Name
Abstract | ||
|---|---|---|
We are living in a world where massive end devices perform computing everywhere and everyday. However, these devices are constrained by the battery and computational resources. With the increasing number of intelligent applications (e.g., augmented reality and face recognition) that require much more computational power, they shift to perform computation offloading to the cloud, known as mobile cloud computing (MCC). Unfortunately, the cloud is usually far away from end devices, leading to a high latency as well as the bad quality of experience (QoE) for latency-sensitive applications. In this context, the emergence of edge computing is no coincidence. Edge computing extends the cloud to the edge of the network, close to end users, bringing ultra-low latency and high bandwidth. Consequently, there is a trend of computation offloading toward edge computing. In this paper, we provide a comprehensive perspective on this trend. First, we give an insight into the architecture refactoring in edge computing. Based on that insight, this paper reviews the state-of-the-art research on computation offloading in terms of application partitioning, task allocation, resource management, and distributed execution, with highlighting features for edge computing. Then, we illustrate some disruptive application scenarios that we envision as critical drivers for the flourish of edge computing, such as real-time video analytics, smart “things” (e.g., smart city and smart home), vehicle applications, and cloud gaming. Finally, we discuss the opportunities and future research directions. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JPROC.2019.2922285 | Proceedings of the IEEE |
Keywords | DocType | Volume |
Backscatter,Wireless communication,Throughput,Radio frequency,Protocols,Resource management,Energy harvesting | Journal | 107 |
Issue | ISSN | Citations |
8 | 0018-9219 | 21 |
PageRank | References | Authors |
0.73 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Lin | 1 | 34 | 3.63 |
| Xiaofei Liao | 2 | 1145 | 120.57 |
| Hai Jin | 3 | 6544 | 644.63 |
| Peng Li | 4 | 196 | 24.77 |