Name
Abstract | ||
|---|---|---|
Complex science workflows involve very large data demands and resource-intensive computations. These demands need reliable high-speed networks, that can optimize performance for application data flows. Characterizing flows into large flows (elephant) versus small flows (mice) can allow networks to optimize performance by detecting and handling demands in real-time. However, predicting elephant versus mice flows is extremely difficult as their definition varies based on networks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.future.2018.11.006 | Future Generation Computer Systems |
Keywords | Field | DocType |
Elephant and mice flows,Wide area networks,Machine learning,Gaussian mixture models | Cluster (physics),Computer science,NetFlow,Initialization,Workflow,Mixture model,Computation,Distributed computing | Journal |
Volume | ISSN | Citations |
94 | 0167-739X | 1 |
PageRank | References | Authors |
0.35 | 21 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mariam Kiran | 1 | 121 | 17.83 |
| Anshuman Chhabra | 2 | 8 | 4.57 |