5.5 A Case Study
5.5.1 Predictive Task Offloading for Fast-Moving Vehicles
56 5 Mobile Edge Computing for Internet of Vehicles ber of inefficient exploration attempts in the training process by deliberately adjusting the state and reward representations. Wang, Ning, et al. [78] presented an imitation learning–enabled online task scheduling scheme. In this scheme, the learning agents find optimal offloading strategies by solving an optimization problem with a few offline samples; near-optimal edge service performance is then achieved at a low learning cost.
5.5 A Case Study 57 L. Through the V2I communication mode, vehicles traveling on a given segment can only access the RSU located in the corresponding segment.
In the scenarios we studied, such as a temporarily deployed vehicular network, the RSUs communicate with each other through wireless backhauls. Each RSU is equipped with an MEC server with limited computational resources. To improve the transmission efficiency of the wireless backhauls, the task input file cannot be transmitted between the RSUs. Moreover, since the task output data size is small, the computation output can be transmitted between RSUs through wireless backhauls.
All the vehicles move at a constant speed. The distribution of the vehicles on the road follows a Poisson distribution with densityλ.
Each vehicle has a computation task. The task can be either carried out locally by the vehicular terminal or computed remotely on the MEC servers. The computation task is denoted as T = {c,d,tmax}, wherecis the amount of the required compu- tational resources,d is the size of the computation input file, andtmaxis the delay tolerance of the task. We further classify the tasks intoStypes and present the tasks as Ti = {ci,di,ti,max},i ∈S. The vehicles can be correspondingly classified according to their computation task types intoStypes. The proportion of vehicles with a task of typeiin the total number of vehicles on the road isρi, wherei ∈SandS
i=1ρi =1.
5.5.1.2 Offloading with Optimal Predictive Transmission
There are two transmission modes for task offloading. One is through a direct V2I mode. In this mode, a vehicle can only offload its task to the MEC server equipped on the RSU that the vehicle can currently access. Considering that a vehicle travels down an expressway at high speed, if its computation task costs a relatively long time, the vehicle can pass by several RSUs during the task execution period. In this case, the output of the computation to be sent back to the vehicle needs to be transmitted from the MEC server that has accomplished the task to the remote RSU that the vehicle is newly accessing. The time overhead and transmission cost of the multi-hop relay seriously degrade the task transmission’s effectiveness.
Another offloading mode is predictive V2V transmission, whose main framework is illustrated in Fig.5.3. In this mode, the vehicles send their task input files to the MEC servers ahead of them, in their direction of travel, through multi-hop V2V relays. Based on the accurate prediction of the file transmission time and the task execution time, as well as the time spents for the vehicle traveling down the road, vehiclek can arrive within the communication area of RSUn at the exact time its task has been completed. The computation output can be transmitted directly from RSUn to the vehicle through V2I transmission without a multi-hop backhaul relay.
Transmission costs for task offloading can thus be reduced.
Letti,v2vdenote the average time delay for the transmission of the input file of a task of typeithrough a one-hop V2V relay. The total time consumption of completing the task in this predictive mode is
ti,j =yj·ti,v2v+ti,upload+ti,r emote+ti,download (5.1)
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Fig. 5.3 Vehicle mobility-aware predictive task data transmission
wherejis the number of hops the upload destination RSU is from the vehicle’s current position, where j >1 means the vehicles adopt predictive mode transmission. We defineyjas the number of V2V relay hops that are required to transmit the input file to an RSU j hops away. Furthermore, the total cost of this type of task offloading is fi,j =yj· fi,v2v+ fi,upload + fi,r emote+ fi,download (5.2) where 1< j≤ Ji,max.
To minimize the offloading cost of both data transmission and task execution while satisfying the latency constraints, the objective function of the optimal offloading schemes is
min{Pi,j}
S i=1
Ji,max
j=0
ρiPi,jfi,j
suchthat ti,j ≤ti,max, i∈ {1,S},j ∈ {0,Ji,max}
(5.3)
The objective function in (5.3) gives the average offloading costs of all types of vehicles when they choose offloading strategies{Pi,j}, where{Pi,j}is the probability of a vehicle of typeichoosing to offload its task to the MEC server jroad segments away from its current position. To solve (5.3), we resort to a game approach to find the optimal offloading strategies of each type of vehicles. This game involves S players, where each player is a set of vehicles with the same type of tasks. We denote the vehicle set with tasks of typei as seti. The strategies of vehicle seti (i = {1,2, . . . ,S})are{Pi,j}. Vehicles in seti can choose to either execute tasks locally or offload them to MEC servers j hops away. The payoff for setiis the sum of the vehicles’ offloading costs. Using a heuristic method in which each vehicle set adopt its best response action given the strategies of other vehicle sets, we can obtain a Nash equilibrium, which is the solution of (5.3).
5.5 A Case Study 59
0.050 0.1 0.15 0.2 0.25 0.3 0.35 0.4
100 200 300 400 500 600 700
Offloading cost
Offloading through direct V2I transmission Offloading through combination-mode transmission
Fig. 5.4 Task offloading costs in terms of vehicles density
5.5.1.3 Performance Evaluation
In the simulation scenario, we consider 10 RSUs located along a four-lane one-way road. The vehicles are traveling at 120 km an hour. Their computation tasks are clas- sified into five types, with the probabilities{0.05,0.15,0.3,0.4,0.1}, respectively.
In addition, we set the computation resource requirement of each type of task at {7,13,27,33,48}units, respectively.
Figure5.4shows the computation offloading costs with different densities of vehi- cles on the road. We compare the performance of our proposed predictive offloading scheme with the V2I direct transmission scheme. It can be seen that the predictive scheme greatly reduces the cost when the road has high vehicle density. In the case of high traffic density, long task execution times on the MEC servers lead to more RSUs that the vehicles have traveled past. Due to the transmission cost of the wire- less backhaul between RSUs, the total costs of the direct V2I scheme rise quickly with an increase in the density λ. However, in the predictive scheme, part of the transmission is offloaded to the V2V relay, which has a lower cost compared with wireless backhaul transmission. Thus, computation offloading costs can be saved.
It is worth noting that the performance improvement brought about by predictive offloading is based on the accurate prediction of vehicle mobility. With the devel- opment of AI technology, the prediction of vehicle mobility patterns has become much more accurate, especially on highways that have stable traffic flows. Thus, this proposed predictive scheme is promising and effective in practical applications.
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Fig. 5.5 Task offloading in an MEC-enabled vehicular network