TECHNICAL FIELD

The subject matter described herein relates in general to connected vehicles and, more specifically, to systems and methods for generating a task offloading strategy for a vehicular edge-computing environment.

BACKGROUND

Connected vehicles—vehicles that can communicate bidirectionally over a network with infrastructure systems and, in some applications, other vehicles—are becoming mainstream and are expected to be widely deployed within a few years. One technology that supports this connected-vehicle future is edge computing. Edge computing can benefit connected vehicles in several ways. For example, edge computing can enable computation-intensive and latency-critical applications despite the computation-resource limitations of the connected vehicles themselves. Also, edge computing permits connected vehicles to share important data such as sensor data with one another. One aspect of vehicular edge computing is developing practical, effective task offloading strategies for diverse terrains and infrastructure deployments.

SUMMARY

Embodiments of a system for generating a task offloading strategy for a vehicular edge-computing environment are presented herein. In one embodiment, the system comprises one or more processors and a memory communicably coupled to the one or more processors. The memory stores a simulation module including instructions that when executed by the one or more processors cause the one or more processors to deploy a simulation platform that simulates a vehicular edge-computing environment in which one or more vehicles perform computational tasks associated with one or more vehicular applications. Data associated with the computational tasks is partitioned into segments, the simulation platform includes a simulated edge server, the simulated edge server includes a policy network, and the policy network includes a neural network. The memory also stores a Deep Reinforcement Learning (DRL) module including instructions that when executed by the one or more processors cause the one or more processors to perform, for each of a plurality of segments, a Deep Reinforcement Learning (DRL) training procedure in which the one or more processors receive, at the simulated edge server, state-space information regarding the one or more vehicles and one or more intermediate network nodes, the one or more intermediate network nodes including at least one of base stations and roadside units; input the state-space information to the policy network; generate, from the policy network, an action concerning a current segment of a particular computational task associated with a particular vehicle among the one or more vehicles, the action including one of offloading the current segment via a specific base station, offloading the current segment via a specific roadside unit, and holding the current segment in a data buffer of the particular vehicle; and assign a reward to the policy network for the action in accordance with a predetermined reward function. The instructions in the DRL module cause the one or more processors, via the DRL training procedure, to produce a trained policy network embodying an offloading strategy for segmentation offloading of computational tasks from vehicles to one or more of an edge server and a cloud server.

Another embodiment is a non-transitory computer-readable medium for generating a task offloading strategy for a vehicular edge-computing environment and storing instructions that when executed by one or more processors cause the one or more processors to deploy a simulation platform that simulates a vehicular edge-computing environment in which one or more vehicles perform computational tasks associated with one or more vehicular applications. Data associated with the computational tasks is partitioned into segments, the simulation platform includes a simulated edge server, the simulated edge server includes a policy network, and the policy network includes a neural network. The instructions also cause the one or more processors to perform, for each of a plurality of segments, a Deep Reinforcement Learning (DRL) training procedure in which the one or more processors receive, at the simulated edge server, state-space information regarding the one or more vehicles and one or more intermediate network nodes, the one or more intermediate network nodes including at least one of base stations and roadside units; input the state-space information to the policy network; generate, from the policy network, an action concerning a current segment of a particular computational task associated with a particular vehicle among the one or more vehicles, the action including one of offloading the current segment via a specific base station, offloading the current segment via a specific roadside unit, and holding the current segment in a data buffer of the particular vehicle; and assign a reward to the policy network for the action in accordance with a predetermined reward function. The instructions also cause the one or more processors to produce, via the DRL training procedure, a trained policy network embodying an offloading strategy for segmentation offloading of computational tasks from vehicles to one or more of an edge server and a cloud server.

Another embodiment is a method of simulating edge-computing deployment in diverse terrains, the method comprising deploying a simulation platform that simulates a vehicular edge-computing environment in which one or more vehicles perform computational tasks associated with one or more vehicular applications. Data associated with the computational tasks is partitioned into segments, the simulation platform includes a simulated edge server, the simulated edge server includes a policy network, and the policy network includes a neural network. The method also includes performing, for each of a plurality of segments, a Deep Reinforcement Learning (DRL) training procedure that includes receiving, at the simulated edge server, state-space information regarding the one or more vehicles and one or more intermediate network nodes, the one or more intermediate network nodes including at least one of base stations and roadside units; inputting the state-space information to the policy network; generating, from the policy network, an action concerning a current segment of a particular computational task associated with a particular vehicle among the one or more vehicles, the action including one of offloading the current segment via a specific base station, offloading the current segment via a specific roadside unit, and holding the current segment in a data buffer of the particular vehicle; and assigning a reward to the policy network for the action in accordance with a predetermined reward function. The method also includes producing, via the DRL training procedure, a trained policy network embodying an offloading strategy for segmentation offloading of computational tasks from vehicles to one or more of an edge server and a cloud server.

