Name
Abstract | ||
|---|---|---|
We present a novel way of considering in-network computing (INC), using ideas from statistical physics. We define degeneracy for INC as the multiplicity of possible options available within the network to perform the same function with a given macroscopic property (e.g. delay). We present an efficient algorithm to determine all these alternatives. Our results show that by exploiting the set of possible degenerate alternatives, we can significantly improve the successful computation rate of a symmetric function, while still being able to satisfy requirements such as delay or energy consumption. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/jsyst.2020.2976873 | arXiv: Distributed, Parallel, and Cluster Computing |
DocType | Volume | Citations |
Journal | abs/1901.02712 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Merim Dzaferagic | 1 | 3 | 3.53 |
| Neal McBride | 2 | 0 | 0.68 |
| Ryan Thomas | 3 | 8 | 1.63 |
| Nicola Marchetti | 4 | 192 | 33.90 |
| Irene Macaluso | 5 | 119 | 22.24 |