Name
Abstract | ||
|---|---|---|
To overcome the limitation of standalone edge computing in terms of computing power and resource, a concept of distributed edge computing has been introduced, where application tasks are distributed to multiple edge clouds for collaborative processing. To maximize the effectiveness of the distributed edge computing, we formulate an optimization problem of task allocation minimizing the application completion time. To mitigate high complexity overhead in the formulated problem, we devise a low-complexity heuristic algorithm called dependency-aware task allocation algorithm (DATA). Evaluation results demonstrate that DATA can reduce the completion time up to by 18% compared to conventional dependency-unaware task allocation schemes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/INDIN41052.2019.8972185 | INDIN |
Field | DocType | Citations |
Edge computing,Heuristic (computer science),Collaborative processing,Real-time computing,Allocation algorithm,Engineering,Optimization problem,Distributed computing | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaewook Lee | 1 | 735 | 50.24 |
| Joonwoo Kim | 2 | 3 | 3.08 |
| Sangheon Pack | 3 | 913 | 117.20 |
| Haneul Ko | 4 | 73 | 17.42 |