# Task Migration with Partitioning for Load Balancing in Collaborative Edge Computing
## 1 Introduction  
In the 5G era, the concept of vehicular networks has extended to the Internet of Vehicles (IoV), in which intelligent and interactive vehicular applications such as autonomous vehicles, path planning and navigation are enabled by communication and computation technologies [1; 2; 3]. These computation-intensive applications frequently require ultra-reliability and low-latency (uRLLC). For example, autonomous driving applications need to process huge amounts of data in real time (as high as 2 GB/s) within about 10 ms, which is a very low delay constraint [2]. Applications for efficient driving, e.g., path planning or navigation generate less data than autonomous driving, but tasks from the applications should be performed within a still-low delay constraint of about 100 ms [4]. Despite improving the computing power of vehicles, processing such applications on vehicular terminals is still difficult while ensuring high computing power and quality-of-service (QoS) requirements, such as low latency. To overcome these limitations and facilitate efficient application processing, multi-access edge computing (MEC) has emerged as a promising technology that can satisfy the demand for the heavy computation of vehicles by providing rapid and sufficient computational resources to vehicles [5]. Thus, by offloading tasks from vehicles to MEC servers (MECSs), the MEC system can facilitate computation-intensive applications with low latency in vehicles with limited computing resources. Meanwhile, compared with conventional centralized cloud computing, offloading computation-intensive tasks to MECSs, which are geographically closer to vehicles, by deploying them at the network edge, can further reduce the network delay. However, because MECSs have limited computing power, the computing power that the vehicle can request from the MECS depends on the MECS load level, i.e., the number of tasks already allocated to the MECS [6]. Due to the mobility of vehicles, vehicles are not evenly distributed across a MEC system; thus, the number of tasks offloaded to a MECS may not be evenly distributed among MECSs. Therefore, some MECSs are heavily congested, whereas others are lightly loaded. If a task is offloaded to a congested MECS, the task may be blocked or the delay will be prolonged. Thus, the delay requirements may be violated. Moreover, the mobility of vehicles causes uncertainty in downloading results [7]. Owing to the limited coverage of MECSs, the vehicle can travel out of the coverage of a MECS that offloads a task during a service session, resulting in a service interruption. Service interruption occurs because of dynamically changing radio association, which can significantly increase the total service latency, particularly in urban areas with highly dense infrastructure. To improve the satisfaction with QoS requirements, references [8; 9; 10; 11; 12; 13] studied service migration. In [8; 9; 10], a service migration that migrates services according to a vehicle's trajectory was proposed to minimize the average completion time of tasks. As they only considered the trajectory of vehicles without considering the load of MECSs, tasks can simultaneously migrate to a specific MECS. Thus, this MECS becomes overloaded, which can result in uneven loads among MECSs and reduce system throughput. Service migration with load balancing was addressed in [11; 12; 13]. In [11], a service migration method was proposed that considered the load difference and migration cost in balancing the loads of MECSs in a MEC system. In [12], service migration was determined using the traffic conditions and loads of each MECS to minimize the migration costs and travel times of vehicles. The load of each MECS in [11; 12] was measured using the number of tasks in the MECS. In [13], the method distributed the load from an overloaded cell to a lightly loaded cell from its neighbors to increase the utilization of radio resources. The load is represented by the resource block utilization ratio of the cells.