=Paper=
{{Paper
|id=Vol-3131/regular10
|storemode=property
|title=Task Offloading Optimization in Vehicular Edge Computing Based on Vehicle Mobility Analysis
|pdfUrl=https://ceur-ws.org/Vol-3131/paper10.pdf
|volume=Vol-3131
|authors=Yarui Li,Feng Zeng,Qiao Chen
|dblpUrl=https://dblp.org/rec/conf/atait/LiZC21
}}
==Task Offloading Optimization in Vehicular Edge Computing Based on Vehicle Mobility Analysis==
https://ceur-ws.org/Vol-3131/paper10.pdf
Task Offloading Optimization in Vehicular Edge Computing Based on
Vehicle Mobility Analysis
Yarui Li, Feng Zeng*, Qiao Chen
School of Computer Science and Technology, Central South University, Changsha Hunan 410083, China
fengzeng@csu.edu.cn
Abstract: Vehicular Edge Computing (VEC), as a promising new paradigm, can improve the QoS of
vehicular applications through computation offloading. However, with the emergence of more and more
computation-intensive and delay-sensitive vehicular applications, VEC servers are facing the challenge
of resource limitation, and the fast movement of vehicles will lead to the switching of task uploading. In
this paper, based on the analysis of the task offloading switching between VEC servers due to the moving
of vehicles, we propose a nonlinear programming model for joint optimization of delay, energy
consumption, payment for vehicle users. Then, we solve the problem based on the KKT conditions to
obtain the task offloading optimization strategy. Simulation results show that the proposed strategy can
ensure that the vehicles perform task offloading switching efficiently between the VEC servers, and can
reduce the average completion time of task offloading and the total cost of vehicle users.
Keyword:Vehicular edge computing, Task offloading, Switching, Mobility
1 Introduction
In recent years, there are more and more emerging vehicular applications such as autopilot, smart cockpits,
and so on. These vehicular applications need high computing power, high network bandwidth, and low
latency. Since cloud servers are far away from vehicles and large amounts of data should be uploaded and
processed, cloud computing are difficult to meet the Quality of Service (QoS) requirements for those smart
vehicular applications. To this end, Vehicular Edge Computing (VEC) emerges as the important technology
to deal with the limitation of the on-board computing, and the main idea is to provide computing services for
vehicles at the edge of vehicle network [1]. With the support of the adjacent edge servers, the vehicles can
obtain timely and efficient computing services.
In a vehicular edge network, the vehicle offloads tasks to the edge server via wireless communication
with the Road Side Unit (RSU) for efficient computing services. Due to the fast moving of vehicles and the
limitation of wireless communication range, vehicles need to go through multiple RSUs to complete task
offloading. When the vehicle which is uploading the task leaves the wireless signal coverage of current
RSU, the vehicle will stop the current uploading and switch to the next VEC server to continue the
uploading. The uploading interruption caused by the mobility of the vehicle may lead to the failure of task
offloading or the delay of task processing, which is a challenge research topic in VEC.
In this paper, we consider the task offloading delay, energy consumption and the payment of vehicle
users, analyze the switching process of task uploading between multiple VEC servers, and propose an
optimization model for task offloading with minimum cost. Based on the KKT conditions, we solve the
problem and obtain the task offloading optimization strategy. Simulation results show that the proposed
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scheme can ensure that vehicle users perform switching of task uploading efficiently, reduce the whole delay
of task offloading and the total cost of vehicle users.
2 Related Work
In recent years, as a promising technology, vehicular edge computing (VEC) can extend the computing
power to the edge of vehicular network and provide computation service for vehicles, which has abstracted
the attention of many scholars.
In VEC, in order to obtain high quality of service, vehicle users can offload the computation tasks to the
VEC servers. There are many research works on the problem of task offloading in VEC. Sun et al [2]
introduced one kind of decision-making and scheduling problem of task offloading, and described the
problem as a mixed integer nonlinear programming problem. The heuristic and genetic algorithms were
proposed to solve the problem, which reduced the delay and energy consumption of task processing and
improved the offloading efficiency in VEC. A context-aware offloading scheme in an opportunistic vehicular
fog computing framework was presented by Rahman et al [3], which took into account the variation of
vehicle speed, direction and position, allowing vehicles to take advantage of nearby opportunistic offloading
to support a vehicle-to-vehicle (V2V) computation offloading which effectively solved the capacity
limitation problem at RSU and provided a sustainable computing environment for vehicle users. In order to
decrease the task execution delay, Zeng et al [4] proposed a new vehicle edge computing framework based
on software defined networks, which introduced the reputation to measure the contribution of each vehicle.
They designed the interaction process as a kind of incentive mechanism based on reputation via using
Stackelberg game modeling, and proposed a genetic optimization algorithm to quickly obtain the optimal
strategy for both sides of the game.
Some researchers analyze the impact of the mobility of vehicles on task offloading. Zhang et al [5]
described task offloading as a finite level Markov decision process (MDP). Considering the uncertain
transition probability under real circumstances, they presented a concrete expression of transition probability
and proposed a robust time-aware task offloading algorithm, then further proved that the proposed algorithm
can reduce the delay of task offloading even under the uncertain high transition probability. Considering the
mobility of vehicles, Liu, Li and Sun [6] studied the problem of task offloading in a dynamic environment
with certain resource and delay constraints. Based on one-to-one and one-to-many matching algorithms, task
assignment was studied on three different speed models (straight road, urban road with traffic lights and
curved road). Their research work reduced the total network delay of task offloading. Li et al [7] proposed
an auxiliary slice network structure, which utilized Mobile Edge Computing (MEC) to host some network
services. They presented a traffic scheduling strategy, designed a new flow scheduling mechanism,
considering the requirements of high mobility and reliability between vehicles.
The VEC servers mentioned above are always assumed to have sufficient resources, however, during
peak demand periods, VEC servers tend to become congested due to the limited resources, resulting in a
decline of the QoS. To this end, some researchers took into account the dynamic management of resources,
and studied resource allocation optimization issues. Salahuddin et al [8] proposed a resource allocation
scheme based on vehicle cloud reinforcement learning technology to minimize the supply cost of vehicle
cloud resources. In order to improve the computing power of vehicle users, some researchers also considered
the factors of user behavior relationship between vehicles. For example, Zhang et al [9] used unmanned
aerial vehicle (UAV) to assist social networking of vehicles, and the combination of social content caching
and wireless resource scheduling was considered to explore the dynamic resource allocation problem of
energy perception, the dynamic power allocation of fixed vehicles was optimized by using Search Algorithm.
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In order to take the use of idle vehicular resources, Zeng et al [10] studied how to effectively and
economically utilize the idle resources in volunteer vehicles to handle the overloaded tasks in VEC servers,
and proposed a fast searching algorithm based on genetic algorithm to find the best pricing strategy for the
VEC server. Wang et al [11] mentioned that a large number of parking vehicles in the parking lots have
underutilized resources which can be used to assist the content provider with collaborative caching popular
content. However, because of the mobility of the vehicle in real time, the communication between the
requesting vehicle and the parked vehicles may be unsustainable. In contrast, it is considered in this paper
that stable communication can be maintained between short distance vehicles under current technical
conditions. While due to the high mobility of the vehicle, if the vehicle users move out of the current RSU’s
coverage and do not complete the uploading, it will cause the interruption of task offloading and decrease
the QoS of VEC. Therefore, we need to deeply analyze the process of task offloading while the moving of
vehicles, and design the optimal scheduling of task.
3 System Description
We assume that the system deploys RSUs on the road side, and each RSU is connected with a VEC
server through the wired link. The set of VEC servers is denoted as = 1 , 2 , …, m . Each server
1≤ ≤ has undertook some computation tasks from vehicle users within its communication range,
and the set of vehicle users is denoted as = 1, 2 , …, , as is shown in figure 1. It is assumed that each
vehicle user has large data volumes and latency-sensitive tasks, these tasks can be offloaded to the VEC
servers or executed on local devices. It is supposed that all RSUs have the same wireless transmission range,
but as the location changing of the vehicles, the vehicles should switch from one RSU to another for data
uploading, since each RSU’s wireless signal can cover the limited area. Therefore, it is necessary to consider
the switching problem between the servers while task offloading. That is, when the vehicle user travels from
the coverage of the VEC server to the coverage of the VEC server , the vehicle user's task offloading
process will change accordingly. This model will be described in more detail below.
Fig.1. Vehicular Edge Network
1) VEC Group
Every RSU is installed around the road, and is equipped with a VEC server. When the vehicle user
offloads the task to the server, the server will assign resources to the vehicle user based on the requirements.
Multiple VEC servers can share their idle resources and cache content, constitute a resource sharing pool, a
VEC group. If the current server does not have enough resources to complete the task, it can request other
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servers in the group to obtain idle resources to handle overloaded tasks. At the same time, when the task
requested by the vehicle user is cached by another server, the current server can also get cache content from
other servers.
2) Vehicle User
The vehicle is equipped with wireless communication devices such as global positioning, Bluetooth,
WiFi, with certain task processing capabilities. When the vehicle does not have enough resources to handle
the task, it can request the VEC server to provide computation service. If the server accepts task processing
request from the vehicle user, it will assign a certain amount of resources for the vehicle user, and the
vehicle user needs to pay a certain fee to the server to obtain a good task offloading service [12].
3) Vehicular Task
For task of vehicle user 1≤ ≤ n , it can be represented by = , , where is the
amount of task size, and is the execution ability required to process the task, measured with the CPU
cycles. Taking the image identification task as an example, is the image file size, and is the amount
of CPU cycles required to finish the image processing. Vehicular task can be executed on a local
vehicular device or a VEC server, but cannot be executed simultaneously on the two.
4 Task Offloading Process
Considering the data uploading switching between VEC servers, we introduce the process of task
offloading. The uploading delay, energy consumption, payment and cache policy are taken into account in
our analysis of task offloading.
1) Task Uploading Delay
When the vehicle user moves from the cover area of the current VEC server to the cover area of the
next VEC server +1 , it is necessary to perform a switching of the vehicular task. Vehicle users unload tasks
through RSU to the edge server which is directly connected with the RSU via wired link, and the wireless
channel between the vehicle and the RSU is affected by radio interference and the noise. According to radio
theory, the signal-to-interference-plus-noise ratio (SINR) between the vehicle users and the RSU can be
represented as
,ℎ ,
Λ, = , (1)
{ \ } ,ℎ ,+ ,
in which { \ } ,ℎ , is the interference of other vehicle users ( ) to , , represents wireless link
transmission power of vehicle user , and , represents communication background noise. It is assumed
that the task unloading bandwidth is the same for every vehicle within the same RSU coverage,
therefore, the transmission rate of vehicle user in RSU i can be represented by , = B log 1 + Λ , .
The transmission delay between the VEC server and its connected RSU is negligible, so the transmission
delay of vehicle user directly unloading the task to VEC server is
,
, = , (2)
,
where , ϵ 0,1 represents the proportion of the task required to handle within the current VEC server
coverage, and the current amount of task unloaded at server is , .
Assumed that the vehicle user completes the task uploading after driving from the first RSU to the jth
RSU, if the server does not store any data related to task (not caching), the server needs to
execute the task. Let the VEC server can provide the processing power as , , then the time of task
execution is
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= . (3)
,
Since the data of output is much less than the amount of task uploaded by the vehicle user, the time
taken by the vehicle user to download the execution results can be ignored. Hence, the total processing time
for vehicle users to complete task offloading is
,
= + = =1
+ . (4)
, ,
2) Energy Consumption of Vehicle Users
The energy consumption of the vehicle user comes from wireless transmission while task
unloading to the server, which is denoted as
,
, = , ,i = , . (5)
,
3) Payment for Task Offloading
The vehicle should pay the data uploading and task execution for VEC servers. The uploading payment
has relation with transmission rate, and we assume the unit cost of the server to receive the uploaded data
is , , the unit task executing cost is , . The total payment of for task offloading can be expressed as:
= =1 , , + , , . (6)
4) Cache Policy
In order to provide a better service experience for vehicle users, the VEC servers will cache some
popular and task-related data. When the task requested by the vehicle user is cached on the server, the result
can be directly got without any processing operation. At this time, the vehicular task execution time = 0,
and the vehicle user pays the execution fee for VEC server , , is also 0, shown in (7):
1 is cached
, = , (7)
0 otherwise
where , ∈ 0,1 indicates whether there is a cache on the VEC server .
5) Local Processing Model
If the total cost of the task offloading is higher than that of the local computation processing, the
vehicle user will perform the vehicular task locally. The local task execution time, energy, and currency
consumption are
,
, = =1
, (8)
,
2
, = =1 , , , (9)
, = , , , (10)
where is coefficient related to the hardware architecture of vehicular device [13], , is local
processing capacity of vehicle user, , is unit cost for vehicle users.
6) Objective (Cost) of Task Offloading
In this paper, we consider the joint optimization of the delay, energy consumption and currency
payment of task offloading for the vehicle users, and the objective of task offloading for vehicle users is to
minimize the delay, energy consumption and the payment for VEC servers. Let , , ϵ 0,1 respectively
indicate that the vehicle user's attention to delay, energy and payment. For vehicle user and its task ,
we define the objective function as (11), and is also called as the cost of task offloading hereafter.
, 2 ,
= 1− =1
+ =1 , , + , , + =1
+ 1−
, ,
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,
, + =1 , + =1 , , + 1− , , , (11)
, ,
In (11), = 0 indicates that the task is executed in local device, = 1 indicates that the vehicle
user offloads the task to the VEC server, and due to the moving of vehicle , it is supposed that the task
offloading is processing from RSU 1 area to RSU j area.
5 Problem Description and Solution
5.1 Problem Description
In VEC, the computing capability and storage space of the VEC server are limited, and we assume that
the maximum computing capability of the VEC server is . For ease of understanding, Table I shows
the main parameters used herein. The joint optimization of offloading delay, energy consumption and the
payment can be modeled as:
=1 , 2 ,
min =1
1− + =1 , , + , , + =1
+
{ ,, ,, } , ,
,
1− , + =1 , + =1 , , + 1− , , , (12)
, ,
1: , >0 (13-a)
2: =1 , ≤ (13-b)
3: =1 , =1 (13-c)
4: = 0,1 (13-d)
5: , = 0,1 (13-e)
6: + + =1 (13-f)
In above model, constraints C1 and C2 ensure that the computing capability , , which is required by
task execution, is a positive value, but the sum of computing capability cannot exceed the maximum
capability . C3 means that the vehicle user can handle the vehicular task after switching over VEC
servers, C4 means that the vehicle user can only execute the task in local device or offload the task to a VEC
server, and C5 represents whether the VEC server caches the task.
Table I Main Notation Used In This Paper
Parameter Meaning
the set of VEC servers
V the set of Vehicle users
the size of task
the CUP cycles for the execution of task
, proportion of the data uploaded by to
, transmission rate of to
, the CUP cycles required by to execute
, 's local processing power
, unit uploading cost
, unit computation cost
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, whether cached on the VEC server
whether to perform tasks locally
, , weight variable
5.2 Problem Solving
∗
When offloading the task, the vehicle user determines the task offloading policy , and the vehicle
user can complete task offloading after traveling through the VEC servers 1 , 2 , …, , …, . With the
∗
given policy , the joint optimization problem can be shown as follows:
∗ =1 , 2 ∗ ,
min ∗ =1
1− + =1 , , + , , + =1
+
{ ,, ,, } , ,
,
1− , + =1 , + =1 , , + 1− , , , 14)
, ,
1: , >0 (15-a)
2: =1 , ≤ (15-b)
3: =1 , =1 (15-c)
4: + + =1 (15-d)
The above optimization problem is a multivariate nonlinear programming problem, we have the
following Lemma.
∗
Lemma 1 Given the uploaded ratio of VEC servers ( , ), the optimization problem (14) is a convex
optimization problem about , . Assumed that the vehicle user's policy set is , the method of the KKT
condition can be used to obtain the optimal solution.
Proof: First, according to (14), the Lagrange function of the above problem can be obtained, as shown
in (16).
∗ =1 , 2
,μ = =1
1− + =1 , , + , , +
,
∗ , ,
=1
+ 1− , + =1 , + =1 , , + 1− , , , +
, , ,
μ =1 , − (16)
In (16), is the variable of non-negative Lagrange multiplier, the and in the above
optimization problem must meet the following equations:
,μ 1− ,
=− 2 + 1− , , +μ=0 (17)
, ,
, ,μ
μ
= =1 , − =0 (18)
Therefore, the optimal solution of the allocated computing capability of the VEC server to the vehicle
user is:
2
∗ 1− ,
, = (19)
=1 1− ,
2
=1 1− ,
μ∗ = − 1− , , (20)
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6 Performance Evaluation
6.1 Parameter Setting
We use Matlab to simulate and evaluate the above solution. In the simulation, the VEC network has a
total of 15 VEC servers connected to the corresponding RSU, the coverage is circular space, and each RSU
has 5-50 vehicle users within its wireless coverage. The channel bandwidth between servers is set to
10-30MHz, and the computing capability of VEC servers is set to 7000-10000. The amount of request data
in the task is 500-3000Mb, and the required CPU cycles are 350-2700 cycles. The local computing
capability of the vehicle is randomly set between 1-5, and the values of the optimization variables , , and
are 0.3, 0.45 and 0.25, respectively. The experiment parameters are shown in Table II.
Table II Simulation experiment parameters
Parameter Value
Number of VEC server 15
Number of vehicle 5-50
Channel bandwidth 10-30MHz
Task data size 500-3000Mb
CPU cycle 350-2700Cycles
variable ， ， 0.3, 0.45 and 0.25
6.2 Experiment Analysis
In the experiment, we analyze the impact of different task processing types on the total cost (objective
function) of vehicles. The figure 2 shows that, as the number of vehicle users requesting task offloading
increases, the total cost for all vehicle users increases. When the number of vehicle users is fixed, the total
cost of task offloading will be lower than that of local processing, since the VEC servers can provide
efficient processing resources with low transmission delay. However, since the total amount of resources of
the VEC server is fixed, when a large number of VEC servers are assigned to nearby vehicles, there will be
competition between vehicles, the price for VEC server resources will increase. We can observe from figure
2 that the total cost with the support of cache processing is the lowest, because when the VEC servers have
the cached task data, the offloaded task does not have to be executed by the VEC server, and the result can
be directly return to requester without task execution. With the support of caching, the execution delay can
be ignored, so that the total cost is lower than that of other processing types.
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Fig.2. Impact of different task processing types on the cost of vehicle users
When task offloading, the driving vehicle users need to switch different number of VEC servers. Since
the processing capability of each server is limit, with the increase of tasks, the allocated resource of each
server for each task will reduce, thus the number of servers switched by vehicle users will increase
accordingly, as is shown in figure 3.
Fig.3. The number of servers switched by vehicle users in task offloading
7 Conclusion
In this paper, we focuses on the task switching and offloading scheme of vehicle users while moving,
establishes an optimal model of task offloading with joint optimization of delay, energy consumption and the
payment for vehicle users. In order to obtain the task offloading optimization strategy, we proposed a
solution based on the KKT conditions, which shows that each server should undertake the optimal amount of
tasks during task unloading considering the moving of vehicles. The simulation results show that the
proposed scheme has good effect, the vehicles perform task offloading switching efficiently between the
VEC servers, and the delay of task offloading and the total cost of vehicle users are decreased.
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