.png){kind=icon}
返回
Reinforcement Learning for Optimizing Delay-Sensitive Task Offloading in Vehicular Edge-Cloud Computing
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2023.3292591.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the appearance of more and more devices connected to the Internet, the world has witnessed an ever-growing number of data to be processed. Among those, many tasks require swift execution time, while the storage and computation capability of Internet of Things (IoT) devices are limited. To address the demands of delay-sensitive tasks, we present a vehicular edge-cloud computing (VECC) network that leverages powerful computation capabilities through the deployment of servers in proximity to task-generated devices, as well as the utilization of idle resources from smart vehicles to share the workload. Because these limited resources are vulnerable to sudden data arising, it is imperative to incorporate cloud servers to prevent system overload. The challenge now is to find a task offloading strategy that collaborates both edges and cloud resources to minimize the total time surpassing the quality baseline of each task (tolerance time) and make all tasks meet their soft deadlines of quality. To reach this goal, we first model the task offloading problem in VECC as a Markov decision process (MDP). Then, we propose advantage-oriented task offloading with a dueling actor-insulator network scheme to solve the problem. This value-based reinforcement learning (RL) method helps the agent find an effective policy when not knowing all the state attributes changes. The effectiveness of our method is demonstrated by performance evaluations based on real-world bus traces in Rio de Janeiro (Brazil). The experimental results show that our proposal reduces the tolerance time by at least 8.81% compared to other RL algorithms and 75% compared to greedy approaches.
Keyword:
Cloud computing
deep Q-learning
delay-sensitive task
task offloading
vehicular edge computing (VEC)
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
分享收藏分享收藏Massive MIMO Systems for 5G and beyond Networks-Overview, Recent Trends, Challenges, and Future Research Direction面向5g及以上网络的大规模MIMO系统-概述、最新趋势、挑战和未来研究方向.png)
SENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容