摘要
以云计算模型为代表的集中式数据处理关键技术已不能高效、及时地处理边缘设备产生的数据。针对这一问题,以"数据处理应更靠近数据源头"为核心理念的边缘计算模型应运而生。首先介绍了微数据中心、微云、雾计算、移动边缘计算等计算范型,并讨论了边缘资源整合的优势。然后,回顾了近年来边缘计算中与资源优化领域相关的工作,以计算、存储和通信3种资源为切入点,分别从计算卸载、分布式缓存和高性能传输这3个研究热点,对国内外的研究进展进行总结和讨论。最后,展望了该领域未来的发展趋势和主要的研究方向。
The traditional centralized architecture, known as cloud computing, cannot accommodate such user demands in an efficient and timely manner. To cope with this problem, edge computing architectures have been proposed with the core concept of that"data processing should be close to the data source". Firstly, paradigms of edge computing was introduced, including micro data center, cloudlet, fog computing, and mobile edge computing, and the advantages of edge computing from the perspective of resource integration was discussed. Then, related works of resource optimization in edge computing was reviewed and summarized, and these works was discussed via three directions, i.e., computation offloading, distributed caching and high performance transmission, corresponding to core resources as computing, storage and communication.Finally, trends of development and future directions were presented as well.
作者
屈志昊
叶保留
陈贵海
唐斌
郭成昊
QU Zhihao;YE Baoliu;CHEN Guihai;TANG Bin;GUO Chenghao(College of Computer and Information, Hohai University, Nanjing 211100, China;Department of Computer Science and Technology, Nanjing University, Nanjing 210046, China;The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 2100007, China)
出处
《大数据》
2019年第2期17-33,共17页
Big Data Research
基金
国家重点研发计划基金项目资助(No.2018YFB1004704)
国家自然科学基金资助项目(No.61832005)
江苏省重点研发计划基金资助项目(No.BE2017152)~~
关键词
边缘计算
计算卸载
分布式缓存
高性能传输
edge computing
computation offloading
distributed caching
high performance transmission
