TY - JOUR
AU1 - Li, Dongxu
AU2 - Wang, Xingfu
AU3 - Lu, Yuyang
AB - Vehicle computing tasks, which need high computational resources and be sensitive to delay, is widely concerned in the field of Mobile Edge Computing (MEC). At present, considerable progress has been made in the research of Vehicle-to-Vehicle (V2V) task offloading. However, in order to further improve the efficiency of task offloading, the base station should also be considered to have reserved computational resources. Determining the level of resources reservation is challengeable, as setting it too low will have bad efficiency in high-task scenarios, while setting it too high will lead to a waste of resources. Hence, we introduce the Edge-server Resources Reservation framework (ERRF), in which we study the impact of traffic flow on the reserved computational resources, introduce an algorithm to calculate a balanced reserved computational resource, and design a pre-processing algorithm for task offloading on this basis. We conduct the simulation and compare it with two V2V task offloading algorithms. Our results demonstrate the effectiveness of our framework, particularly in terms of average latency and task success rate across different scenarios.
TI - ERRF: an edge-server resources reservation framework in internet of vehicle
JF - Proceedings of SPIE
DO - 10.1117/12.3030350
DA - 2024-05-16
UR - https://www.deepdyve.com/lp/spie/errf-an-edge-server-resources-reservation-framework-in-internet-of-mcbwzcC2jN
SP - 131600Z
EP - 131600Z-6
VL - 13160
IS - 
DP - DeepDyve
ER -