtask can be completely combined into the original task, C2 indicates that the time delay after the task is reprocessed is not greater than the time delay constraint of the task, C3 indicates that the computing power of other edge nodes and cluster head vehicles can meet the computing power required by the task, and C4 indicates that the sum of the data size of each subtask is equal to the original task size.
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