Name
Abstract | ||
|---|---|---|
In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues. So for edge computing benchmarking, we must take an end-to-end view, considering all three layers: client-side devices, edge computing layer, and cloud servers. Unfortunately, the previous work ignores this most important point. This paper presents the BenchCouncil’s coordinated effort on edge AI benchmarks, named Edge AIBench. In total, Edge AIBench models four typical application scenarios: ICU Patient Monitor, Surveillance Camera, Smart Home, and Autonomous Vehicle with the focus on data distribution and workload collaboration on three layers. Edge AIBench is publicly available from http://www.benchcouncil.org/EdgeAIBench/index.html. We also build an edge computing testbed with a federated learning framework to resolve performance, privacy, and security issues. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-32813-9_3 | Bench |
Field | DocType | Citations |
Edge computing,End-to-end principle,Computer science,Workload,Testbed,Computer network,Cloud server,Home automation,Patient monitor,Benchmarking | Conference | 1 |
PageRank | References | Authors |
0.40 | 0 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianshu Hao | 1 | 2 | 1.77 |
| Yunyou Huang | 2 | 2 | 3.46 |
| Xu Wen | 3 | 1 | 0.40 |
| Wanling Gao | 4 | 299 | 19.12 |
| Fan Zhang | 5 | 4 | 0.81 |
| Chen Zheng | 6 | 1 | 0.40 |
| Lei Wang | 7 | 577 | 46.85 |
| hainan ye | 8 | 7 | 3.93 |
| Kai Hwang | 9 | 1 | 0.40 |
| Zujie Ren | 10 | 1 | 0.40 |
| Jianfeng Zhan | 11 | 767 | 62.86 |