.png){kind=icon}
返回
When Cache Meets Vehicular Edge Computing: Architecture, Key Issues, and Challenges
.png){kind=icon}
PRE
AI
DOI：10.1109/MWC.202.2100159.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Task-oriented caching in vehicular edge computing (VEC) has become a hot topic recently, which brings in many opportunities for capability limited vehicles and makes VEC task offloading and service provision more cost-effective. Most works on caching assisted task offloading in VEC focused mainly on the reuse of computation results and neglected the characteristics pertaining to VEC. In effect, task-oriented caching in VEC is time-sensitive, requiring multiple-choice and multi-granularity. We need to address the issues on different computing and storage capabilities between vehicles and edge server with their diverse applications. Therefore, caching enabled task offloading in VEC aims for exploitation and enhancement of the functions in edge computing. To investigate in depth the benefits of caching enabled task offloading in VEC, we first provide an architectural overview on caching enabled vehicular edge computing (CVEC). Then, we strive to address the most concerned issues, that is, where, what, how and when to cache the tasks in CVEC. Specifically, three caching strategies are introduced to address what to cache in CVEC. In addition, caching strategies are evaluated with respect to response latency and energy consumption at edge servers. Simulation results reveal that caching enabled task offloading in CVEC can remarkably improve the performance of VEC. We also take a look at the opportunities and discuss the challenges in CVEC to shed light on future research directions on CVEC.
Keyword:
Task analysis
Computer architecture
Edge computing
Wireless communication
Servers
Cooperative caching
Optimization
Vehicular ad hoc networks
期刊
IF:
11.5
论文数:
2.8K
被引数:
1.3W
机构
引用论文
A Novel Mobile Edge Network Architecture with Joint Caching-Delivering and Horizontal Cooperation
OAAI
.png){kind=icon}
OAAI分享收藏分享收藏
查看更多内容