Abstract
Multi-access edge computing (MEC) has emerged as a promising technology to facilitate efficient vehicular applications, such as autonomous driving, path planning and navigation. By offloading tasks from vehicles to MEC servers (MECSs), the MEC system can facilitate computation-intensive applications with hard latency constraints in vehicles with limited computing resources. However, owing to the mobility of vehicles, the vehicles are not evenly distributed across the MEC system. Therefore, some MECSs are heavily congested, whereas others are lightly loaded. If a task is offloaded to a congested MECS, it can be blocked or have high latency. Moreover, service interruption would occur because of the high mobility and limited coverage of the MECS. In this paper, we assume that the task can be divided into a set of subtasks and computed by multiple MECSs in parallel. Therefore, we propose a method of task migration with partitioning. To balance loads, the MEC system migrates the set of subtasks of tasks in an overloaded MECS to one or more underloaded MECSs according to the load difference. Simulations have indicated that, compared with conventional methods, the proposed method can increase the satisfaction of quality-of-service requirements, such as low latency, service reliability, and MEC system throughput by optimizing load balancing and task partitioning.
Highlights
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]
To overcome these limitations and facilitate efficient application processing, multiaccess 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]
As the vehicle has already offloaded its task to the serving MEC servers (MECSs) and the task is placed in its waiting queue, the task must be completed within a time, excluding the transmission delay under the delay constraint
Summary
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]. If the set of subtasks of a task migrates to the MECS without considering the loads of the MECSs, the total service delay may increase. This becomes useless if the subtasks migrate to a congested MECS or a specific MECS simultaneously. The set of subtasks in an overloaded MECS is migrated to one or more underloaded MECSs depending on the load difference between the overloaded and underloaded MECSs. Balancing the loads in the MEC system through task partitioning and migration can increase both the satisfaction of QoS requirements, such as low latency and service reliability, and MEC system throughput.
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