Search

In vehicular edge computing (VEC) networks, the rapid expansion of intelligent transportation and the corresponding enormous numbers of tasks bring stringent requirements on timely task offloading. However, many tasks typically appear within a short period rather than arriving simultaneously, which makes it difficult to realize effective and efficient resource scheduling. In addition, some key information about tasks could be learned due to the regular data collection and uploading processes of sensors, which may contribute to developing effective offloading strategies. Thus, in this paper, we propose a model that considers the deterministic demand of multiple tasks. It is possible to generate effective resource reservations or early preparation decisions in offloading strategies if some feature information of the deterministic demand can be obtained in advance. We formulate our scenario as a 0-1 programming problem to minimize the average delay of tasks and transform it into a convex form. Finally, we proposed an efficient optimal offloading algorithm that uses the interior point method. Simulation results demonstrate that the proposed algorithm has great advantages in optimizing offloading utility.

In vehicular edge computing (VEC) networks, the rapid expansion of intelligent transportation and the corresponding enormous numbers of tasks bring stringent requirements on timely task offloading. However, many tasks typically appear within a short period rather than arriving simultaneously, which makes it difficult to realize effective and efficient resource scheduling. In addition, some key information about tasks could be learned due to the regular data collection and uploading processes of sensors, which may contribute to developing effective offloading strategies. Thus, in this paper, we propose a model that considers the deterministic demand of multiple tasks. It is possible to generate effective resource reservations or early preparation decisions in offloading strategies if some feature information of the deterministic demand can be obtained in advance. We formulate our scenario as a 0‐1 programming problem to minimize the average delay of tasks and transform it into a convex form. Finally, we proposed an efficient optimal offloading algorithm that uses the interior point method. Simulation results demonstrate that the proposed algorithm has great advantages in optimizing offloading utility. [ABSTRACT FROM AUTHOR]

Vehicle edge computing (VEC) is expected to be widely used in 5G wireless network in the future owing to its low latency, high reliability, and easy deployment. Unfortunately, the computation resources of roadside units (RSUs) equipped with computing ability are limited, which makes us need to seek other computing nodes. In this article, we explore an offloading model to meet this challenge. Future vehicles equipped with computing abilities can provide VEC services in vehicular fog networks (VeFN), which greatly reduces task delay and improves the efficiency of transportation system. We consider that a package consisting of several tasks is offloaded to vehicles with different computing abilities via multicast, and these vehicles process tasks at the same time. First, we use the mathematical method to construct the system utility function, aiming to obtain low delay and low computing cost, and produce a 0-1 programming problem with inequality constraints. We then solve the slack form of the primary problem based on the interior point method. Finally, a low-complexity algorithm is proposed to optimize the task delay and cost. Numerical results show that the algorithm has fast convergence speed and superior performance.