Name
Abstract | ||
|---|---|---|
On the one hand, edge computing has the advantage of distributing the load to the edges of a computer network. Local computation at the edge is bandwidth-efficient and anonymous. On the other hand, cloud computing is the choice when it comes to computationally demanding tasks and big data. In this paper, we argue for edge-cloud computing (which blends the two together) with an experimental study on the impact of execution location on application performance. We answer the question of how to determine whether it should compute a task at the edge or on the cloud and what the criteria are. We analyze the factors of response time, memory space, data availability and privacy policy. We experimentally evaluate the impact of these factors on execution location based on a network visualizer software. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BigCom51056.2020.00028 | 2020 6th International Conference on Big Data Computing and Communications (BIGCOM) |
Keywords | DocType | ISBN |
location of execution,edge computing,cloud computing | Conference | 978-1-7281-8276-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimitrios Melissourgos | 1 | 0 | 0.34 |
| Sishun Wang | 2 | 0 | 0.34 |
| Shigang Chen | 3 | 2568 | 187.11 |
| Youlin Zhang | 4 | 10 | 5.26 |
| Olufemi Odegbile | 5 | 0 | 0.34 |
| Yuanda Wang | 6 | 1 | 2.04 |